drug trafficking research paper

• Open access
• Published: 31 January 2017

Drug supply networks: a systematic review of the organizational structure of illicit drug trade

• Gisela Bichler   ORCID: orcid.org/0000-0001-8686-9353 1 ,
• Aili Malm 2 &
• Tristen Cooper 3  

Crime Science volume  6 , Article number:  2 ( 2017 ) Cite this article

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This study presents a systematic review of research using social network analysis to document the structure of organized crime groups involved in drug trafficking. Our first objective is to determine whether the balance of evidence supports the argument that organized crime groups are loosely structured. Second, we aim to assess the relative importance of targeting individuals high in social capital (structural position) versus human capital (access to resources) in efforts to disrupt operations. Examining 34 studies, describing 54 illicit drug supply networks, we find five implications for anti-drug policy, and propose seven recommendations to facilitate meta-analysis and improve cross-network comparison.

Qualitative research examining organized crime groups, with an emphasis on drug trafficking activity, finds varying group structure within a loosely connected trade network. While these assessments are useful, there is little consistency in how authors operationalize organizational forms (as noted by Dorn et al. 2005 ), in part because these analyses are unable to map the actual structure of the group. Deepening our understanding of how illicit drug markets operate is pivotal to designing effective policy and crime control strategies. If structure varies, perhaps by market niche, drug trafficked or group characteristics, then we must tailor crime control efforts so they target the vulnerabilities of specific types of operations. Structure matters.

To standardize our descriptions of criminal group structures and begin the process of verifying the suppositions generated by qualitative research, scholars are turning to social network analysis (SNA). Capitalizing on a suite of empirical tools—theory, metrics, and analytics—crime scientists use SNA to document the interdependence among actors involved in drug trafficking. Rather than describing a group in general terms using researcher generated typologies, SNA studies use common metrics to characterize group structure, pinpoint specific actors and groups that control key market activities, i.e., importing drugs, laundering proceeds, etc., and identify individuals positioned to reestablish trade activity when central figures are removed. Thus, SNA provides an opportunity to re-examine what we think we know about market structure from a fresh perspective.

Examining SNA research of drug trafficking organizations, this systematic review of 34 published studies, describing 55 trade networks, is the first to synthesize what we currently know about the structure of illicit drug trade. We begin with a brief overview of landmark qualitative research and describe how SNA can contribute to the study of the organizational structure of crime groups. Then, we outline our document search protocol, and detail our methods. The results are partitioned into two sections. First, we examine network structure and find evidence confirming the idea that groups and drug markets are loosely organized and that groups have identifiable central figures. Second, we consider the relative importance of social capital (e.g. network position) and human capital (e.g. access to resources), confirming a correlation between social and human capital and that network vulnerabilities, representing key persons, are identifiable. We conclude with a discussion of the implications these findings have for crime control policy and provide direction for future research to facilitate meta-analyses and improve cross-network comparison.

Structure of drug trafficking groups

Group structure.

Contrary to media inspired conceptualizations of organized crime, qualitative research investigating the configuration of drug trafficking organizations finds varying group structures within a loosely connected trade network (for a review of some of this literature see Natarajan and Hough 2000 ). While a thorough review of the field is beyond the scope of the present study, a number of seminal research projects inform hypotheses about the structure of groups involved in illicit drug markets.

Adler ( 1985 ) showed early on that organized crime groups tend to operate similar to legitimate business. Using ethnographic methods, she revealed that drug trafficking operations are loosely structured, often involving informal agreements among market participants. Arguing that market structure is to some extent dependent upon the source of the drug handled; the specialized importation of foreign drugs requires fewer people and less formal structure than domestic drug production. Domestic drug production is also more likely to mimic a legitimate organization due to local competition.

Interviewing 40 incarcerated high-level cocaine and marijuana traffickers, Reuter and Haaga ( 1989 ) discovered that their networks typically take the form of small partnerships consisting of temporary and dynamic coalitions of dealers. Acknowledging methodological limitations associated with the sample, Reuter and Haaga make several key observations about the markers of ‘success’ in the industry that are of relevance to the present study. (1) There are few barriers to getting involved in the market; namely, access to capital, effort, luck, and use of violence are not required for success. (2) Traffickers are not limited to working regionally—the market is national. In the wholesale market, experience and the willingness to make and take opportunities limits involvement. (3) Large or long lasting networks exist, but they are not required for success in high-level drug operations.

To this point, Eck and Gersh ( 2000 ) examined 620 cases gathered from federal, state, and local drug investigations in the Washington-Baltimore High Drug Trafficking area (W/B HIDTA) from 1995 to 1997. The results show that 60.4% of cases involved individuals or actors conspiring with a loose-knit association. Further, of the 39.1% involved in some form of a criminal organization, most (66.7%) comprised groups of less than 21 people. After studying operations in greater detail (e.g., communications patterns, transactions, and security), the authors conclude that drug trafficking more closely resembled a cottage industry of small, somewhat temporary sets of people, and that there were few instances of large, hierarchically-organized distribution networks.

Qualitative studies of drug operations trafficking crack, cocaine, and heroin throughout the 1990s and 2000s found similar results. For instance, Dorn et al. ( 2005 ) reviewed upper-level drug trafficking literature, concluding that drug traffickers are diverse and driven by different motivations. These differences are reflected in group structure and vulnerability: business criminals motivated by profit are more likely to have a durable core with several connections to different groups and individuals than ideologically motivated offenders (Dorn et al. 2005 ). In his interviews with Colombian, drug cartel informants, Kenney ( 2007 ) shows that trafficking networks are flexible and react to opportunities and constraints by expanding and contracting in size and reach. Research by Spapens ( 2010 , 2011 ) also supports these findings. He shows that drug market monopolies are rare and difficult to maintain. He highlights the differences between legitimate and criminal markets, focusing on the need for trust in illicit business.

This literature led to the following working hypothesis: while several structures exist, most operations are loosely connected networks that can quickly react to shifting market conditions . What is not clear from this body of work, however, is whether mapped networks exhibit loose connectivity and to what extent this structure pertains to specific, clearly defined groups of actors, and to what extent these patterns characterize general market structure. A key issue in understanding the form and function of a network is to establish membership boundaries, because including peripheral individuals who are not really part of the group can significantly alter how we describe the network. A dense, cohesive group with a single leader will appear to look like a loosely connected set of clusters if people linking groups together are also included. Thus, it is important to consider group structure (within a definable crime group) and market structure (connections between different groups in a distribution chain) independently—it is possible that within group structure can be hierarchical even when the market as a whole exhibits the properties of a loosely connected network. Moreover, with each author developing their own typology of group structure it is difficult to conduct the cross-study comparisons needed to establish general patterns. Standardized metrics are needed to describe the nature and distribution of organizational structures.

Role differentiation

The importance of role differentiation by activity (i.e., fetching precursor drugs versus cooking methamphetamine) or market niche (i.e., cross-border smuggling versus wholesale supplying) also emerges from the review of qualitative research. Variation in organizational structure means that disruption efforts will need to be tailored to the type of operation and the inherent resilience of the group structure. For instance, through extensive interviewing of 296 subjects involved in crack, cocaine, and heroin distributions, Johnson et al. ( 2000 ) found evidence of role differentiation in response to police attention: countermoves involved parsing drug market activities into specific tasks, (e.g., separating holders, transporters, deliverers, money counters, versus guards, etc.) in order to be flexible and resilient to crime suppression activities. Their research also uncovered that market niches, such as low-level distribution, dealing, and upper-level distribution, show variation in organizational structure. This suggests that market forces at each level of trade impose unique constraints upon individuals engaged in drug trafficking.

Even within money laundering, a function we generally recognize as a relatively specialized facet of drug trafficking, we find evidence of the varied, and thus flexible, nature of operations (Schneider 2010 ; Soudijn 2012 ). For example, studying 31 Dutch cases involving large-scale cocaine importation, Soudijn ( 2014 ) discovered that only half of the investigations (14 cases) involved people providing financial services typically associated with money laundering. Contrary to conventional wisdom, however, the study uncovered a wide range of financial activity and financial facilitators were not accountants or lawyers; rather, individuals were involved in either sending money between countries (e.g., smuggling cash and hawala banking) or they participated in activities to give money a legal appearance, such as investing in the legal economy. Though not commented on by Soudijn ( 2014 ), this suggests human capital—individual resources and skill sets—influences whether, and in what capacity, someone is involved in drug market activities. Human capital may also differentiate leaders and critical personnel from easy to replace subordinates.

More recently, Natarajan et al. ( 2015 ) examined 89 organizations uncovered through major investigations of the Drug Enforcement Administration (50 cases constituted the nation-wide sample) or prosecuted in New York City (39 cases) from 1997 to 2007 with the aim of testing a system of classifying groups along two dimensions—organizational structure and tasks. Most notably, they find that data source impacts structural variation. For instance, when using New York City data, 12.8% of groups have a corporate organizational style and 30.1% were communal businesses, whereas, federal cases tended to involve corporate (54%) or communal businesses (42%). Where corporate organizational style includes a formal hierarchy and division of labor and communal businesses are comprised of members linked by at least one common characteristic, i.e., religion, nationality, neighborhood, or race. Additionally, 41% of New York cases and 62% of federal cases concerned groups involved in multiple niches (e.g., smuggling, wholesale, and regional distribution). Again, having a flexible, informal structure and being involved in a range of activities speaks to the potential impact that the collective resources and individual human capital play in shaping operational structure.

These studies suggest that drug trafficking is comprised of entrepreneurs exploiting their social and human capital. Our second working hypothesis follows from this idea. The hypothesis states, varying structural properties emerge for different types of market involvement and that market leaders and critical personnel (central individuals) are those with the greatest human capital. Soudijn ( 2014 ) and Natarajan et al. ( 2015 ), however, raise the concern that what we think we know about organizational structure is to a large part, pre-determined by the focus of and resources deployed during investigations, as well as the prosecutorial discretion of attorneys at the local and federal levels. Thus, we may find that variation in the predominance of central individuals is contingent on the scope of the study and source of information.

Network analysis of trafficking networks

While SNA-oriented study of organized crime is relatively new, the material advantage of using network science to study criminal organizations was lauded over two decades ago [see for example Jackson et al. ( 1996 ) and Sparrow ( 1991 )]. Because we are still in a relatively nascent stage of development, crime scientists are still working through SNA theory and metrics to identify the most appropriate mechanisms to test our ideas about the structure of crime groups. With this caveat in mind, two themes dominate our efforts to map the structure of illicit drug trafficking.

Criminal network structures

Crime scientists working with SNA have come to view criminal networks differently from other social networks because they operate in hostile environments. For instance, Morselli writes, “Criminal networks are not simply social networks operating in a criminal context. The covert settings that surround them call for specific interactions and relational features within and beyond the network (2009; 8).” With various agents of the criminal justice system working to constrain illicit trade, individuals profiting from criminal enterprise must work in secrecy, under a cloak of invisibility; whereas, legitimate trade activity may organize to maximize the efficiency of operations. This ongoing challenge shapes how the group, and overall market, operates. As stated in our first working hypothesis, qualitative investigations suggest that drug operations are primarily loosely connected networks capable of rapid change in response to shifting market conditions. While direct SNA metrics of these concepts do not exist, we can explore comparable concepts of network density (or sparseness) and centrality, and what this means for operational structure.

Figure  1 illustrates the difference between dense and sparse operations and introduces two types of central positioning—hubs and brokers [(see Borgatti and Everett 1992 , 2006 ) or for a more information about network centrality and associated metrics visit https://en.wikipedia.org/wiki/Centrality or http://www.faculty.ucr.edu/~hanneman/nettext/C10_Centrality.html ]. This begins our discussion of how operational structures may indicate a preference for efficiency or secrecy (security). Note that in this hypothetical example, the circles represent people involved in the manufacture and trafficking of methamphetamine and the arrowheads indicate the flow of communications through the network.

Network structures characterizing security and efficiency

If we look at person 6, denoted by a grey circle, in Fig.  1 a, we see their position in the network allows them to exchange information with most others in the network. This information exchange is efficient and may be quick as there are few intermediaries required to reach other group members. In this example, density is high, meaning that most people connect directly to each other. Higher network density positively effects the network’s efficiency, provided messages take direct paths through the network. Arguably, this structure may also increase trust among individuals in the network [see Coleman ( 1988 ) and Granovetter ( 1981 ) for more details on trust and network closure]. An additional benefit is that with the removal of any individual, the group would continue to function: it is highly resilient to attack because of its high level of interconnectivity. While more efficient and resilient to attack, the structure reduces operational security. This means the network is not “secure” against efforts by law enforcement to uncover information about operations. For example, if we arrest person 6, or anyone else for that matter, they have knowledge of all group members and could implicate everyone in an investigation. Compare this network structure to Fig.  1 b; here, we see that if person 6 were to act as an informant, they could only implicate the person they receive information from, person 3, and the person they transmit information to, person 8. The network is relatively secure, because it is sparse and few connections exist among people in the group. The drawback is that rebuilding operations can be lengthy and difficult when crime control efforts remove a centrally placed individual.

Sparse, or loosely connected networks, typically include individuals centrally positioned as hubs and brokers. Individuals with a lot of direct connections (such as person 3 in Fig.  1 b), relative to others in the network, are hubs. Theoretically, we consider hubs to have the greatest degree of influence in the network; they can directly share information with more people than anyone else can. Brokerage is a different idea about central positioning—brokerage positions enable someone to control the flow of information between any randomly selected pair of other actors in the network. Returning to Fig.  1 a, since any effort to communicate with person 1 or 2 must go through person 3, person 3 is in a better position to broker information within the group. These structural positions offer a strategic advantage for crime control when networks are sparse: disruption efforts that aim to remove central actors, namely hubs and brokers, stand the greatest chance to disrupt network functions.

Social and human capital

Another social network argument is that individuals positioned with ties to unique clusters of people have greater social capital (Burt 1992 , 1997 ). Bridging different groups of people has a strategic advantage; individuals become indispensable to the overall group because they alone “hold” the group together and they ensure that they are the first to hear new information as it passes through the network. When combined with human capital, that is having unique skills or access to resources, a well-equipped bridge has great potential to maximize their success. When applied to organized crime groups and drug markets, we may hypothesize that varying structural properties emerge for different types of market involvement. Owing to the idea that some activities are more important to operations (e.g., money laundering and smuggling) and that, market leaders and critical personnel (central individuals within a group or connecting different groups) are those with the greatest human capital.

As illustrated in Fig.  2 a, individuals 3 and 6 have equivalent social capital. They each have efficient connections, meaning they established a single relation with each of three different clusters of people. Since the clusters do not have other connections joining them to the other groups of people, individuals 3 and 6 have unique positions. Sitting between several subgroups, they have the opportunity to reap the most benefit from the information they access from each cluster. This network position presents opportunities to use or act on information first and may serve to enhance the success of persons 3 and 6. In doing so, their actions may enhance the overall success of the entire network. Notably, if we factor for the ability to act on this information, meaning that we consider the individual attributes and resources of each person, we may discover that despite having similar social capital, person 3 (the meth cook as indicated in panel b), has greater human capital, and so, may be better able to use their social capital to their advantage. The argument being, couriers have a less specialized skillset making person 6 easy to replace, whereas with greater individual resources, the meth cook would be harder to substitute. In this scenario, positional advantage is not sufficient; it is only when the information benefit accrued from social position intersects with human capital that material advantages are likely realized.

Network structures characterizing social and human capital

Present study

Adapting the working hypotheses derived from qualitative research to fit within an SNA framework, we sought to answer two sets of questions.

Does the SNA literature identify specific network structures common to drug trafficking organizations that are consistent with the findings of qualitative research? If so, is there a difference between group structures and market structures? Moreover, given methodological shortcomings, what strategic implications can we derive from these findings to aid crime control efforts aimed at disrupting drug trade?

What is the relative importance of social capital (position within the network) and human capital (access to unique resources and skills) in determining who are the critical actors or groups within an illicit drug market? By using such information, do crime control efforts gain an advantage in efforts to disrupt market activity? Do the methodological shortcomings associated with studying criminal networks influence these findings?

Source identification

To ensure our search protocol was systematic, thorough, and efficient (fewest number of false positives), we identified an optimal set of search terms through an iterative process using a notable hit weight selection criterion. Footnote 1 To calculate the notable hit weight for each set of possible search terms we divided the number of articles found on Google Scholar written by notable authors Footnote 2 by the total number of matches identified. We assume that a search term or phrase that returns a high yield of materials produced by known, active scholars in the field will likely be more effective in uncovering similar types of research produced by other authors with a smaller body of work. Starting with a preliminary set of potential terms, Footnote 3 drawn from the keywords listed in articles written by notable authors, we systematically removed all poorly preforming items. For instance, we removed the search term “co-offending” because it generated too many false positives—few articles pertained to drug trafficking networks. As reported in Table  1 , this process resulted in six best preforming sets of keywords. Next, we tested various keyword combinations to build an optimal set of terms, settling on “Illicit drug network structure analysis trafficking” + “network analysis” which achieved a notable hit rate of 90:1,560, a value that was 2.8 standard deviations above the mean of all other tests. Footnote 4

We used EBSCO Host, JSTOR, Simon Fraser University’s Fast Search, and Google Scholar to search for sources. Each document was scanned against a set of inclusion criteria: the document must be published in English, with a scholarly outlet, after 1990, and contain social network analysis of at least one drug trafficking network using one or more recognized social network metric and/or analytic procedure. Moreover, the specified focus of the research had to be drug trafficking; articles looking at the overall structure of organized crime groups (e.g., Campana 2011 ; Varese 2011 , 2012 ) were not included as all legitimate and multiple types of illicit activity were combined into a single network and our aim was to investigate only the portion of their operations involved in illicit drug trafficking. Each source appearing to satisfy the criteria based on a scan of the title, abstract and results was retained, and later read closely to confirm eligibility. Additionally, the research team examined the references of all sources for additional articles.

Figure  3 illustrates the screening process used to identify suitable studies. Thirty-four sources met the inclusion criteria (see reference section “Sources for systematic review”). Of note, many potential items were excluded due to a lack of network statistics: we were unable to include several important studies discussing the utility of SNA or theoretical concepts of interest to the study of dark networks (e.g., Kenney 2007 ; Spapens 2010 ) and seminal inquiries into group structure using qualitative methods (e.g., Natarajan et al. 2015 ; Soudijn 2014 ; Spapens 2011 ), because they did not present actual network metrics.

Illustration of study identification process

Description of sources

Of the 34 studies identified, 76% were case studies; where 41% focus on the workings of a particular group and 35% examine the distribution chain involving a central group but including all of their associations to other groups (see Table  2 ). About 18% of the studies investigated a population of actors known to be involved in drug-related criminal enterprise. Footnote 5 Concerning geographic coverage, most research examined groups with central operations in North America or the Mediterranean. Notably, only one study sought to examine a global network (not reported in the Table  2 ). Collectively, most of these studies examine organized crime groups involved to some extent in the trafficking of cocaine. While we searched articles from 1990 to 2015, 71% of sources were published since 2010. As reported in Table  2 , most research aims to describe networks or explore research questions about the structure of the drug trafficking organization, using descriptive statistics or simple hypothesis tests. About 79% of studies are cross-sectional, with data aggregated from police intelligence information. Publication venues tended to be peer review (82%) and only 21% of studies were funded.

Sample description

In this study, we originally intended to use the network as the primary unit of analysis instead of published studies. Footnote 6 The 34 sources identified describe 55 networks, some of which are subnetworks based on different extractions. The meta-analytic database constructed for this study included

Details about the methods used and the network generation process (e.g., description of data source, a sampling description, time frame, boundary specification, Footnote 7 type of drugs trafficked by the network, and the directionality and valuation of connections Footnote 8 );

descriptions of each network (e.g., total number of actors in the network, number of connections among them, density, number of components, as well as the average, standard deviation, and degree of centralization for measures of actor positioning); and,

specifics about the analysis performed, i.e., whether the analysis was dynamic or cross-sectional, what tests were performed to answer stated research questions or hypotheses, and if they conducted a sensitivity analysis.

Much to our chagrin, there was little consistency in the information reported about each network, few commonalities exist in the analysis conducted, and stated research questions or hypotheses varied widely. For example, one of the most fundamental descriptive statistics to report about a network is density—the number of observed connections in the network relative to the number of links that could be present if all actors connected to each other. Descriptions of only 26 networks (48.1% of the networks discussed in the source articles) reported density. The simplest information, the number of actors and links present in the network, was more broadly reported, 85.2 and 53.7% respectively. This is not particularly encouraging given these details are equivalent to reporting the sample size in other research domains. Common descriptive statistics are even more elusive: only 12% of the studies report all standard descriptive statistics for each network (e.g., average and standard deviation for degree centrality, density, average path length, and number of actors and links) and 24% of articles report standardized values, required to directly compare different networks. Moreover, studies ranged in methodologies from descriptive core analysis (e.g., study Bouchard and Konarski 2014 ) to regression models (e.g., study Grund and Densley 2012 ) to simulation experiments (e.g., study Duijn et al. 2014 ). Not all is lost, however, as two critical themes emerged from our inspection of network-based studies of organized crime structure; in the results section that follows, we discuss the criminal network structure, specifically the trade-off between efficiency and security, and the relative importance of human capital versus social capital.

Criminal network structure

Our first set of research questions examine whether the SNA literature finds specific network structures common to drug trafficking organizations; if there are differences between group structures and market structures; and, whether these structural patterns offer strategic implications to aid crime control efforts aimed at disrupting the illicit drug trade. Of the 34 studies included in this review, 14 examine operational structure in detail (see Table  3 ) and characterize the research objective as exploring the efficiency and security trade-off. Footnote 9 Networks engaging in illicit activity have to balance the need for efficient business connections and communication with security and secrecy (Baker and Faulkner 1993 ; study Morselli et al. 2007 ). Theoretically, this balance is not as important in legitimate, conventional networks (study Duijn et al. 2014 ). This trade-off might account for operational structures described in qualitative research—the preponderance of loosely structured networks of entrepreneurs.

Across the 15 networks described in these 14 studies, the statistics used fall under two broad categories—centrality and embedding. Centrality measures are marginally more common. Specifically, eight out of 14 (57%) studies used both degree and betweenness centrality, and three studies used closeness centrality. Degree centrality is the count of ties attached to a given individual (Freeman 1979 ). Individuals with high degree centrality have more connections. Betweenness centrality is the number of times that an individual sits along the shortest path between all others in the network (Freeman 1979 ), and represents the extent that an individual mediates connections and information. Closeness centrality assesses the ability of an actor to communicate along the shortest path to all others in the network (Freeman 1979 ). Irrespective of the type of centrality, when networks exhibit lower overall centralization, it means that a smaller portion of the network is dependent on a single actor. Notably, this structural dependence does not necessarily suggest hierarchical control: centralization would only be interpreted as reflecting hierarchical organization if the direction of actor connections indicated chain of command. Overall, the studies show that drug trafficking networks have higher centralization than conventional networks (study Calderoni et al. 2014 ), simulated networks (study Malm and Bichler 2011 ), and terrorist organizations (studies: Morselli et al. 2007 ; Xu and Chen 2008 ). The studies also show that centralization increases with the threat of law enforcement targeting (Morselli and Petit 2007 ).

Six out of 14 studies (43%) reported embedding measures. Statistics measuring how individuals are embedded in larger social structures include density, path length, clustering, efficiency, and transitivity. Footnote 10 Embedding measures are tools that allow social network analysts to contextualize and understand the entire population and how network structure constrains or enables actors in the network. Of note, only two studies combined centrality and embedding statistics to explain the security and efficiency trade-off (studies: Calderoni et al. 2014 ; Morselli et al. 2007 ). Overall, the studies show that individuals in drug trafficking networks are more embedded than conventional networks, and less embedded than terrorist organizations, as indicated by lower path length and clustering coefficients (studies: Mainas 2012 ; Morselli et al. 2007 ; Xu and Chen 2008 ).

A group’s objectives and operational tempo appear to moderate its network structure. Networks whose primary purpose is to make money tend to favor efficiency (greater density), while networks with more ideological goals or a longer time to act favor sparseness with fewer central actors (studies: Bright and Delaney 2013 ; Morselli et al. 2007 ): this finding generally concurs with Dorn and colleagues (2005). Overall, the studies included in this review show that drug trafficking network structure appears to be lower in centralization and density than both legitimate networks (studies: Calderoni et al. 2014 ; Duijn et al. 2014 ; Malm et al. 2010 ) and general co-offending networks (study Duijn et al. 2014 ), and more centralized and dense than terrorist networks (studies: Mainas 2012 ; Morselli et al. 2007 ; Xu and Chen 2008 ). Footnote 11

Only two of the studies looked at change in organizational structure over time and both used descriptive statistics; none incorporated dynamic simulation-based models such as exponential random graph models (ERGM). Bright and Delaney ( 2013 ) found that as a drug network’s profit orientation increases, the structure centralizes and changes from one favoring security to efficiency. They also show that a shift in roles and increase in size favors efficiency over security (study Bright and Delaney 2013 ). Morselli and Petit ( 2007 ) investigated how law enforcement targeting effects the efficiency and security trade-off (study Morselli and Petit 2007 ). They concluded that as law enforcement targeting and seizures increase, network structure centralizes (density increases) to become more secure.

Eight of the studies assessed the network position of group leaders (studies: Calderoni 2014 ; Calderoni et al. 2014 ; Duijn et al. 2014 ; Hofmann and Gallupe 2015 ; Malm et al. 2008 ; Morselli 2009 , 2010 ; Xu and Chen 2008 ). Footnote 12 The rationale for this focus is that leaders of groups who favor security will seek to protect themselves from the gaze of law enforcement by distancing themselves from others in the group. The results of this research are also mixed. The majority of the studies show that group leaders have both high betweenness and degree centrality, suggesting that they are central actors (studies: Calderoni 2014 ; Calderoni et al. 2014 ; Duijn et al. 2014 ; Hofmann and Gallupe 2015 ; Tenti and Morselli 2014 ); however, Morselli ( 2009 , 2010 ) found that leaders were peripheral to the core of group communication. Incorporating geographic distance with social network metrics, Malm et al. ( 2008 ) found that leaders were central in the network, but distanced themselves geographically from drug production sites (study Malm et al. 2008 ).

As mentioned at the outset of this article, it is important to determine the structural differences of groups operating within the drug market and the market itself. The majority of the studies focused on market structure, including groups and individuals occupying different niches (studies: Calderoni 2014 ; Calderoni et al. 2014 ; Mainas 2012 ; Malm and Bichler 2011 ; Morselli 2009 , 2010 ; Morselli and Petit 2007 ; Tenti and Morselli 2014 ). Only a few studies focused specifically on groups (studies: Bright and Delaney 2013 ; Hofmann and Gallupe 2015 ; Morselli et al. 2007 ; Xu and Chen 2008 ). The findings indicate little difference between market and group structure. The articles reviewed unanimously show that both groups and networks operating within a drug market expand outward from a core in short chain-like structures, rather than from multiple cells (studies: Calderoni et al. 2014 ; Duijn et al. 2014 ; Mainas 2012 ; Malm and Bichler 2011 ; Natarajan 2000 ; Tenti and Morselli 2014 ; Xu and Chen 2008 ). These results also confirm that drug markets conform to small-world properties, Footnote 13 where communication can reach every member of the group with a relatively small number of intermediaries, and network structures are relatively sparse (studies: Mainas 2012 ; Malm and Bichler 2011 ; Morselli and Petit 2007 ; Salazar and Restrepo 2011 ; Xu and Chen 2008 ). Generally, these findings are consistent with the results of qualitative research. It follows that law enforcement efforts to disrupt illicit drug trafficking should consider the structure of the target network when developing strategies (e.g., study Malm et al. 2010 ); however, it is important to be cognizant that their targeting may affect network structure (e.g., study Morselli and Petit 2007 ).

There is however, a caveat to these findings. There is little consistency in the examined research as to how structural characteristics reflect the concepts of efficiency and security. One group of research finds that high centralization (usually coinciding with lower density) reflects more efficient and less secure networks (studies: Bright and Delaney 2013 ; Calderoni et al. 2014 ; Mainas 2012 ; Malm and Bichler 2011 ; Morselli et al. 2007 ). The other group suggests that decreased centralization (increased density) reveals more efficiency and less security (studies: Duijn et al. 2014 ; Morselli 2010 ; Morselli and Petit 2007 ; Salazar and Restrepo 2011 ). The research focus is one possible source for this contradiction. The former group investigates the natural evolution of drug trafficking groups or compares drug trafficking groups to other groups with notably different objectives and frequency of action; whereas, much of the latter group investigates changing network structure due to increased enforcement activity. Thus, while we see some consistency in the metrics used to describe drug trafficking networks, researchers need to consider the context of networks when applying theory and drawing theoretical conclusions.

Relative importance of social capital versus human capital

Individuals positioned with ties to unique clusters of people have greater social capital (Burt 1992 , 1997 ); however, if we factor for individual attributes and resources, we may find that the role a person plays in the drug distribution process is more telling. Thus, our second focal area is to examine role differentiation and to determine whether the existing SNA literature reveals something about the relative importance of social capital (position within the network) and human capital (access to unique resources and skills) when we attempt to identify critical actors or groups within an illicit drug market. In examining this issue, we ask whether efforts to disrupt market activity gain an advantage by considering these factors. Of the 34 studies looking into the organizational structure of illicit drug trade, 12 examine the social capital of individuals involved in drug market activity by the resources possessed or role they play within a specific group’s operation or across a drug distribution chain. Footnote 14 Across the 16 networks described, two SNA metrics are commonly used to assess social capital—degree and betweenness centrality. Footnote 15 The attributes used to reflect human capital are typically associated with operational roles (reported for 87.5% of the observed networks) or status and/or rank within the group (50% of the observed networks). Only two studies specifically investigate human capital using actor attributes associated with access to resources and involvement in specific activities.

Operational roles

While each author used a different set of operational roles to classify drug market participants, evidence is beginning to emerge to suggest that traffickers, those involved in smuggling or organizing shipping consignments, have higher social capital than individuals or groups involved in other roles, for example:

Examining two case studies, Calderoni ( 2012 , 2014 ) observed higher average degree and betweenness centrality, but lower clustering coefficients for individuals involved in finding drugs abroad and importing to Italy.

Investigating all known drug groups working in Western Canada (British Columbia and the Yukon Territory), Malm and Bichler ( 2011 ) considered simple involvement (single niche of activity) versus complex activity (participation in two or more operational roles), finding that the people involved in complex transport and complex supply had higher average degree and betweenness centrality scores and low clustering coefficients (study Malm and Bichler 2011 ).

Studying four mid-level criminal organizations operating in the Spanish cocaine market, Framis ( 2014 ) discovered that within each group someone involved with importing/transporting was most central to operations (study Framis 2014 ).

Notably, only one study explored the social capital of one smuggler throughout their career. Morselli ( 2001 ) found that while the network structure varied, at no time was it a hierarchical drug trafficking organization (study Morselli 2001 ). Arguably, at the most successful period of his trafficking career, the central figure of this network (Mr. Nice) exhibited his greatest level of social capital: highest level of efficiency and lowest effect size—two measures of social capital suggested by Burt ( 1992 , 1997 ). Interestingly, consignments were of medium size (averaging about 3 kg) and showed little fluctuation.

Examining group structure and between group crime activities for 9 groups of co-offenders involved in Italian the cocaine market, Tenti and Morselli ( 2014 ) discovered that groups occupying the same niche in the drug distribution chain exhibited variation in structure (study Tenti and Morselli 2014 ). Moreover, highly central individuals were located a different levels of drug distribution; highly central people did not concentrate in a particular role within the industry. Since the configuration of groups varied, so too, did the network resilience among groups. With many partnership agreements approximating a resource-sharing organizational model, the chain-like structure of the network exhibited low density with interacting clusters (subsets) of people.

Only two studies specifically investigate the relative importance of money laundering. Malm and Bichler ( 2013 ) find that self-launderers who were also involved in smuggling or supply were the most highly ranked brokers in the network: this is indicative of higher social capital (study Malm and Bichler 2013 ). Moreover, there were considerably more self-launders found in the network of co-offenders (82/102 or 80%) suggesting that with recent technological developments in the financial sector (i.e., hawala banking, bitcoins, person-to-person transfers) money laundering is “de-professionalizing.” Morselli and Giguere ( 2006 ) add that drug distribution networks include influential participants who appear to work in legitimate occupational settings; of these individuals, those involved in financial activities are critical seeds, introducing others to the network. The authors conclude that these seeds are likely to hold the key to understanding the opportunity structure of criminal enterprise (study Morselli and Giguere 2006 ).

Access to resources/specialized skills

Few studies directly examine the relative importance of human capital (access to resources and specialized skills) in comparison to social capital. In a series of studies examining the operations of a methamphetamine trafficking group working out of Australia, Bright and colleagues (studies: Bright et al. 2012 , 2014a , b , Bright and Delaney 2013 ) demonstrate the high correlation between human capital and social capital, where the human capital measure captured a range of tangible and intangible resources. Investigating the market disruption potential of using social and human capital to identify targets for law enforcement action these authors find that degree targeting (individuals with the highest degree centrality) and a mixed identification strategy incorporating human capital and social capital perform the best as gauged by facilitating the greatest reduction in the size of the largest component (group of connected individuals) and maximum disruption of market functionality (study Bright et al. 2014b ). These authors, however, caution that law enforcement strategies must remain flexible because people transit in and out of networks causing centrality scores to fluctuate over time (study Bright and Delaney 2013 ). A point well supported by Duijn et al. ( 2014 ).

Testing the simulated effectiveness of a wider range of targeting tactics, Duijn et al. ( 2014 ) show that several strategies have the potential to disrupt the Dutch marijuana industry (study Duijn et al. 2014 ). Comparing five disruption strategies (e.g., random, human capital, degree centrality, betweenness centrality, and human capital and degree centrality) and three recovery mechanisms (e.g., random, preference by social distance or degree centrality), networks were found to exhibit greater density, and thus more resiliency, after attacks targeting those with the most human capital. Moreover, individuals playing instrumental roles were more vulnerable (e.g., coordinators and international traders). These authors assert that disruption strategies must be long term efforts as networks recover to attack in such a manner that they become more efficient and resilient (as discussed earlier in this article).

Disrupting drug markets

This study sought to synthesize what we know about the structure of drug trafficking organizations as revealed by SNA scholarship. We reason that qualitative research involving conventional analytic techniques is invaluable to developing ideas about the structure of criminal enterprise, and that to build on this body of work we must apply SNA metrics. By applying SNA metrics, we can standardize how we describe network structures thereby supporting cross-study comparisons about the relative positional importance of people and groups. In doing so, SNA-based research offers a strategy to validate hypotheses in a way that provides direct crime control implications. While still in a preliminary stage of development, five implications emerge from this systematic examination of the body of SNA research in this area.

Drug trafficking networks are more apt to be sparse with central individuals connecting the group and linking between different groups suggesting an operational preference for security. This result suggests that targeting central individuals may fragment the network (e.g., study Duijn et al. 2014 ); however, results also suggest there may be a number of individuals waiting to replace central figures. Police need to recognize that increased law enforcement attention will cause the organization to adapt and become more decentralized, thus more difficult to target specific individuals (e.g., study Morselli and Petit 2007 ). Disruption is a long-term strategy (e.g., studies: Bright et al. 2012 , 2014a , b , Bright and Delaney 2013 , Duijn et al. 2014 ).

Leaders of drug trafficking networks and those with important roles are identifiable through centrality analysis, if there is sufficient information about group connections (e.g., studies Calderoni et al. 2014 ; Duijn et al. 2014 ). Notably, missing or dated information can obscure central actors. Thus, efforts to map criminal networks should regularly extend beyond criminal justice sources to include current affiliations, family connections, and legitimate business relations to ensure the most important actors in the group are correctly identified.

Use a range of metrics and analytic techniques to identify central players to target, i.e., spectral embedding (study Calderoni et al. 2014 ), attributes/roles (studies Bright et al. 2014a , b ), meeting participation and communication style (studies: Calderoni 2012 ; Calderoni 2014 ), and legitimate connections (study Morselli and Giguere 2006 ). There are many centrality statistics, each tapping a different aspect of social structure, when combined with decisions about actors and which relations to code, this creates a versatile tool kit through which to understand group structure. Examining the structure from different perspectives will better expose network vulnerabilities.

While disruption efforts will vary in effect, degree targeting or degree/human capital strategy performs best. Removing well-positioned and well-resourced actors from the trade network should split the network into smaller components and maximize the potential disruption of market activity (study Bright et al. 2014b ). While it is possible to trigger cascading failure with targeted progressive attacks on bridges (study Xu and Chen 2008 ), the success of crime control policy will depend on the drug market niche that the targeted individual occupies and the complexity of their drug market involvement (study Malm and Bichler 2011 ). Analysts are advised to identify central actors for each market niche.

Map networks repeatedly (over time). Anti-crime strategies need to be flexible as networks continually evolve; attacks on the network, from within due to conflict and launched externally by the criminal justice system, lead to structural evolution. Actors change, centrality scores fluctuate, and roles/attributes adjust (e.g., study Bright and Delaney 2013 ). Targeting individuals for removal from the network based on human capital stands to increase network density, exposing more of the network as replacement people often create new paths and shortcuts, which raises the efficiency, and inherent resiliency, of the network (e.g., study Duijn et al. 2014 ). Thus, old network maps are useful as benchmarks against which to assess the efforts of disruption tactics.

At this point, we draw the readers’ attention to the methodological shortcomings associated with studying drug trafficking networks.

Research limitations

Due to three methodological shortcomings, the findings reported above are of limited generalizability. First, as reported in Tables  3 and 4 , this body of work primarily uses evidence presented in court or information extracted from police data (intelligence, investigations, or co-arrests). As most researchers using juridical materials to glean network information from electronic surveillance generated during the investigation, police information is the principal data source. Footnote 16 Only one study (study Morselli 2001 ), explores a network built from an autobiography supplemented with investigatory (DEA electronic surveillance) and news reports. This overreliance on data from the criminal justice system means that we must acknowledge that the efficiency and security trade-off and the intersection between human and social capital may be different among groups involved in illicit drug trade that do not come to the attention of law enforcement. Moreover, observed networks may be incomplete, as some actors involved may remain unidentified or mislabeled as being unimportant in the drug trade.

Second, the search strategy used in this systematic review was limited to articles written in English, accessible through a scholarly outlet, and published since 1990. Therefore, it is quite possible that relevant studies were missed. In this vein, most of these studies examine markets based in Western, consumer nations with extensive coastlines, few shared land borders, and pivotal positions in world trade based on port/shipping activity. Footnote 17 When considered in tandem with the reliance on law enforcement data as discussed above, it is not surprising that the investigatory focus of most of these studies is on groups importing or distributing cocaine and other illicit drugs produced externally. Notable exceptions being Duijn and colleagues (study Duijn et al. 2014 ) who studied cannabis cultivation in the Netherlands and the research group led by David Bright (studies: Bright et al. 2014a , b ; Bright et al. 2012 ) who investigated the methamphetamine trade in Australia. Being situated as an entrepôrt may affect the capital, human and social, associated with individuals involved in smuggling or transportation roles. Moreover, this context may also unduly accentuate the central positioning of individuals found to be involved in smuggling activity. Had researchers undertaken similar studies in countries with less exposure to the currents of global trade, the organizational structures observed may be considerably different.

The third methodological issue to acknowledge is that most of the observed networks (76%) were case studies—43% focused on a specific group and 33% mapped a distribution chain. This disproportional inclusion of purposively selected case studies, chosen in part due to data availability, leads us to conclude that it is premature to suggest that these findings apply to all groups involved in illicit drug trade. With more egocentric and population-based research, we will be better equipped to assess the generalizability of these case studies.

Advancing the field

Synthesizing prior research through meta-analyses and systematic reviews is a critical exercise in the advancement of scientific inquiry. Notably, the nascent stage of SNA research in crime science hampered our attempt to synthesize what we know about the organizational structure of illicit drug trade. Reported results were primarily descriptive rather than explanatory, and there was a marked lack of consistency in reporting standards and methods. Moving forward, in order to facilitate cross-case comparison, replication, and rigorous meta-analyses, we suggest that research using social network analysis should incorporate the following reporting standards.

Researchers and analysts must clearly describe how they generated the networks. Network generation involves making decisions about what constitutes a link between actors and where to obtain information about these connections, as well as whether the relations have an inherent value or directionality. Thus, a clear statement is needed to alert the reader as to what constitutes a tie, the different types of ties (if appropriate), whether ties were valued (or binary), and whether the network was directed (or symmetrical). After generating the initial network, researchers often extract a subsample for analysis (i.e., principal component). A clear explanation of subsample extraction is necessary. It is important to provide these details as decisions made here, may radically influence the results. Even though editorial preferences will relegate some of these details into footnotes, it is important not to cut this information in the final edits. This information reveals how methodological decision-making influences the results and helps to promote replication.

Structural differences among groups are often associated with how individuals are connected; thus, investigations must be specific about what constitutes a connection between people (or groups). This means that we should invest more effort into uncovering what the important binding mechanisms are and what advantages different types of bonds have for drug trafficking operations (study Malm et al. 2010 ), i.e., brokers who do not use violence are more trusted and deeply connected than brokers who do (study Morselli 2001 ), and family/kinship relations are used for money-laundering and these connections are difficult to sever (studies: Hofmann and Gallupe 2015 ; Malm and Bichler 2013 ).

Researchers and analysts must describe sampling procedures and how they determined network boundaries. SNA research uses many different sampling strategies, including hybridized techniques using multiple procedures. Even within research using a case-study approach, focal individuals referred to as seeds, are often selected as the starting point around which a network is generated. Using selection criteria, we add individuals to the network based on some type of association with the seed individuals. While most authors often provide this information, they do not always explain where the network stops—the network boundary. Conceivably, one could continue for several steps out from a focal individual, i.e., should the friend of a friend of a friend still part of the same group. A pre-determined network boundary should be established and reported. Without these details, replication and cross-study comparison is limited.

Irrespective of the stated research objectives, we must report a set of basic descriptive statistics. Due to the novelty of SNA research in criminology, authors tend to devote attention to explaining the metrics used to answer research questions. There are a set of descriptive statistics, however, that provide a framework for understanding structure. Basic descriptive statistics to report include: the number of nodes and links, density, number of components, average path length, average degree, and degree centralization for each network under examination. If a subset is drawn, then two sets of values may be necessary—descriptive statistics for the full network and descriptive statistics for the subset. The scientific method stipulates that basic descriptive statistics are required when reporting results; SNA is not exempt from this foundational tenet.

We need to report standardized values for all metrics used to test hypotheses or answer research questions. Network size influences many statistics, and as such, statisticians have developed standardized versions of the key metrics, referred to as normalized values. While raw values have an inherent interpretability, and as such, are widely preferred, study results should also include normalized values where possible (i.e., standardized centrality measures). Reporting normalized values will enable meta-analyses that will advance this field of research.

With such a rich body of existing research, replication should take precedence. In what was likely an endeavor to explore the breadth of SNA methods and techniques, scholars in this field examined networks in a variety of ways. Now, the focus can shift to replication, to see if different networks share similar properties when the same analytics are applied. To this end, research collaborations among scholars working in different nations might help to foster more replication and cross-network comparison.

It is important to construct titles, select keywords, and write abstracts using standard terms and phrases to ensure that related research is identified, irrespective of search engine used. In this study, we found 26% of the source articles by reading articles and examining the references listed. Moreover, we had high false positives: wading through hundreds of documents to find a handful of appropriate items is not efficient. Including standard terms and phrases would improve the research process twofold. First, it will decrease the likelihood of missing important studies when conducting a literature review; and second, standardized language will improve the efficiency of source identification during meta-analyses and systematic reviews.

Conclusions

Despite the early stage of SNA research in crime science, there are reasons to be optimistic. Data sharing and research collaborations that seek to compare criminal networks are forming. These partnerships often include an international group of scholars who facilitate cross-country network comparisons and a sharing of expertise (i.e., the University of New South Wales Criminal Networks Research Group http://www.cnrg.unsw.edu.au/ ). Notably, the Illicit Networks Workshop, a working group dedicated to the advancement of a networked criminology, is currently in its eighth year of bringing together scholars from across the world to share ideas and research (Malm and Bichler 2015 ).

Research funding opportunities for social network research in criminal justice are also emerging. For example, the Violence Reduction Initiative has held webinars bringing together practitioners and academics to educate and share experiences in using SNA for crime reduction. Additionally, the National Institute of Justice has solicited research proposals for the application of SNA to reduce violent crime and increase predictive policing capabilities. We encourage more funding agencies to support research specifically focused on the application of SNA to criminal networks.

In conclusion, while we were limited in our analyses by the lack of standardized reporting and methods used over the 34 studies we reviewed, this systematic review still enables us to answer our three research questions and greatly contributes to the field of organized crime and drug research. The studies included in this review enable us to assert with confidence that drug trafficking networks tend to spread from a relatively dense core in short chain-like structures. The studies also show that these structures are apparent across the drug distribution system. Disruption strategies targeting individuals with high centrality and human capital are likely to include the leaders and other visible members of the drug distribution network, and this should, lead to a more successful crime control.

As aptly pointed out by one of the reviewers, systematic reviews are typically inefficient, partly to ensure that the search uncovers the population of studies or as much of the population as possible. Given the high volume of materials uncovered in preliminary tests of search terms that did not include the type of study we sought, the research team decided to develop a process to improve search efficiency without losing our ability to identify obscure publications.

The list of scholars known to use social network analysis in studies of drug trafficking groups includes: Gisela Bichler, Martin Bouchard, David Bright, Francesco Calderoni, Paolo Campana, Aili Malm, Carlo Morselli, and Mangai Natarajan.

The original terms to describe the research were: drug trafficking, organized crime groups, cartel, social network analysis, group structure, drug markets, co-offending networks, and illicit drug distribution.

This means that there were potentially 90 useful documents within a pool of 1560 items uncovered in the key word search.

Research investigating a population of individuals known to be involved in drug trafficking typically involves a data mining process wherein the network generated includes everyone known to police. This often involves consolidating information from different data systems. From this point, selection criteria are applied to hone the file, i.e., for a co-offending relation to exist the individuals must be known to commit at least two crimes together. This contrasts with case study approaches, which define a group of individuals, usually by known membership or coactivity with known members of a group.

As noted by one of the reviewers, in systematic reviews and meta-analyses, there is a parallel distinction between using findings and studies. We argue that networks (i.e. reviews focused on research findings) are appropriate because in much of the scientific literature, a single report will describe, and often systematically compare, the structure of multiple networks. This means that if the article were the unit of analysis, the research team would have to select one network for inclusion in the study. Choosing between networks leaves the current project open to the criticism that researcher bias tainted the selection process. Thus, the research team decided to use all networks described in each study. As the reader will learn shortly, a problem occurred forcing us to report on studies.

To build a network, it is essential to predefine who is eligible for inclusion in the “group”. This is an important decision in the research process as being overly restrictive or too broad may significantly alter the results.

Directed networks are such that the connections among actors have an inherent directionality because whatever is passing through the network (i.e., drugs, information, and money) flows from one person to another. Moreover, connections can be valued to indicate the amount of something passing between actors, the value of the exchange, or the strength of the relationship. Specifying these details about how the network was constructed is critical as it changes the way we interpret structural statistics.

Seven other studies (studies Berlusconi 2013 ; Boivin 2014 ; Bouchard and Konarski 2014 ; Calderoni and Piccardi 2014 ; Canter 2004 ; Grund and Densley 2012 ; Hutchins and Benham-Hutchins 2010 ; Salazar and Restrepo 2011 ) examine network structure and employ centrality and embeddedness measures, however, significant divergence in research aim and theoretical framework prohibit their inclusion here. With this said, results are included in this section where appropriate, in the text or as a footnote. Of interest to the reader, only one study (study Canter 2004 ) attempts to use six indices of organizational structure to generate a typology of criminal organizations. This study finds a range of structure from very loose networks with no central figures to highly structured operations. Two factors account for this variation—size of the group and centrality of leadership. Canter (study Canter 2004 ) concludes that there are three types of criminal organizations—ad hoc groups, oligarchies, and organized criminals, the former exhibiting the smallest group size and the latter being the largest.

Path length refers to the average geodesic distance (average length of the shortest paths) linking each pair of people in the network; the clustering coefficient captures the extend of clumping (areas of high and low density) in the network; efficiency is a standardized metric (controlling for network size) that captures the non-redundant nature of an individual’s connections, meaning that they have ties to unique clusters of people that do not otherwise connect; and, transitivity refers to the occurrence of triadic configurations (sets of three people all connected to each other) relative to intransitive structures (groups of three where there are only two links among actors).

One study explored internal co-offending (study 16), finding that members of an ethnically diverse, but racially homogeneous street gang were more likely to co-offend with other gang members from the same ethnic group, suggesting the existence of distinct internal co-offending structures.

Notably, several other studies examined group leaders, albeit from different perspectives. For instance, two studies used centrality statistics to help uncover core-periphery structures (studies Baker and Faulkner 1993 ; Borgatti and Everett 1992 )—this perspective argues that within each network a cluster of core actors dominate, and accrue the most benefit, from the network. Adopting a world-system perspective, Boivin (study Boivin 2014 ) examined the relative position of nations within global drug distribution, comparing cocaine, heroin, and marijuana markets to legitimate trade relations. He found distinct clustering of core nations and greater centralization in cocaine distribution than marijuana. All networks were significantly less dense than legitimate trade networks. Bouchard and Konarski (study Bouchard and Konarski 2014 ) examined whether a small set of targeted gang members were in fact central to the co-offending network and part of a core group of members; however, only 4 of the 6 most central individuals in the core group were identified by law enforcement. Another set of studies concluded that the central figures in drug markets and groups could be identified using SNA techniques with networks generated from law enforcement and surveillance data (studies Berlusconi 2013 ; Hutchins and Benham-Hutchins 2010 ). Notably, Berlusconi (study 1) found that degree and betweenness centrality are robust in identifying key players under conditions of missing data; and, Hutchins and Benham-Hutchins (study Hutchins and Benham-Hutchins 2010 ) suggest that SNA data mining techniques offer efficient methods for identifying distinct clusters despite low network density and that a small number of highly central brokers (betweenness centrality) are visible. As these studies were not explicitly aiming to examine structure from a security and efficiency perspective they are not included in Table  3 .

Networks exhibit small world properties when “global” connectivity patterns generate networks wherein all actors connect to each other through relatively short chains—typically, six or fewer segments connect all actors in the network (e.g., Granovetter 2003 ; Watts and Strogatz 1998 ). Illustrating this point with an examination of a Colombian drug trafficking network—Cartel del Norte del Valle—Salazar and Restrepo (study 32) document that the average distance between any pair of members was 3.7, in part due to high levels of betweenness. They also found that information shocks, search for threatening nodes, and ultimately, lethal violence flowed through the network in clusters. US policy and major interdiction efforts over the course of 15 years resulted in a decline of centrality and network size, which corresponds with a lengthening of average distance among members from 3.7 to 4.6.

To be included in this analysis, the study must report average scores by role using a graphic or statistic. Notably, we excluded three important studies for this reason. (1) Coding communications about illicit activity for a group of 294 individuals involved in heroin dealing in New York City in the 1990s, Natarajan ( 2000 ) maps the organizational structure by role, but does not provide average metrics, nor does she specifically investigate social capital (study Natarajan 2000 ). (2) Natarajan ( 2006 ) examines social power using Bonacichi’s power analysis and only reports mean power scores for core members (a group of 38 individuals critical to heroin trafficking) that include sellers, retailers, brokers, and secretaries (study Natarajan 2006 ) and did not report power scores by role. (3) Garay-Salamanca and Salcedo-Albarán ( 2012 ) looked at the social capital of key leaders; however, they did not examine the position or human capital of other members of the three networks investigated.

This widespread use of general centrality measures is a bit surprising given that the originators of social capital theory suggested other metrics to operationalize this concept. For more information about a set of alternative measures of social capital, see Burt ( 1992 , 1997 ).

Several authors, Varese and Campana (Campana 2011 , Campana and Varese 2012 , Varese 2011 , 2012 ), Natarajan (studies: Natarajan 2000 , 2006 ), and Calderoni (Calderoni et al. 2014 , 2015 , and studies: Calderoni 2012 , 2014 ) to name a few, aptly demonstrate the utility of using police data, particularly information gleaned from wiretaps. Berlusconi ( 2013 ) supports this research and concludes that structural measures are robust when the data come from a purposive sample of all wiretaps among all criminal network members (study Berlusconi 2013 ). However, reliance on a single source or type of law enforcement data raises important methodological concerns, namely self-censorship, coverage gaps, unknown network boundaries, limited sample sizes, biased samples, and potential incongruence between the content of the conversation and subsequent action (Varese 2012 ).

Boivin ( 2014 ) is a notable exception: His research used drug seizure information for 194 countries reported to the United Nations Office on Drugs and Crime (UNODC) by organizations tasked with monitoring and controlling drug trafficking (study Boivin 2014 ).

Adler, P. (1985). Wheeling and dealing: An ethnography of an upper-level drug dealing and smuggling community . New York: Columbia University Press.

Google Scholar  

Baker, W. E., & Faulkner, R. R. (1993). The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry . New York, NY: American Sociological Review.

Borgatti, S., & Everett, M. (1992). Notions of position in social network analysis. Social Methods, 22, 1–35.

Article   Google Scholar  

Borgatti, S., & Everett, M. (2006). A Graph-theoretic perspective on centrality. Social Networks, 28, 466–484.

Burt, R. S. (1992). Structural holes: The social structure of competition . Cambridge: Harvard University Press.

Burt, R. S. (1997). The contingent value of social capital. Administrative Science Quarterly, 42 (2), 339–365.

Calderoni, F., Berlusconi, G., Garofalo, L., Giommoni, L., & Sarno, F. (2015). The Italian mafias in the world: A systematic assessment of the mobility of criminal groups. European Journal of Criminology . doi: 10.1177/1477370815623570 .

Campana, P. (2011). Eavesdropping on the Mob: The functional diversification of mafia activities across territories. European Journal of Criminology, 8 (3), 213–228.

Campana, P., & Varese, F. (2012). Listening to the wire: Criteria and techniques for the quantitative analysis of phone intercepts. Trends in Organized Crime, 15, 13–30.

Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120.

Dorn, N., Levi, M., & King, L. (2005). Literature review on upper level drug trafficking . London: Home Office.

Eck, J. E., & Gersh, J. H. (2000). Drug trafficking as a cottage industry. In M. Natarajan & M. Hough (Eds.), Illegal drug markets: From research to prevention policy, crime prevention studies (Vol. 11, pp. 241–272). Monsey, NY: Criminal Justice Press.

Freeman, L. F. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215–239.

Granovetter, M. (1981). The strength of weak ties: A network theory revisited . New York, NY: State University of New York, Department of Sociology.

Granovetter, M. (2003). Ignorance, knowledge, and outcomes in a small world. Science, 301, 773–774.

Jackson, J., Herbrink, J., & Jansen, R. (1996). Examining criminal organizations: Possible methodologies. Transnational Organized Crime, 2 (4), 83–105.

Johnson, B. D., Dunlap, E., & Tourigny, S. C. (2000). Crack distribution and abuse in New York. In M. Natarajan & M. Hough (Eds.), Illegal drug markets: From research to prevention policy, crime prevention studies (Vol. 11, pp. 19–57). Monsey, NY: Criminal Justice Press.

Kenney, M. (2007). The architecture of drug trafficking: Network forms of organisation in the Colombian cocaine trade. Global Crime, 8, 233–259.

Malm, A., & Bichler, G. (2015). Seeds of collaboration: The Illicit networks workshop. Journal of Contemporary Criminal Justice, 31 (3), 236–242.

Natarajan, M., & Hough, M. (2000). Illegal drug markets: From research to prevention policy, crime prevention studies (Vol. 11). Monsey, NY: Criminal Justice Press.

Natarajan, M., Zanella, M., & Yu, C. (2015). Classifying the variety of drug trafficking organizations. Journal of Drug Issues, 45 (4), 409–430.

Reuter, P., & Haaga, J. (1989). The Organization of high-level drug markets: An explorative study . Santa Monica, CA: RAND.

Schneider, Friedrich. (2010). Turnover of organized crime and money laundering: Some preliminary empirical findings. Public Choice, 144, 473–486.

Spapens, T. (2010). Macro networks, collectives, and business processes: An integrated approach to organized crime. European Journal of Crime, Criminal Law and Criminal Justice, 18, 185–215.

Spapens, T. (2011). Interaction between criminal groups and law enforcement: The case of ecstasy in the Netherlands. Global Crime, 12 (1), 19–40.

Sparrow, M. S. (1991). The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks, 13, 251–274.

Soudijn, Melvin R. J. (2012). Removing excuses in money laundering. Trends in Organized Crime, 15 (2), 146–163.

Soudijn, M. R. J. (2014). Using strangers for money: A discussion on money-launderers in organized crime. Trends in Organized Crime, 17, 199–217.

Varese, F. (2011). How mafias take advantage of globalization: The Russian mafia in Italy. British Journal of Criminology, 52 (2), 235–253.

Varese, F. (2012). The structure and the content of criminal connections: The Russian mafia in Italy. European Journal of Criminology, 29 (5), 899–909.

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.

Sources for systematic review

Berlusconi, G. (2013). Do all the pieces matter? Assessing the reliability of law enforcement data sources for the network analysis of wire taps. Global Crime, 14 (1), 61–81.

Boivin, R. (2014). Drug trafficking networks in the world economy. In C. Morselli (Ed.), Crime and Networks (pp. 182–191). New York, NY: Routledge.

Bouchard, M., & Konarski, R. (2014). Assessing the core membership of a youth gang from its co-offending network. In C. Morselli (Ed.), Crime and networks (pp. 81–93). New York, NY: Routledge.

Bright, D. A., Greenhill, C., Reynolds, M., Ritter, A., & Morselli, C. (2014a). The use of actor-level attributes and centrality measures to identify key actors: A case study of an Australian drug trafficking network. Journal of Contemporary Criminal Justice, 31 (3), 262–278.

Bright, D. A., & Delaney, J. J. (2013). Evolution of a drug trafficking network: Mapping changes in network structure and function across time. Global Crime, 14, 238–260.

Bright, D. A., Greenhill, C., & Levenkova, N. (2014b). Dismantling criminal networks: Can node attributes play a role. In C. Morselli (Ed.), Crime and networks (pp. 148–162). New York, NY: Routledge.

Bright, D. A., Hughes, C. E., & Chalmers, J. (2012). Illuminating dark networks: A social network analysis of an Australian drug trafficking syndicate. Crime, Law and Social Change, 57 (2), 151–176.

Calderoni, F. (2012). The structure of drug trafficking mafias: The ‘Ndrangheta and cocaine. Crime, Law and Social Change, 58 (3), 321–349.

Calderoni, F. (2014). Strategic positioning in mafia networks. In C. Morselli (Ed.), Crime and networks (pp. 163–181). New York, NY: Routledge.

Calderoni, F., & Piccardi, C. (2014). Uncovering the structure of criminal organizations by community analysis: The infinito network. 10th international conference on signal-image technology & internet-based systems (pp. 301–308. doi: 10.1109/SITIS.2014.20 .

Calderoni, F., Skillicorn, D., & Zheng, Q. (2014). Inductive discovery of criminal group structure using spectral embedding. Information & Security, 31, 49–66.

Canter, D. (2004). A partial order scalogram analysis of criminal network structures. Behaviormetrika, 31 (2), 131–152.

Duijn, P. A. C., Kashirin, V., & Sloot Peter, ma. (2014). The relative ineffectiveness of criminal network disruption. Scientific Reports, 4 (4238), 1–15.

Framis, A. (2014). Illegal networks or criminal organizations: Structure, power, and facilitators in cocaine trafficking structures. In C. Morselli (Ed.), Crime and Networks (pp. 131–147). New York, NY: Routledge.

Garay-Salamanca, L. J., & Salcedo-Albarán, E. (2012). Institutional impact of criminal networks in Colombia and Mexico. Crime Law and Social Change, 57, 177–194.

Grund, T. U., & Densley, J. A. (2012). Ethnic heterogeneity in the activity and structure of a black street gang. European Journal of Criminology, 9 (4), 388–406.

Hofmann, D. C., & Gallupe, O. (2015). Leadership protection in drug trafficking networks. Global Crime, 16 (2), 123–138.

Hutchins, C. E., & Benham-Hutchins, M. (2010). Hiding in plain sight: Criminal network analysis. Computational and Mathematical Organizational Theory, 16, 89–111.

Mainas, E. D. (2012). The analysis of criminal and terrorist organizations as social network structures: A quasi-experimental Study. International Journal of Police Science and Management, 14 (3), 264–282.

Malm, A., & Bichler, G. (2011). Networks of collaborating criminals: Assessing the structural vulnerability of drug markets. Journal of Research in Crime and Delinquency, 48 (2), 271–297.

Malm, A., & Bichler, G. (2013). Using friends for money: The positional importance of money-launderers in organized crime. Trends in Organized Crime, 16 (4), 365–381.

Malm, A., Bichler, G., & Van De Walle, S. (2010). Comparing ties that bind criminal networks: Is blood thicker than water? Security Journal, 23, 52–74.

Malm, A., Kinney, J. B., & Pollard, N. (2008). Social network and distance correlates of criminal associates involved in illicit drug production. Security Journal, 21, 77–94.

Morselli, C. (2001). Structuring Mr. Nice: Entrepreneurial opportunities and brokerage positioning in the cannabis trade. Crime, Law and Social Change, 35, 203–244.

Morselli, C. (2009). Hells angels in springtime. Trends in Organized Crime, 12, 145–158.

Morselli, C. (2010). Assessing vulnerable and strategic positions in a criminal network. Journal of Contemporary Criminal Justice, 26 (4), 382–392.

Morselli, C., & Giguere, C. (2006). Legitimate strengths in criminal networks. Crime, Law & Social Change, 45, 185–200.

Morselli, C., & Petit, K. (2007). Law-enforcement disruption of a drug importation network. Global Crime, 8 (2), 109–130.

Morselli, C., Giguère, C., & Petit, K. (2007). The efficiency/security trade-off in criminal networks. Social Networks, 29, 143–153.

Natarajan, M. (2000). Understanding the structure of a drug trafficking organization: A conversational analysis. In M. Natarajan & M. Hough (Eds.), Illegal drug markets: From research to prevention policy, crime prevention studies (Vol. 11, pp. 273–298). Monsey, NY: Criminal Justice Press.

Natarajan, M. (2006). Understanding the structure of a large heroin distribution network: A quantitative analysis of qualitative data. Journal of Quantitative Criminology, 22 (2), 171–192.

Salazar, B., & Restrepo, L. M. (2011). Lethal closeness: The evolution of a small-world drug trafficking network. Desafios, 23 (2), 197–221.

Tenti, V., & Morselli, C. (2014). Group co-offending networks in Italy’s illegal drug trade. Crime, Law and Social Change, 62, 21–44.

Xu, J., & Chen, H. (2008). The topology of dark networks. Communications of the ACM, 51 (10), 58–65.

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Authors’ contributions

GB was principal investigator, leading the team of co-authors, AM and TC, who all were involved in executing the study and drafting the manuscript. All authors read and approved the final manuscript.

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Gisela Bichler is a Professor in the Department of Criminal Justice at California State University, San Bernardino. Generally, Dr. Bichler’s research explores the interplay between the environment and offending behavior, using quantitative methodology from social network analysis and crime pattern analysis. Recent publications include the Journal of Research in Crime and Delinquency, Crime and Delinquency, Crime Patterns and Analysis , Global Crime and the Security Journal . She is founder and co-director of the Center for Criminal Justice Research—CSUSB. Email address: [email protected].

Aili Malm is an Associate Professor in the Department of Criminal Justice at California State University Long Beach. Generally, Dr. Malm’s published research centers on the intersection between policing and social policy, concentrating in topics associated with social network analysis. Recent publications include the Journal of Research in Crime and Delinquency, Crime and Delinquency, Crime Patterns and Analysis, Global Crime , and the Security Journal . Email address: [email protected].

Tristen Cooper is a Research Associate with the Center for Criminal Justice Research, California State University, San Bernardino. His research interests include data mining, social network analysis, and national securities studies.

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Bichler, G., Malm, A. & Cooper, T. Drug supply networks: a systematic review of the organizational structure of illicit drug trade. Crime Sci 6 , 2 (2017). https://doi.org/10.1186/s40163-017-0063-3

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Article contents

International drug trafficking: past, present, and prospective trends.

• Luca Giommoni , Luca Giommoni School of Social Sciences, Cardiff University
• R.V. Gundur R.V. Gundur The Centre for Crime Policy and Research, Flinders University
•  and  Erik Cheekes Erik Cheekes School of Social Sciences, Cardiff University
• https://doi.org/10.1093/acrefore/9780190264079.013.470
• Published online: 27 August 2020

Since the early 20th century, the illegal drug trade has received increasing focus throughout the world. However, the use of mind-altering substances predates attempts to prohibit or regulate them. Early control efforts date back to the teachings of Mohammed in the Koran, though wider-scale control efforts did not occur until the 18th century. Since that time, both the production of mind-altering substances and their regulation or prohibition has been commonplace throughout the world. Several illicit markets exist in response to the ongoing demand. Four notable products are cocaine, heroin, cannabis, and synthetically produced, mind-altering substances that are sold predominantly to users in North America and Europe.

The production, transportation, and usage of these substances are all impacted by the histories and geographies of the producer, intermediary, and user countries. Shifts in tolerance of certain substances; geopolitical events, such as war; international policy and policing initiatives, such as the implementation of the United Nations Convention Against Illicit Traffic in Narcotic Drugs and Psychotropic Substances of 1988 and improved means of detecting illicit payloads at international boarders; and changes in demand for specific products have all influenced how trafficking routes and the organizations that participate in the drug trade form and adapt.

Regardless of these changes, one constant is that no aspect of the drug trade has ever been dominated by a single, monolithic organization; several illicit enterprises have historically come together to form the often supply global chains. In the 2011, the first darknet market, the Silk Road, emerged as a means by which some buyers and sellers could connect, thus potentially reducing the links of the supply chain. Ongoing changes in technology as well as shifts in the regulatory frameworks on controlled substances will impact illicit substances that are sold and how buyers and sellers interact, and will require innovated research strategies to evaluate their evolution.

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• international drug trafficking

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The Discourse of Drug Trafficking from Global Perspective

11 Pages Posted: 12 Oct 2020

Emmanuel Uzuegbu-Wilson

Babcock University - School of Law and Security Studies

Date Written: October 4, 2020

This study examined drug trafficking phenomenon from the global perspective. The study employed a desk review research approach with the reports and evaluations obtained from secondary sources of data analyzed through content analysis. The study found that globalization is posing an entirely new challenges or threats to human security which include the global expansion of the drug trade. The study also found that the debilitating effect of the drug trade on societies coupled with its links to other transnational threats can leads to prolonged drug war. The study recommended that international drug control efforts should focus on economic development aimed at undermining the incentives for producing illicit narcotic drugs. To ease the implementation of policy measures aimed at combating drug trafficking and insecurity across nation states, political leaders should muster the political will to eradicate corruption at all levels while also addressing the structural vulnerabilities which their country presents. The capacity of inter-institutional agencies that are directly involved in counter-narcotic strategies and policies across states should also be strengthened in order to bring about cohesion in policy harmonization with global partners.

Keywords: Drug Trafficking, Globalization, Narcotics, Security, Transnational Organized Crime

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Emmanuel Uzuegbu-Wilson (Contact Author)

Babcock university - school of law and security studies ( email ).

Ilishan, Ogun State 110011 Nigeria

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Online and offline determinants of drug trafficking across countries via cryptomarkets

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• Luca Giommoni   ORCID: orcid.org/0000-0002-3127-654X 1 ,
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Drug cryptomarkets are a significant development in the recent history of illicit drug markets. Dealers and buyers can now finalize transactions with people they have never met, who could be located anywhere across the globe. What factors shape the geography of international drug trafficking via these cryptomarkets? In our current study, we test the determinants of drug trafficking through cryptomarkets by using a mix of social network analysis and a new dataset composed of self-reported transactions. Our findings contribute to existing research by demonstrating that a country’s level of technological advancement increases the probability of forming trafficking connections on cryptomarkets. Additionally, we found that a country’s capacity to police cryptomarkets reduces the number of trafficking connections with other countries. We also observed that trafficking on cryptomarkets is more likely to occur between countries that are geographically close. In summary, our study highlights the need to consider both online and offline factors in research on cryptomarkets.

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Do police crackdowns disrupt drug cryptomarkets a longitudinal analysis of the effects of operation onymous, personal use, social supply or redistribution cryptomarket demand on silk road 2 and agora.

Potential Influences of the Darknet on Illicit Drug Diffusion

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Introduction

Silk Road’s inception in February 2011 ushered in a novel era in illicit drug transactions. In the past, individuals who sought illicit drugs had to meet dealers in person to finalize transactions. Cryptomarkets, however, heralded a shift in this convention. These anonymous online markets, accessible exclusively via the darknet (Aldridge, 2019 ), enabled the purchase of both illicit drugs and other commodities, licit or not, without requiring personal contact with transaction partners. As a result, drug dealers could extend their businesses, dealing with people unknown to them and receiving anonymous cryptocurrency payments (Ouellet et al., 2022 ).

This shift in drug dealing paradigms posed significant challenges to law enforcement authorities, given cryptomarkets’ potential to reshape the structure and scale of drug trafficking. Ordinarily, illicit drugs traverse multiple international borders before reaching their final consumers. Cryptomarkets, on the contrary, provide the means to streamline supply chains by sourcing directly from drug-producing countries, bypassing intermediaries. This model has the potential to boost the global reach of drug traffickers and heighten their profits.

However, current evidence suggests that cryptomarkets haven’t significantly disrupted traditional drug trafficking routes. Most cryptomarket vendors operate from consumer countries like the United Kingdom, EU countries, USA, Canada, and Australia. This pattern extends to buyers and revenue as well. Only a handful of transactions occur directly between producer countries, such as Afghanistan for heroin and Colombia for cocaine, and destination or consumer countries (Kruithof et al., 2016 ). Furthermore, cryptomarkets represent only a small fraction of the overall illicit drug market (Kruithof et al., 2016 ).

Despite some understanding of the geographic dispersion of cryptomarkets, the driving forces behind international trafficking on these platforms remain unclear. What factors influence a participant’s decision to buy from or sell to another country on cryptomarkets? And more broadly, what shapes the geography of international drug trafficking via cryptomarkets?

This article will attempt to answer these questions through social network analysis techniques and a new dataset of self-reported cryptomarket transactions. It aims to contribute to existing literature by: (1) exploring the impact of both offline (like geographic distance) and online variables (like technological development) on trafficking between countries on cryptomarkets, and (2) proposing that a country’s ability to regulate cryptomarkets can dissuade the establishment of online drug trafficking channels.

In the following sections, we will delve into relevant literature, formulate and test hypotheses assessing the relative influence of offline and online factors on drug trafficking via cryptomarkets. We will then introduce our novel crowdsourcing methodology and proceed to discuss the results. Our findings imply that countries involved in drug trafficking are not randomly connected; local factors indeed affect the movement of drugs from one country to another via cryptomarkets.

Online drug trafficking flows

Cryptomarkets, accessible solely via the darknet, are anonymous online marketplaces (Aldridge & Décary-Hétu, 2014 ). Ross Ulbricht inaugurated the first such market in 2011, driven by a libertarian ethos. He envisaged these cryptomarkets as open platforms for vendors to list their products and services (Barratt, 2012 ). Buyers could browse these listings, select their preferred product, and place an order. Transactions were conducted in bitcoin, and all connections were protected by the darknet, ensuring the anonymity of all parties (Martin et al., 2020 ).

Given the intensely competitive nature of cryptomarkets, vendors are required to disclose substantial information as part of their business. With few discernible differences between vendors selling similar products like cocaine (EMCDDA, 2023 ), they share information regarding their location, experience, and the quality of their products and services. Conversely, buyers on cryptomarkets have minimal incentives to reveal personal information as it would compromise their anonymity.

Initially, cryptomarkets were heralded as revolutionary criminal innovations (Aldridge & Décary-Hétu, 2014 ), posing a potential threat to traditional illicit drug markets (Barratt, 2012 ; Martin, 2014 ). For the first time, drug buyers could order any drug of their choice whenever they wished (Barratt et al., 2016a ). This suggested a shift in the international illicit drug trafficking network, bridging the gap between buyers and producers.

However, the first analysis of a cryptomarket by Christin ( 2013 ) challenged this theory. He found that the most active countries distributing illicit drugs on cryptomarkets, namely the United States, the United Kingdom, the Netherlands, Canada, and Germany, were more commonly known as consumer or transit countries rather than producers (UNODC, 2021 ). Consistently, these countries, along with Australia and other European nations, have been reported as the principal origins of all illicit drugs sold on cryptomarkets (Demant et al., 2018 ; Kruithof et al., 2016 ; Munksgaard et al., 2021 ). In many instances, cryptomarkets act as the final link in the distribution chain of illicit drugs (Duxbury & Haynie, 2018 ). Thus, drug traffickers typically import illicit drugs in bulk before redistributing them to buyers via cryptomarkets.

Norbutas’ study ( 2018 ) supports this, revealing that cryptomarket buyers often purchase from multiple vendors within their own country. In the case of international transactions, buyers usually prefer vendors from the same continent. Demant et al. ( 2018 ) drew similar conclusions, noting a propensity for national purchases over international ones.

Risk factors potentially explain this behavior. Décary-Hétu et al. ( 2016 ) found international drug shipments to be riskier, and a vendor’s circumstances significantly influenced their decision to operate internationally. Factors such as the volume of drugs sold, perceived law enforcement efficiency, and national demand could determine a vendor’s willingness to risk exporting illicit drugs via cryptomarkets. For example, vendors in countries with stringent border law enforcement like Finland, Australia, the United States, and Canada are often reluctant to ship internationally (Kruithof et al., 2016 ). However, the origin of the drugs could also affect their online sales. Countries like Germany, Canada, and the Netherlands, known for producing synthetic drugs like ecstasy, MDMA, and amphetamines (UNODC, 2022 ; EMCDDA, 2023 ), seem to favor international exports over domestic sales (Broséus et al., 2017 ).

Over the past decade, several studies have investigated international drug trafficking, its structure, and the various factors shaping offline trafficking routes. For instance, Boivin ( 2014a ) determined that drug trafficking typically follows specific routes and countries usually have a limited number of trading partners. Meanwhile, Chandra et al. ( 2011 ) discovered different countries playing key roles in the distribution of cocaine and heroin in Europe. This reflects the multifaceted nature of illicit drug markets and their ability to adapt to internal and external shocks. Utilizing various network techniques, Giommoni et al. ( 2017 ) found that geographical proximity and intense migration flows increased the likelihood of drug exchanges between countries. Interestingly, the risk of interception and arrest did not deter traffickers from exporting illicit drugs.

Our understanding of the factors influencing international drug trafficking via cryptomarkets remains limited. Previous research largely focuses on the concentration of sales, sellers, or buyers within countries instead of cross-border trafficking (Broséus et al., 2017 ; Christin, 2013 ; Demant et al., 2018 ; Dittus et al., 2018 ; Kruithof et al., 2016 ; Morelato et al., 2018 ; Soska & Christin, 2015 ). This methodology can highlight active countries within the cryptomarket ecosystem but provides little insight into why some countries export to or import from others. Cryptomarket data also tend to be inadequate for tracking trafficking flows (Broséus et al., 2017 ; Dittus et al., 2018 ; Morelato et al., 2018 ), with nearly 40% of sellers claiming to ship ‘worldwide,‘ which hampers precise mapping of trafficking routes (Broséus et al., 2017 ).

Moreover, the majority of these studies are descriptive, offering limited explanation of the various factors shaping trafficking routes on cryptomarkets (Broséus et al., 2017 ; Christin, 2013 ; Demant et al., 2018 ; Dittus et al., 2018 ; Kruithof et al., 2016 ; Morelato et al., 2018 ; Soska & Christin, 2015 ). An exception is Norbutas’ study ( 2018 ), which explored the structure of the now-defunct cryptomarket Abraxas, highlighting the geographic constraints of drug transactions. However, his analysis, conducted seven years ago, focused solely on geographic distance and neglected other influential factors. The unanswered questions include whether countries with more advanced communication and information infrastructures are more likely to engage in international drug trafficking on cryptomarkets, or whether the level of cryptomarket law enforcement deters international trading. In general, what online factors, besides geographic distance, impact the formation of drug trafficking routes on cryptomarkets? These queries remain largely unaddressed and require further empirical exploration.

The current study

In this study, we aim to examine the factors influencing drug trafficking via cryptomarkets. This constitutes the first investigation into how both online and offline elements shape drug trafficking routes via these cryptomarkets. Although previous research identified geography as a significant determinant of drug trafficking (Broséus et al., 2017 ; Demant et al., 2018 ; Dittus et al., 2018 ; Norbutas, 2018 ), the role of online and offline factors in creating drug trafficking links between nations has not yet been explored. From previous research, we formulate the following hypotheses:

Hypothesis 1[H1]: A country’s technological advancement level has a positive correlation with the formation of drug trafficking routes via cryptomarkets.

Cryptomarkets do not operate in isolation. They require specific technological infrastructures (Martin, 2014 ). Participants need access to a reliable internet connection and bitcoins for drug transactions (Aldridge & Décary-Hétu, 2016a ). Furthermore, they need resources to acquire the knowledge needed to operate efficiently on cryptomarkets. While some countries have these resources readily available, others do not. We hypothesize that countries with a more developed digital infrastructure – an online factor – are more likely to innovate in their drug trafficking and form drug trafficking connections with other nations. A country with a significant role in offline trafficking might have strong economic incentives to join cryptomarkets but might struggle to access them if high-speed connections and cryptocurrencies are scarce. Conversely, countries with highly developed technological infrastructures might easily participate in drug trafficking via cryptomarkets, even with a minimal economic return. Previous studies have demonstrated the positive impact of internet penetration on innovation development (Xiong et al., 2022 ) and international tourism expenditures (Lorente-Bayona et al., 2021 ). We anticipate the same for online drug trafficking.

Hypothesis 2[H2]: The farther two countries are from each other, the less likely they are to trade drugs on cryptomarkets.

The influence of geographic distance on legitimate and illicit trade is well-documented (Caulkins & Bond, 2012 ; Disdier & Head, 2008 ; Paoli & Reuter, 2008 ; Reuter, 2014 ). Distance augments transportation costs and the risk of interception and arrest. Although cryptomarkets primarily operate online, geographic distance – an offline factor – also affects them for similar reasons. Norbutas ( 2018 , p. 98) concluded in his analysis of the cryptomarket Abraxas that “buyers might be more willing to order domestically to avoid increased risks of package interception, potential arrest, and long shipping times.“ We, therefore, anticipate that geographic proximity plays a role in establishing drug trafficking routes via cryptomarkets.

Hypothesis 3[H3]: Sharing a common language increases the likelihood that two countries will trade drugs via cryptomarkets.

Language – an offline factor – can aid drug trafficking in two ways. Firstly, cultural affinity – such as speaking the same language – has been shown to reduce uncertainties by providing non-economic factors for buyers and sellers to trust each other. This principle applies to legal goods (Prashantham et al., 2015 ; Rauch & Trindade, 2002 ; Sgrignoli et al., 2015 ), and even more so to drug markets, where participants cannot rely on legal authorities to enforce agreements and are perpetually at risk of arrest (Paoli, 2002 ). Thus, language diminishes uncertainties between the two parties of a deal (Combes et al., 2005 ; Kleemans & Van de Bunt, 1999 ; Paoli & Reuter, 2008 ). Secondly, buyers and sellers must be able to read and write in the same language to understand the terms of a deal. For example, all other factors being equal, the USA is more likely to trade with the UK than with Brazil, given the larger English-speaking population in the former.

Hypothesis 4[H4]: A country’s ability to police cryptomarkets negatively correlates with the formation of drug trafficking routes.

In theory, the primary costs for drug dealers are those imposed by enforcement authorities, such as arrest, imprisonment, seizures, and confiscation (Caulkins & Reuter, 2010 ; Kuziemko & Levitt, 2004 ; Reuter & Kleiman, 1986 ). The higher the level of enforcement in a country, the less appealing it becomes to cryptomarket participants, as this increases their punishment risk. Although various theories suggest this, evidence shows that the intensity of enforcement – an offline factor – does not impact the formation of trafficking routes (Berlusconi et al., 2017 ; Boivin, 2014b ; Giommoni et al., 2017 ). Previous research found that perceived law enforcement effectiveness reduces international shipping of listings (Décary-Hétu et al., 2016 ). Effective law enforcement tactics might increase border inspections and disrupt the delivery of drugs purchased online. Therefore, we assume that enforcement and control levels can deter participants from trafficking drugs internationally on cryptomarkets due to the multi-faceted and somewhat disruptive nature of police operations against cryptomarkets (Décary-Hétu & Giommoni, 2017 ; Martin et al., 2020 ; Soska & Christin, 2015 ). A cybercrime report by Chainalysis ( 2021 ) suggests that, except for Russia, cryptomarkets have indeed experienced some disruption, as their size and scope have not significantly increased since 2018.

Methodology

The data for this study were sourced from the crowd-sourcing project DrugRoutes, which we launched online on January 1, 2020. DrugRoutes was an online platform that gathered transaction data directly from individuals who had bought or sold drugs on cryptomarkets. The website, accessible via the clear web or the darknet, allowed users to anonymously share information regarding their latest cryptomarket transactions. The data gathered included the specific type of illicit drug involved, the quantity traded, the transaction amount, the transaction date, the countries of origin and destination, and confirmation of parcel receipt. To encourage participation, DrugRoutes openly shared the collected data, enabling cryptomarket users to identify the most popular routes. Consistent with previous studies (Barratt et al., 2016b ; Martin et al., 2019 ), our methodology aimed to create a safe space for cryptomarket participants to contribute information for research purposes.

Every submission to the project underwent moderation by the authors to filter out potential spam. Submissions deemed too deviant from the prevalent cryptomarket prices per unit at the time were labeled as spam and excluded from the dataset. The research team cross-referenced the price per unit from multiple listings on several cryptomarkets and calculated an average. A transaction price from the same origin country that deviated more than one standard deviation from the mean was regarded as spam and removed from the dataset. We also removed multiple submissions made within seconds of each other as potential spam. While DrugRoutes was one of the few crowd-sourcing initiatives collecting information on illicit drug transactions (for example, see Government of Canada, 2022 ), it stands out as the only one incorporating successful delivery of illicit drugs. The research team advertised the crowd-sourcing platform on approximately 140 darkweb platforms, and the consent form and contact information were readily available on the website.

In total, we collected 1,364 submissions between 2020 and 2022, all of which were confirmed to be authentic and genuine. Below, we present some descriptive statistics to demonstrate the nature and characteristics of the collected sample. Figure  1 highlights the top fifteen buyer countries, while Fig.  2 displays the percentage of international transactions for these same countries. In line with several other studies, the United States is the primary buyer country (Aldridge & Décary-Hétu, 2014 ; Christin, 2013 ; Soska & Christin, 2015 ), followed by three European nations (Germany, France, and the United Kingdom), and then Canada and Australia. Figure  2 complements Fig.  1 by indicating which countries are more open to sourcing drugs internationally and which prefer to make purchases within national borders. Turkey, India, and Belgium concentrate most of their purchases internationally, while Russia, the USA, and Canada mainly fulfill their online drug demands domestically.

Top 15 buyer countries. National and international transactions

National V International transactions for the top 15 buyer countries (%)

Figure  3 broadens the scope of what we observed for the top buying countries, offering insight into the most prominent selling countries. Firstly, there are noteworthy differences between the two lists. Although the United States tops both rankings, several countries featured in Fig.  1 are absent from Fig.  3 , including the Netherlands, Mexico, Colombia, and Afghanistan. Figure  4 can assist us in understanding the roles these countries play in international trafficking via cryptomarkets. Countries like the United States, Australia, Italy, and Russia primarily cater to domestic markets, whereas the Netherlands, Mexico, Colombia, and Afghanistan focus at least 75% of their sales on international transactions. This corroborates literature on offline drug trafficking that designates these countries as either producers (Afghanistan and Colombia) or transit points before drugs reach their final destinations (UNODC, 2021 ).

Top 15 seller countries. National and international transactions

National V International transactions for the top 15 seller countries (%)

As this paper is exclusively concerned with international transactions, the subsequent analyses will omit data that pertain strictly to domestic trade. Table  1 presents the total number of international transactions recorded on DrugRoutes, differentiated by substance type. Cannabis is the most traded drug, accounting for over a quarter of transactions, followed by cocaine and LSD. MDMA and amphetamines constitute 8% and 6% of all transactions, respectively. Notably, the five most popular substances account for 70% of all transactions, with the remaining nine substances representing the remaining 30%. This distribution aligns with previous research based on the analysis of cryptomarket webometrics (Aldridge & Décary-Hétu, 2014 ), lending credence to the reliability of our data in mapping online drug trafficking routes.

Dependent variable

This study views drug trafficking on cryptomarkets as a network of relationships between countries. This perspective aligns with previous literature analyzing drug trafficking across nations (Aziani et al., 2021 ; Bichler & Jimenez, 2022 ; Boivin, 2014b ), and recent studies investigating the geographic structure of drug trafficking on cryptomarkets (Broséus et al., 2017 ; Norbutas, 2018 ).

We utilize data from DrugRoutes to identify relationships between countries. DrugRoutes solicited information from cryptomarket participants about their home country and the country with which they most recently transacted. Consequently, we establish a link from Germany to Spain if a participant based in Germany reports purchasing drugs from a dealer in Spain, or if a Spanish drug dealer declares having shipped drugs to Germany. Using this method, we identified a total of 731 different transactions involving 372 dyads across 42 pairs of countries.

The network of drugs trafficked via cryptomarkets is characterized by two distinctive features. First, we only consider a connection if at least two submissions are reported for a pair of countries. For example, we dismissed the connection between Albania and Ireland since we have only one observation following this route. These connections are more likely to be random or sporadic links between countries and, therefore, are not included in our analysis. The final network is predicated on a total of 100 exchanges between any two countries.

Secondly, we do not differentiate between substances. For example, a connection between Spain and Germany for cannabis is regarded in the same way as a connection between France and Germany for cocaine. Given that we have only a few transactions for most substances, creating individual networks for each illicit drug type would result in very small networks. As a result, we opted to group all drug types together to avoid information loss. More crucially, we anticipate the independent variables to exert a similar effect on cryptomarket transactions, irrespective of the drug type. This approach also enables us to compare our findings to previous studies that do not differentiate between substances (Broséus et al., 2017 ; Morelato et al., 2018 ; Norbutas, 2018 ).

Independent variables

This study employs both nodal and relational attributes data to decipher the factors that influence the geographic arrangement of drug trafficking through cryptomarkets. Nodal attributes represent unique characteristics of the countries comprising the network, such as a country’s gross domestic product (GDP) per capita or its population size. On the other hand, relational attributes provide insights about the connections between any two countries within the network, like the distance between Spain and Germany. Table  2 presents all variables used in this analysis, detailing the source, reference period, the nature of the variable (i.e., nodal or relational attribute), and relevant descriptive statistics.

We utilized the Information and Communication Development (ICT) index as a country-level indicator of technological progress to verify our initial hypothesis. This empirically derived index comprises three weighted sub-indices (infrastructure access, intensity, skills) and facilitates cross-national comparisons (ITU, 2020 ).

We examined the impact of geographic proximity ( H2 ) and social proximity ( H3 ) using two matrices; one calculating the geographic distance between countries, and another evaluating the prevalence of a language spoken by a minimum of 9% of the population in any pair of countries. Both variables were sourced from the Centre d’Études Prospectives et d’Informations Internationales (CEPII) and have been previously employed in several studies to measure social and geographic proximity (Favarin & Aziani, 2020 ; Giommoni et al., 2017 ).

We operationalized a country’s capacity to olice cryptomarkets ( H4 ) using the Global Cybersecurity Index (GCI) developed by the International Telecommunications Union (ITU) of the United Nations. The GCI assesses each country’s developmental stage in five areas: legal measures, technical measures, organizational measures, capacity development, and cooperation. Our decision to use this index was motivated by three factors. First, since cryptomarkets operate online, it seemed logical to employ a variable examining a country’s online resilience, rather than conventional offline law enforcement metrics (e.g., number of police forces or arrests). Many interventions against cryptomarkets involve specialized cybercrime policing units such as the Netherlands National High Tech Crime Unit or the Dark Web Intelligence, Collection, and Exploitation team within the British National Crime Agency. Second, proxies indicating the level of enforcement across countries are notoriously deficient and lack comparability (Aebi & Linde, 2015 ; Kilmer et al., 2015 ). Third, several countries examined in this analysis neither collect nor report any data related to cybercrime.

Exponential Random Graph Models (ERGMs) were employed to ascertain the factors influencing the geographic arrangement of drug trafficking through cryptomarkets. ERGMs comprise a category of statistical models applicable to relational data, evaluating the likelihood of a connection between two countries in the network based on the individual country attributes (e.g., their ICT index) and attributes of country pairs (e.g., geographical distance between two countries). Unlike traditional statistical models, ERGMs don’t assume observation independence, thereby allowing for testing or controlling network attributes such as the propensity towards centralization (Lusher et al., 2013 ; Robins et al., 2007 ).

Besides the independent variables previously discussed, one of the models incorporates two controls to compensate for outdegree centralization and reciprocity. The latter accounts for the likelihood of reciprocal connections between any two given countries, while we employed the GWODEGREE parameter to control for a country’s probability of establishing a new outgoing tie based on the number of existing ties with other countries (Hunter, 2007 ). All network analyses were conducted utilizing the Statnet suite of packages for R (Butts, 2008 ; Handcock et al., 2018 ; R Core Team, 2021 ). The model encompassing parameters for centralization and reciprocity employed Markov Chain Monte Carlo simulation methods to approximate the maximum likelihood (Hunter et al., 2008 ). Appendix 2 features goodness-of-fit plots that compare observed networks with simulated ones, evaluating the overall fit of the models discussed in the “ Results ” section below.

This study was approved by the Social Science Research Ethics Committee at Cardiff University (SREC/3197), underscoring our commitment to ethical considerations. The research was guided by two fundamental principles: (1) the process of data collection and analysis should not expose any party involved to potential harm, and (2) no personally identifying information would be collected or disclosed at any stage of the research. Although DrugRoutes was accessible on both the clear and dark web, we did not gather any sensitive data such as IP addresses or geolocation of submissions. It’s also crucial to clarify that while the platform disseminated information about prevalent drug trafficking routes on the dark web, it didn’t indicate the routes least likely to be intercepted. Our objective was to illuminate the operations of cryptomarkets, not to furnish guidance on successful strategies for online drug trafficking.

Table  3 provides descriptive statistics of the drug trafficking network through cryptomarkets, with Fig.  5 offering a visual depiction of the network. Comprising 42 countries and 100 links, the network represents close to 6% of all possible links. This observation aligns well with prior studies indicating that offline drug trafficking has a low density and tends to concentrate along specific routes (Boivin, 2013 , 2014a ; Giommoni et al., 2017 ).

Trafficking network via cryptomarkets

Despite the sparse density, countries usually obtain illicit drugs from multiple sources, as on average, each country imports from more than two nations and conducts trade with nearly five countries. However, the number of connections is not evenly distributed, as demonstrated in Fig.  6 . While most countries export to one or a few countries, a handful export to numerous others. This suggests that, akin to offline drug trafficking, some countries play a central role in trafficking via cryptomarkets. Germany (20), the Netherlands (20), and the USA (15) stand out due to the number of outgoing ties with other countries. This is not entirely unexpected – prior studies have revealed that the USA dominates cryptomarket transactions (Aldridge & Décary-Hétu, 2016b ; Christin, 2013 ), while Germany and the Netherlands are known for their key roles as redistribution centers for heroin, cocaine, and cannabis within Europe (Aziani et al., 2021 ; Lahaie et al., 2015 ; Paoli & Reuter, 2008 ).

Scatterplot between indegree and outdegree along with their distribution

The distribution of incoming ties is more evenly spread than that of outgoing ties (in-degree centralisation stands at 0.36, while out-degree centralisation is at 0.49), with forty-one countries having between zero and nine connections. With seventeen incoming ties, the USA emerges as a clear outlier. This can be explained by both online and offline factors. Firstly, as noted earlier, the USA accounts for a significant majority of illicit drug transactions on cryptomarkets. Secondly, with a population exceeding 300 million, it is one of the world’s primary consumer markets for illicit drugs.

Reciprocity offers further valuable insights into the network. It reveals that 26% of connections are reciprocated, meaning that in a quarter of the cases, Country A imports from Country B, and conversely, Country B imports from Country A. This feature characterises drug trafficking via cryptomarkets, a phenomenon less common in offline drug markets. Offline trafficking generally follows a single direction - for instance, the UK imports from the Netherlands, but the Netherlands does not reciprocate. However, this dynamic occurs within cryptomarkets, albeit on a small scale. Cryptomarkets can broaden geographic and informal networks by offering alternative paths to traditional routes. For example, even though illicit drugs usually move from the Netherlands to the UK, Dutch-based buyers might find a better deal in the UK. It’s also possible that reciprocity is an artifact of the drugs traded on cryptomarkets. As most transactions involve cannabis, which is produced in almost every country, drug trafficking is less tied to a single direction and more open to reciprocal exchanges.

Table  4 details the estimates and standard errors from ERGMs of drug trafficking via cryptomarkets. Model 1 includes all variables operationalising our four hypotheses. Most of these variables are significantly associated with the dependent variable, and their direction aligns with our predictions. The exceptions are the ICT index for exporter countries and common language. While the ICT index standard errors are relatively small and close to the significance threshold of 0.05, the standard errors for common language are considerably larger.

Model 2 is the final model, incorporating structural effects to manage the impact of exporters and mutual connections. This model reveals that social proximity, defined by the existence of a language spoken by at least 9% of the population in any pair of countries, is significantly associated with traditional trafficking but doesn’t explain cryptomarket trafficking. Two reasons can account for this variance.

First, cryptomarkets replace social proximity with a collection of deliberately crafted mechanisms aimed at fostering trust among participants (Martin et al., 2019 ; Munksgaard, 2021 ). The question of how a buyer can trust a seller on cryptomarkets arises — how can they ascertain the seller will not abscond with their money? The answer lies in the transparency of past transactions, feedback from prior buyers, and a series of strategies that allow buyers to form informed judgments about a vendor’s trustworthiness and reliability (Tzanetakis et al., 2016 ). Cryptomarkets have engineered mechanisms to identify reliable partners and mitigate deceitfulness, rendering social proximity unnecessary.

Second, there are active cryptomarkets in various languages at any given time. Participants have access to online markets in any language, and they do not need to learn or use another language to buy drugs online. However, proficiency in English, or at least the ability to read and write in English, is a requirement for participating in certain cryptomarkets. Hence, the ecosystem’s diversity reduces the significance of language.

This study, for the first time, underscores the role that a country’s information technology infrastructure plays in facilitating drug trafficking transactions on cryptomarkets. Digital restrictions are as crucial as offline restrictions and account significantly for importing countries. To sell or buy drugs online, participants need access to a high-speed internet connection, the Tor browser or an alternative anonymous network like I2P, and the capability to set up an anonymous Bitcoin wallet (Basheer, 2022 ). Usually, these are not enough; participants in cryptomarkets often need to take extra steps to increase their anonymity, such as setting up encrypted emails, encrypting all communications, and using a VPN (Horton-Eddison et al., 2021 ).

In some countries, these technologies are readily available, contributing to digital skills being more widespread among the population. However, this might not be the case in other countries that could potentially benefit from joining cryptomarkets. For example, Colombian dealers could reap substantial profits from selling on cryptomarkets, as the domestic wholesale price for a kilogram of cocaine is about $1,500, while it is $45,000 in the UK (UNODC, 2021 ). But the internet penetration in Colombia is 65%, with a significant proportion of those without internet access likely residing in more rural areas where cocaine production is more concentrated. By contrast, internet penetration in the UK is 95%. This disparity helps illuminate — albeit incompletely — why the UK has a more central role in drug cryptomarket trafficking than Colombia.

The degree of a country’s cybersecurity commitment inversely impacts the likelihood of outgoing trafficking connections with other nations. In essence, the more equipped a country is to combat cybercrime, the fewer outgoing connections it has. This observation is contrary to much of the empirical research on drug law enforcement and some studies on policing cryptomarkets. Research on international drug trafficking drivers reveals that stringent law enforcement actions do not deter a country from establishing trafficking connections (Aziani et al., 2021 ; Berlusconi et al., 2017 ; Giommoni et al., 2017 ). Likewise, police operations’ impact on cryptomarkets is often minimal. The majority of studies concur that cryptomarket participants tend to adjust to law enforcement interventions by implementing extra security measures or shifting to other markets (Décary-Hétu & Giommoni, 2017 ; Ladegaard, 2018 , 2019 ; van Wegberg & Verburgh, 2018 ). Police successes, at best, are fleeting and diminish over time.

There are a few reasons why a more advanced cybersecurity infrastructure might make drug trafficking via cryptomarkets less attractive. Firstly, the Global Cybersecurity Index evaluates a country’s readiness to confront cybercrime. Individual police operations may yield limited success, but the overall cybersecurity infrastructure could deter people from exporting illicit drugs via cryptomarkets. This might seem counterintuitive, but it aligns with traditional drug trafficking. Most police operations are deemed limited in duration and scope, but illicit drugs remain less accessible and costlier than if legalized (Kleiman, 2009 ; Pollack & Reuter, 2014 ). The mere existence of drug law enforcement influences drug markets. The second reason is more technical, relating to how we measure levels of enforcement. The GCI is a composite indicator accounting for different aspects of cybersecurity, such as organizational measures, capacity development, and cooperation. There is not a comparable measure for a country’s ability to counter international drug trafficking, leading most studies to resort to questionable proxies like the number of police officers per capita (Giommoni et al., 2017 ). We posit that the GCI provides a more robust and comprehensive indicator of enforcement against cybercrime, including cryptomarkets.

Both models also indicate that trafficking is likelier between geographically proximate countries. Mirroring trends in legal trade and offline trafficking, geographic distance escalates the costs associated with drug trafficking via cryptomarkets (Caulkins & Bond, 2012 ; Disdier & Head, 2008 ; Favarin & Aziani, 2020 ). Long-haul deliveries require more ingenious methods of drug concealment, incur lengthier shipping times, and may increase the risk of interception (Décary-Hétu et al., 2016 ; Norbutas, 2018 ). Despite dealers’ profit-driven motives, their main objective is to evade arrest (Caulkins & Reuter, 2010 ; Pollack & Reuter, 2014 ). Geographic distance amplifies this risk and can discourage dealers from engaging in long-distance transactions.

Finally, the significant and negative direction of the exporter effect indicates that a country’s likelihood of forming a new outgoing tie decreases as its existing ties increase. This implies that the number of countries with cryptomarket consumers is limited and that certain countries may have nearly saturated at least their regional market once they attain a certain size. The reciprocity variable, being positive and significant, suggests a tendency towards mutual ties in the network. This might indicate that cryptomarket participants are conscious of the relative safety of transactions on cryptomarkets. If drugs can be successfully transported in one direction, consumers might be more inclined to order drugs internationally in the opposite direction, particularly if less.

Conclusions

This study presents compelling insights into the mechanics of drug trafficking within cryptomarkets. It challenges conventional notions of social proximity, demonstrating that shared language or traditional relationships are less relevant in these digital platforms. Instead, the establishment of trust-based mechanisms and multilingual capacities are more integral to interactions in cryptomarkets.

Furthermore, it highlights the significant role of a country’s digital infrastructure and its IT capabilities in shaping its involvement in online drug trafficking. The level of internet penetration, availability of digital anonymity tools, and population’s digital literacy can affect the scale and nature of a country’s participation in these cryptomarkets.

The study also underlines the unexpected effects of strong cybersecurity infrastructure. Contrary to the conventional enforcement approach, which often fails to deter trafficking, advanced cybersecurity measures seem to reduce a country’s involvement in the online drug trade. This finding could reshape our understanding of effective strategies to combat online drug trafficking.

Geographical proximity remains a critical factor, even in digital markets (Décary-Hétu et al., 2016 ; Norbutas, 2018 ). The risks and costs associated with longer shipping distances can deter dealers from international transactions, reflecting the influence of physical logistics on online trade. Overall, these findings suggest that cryptomarkets operate under different dynamics than traditional markets and need unique strategies for intervention and control.

This paper provides key methodological advancements in studying online drug trading. By harnessing crowd-sourced data, we haveve managed to explore this field more deeply than ever before. While cryptomarkets tend to provide elusive details about buyer locations, our platform has proven successful in gathering and examining data on trafficking routes. As a result, we now have an unparalleled glimpse into the pathways of drug movement across international borders through the dark web.

Yet, this innovative approach is not without its challenges. The data collection process led to an unrandomized sample, due to a self-selection bias among participants. With no concrete understanding of why some users shared information and others did not, our findings could potentially be skewed. However, such biases are common in research involving illicit activities like drug trading. To ensure the accuracy of our findings, we implemented stringent checks to remove spam, outliers, and infrequent connections between countries.

Moving forward, fostering stronger relationships with participants in illicit drug markets is crucial for the success of crowd-sourcing platforms. Although launching and maintaining such platforms like DrugRoutes requires significant effort and resources, they offer a unique opportunity to gain a deeper understanding of criminal behaviour and to prevent crime in our increasingly digital society. It provides a fresh, innovative approach amidst a growing array of diverse methods for studying and comprehending online drug markets (Barratt & Maddox, 2016 ; Munksgaard & Martin, 2020 ).

Data availability

Data supporting this study are openly available at https://doi.org/10.17035/d.2023.0267197475 .

Reference list

Aebi, M. F., & Linde, A. (2015). The epistemological obstacles in comparative criminology: A special issue introduction. European Journal of Criminology, 12 (4), 381–385. https://doi.org/10.1177/1477370815595311

Article   Google Scholar  

Aldridge, J. (2019). Does online anonymity boost illegal market trading? Media Culture & Society . https://doi.org/10.1177/0163443719842075

Aldridge, J., & Décary-Hétu, D. (2014). Not an “Ebay for Drugs”: The cryptomarket “Silk Road” as a paradigm shifting criminal innovation (SSRN Scholarly Paper ID 2436643). Social Science Research Network. http://papers.ssrn.com/abstract=2436643

Aldridge, J., & Décary-Hétu, D. (2016a). Cryptomarkets and the future of illicit drug markets. The Internet and Drug Markets , (2016), 23–32.

Aldridge, J., & Décary-Hétu, D. (2016b). Hidden wholesale: The drug diffusing capacity of online drug cryptomarkets. International Journal of Drug Policy . https://doi.org/10.1016/j.drugpo.2016.04.020

Aziani, A., Berlusconi, G., & Giommoni, L. (2021). A quantitative application of enterprise and social embeddedness theories to the transnational trafficking of cocaine in Europe. Deviant Behavior, 42 (2), 245–267.

Barratt, M. J. (2012). Silk road: Ebay for drugs. Addiction, 107 (3), 683–683. https://doi.org/10.1111/j.1360-0443.2011.03709.x

Barratt, M. J., Ferris, J. A., & Winstock, A. R. (2016). Safer scoring? Cryptomarkets, social supply and drug market violence. International Journal of Drug Policy . https://doi.org/10.1016/j.drugpo.2016.04.019

Barratt, M. J., Lenton, S., Maddox, A., & Allen, M. (2016). What if you live on top of a bakery and you like cakes?’—Drug use and harm trajectories before, during and after the emergence of Silk Road. International Journal of Drug Policy . https://doi.org/10.1016/j.drugpo.2016.04.006

Barratt, M. J., & Maddox, A. (2016). Active engagement with stigmatised communities through digital ethnography. Qualitative Research, 16 (6), 701–719. https://doi.org/10.1177/1468794116648766

Basheer, R. (2022). Cryptomarkets’ phenomenon: A conceptualization approach. Human Behavior and Emerging Technologies, 2022 , e6314913. https://doi.org/10.1155/2022/6314913

Berlusconi, G., Aziani, A., & Giommoni, L. (2017). The determinants of heroin flows in Europe: A latent space approach. Social Networks, 51 , 104–117.

Bichler, G., & Jimenez, I. (2022). Drug smuggling seizures: The effects of reporting consistency and quality on the observed transnational structure. Journal of Drug Issues , 00220426221107550. https://doi.org/10.1177/00220426221107550

Boivin, R. (2013). Drug trafficking networks in the world economy. In C. Morselli (Ed.), Crime and networks (pp. 182–194). Routledge.

Google Scholar  

Boivin, R. (2014). Macrosocial network analysis: The case of transnational drug trafficking. In A. J. Masys (Ed.), Networks and Network Analysis for Defence and Security (pp. 49–61). Springer.

Chapter   Google Scholar  

Boivin, R. (2014). Risks, prices, and positions: A social network analysis of illegal drug trafficking in the world-economy. International Journal of Drug Policy, 25 (2), 235–243.

Broséus, J., Rhumorbarbe, D., Morelato, M., Staehli, L., & Rossy, Q. (2017). A geographical analysis of trafficking on a popular darknet market. Forensic Science International, 277 , 88–102. https://doi.org/10.1016/j.forsciint.2017.05.021

Butts, C. T. (2008). Social network analysis: A methodological introduction. Asian Journal of Social Psychology, 11 (1), 13–41.

Caulkins, J. P., & Bond, B. M. (2012). Marijuana price gradients implications for exports and export-generated tax revenue for California after legalization. Journal of Drug Issues, 42 (1), 28–45.

Caulkins, J. P., & Reuter, P. H. (2010). How drug enforcement affects drug prices. Crime and Justice, 39 (1), 213–271.

Chainalysis. (2021). The Chainalysis 2021 Crypto Crime Report . https://go.chainalysis.com/2021-Crypto-Crime-Report.html

Chandra, S., Barkell, M., & Steffen, K. (2011). Inferring cocaine flows across Europe: Evidence from price data. Journal of Drug Policy Analysis, 4 (1), 1941–2851.

Christin, N. (2013). Traveling the silk road: A measurment analysis of a large anonymous online marketplace . Proceedings of the 22nd International Conference on World Wide Web.

Combes, P. P., Lafourcade, M., & Mayer, T. (2005). The trade-creating effects of business and social networks: Evidence from France. Journal of International Economics, 66 (1), 1–29. https://doi.org/10.1016/j.jinteco.2004.07.003

Décary-Hétu, D., & Giommoni, L. (2017). Do police crackdowns disrupt drug cryptomarkets? A longitudinal analysis of the effects of Operation Onymous. Crime Law and Social Change, 67 (1), 55–75. https://doi.org/10.1007/s10611-016-9644-4

Décary-Hétu, D., Paquet-Clouston, M., & Aldridge, J. (2016). Going international? Risk taking by cryptomarket drug vendors. International Journal of Drug Policy . https://doi.org/10.1016/j.drugpo.2016.06.003

Demant, J., Munksgaard, R., Décary-Hétu, D., & Aldridge, J. (2018). Going local on a global platform: A critical analysis of the transformative potential of cryptomarkets for organized illicit drug crime. International Criminal Justice Review, 28 (3), 255–274. https://doi.org/10.1177/1057567718769719

Disdier, A. C., & Head, K. (2008). The Puzzling persistence of the distance effect on bilateral trade. Review of Economics and Statistics, 90 (1), 37–48. https://doi.org/10.1162/rest.90.1.37

Dittus, M., Wright, J., & Graham, M. (2018). Platform criminalism: The last-mile geography of the darknet market supply chain. ACM The Web Conference 2018 . https://ora.ox.ac.uk/objects/uuid:1ecd2466-4849-4383-b556-7c9c9e7ffef4

Duxbury, S. W., & Haynie, D. L. (2018). The network structure of opioid distribution on a darknet cryptomarket. Journal of Quantitative Criminology, 34 (4), 921–941.

EMCDDA. (2023). European drug report 2023: Trends and developments . Lisbon: European Monitoring Centre for Drugs and Drug Addiction. https://www.emcdda.europa.eu/publications/european-drug-report/2023_en

Favarin, S., & Aziani, A. (2020). The global waste trafficking and its correlates. Journal of Contemporary Criminal Justice, 36 (3), 351–383. https://doi.org/10.1177/1043986220939701

Giommoni, L., Aziani, A., & Berlusconi, G. (2017). How do illicit drugs move across countries? A network analysis of the heroin supply to Europe. Journal of Drug Issues, 47 (2), 217–240. https://doi.org/10.1177/0022042616682426

Government of Canada. (2022). StatsCannabis . https://surveys-enquetes.statcan.gc.ca/cannabis/

Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., Krivitsky, P. N., Bender-deMoll, S., & Morris, M. (2018). Statnet: Software tools for the statistical analysis of network data (2018.10) [Computer software]. The Statnet Project. www.statnet.org

Horton-Eddison, M., Shortis, P., Aldridge, J., & Caudevilla, F. (2021). Drug cryptomarkets in the 2020s: Policy, enforcement, harm, and resilience . Swansea: Global Drug Policy Observatory

Hunter, D. R. (2007). Curved exponential family models for social networks. Social Networks, 29 (2), 216–230.

Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). ERGM: A package to fit, simulate and diagnose exponential-family models for networks. Journal of Statistical Software , 24 (3).

ITU. (2020). The ICT Development Index (IDI): Conceptual framework and methodology . https://www.itu.int/en/ITU-D/Statistics/Pages/publications/mis2017/methodology.aspx

Kilmer, B., Reuter, P. H., & Giommoni, L. (2015). What can be learned from cross-national comparisons of data on illegal drugs? Crime and Justice, 44 (1), 227–296.

Kleemans, E., & Van de Bunt, H. G. (1999). The social embeddedness of organized crime. Transnational Organized Crime, 5 (1), 19–36.

Kleiman, M. (2009). When brute force fails: How to have less crime and less punishment . Princeton University Press.

Kruithof, K., Aldridge, J., Décary-Hétu, D., Sim, M., Dujso, E., & Hoorens, S. (2016). Internet-facilitated drugs trade (pp. 21–32). RAND Corporation.

Kuziemko, I., & Levitt, S. D. (2004). An empirical analysis of imprisoning drug offenders. Journal of Public Economics, 88 (9–10), 2043–2066.

Ladegaard, I. (2018). We know where you are, what you are doing and we will catch you: Testing deterrence theory in digital drug markets. The British Journal of Criminology, 58 (2), 414–433. https://doi.org/10.1093/bjc/azx021

Ladegaard, I. (2019). Crime displacement in digital drug markets. International Journal of Drug Policy, 63 , 113–121. https://doi.org/10.1016/j.drugpo.2018.09.013

Lahaie, E., Janssen, E., & Cadet-Taïrou, A. (2015). Determinants of heroin retail prices in metropolitan France: Discounts, purity and local markets. Drug and Alcohol Review . https://doi.org/10.1111/dar.12355

Lorente-Bayona, L. V., Gras-Gil, E., & del Rocío Moreno-Enguix, M. (2021). Internet penetration and international travel and tourism expenditure: The role of foreign exchange control. Tourism Economics, 28 (8), 2050–2067.

Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2013). Exponential random graph models for social networks: Theory, methods, and applications . Cambridge University Press.

Martin, J. (2014). Drugs on the dark net: How cryptomarkets are transforming the global trade in illicit drugs . Palgrave Pivot.

Martin, J., Cunliffe, J., & Munksgaard, R. (2019). Cryptomarkets: A research companion . Emerald Publishing Limited.

Book   Google Scholar  

Martin, J., Munksgaard, R., Coomber, R., Demant, J., & Barratt, M. J. (2020). Selling drugs on darkweb cryptomarkets: Differentiated pathways, risks and rewards. The British Journal of Criminology, 60 (3), 559–578. https://doi.org/10.1093/bjc/azz075

Morelato, M., Broséus, J., De Grazia, A., Tahtouh, M., Esseiva, P., & Roux, C. (2018). Forensic drug intelligence and the rise of cryptomarkets. Part II: Combination of data from the physical and virtual markets. Forensic Science International, 288 , 201–210. https://doi.org/10.1016/j.forsciint.2018.05.002

Munksgaard, R. (2021). Trust and exchange: The production of trust in illicit online drug markets . https://papyrus.bib.umontreal.ca/xmlui/handle/1866/25234

Munksgaard, R., Décary-Hétu, D., Malm, A., & Nouvian, A. (2021). Distributing tobacco in the dark: Assessing the regional structure and shipping patterns of illicit tobacco in cryptomarkets. Global Crime, 22 (1), 1–21. https://doi.org/10.1080/17440572.2020.1799787

Munksgaard, R., & Martin, J. (2020). How and why vendors sell on cryptomarkets. Trends and Issues in Crime and Criminal Justice [Electronic Resource], 608 , 1–12. https://doi.org/10.3316/agispt.20201215041193

Norbutas, L. (2018). Offline constraints in online drug marketplaces: An exploratory analysis of a cryptomarket trade network. International Journal of Drug Policy, 56 , 92–100. https://doi.org/10.1016/j.drugpo.2018.03.016

Ouellet, M., Décary-Hétu, D., & Bergeron, A. (2022). Cryptomarkets and the returns to criminal experience. Global Crime, 23 (1), 65–80. https://doi.org/10.1080/17440572.2022.2028622

Paoli, L. (2002). The paradoxes of organized crime. Crime Law and Social Change, 37 , 51–97.

Paoli, L., & Reuter, P. H. (2008). Drug trafficking and ethnic minorities in Western Europe. European Journal of Criminology, 5 (1), 13–37.

Pollack, H. A., & Reuter, P. (2014). Does tougher enforcement make drugs more expensive? Addiction, 109 (12), 1959–1966. https://doi.org/10.1111/add.12497

Prashantham, S., Dhanaraj, C., & Kumar, K. (2015). Ties that bind: Ethnic ties and new venture internationalization. Long Range Planning, 48 (5), 317–333. https://doi.org/10.1016/j.lrp.2015.02.005

R Core Team. (2021). R: A language and environment for statistical computing (4.1.1) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org

Rauch, J. E., & Trindade, V. (2002). Ethnic chinese networks in international trade. The Review of Economics and Statistics, 84 (1), 116–130.

Reuter, P. H. (2014). The mobility of drug trafficking. In LSE (Ed.), Ending the Drug Wars. Report of the LSE Expert Group on the Economics of Drug Policy . The London School of Economics and Political Science.

Reuter, P. H., & Kleiman, M. A. R. (1986). Risks and prices: An economic analysis of drug enforcement. Crime and Justice, 7 , 289–340.

Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to Exponential Random Graph (p*) models for social networks. Social Networks, 29 , 173–191.

Sgrignoli, P., Metulini, R., Schiavo, S., & Riccaboni, M. (2015). The relation between global migration and trade networks. Physica A: Statistical Mechanics and its Applications, 417 , 245–260. https://doi.org/10.1016/j.physa.2014.09.037

Soska, K., & Christin, N. (2015). Measuring the longitudinal evolution of the online anonymous marketplace ecosystem . Proceedings of the 24th USENIX Security Symposium, Washington, D.C.

Tzanetakis, M., Kamphausen, G., Werse, B., & von Laufenberg, R. (2016). The transparency paradox. Building trust, resolving disputes and optimising logistics on conventional and online drugs markets. International Journal of Drug Policy, 35 , 58–68. https://doi.org/10.1016/j.drugpo.2015.12.010

UNODC. (2021). World Drug Report 2021 . United Nation Office on Drugs and Crime. https://www.unodc.org/unodc/en/data-and-analysis/wdr2021.html

UNODC. (2022). World drug report 2022 . Vienna: United Nation Office on Drugs and Crime. https://www.unodc.org/unodc/data-and-analysis/world-drug-report-2022.html

van Wegberg, R. S., & Verburgh, T. (2018). Lost in the dream? Measuring the effects of operation bayonet on vendors migrating to dream market. WebSci’18 . http://resolver.tudelft.nl/uuid:8c080055-37fb-4f53-a949-099110f91659

Xiong, F., Zang, L., & Gao, Y. (2022). Internet penetration as national innovation capacity: Worldwide evidence on the impact of ICTs on innovation development. Information Technology for Development, 28 (1), 39–55. https://doi.org/10.1080/02681102.2021.1891853

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This work was supported by the Economic and Social Research Council under Grant ES/S008853/1.

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Luca Giommoni

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David Décary-Hétu & Andréanne Bergeron

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All authors contributed to the study conception and design. AB and DDH collected and cleaned and prepared the data. LG and GB performed all the analysis and wrote the first draft of the sections Introduction, The current study, Methodology and Results. AB and DDH drafted the literature review and conclusions. All authors read and approved the final manuscript.

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Goodness of fit diagnostics for Model 1

Goodness of fit diagnostics for Model 2

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Giommoni, L., Décary-Hétu, D., Berlusconi, G. et al. Online and offline determinants of drug trafficking across countries via cryptomarkets. Crime Law Soc Change 81 , 1–25 (2024). https://doi.org/10.1007/s10611-023-10106-w

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• J Migr Health

The prevalence of drug use and illicit trafficking: A descriptive cross sectional study of irregular migrant returnees in Nigeria

Ikenna daniel molobe.

a Unified Initiative for a Drug Free Nigeria (UIDFN), Lagos, Nigeria

b Non-Communicable Disease Research Group, University of Lagos, Lagos, Nigeria

Oluwakemi Ololade Odukoya

c Department of Community Health and Primary Care, College of Medicine, University of Lagos, Nigeria

• • Drug use prevalence in irregular migration among study participants is 61.3%.
• • Alcohol and marijuana is mostly the drug use among irregular migrants.
• • Migration frustration and trauma are the major reason for drug use.
• • Marijuana is most trafficked drug by irregular migrants.

The study assessed the prevalence of drug use and drug trafficking among Nigerian returned migrants from Libyan detention centers in the transit or destination along the Mediterranean irregular migration route. This is a descriptive cross sectional study. The study population was restricted to migrants who returned from May 2017 and April 2018. A total of 382 (238 male and 144 female) were contacted and provided information for this study. These participants were recruited using judgemental and snowballing techniques. Both quantitative and qualitative methods of data collection were used. Results showed that 61.3% of the respondents used drugs during their migration. Drug use was predominant among those in the younger age group (26–30) accounting for 24.9%. The study revealed reasons for respondents’ drug use which were as a result of migration frustration and trauma, or compulsion. The findings on drug trafficking revealed that 15.7% of the respondents engaged in drug trafficking during their migration, and 28% of the respondents that trafficked drugs had previous experience of arrest or detention by law enforcement agent (in Libya). Findings from this study showed high prevalence of drug use among irregular migrants. Experience of migration frustration and trauma were among the factors that contribute to drug use among the migrant population. The study also discovered that some of the migrants who got into illicit drug trafficking were to raise money for survival while some were compelled into the business. The high prevalence of drug use among irregular migrants from findings draw attention to the important implications for public health and social security, while drug trafficking in existence among migrants calls for need of social reintegration.

Introduction

Irregular migration, according to the International Organization for Migration (IOM), can be defined as entry into another country in contravention of immigration laws of that country. Therefore, irregular migrants include, among others, clandestine, illegal, unauthorized, unlawful, undocumented, aliens without residence status, illegalized people, non-compliant, and without documents ( International Organization for Migration 2011 ).

According to IOM, many young people felt deceived about better options for life and work if they migrate ( International Organization for Migration 2011 ). These young people often fall into wrong hands of organized recruiters who facilitate their movement outside the country ( International Organization for Migration 2004 ). In reports, such as of the United Nations Office on Drugs and Crime (UNODC) and United Nations Interregional Crime and Justice Research Institute (UNCRI) on organized crime and irregular migration, human trafficking from Africa to Europe; some of these migrants are introduced into drug trafficking ( UNCRI 2004 ; UNODC 2006 ). These migrants also face a lot of hardship and could be frustrated both on their way and to their final destination ( UNODC 2011 ). Sometimes irregular migrants end up being stranded as many go into drug peddling, commercial sex work, street begging ( UNCRI 2004 ; UNODC 2011 ; García and Laura, 2009 ), or even drug use as a result of psychological trauma ( Borges et al., 2012 ). Some irregular migrants end up in prisons as a result of penalty against crimes committed and therefore constituting nuisance to the country where they migrated into ( García and Laura, 2009 ).

As reported by the International Centre for Migration Policy Development (ICMPD), victims in most cases appear to be aware that they will be expected to work as drug peddlers at their destination, but it seems that they are often ignorant of the full implications ( International Centre for Migration Policy Development 2004 ). Having committed themselves to drug traffickers or to the consortium that will control their activities, they become bond slaves without the right to opt out of their contract and are liable to be subjected to abuse of various sorts ( International Centre for Migration Policy Development 2004 ).

Drug trafficking and drug use are present in transnational migration and among irregular migrants. As revealed in the study on Mexican immigration to the US and drug opportunities, irregular migrants are exposed to drugs and drug related behaviours and traditional approaches to address this are sparse ( Borges et al., 2012 ). As a consequence, in such communities, a drug use culture is evolving; a culture that, if allowed to take root, will set the foundation for potentially serious future drug problems.

Studies, as described in the epidemiology of substance use among forced migrants by Horyniak, Melo, Farrell, Ojeda, and Strathdee; and the healthy immigrant effect: patterns and evidence from four countries by Kennedy, Kidd, McDonald, and Biddle; irregular migrants may be at risk for drug use for some reasons, including coping with traumatic experiences, pre-and post-migration stress, co-morbid mental health disorders, acculturation challenges, and social and economic inequality ( Horyniak et al., 2016 ; Kennedy et al., 2015 ). Migrants, who also have passed through ill-treatment and abuse such as experienced by irregular migrants, can face marginalization, stigmatization and discrimination ( Fozdar and Hartley, 2014 ; Capps et al., 2015 ), which is an important factor in determinants of health, and may contribute to feelings of stress and loss of hope which may, in turn, lead to drug use problem ( Frohlich and Potvin, 2008 ; Steel et al., 2009 ). In addition, study by Anikeeva, Bi, Hiller, Ryan, Roder and Han; and Gushulak, Pottie, Roberts, Torres, and DesMeules, express that migrant health decreases over time to a range of factors, including reintegration challenges and barriers to health service use ( Anikeeva et al., 2010 ; Gushulak et al., 2011 ; Gorman, 2014 ; Mojtabai et al., 2014 ).

Activities of drug trafficking networks and among irregular migrants constitute the extent of this drug problem. Drug traffickers and trafficking networks have found irregular migrant communities (migrants with false or no legal documents, smuggled migrants and trafficked persons) and their social networks very useful for their activities ( Horyniak et al., 2016 ). Over the years, through these networks, countless migrants have made their way north to make a living ( International Organization for Migration 2016 a, 2016 b). In specific terms, drug trafficking organizations contribute in facilitating the movement of illegal migrants into other countries using highly functional groundwork within the social network to achieve their purpose. At present, drugs flow through these networks, and an undetermined number of irregular migrants are being used for major distribution ( Slack and Whiteford, 2010 ; Triandafyllidou et al., 2012 ). In the same communities, drug trafficking organizations and networks also target at-risk young people turning them into drug consumers as well as recruiting them to sell drugs, in some cases there are some migrants whose sole objective in migrating is to traffic in and sell drugs ( García, 2007 ; Cook, 2007 ).

Nigeria presently faces a high rate of unemployment and conflicts, and youths are illegally migrating to other countries and involved in drug trafficking and drug use during their migration. Some of them were introduced by organized recruiters or migrant smugglers who facilitate their movement with drug trafficking within and outside Nigeria through their network system ( Carling, 2014 ). Some of the irregular migrants that got stranded take to drug peddling, commercial sex work, street begging, among others, in order to survive. Some also fall victims of drug use as a result of psychological trauma ( Borges et al., 2012 ), while some end up in prisons due to crime committed and thereafter repatriated to Nigeria after serving their time. On return to Nigeria, some of these migrants, particularly young people, are roaming the street without any meaningful achievement while some have re-migrated or suffered psychological trauma due to frustration. Some have also fall victim of drug use due to post migration trauma ( International Centre for Migration Policy Development 2004 ; International Organization for Migration 2016 a, 2016 b). Europe has become one of the major continents with high rate of irregular migrants from Nigeria ( UNODC 2011 ).

Study objectives

• 1 To assess the prevalence of drug use among irregular migrants.
• 2 To examine the patterns of drug use among illegal migrants.
• 3 To investigate the nature of illicit trafficking among irregular migrants.

Description of the study setting

This study was carried out in Edo and Delta states of Nigeria among identified migrant returnees who were brought back from Libya by IOM and the Federal Government of Nigeria. These returnees were migrants of irregular migration who travelled by road and through the Sahara desert and intended to migrate to Europe through the Mediterranean Sea in search for greener pasture, and were stranded in Libya. According to the IOM Nigeria report, Edo states and Delta states account for communities of high emigration and high returns among the irregular migrant population in Nigeria. This informed the rationale behind chosen the two States for the study. Again, the states accounted for high potential migrants, and stranded and transiting migrants along Mediterranean Sea. Going by the IOM report of 2018; 6, 978 migrants returned as at February which extended to 7709 as at March and 8500 as at April; and Libya accounts for the highest population of migrants by countries; among these returnees ( International Organization for Migration 2018 ).

Type of study design

The study adopted the descriptive cross sectional survey design, as the research was only interested in determining the independent and dependent variable without manipulating any of them.

Description of the study population

The population of the study was restricted to migrant returnees from Libya who returned between May 2017 and April 2018. The criterion for participation in the survey was that the respondents must have returned three months and have had reunion with their family before the interview or had undergone reintegration programme. The migrant returnees who have undergone reintegration programme are those that have received support from organizations upon their return aims to address their economic, social or psychosocial needs. The age consideration for the selection of respondents was within 18 – 50 years. This age bracket was the most predominant among the irregular migrants according to IOM. It was ensured that both genders were represented in the study.

Sampling techniques

The study used judgemental and snowball sampling methods, and copies of questionnaire were properly administered to 382 participants (238 male and 144 female). In applying both sampling techniques, the study employed the use of a migration consultant, who is also a migrant returnee, working with the migrant returnees on reintegration and familiar with the community, who assisted in identifying and contacting the returnees (judgemental) and each identified migrant returnee assisted in recruiting other returnees (snowball) within their social network for this study. The sampling methods were adopted due to difficult to locate and hidden nature of the population. In addition to the administration of questionnaire, 4 focused group discussion (FGD) were conducted with 12 selected participants in each group. The FGD comprised 2 male and female sessions respectively. In total were 24 male and female FGD participants respectively. In-depth Interview (IDI) was conducted among 10 selected participants.

Data collection instruments and procedure

The study employed both quantitative and qualitative methods. The research participants responded to In-depth Interview (IDI) and Focus Group Discussion (FGD) guide and interviewer administered structured questionnaire. The instruments were developed by the authors from the literature review and with consultations and guide from migration experts and professionals in the field of drugs and addiction. In addition, the IDI and FGDs were conducted with the participants to gain a deeper understanding of their experiences. Adopting this mixed method is important because it assists in developing robust explanations to the complexities of irregular migration, drug trafficking and drug use.

Through non-probabilistic purposeful sampling, 10 volunteer migrant returnees who had engaged in either drug trafficking or drug use were interviewed and it was ensured that important elements were selected to participate in the interview. Attention was focused on selecting informants from diverse groups that represent the community, such as irregular migrants, smuggled migrants, and trafficked persons. A total of 4 FGD sessions were conducted: 2 male and female sessions, respectively.

Methods of data analysis

The quantitative data was analyzed using SPSS statistical tool (version 21.0). The data presented in this study is in accordance with the stated method, sample size, data collection instrument and method of data analysis. The quantitative analysis of findings and variables were based on descriptive statistics using frequency distribution and chi-square. The qualitative primary data obtained were manually analyzed by content analysis, reviews and discovery process, and by generating themes from FGD and in-depth interviews. Responses were coded into themes accordingly and categories abstracted into sub themes and data saturation was reached when no new information is discovered in data analysis. Comparison and verifying of conclusions were carried out and organized according to the set objectives of the study. Remarkable and very important quotes from the respondents were noted and referenced in the report.

Ethical approval

Ethical approval for this study was received from the Health Research and Ethics Committee of Lagos University Teaching Hospital (LUTH). Confidentiality and anonymity was assured and informed oral consent obtained from all the respondents after explaining the purpose of the study in detail.

Table 1 shows the summary of the socio-demographic characteristics of the respondents. The male constitutes the highest number in the study participants, representing 238(62.3%). Most of the respondents 259(67.8%) only have secondary school level education. Marital status showed that most of the returnees 226(59.2%) were single. The age group of 26 – 30 years was the most dominant among the study population.

Demographics characteristics of the respondents.

Country of migration

In terms of the country in which the respondents intend to migrate for a new living, this study revealed, as shown in Fig. 1 , several countries in Europe and few countries in North Africa. Italy was the most frequent, with 36.9% among the respondents, followed by Libya (23.6%). Germany (18.1%) and France (11.3%) also recorded high response rate among the respondents. As revealed in the study, the road was the main route of migration by all the respondents. Among 76.1% of the respondents who had the intention to cross through the Mediterranean sea, 52.5% of them were those who have already entered the Mediterranean Sea but were either stranded or caught on the sea and consequently could not cross to Europe or concluded their journey to their intended country of destination ( Fig. 2 ).

Respondents intended country of destination.

Respondents’ transit countries.

Transit countries

Niger was a major route and transit country for illegal migration. All the respondents passed through and stopover in Niger. The major stopover cities in Niger were Agadez and Zinder along the desert area. Another major transit country was Chad 134(35.1%), as some passed from Niger to Chad and then entered Libya ( Fig. 2 ). Libya was the major transit for those destined for countries in Europe. Most of the respondents 368(96.3%) do not possess legal travel documents while crossed the borders. The majority of them were smuggled 374(97.9%) and trafficked 369(96.6%) into other countries during the migration. Those smuggled were migrants of whom their network recruiters illegally facilitated their entry into another country, while those trafficked were exploited by traffickers for the purpose of force labour or commercial exploitation. However, as respondents revealed, most of those smuggled from one country to another were also trafficked at some point during their migration.

Drug use among migrants

Respondents that have used drugs during their migration.

The study showed that 234(61.3%) of the respondents have used drugs during their migration. Drug use was predominant among those in the younger age group (26 – 30) accounting for 24.9%. Among male respondents, more than three quarter 170(71%) of them used drugs, while about 64(44%) among female respondents also used drugs. Of the total 234 respondents (61.3%) who reported to have engaged in drug use during migration, about 35(15%) of them were reported to have been engaged in drug use before leaving the shores of Nigeria.

Reason for drug use during migration

The respondents when asked the reason for their drug use during migration, among the 234 respondents that have used drugs, ‘frustration’ 189(49.5%) was the leading cause of their drug use. This was followed by their being ‘stranded’ 109(28.5%) and ‘trauma’ accounting for 95(24.9%) among the respondents. One respondent stated, “I lost my two brothers inside the sea and because of that I take drugs to forget thinking, and also I was jailed in prison” (IDI-10, male respondent). Few of the respondents revealed that eagerness to continue the journey (9.4%), peer pressure 25(6.5%), no employment in the country of migration 14(3.7%) and introduction to drug business 13(3.4%) as factors that led to their drug use. Some respondents who shared their experiences stated that they use drugs to make them feel less hungry, while some were compelled to use these drugs as the following statement reflect: “In Libya, those who kidnapped us also give us these drugs, so that we will do whatever they ask us to do” (FGD-09, male respondent). Some of the respondents shared their experiences regarding taken these drugs under compulsion, as one respondent explained, “The Libyans forced me to take drugs. I was selected to take care of my fellow returnees and for this purpose they use to give us drugs to make us aggressive” (IDI-03, female respondent).

Access to drugs was noted to have also contributed to the reason for the drug use among the respondents, while some of these drugs were sold in the camp or prisons in Libya. Examples of these drugs include marijuana, hashish, shisha and tramadol.

Types of drugs used during migration

In terms of drug-types used during migration as shown in Fig. 3 , the largest number of the respondents indicated the use of alcohol (43.2%), followed by marijuana (33.8%) and hashish (24.6%). Tobacco (cigarette) use was 18.1%, Tramadol use 15.7%, shisha (14.9%) and codeine accounted for 11.3%. Other drug-types found in used by the respondents were opium (5.9%), cocaine (1.8%) and flunitrazepam (1.8%).

Type of drugs used by the respondents during migration.

Illegal activities engaged in by the respondents during migration and are linked to drug use

Respondents were asked whether they got involved in any illegal activities or crime in the country of migration, and if the crime is related to drug use. In their responses, the illegal activities or crime engaged by the respondents during migration were drug business (50.8%), commercial sex work and stealing, 40.7% and 8.5%, respectively. Likewise, some migrants in cooperation with the Libyans were involved in abduction of other migrants and negotiation of ransom. Most illegal business actitivies were performed in Libya by the Nigerian migrants. While some engaged in these activities as a means of survival, some were forced into the acts through trafficking. Among the respondents involved in illegal business activities or crime during migration, 22% of the respondents’ illegal businesses have a link to drug use. On further exploration, the respondents talk about drugs being given to them by the traffickers before embarking on stealing or prostitution. The drug business led some into drug use while possessing the drugs.

Respondents’ drug use and problematic drug users

Table 2 showed the prevalence of drug use among the migrant returnees in the survey. Of the 234 (61.3%) who use drugs, 124 (52.99%) responded that they became problematic drug users or drug dependent. And only 38 (30.64%) of them seek for help in Libya. A respondent said, “My friend introduced me to drugs in Niger during our journey. I was addicted to these drugs, particularly shisha, because if I don't take it I will not be okay, whenever I take it I will be calm and will be okay. It was not easy for me to stop drugs, because I was actually getting used to it” (IDI-07, male respondent) Table 3 .

Respondents drug use and problematic drug use.

Kind of help support in Libya.

On further probe, the respondents were of the views that help for problematic drug use or treatment seeking could not be easy for the irregular migrants and most were held hostage in the transit countries. In Libya, the respondents who became problematic drug users, 6.45% seek for help in a treatment center (health clinic or Nongovernmental Organisation [NGO]), 3.23% seek for rehabilitation in NGO or faith-based centre, while 0.81% seeks for counselling in NGO. Other respondents (20.15%) only discuss their drug use disorder with their friends.

Respondents who use drugs on their return to Nigeria

The study also depicts that among the total respondents (382) in the survey, 147 (38.5%) used drugs on their return to Nigeria. This result, when compared to percentage that used drugs in immigration, 234(61.3%) used drugs during their migration while 147(38.5%) used drugs on their return to Nigeria. The respondents when asked the reason for drug use when they returned to Nigeria, 80 (30.4%) responded that they were already dependent on drugs, 76 (28.90%) responded that they were frustrated on their return to Nigeria, 63 (24%) responded that they resort to drug use due to lack of employment, and 44 (16.7%) alleged that pre and post-migration stress and trauma contributed to their drug use problem in Nigeria.

Drugs used by the respondents on their return to Nigeria

The drugs used by the respondents on their return to Nigeria where alcohol (34.1%), marijuana (25.8%), cigarette tobacco (12.1%), shisha (10.2%), tramadol (7.3%), codeine (6.7%), hashish (2.2%), flunitrazepam (1.0%) and cocaine (0.6%). Alcohol and marijuana abuse were most dominant among the respondents ( Fig. 4 ).

Drugs used by respondents on their return to Nigeria.

Respondents that continued drug use after undergoing reintegration programme or after three months reunion with their families in Nigeria

Of the 147 (38.5%) among the study population that uses drugs on their return to Nigeria, 136 (93%) continued to use drugs after undergoing reintegration programme or after 3 months reunion with their family. One respondent stated, “I got addicted to drugs. There are times if I have not taken these drugs, it will look as if I am shivering, but I hide it from my parents when I came back to Nigeria, I do go to buy these drug such as tramadol and marijuana, and once I take it, I will just take tom-tom (candy)” (IDI-05, female respondent).

The qualitative study of this survey revealed that the reintegration programmes undergone by the respondents were mainly on business and entrepreneurship skill training and empowerment programmes in the aspect of business establishment for the migrant returnees. The training comprised enterprise development modules such as how to generate business ideas, how to start your business, how to develop a business plan, record keeping, and business partnership and cooperatives. None of these programmes has a psychosocial and behavioural therapy component that addresses the issue of drugs and addiction among the migrant returnees. The result of this study shows that the current reintegration programme and family reunion has not expressively reduced drug use among these migrant returnees.

In relation to the reason for the migrant continued drug use after their reintegration programme or three months family reunion, 59 (43.4%) were still dependent on drugs, 31 (22.8%) were still frustrated, 28 (20.6%) were still passing through pre and post-migration stress and trauma, and 18 (13.2%) still use drugs as a result of no employment. The respondents 58(39.7%) who use drugs complained to have other medical effects associated with their drug use, such as acute cough, amnesia, chest pain, constant fever, headache, high blood pressure, internal pain, stomach pain, throat pain, itching and mental challenge.

Respondents who have been involved in treatment programme specifically related to drug use in Nigeria

The study shows that most respondents 125(85.3%) that use drugs did not seek treatment since their return to Nigeria. Of those who did seek treatment, 22(14.7%) were males. None of the females seek for treatment upon their return in Nigeria. Stigmatization remains one of the reasons for none-treatment seeking among the migrant returnees.

Stigmatization and discrimination face by the migrant returnees upon their return and as a result of their drug use

The respondents in this study complained that they have faced stigmatization and discrimination upon their return to Nigeria. Apart from the stereotype stigma that have been placed on migrant returnees from Libya who are associated with irregular migration and human trafficking, the migrants’ drug use problem also increases the societal stigma and discrimination encountered by these population as the following statements illustrate: “Some of our girls that travel to Libya and came back, if people knew you came back from Libya, it is big stigma in Edo state, they feel maybe you have smoked or taken the whole drugs in Libya and they see us as abnormal human beings”( FGD-19 , female respondents); “Because of the stigma, most of us that came back from Libya, our smoking and drug taking increased, immediately we came back, with countless interviews, facing cameras. With your face on TV, your friend may call his family that they saw you and that you are back to the country from Libya, walking on the streets, some people start murmuring against you, that he is a Libyan returnee, he has been involved in crime in Libya, these are stigma, and it makes one go crazy” (FGD-05 – male respondents).

Drug trafficking among migrants

Respondents’ engagement in illicit drug peddling or trafficking in country of migration.

Of the total respondents of 382, 60 (15.7%) disclosed that they trafficked drug during their migration (in Libya or other transit countries) and among them were 40 (10.5%) who also carry these drugs on their way through the journey. On further probes, the respondents revealed that the majors ways of doing this is to boycott border checks through alternative (secret) route or to hide it in a secret place in the migrant luggage, cloths, underwear or food. Some of the respondents disclosed that they kept the drugs in their body by swallowing them while others ‘settle’ by bribing the officers on duty. Among the total female respondents (144) in the study, 4% of them were reported to be involved in drug trafficking. The information gathered from FGD further revealed that pregnant women among migrants are also used in the drug trafficking.

Drugs trafficked or peddled by respondents during migration

The respondents that attested to their involvement in either drug business, mentioned the following drugs as the major drug with highest drug trafficked volume or that move sales of which they were involved, and these were: Marijuana (76.7%), Hashish (50%) and Tramadol (43.3%). Other drugs trafficked or peddled were listed in Fig. 5 . The local gin (spirit) observed in this study were those not under normal government regulation. This illicit gin has been reported to be toxic and banned in some countries such as in Nigeria by the National Agency for Food and Drug Administration (NAFDAC).

Drugs trafficked or peddled by migrant during migration.

Groups or persons responsible for respondents’ introduction to drug business

Among groups or persons through whom respondents were introduced to drug business, findings shows that the highest influence on the migrants towards drug business were through migrant smugglers (46.7%), followed by human traffickers (45%) and friends (25%). Some of the respondents also disclosed through in-depth interviews and focus group discussions that their family members introduced them to drug business. Further investigations revealed that some respondents in this category got into drug business in Libya or transits countries due to survival and frustration experience.

The respondents also claimed compulsion by some Libyan rebels or officers in their prisons or refugee camp to sell drugs for them, while some of migrants were people that started drug business before leaving Nigeria. The reasons for being involved in illicit drug business during migration, as mentioned by the respondents who were directly involved in drug business, the most reason stated was to raise money (33%) for their journey. This was followed by compulsion or being forced into drug business (32%), as this was supported by information obtained from the focused group discussion in which many of the migrant claimed that their camp officers and human traffickers put them under threats and compulsion to the drug business. Other major reasons for the respondents’ engagement in dug business were frustration (3%) and solely for survival (i.e. to earn a living) (15%). The other remaining respondents (17%) involved in the drug business did not give any tangible reasons for their involvement in drug business. The following statements illustrate: “ Some of this human traffickers will approach you that they can help you go to Europe, they will charge you 700,000 Naira and you will pay them, but once you get to Agadez in Niger, there will be a disconnect, these traffickers phone numbers will be switched off, then other traffickers will come in and approach you to carry drugs for them, since you don't have money with you, you will agree to do it, poverty, frustration are responsible for drug trafficking, sometimes this human traffickers force it on you” (FGD-02, male respondents); “I deal on drugs, I sell drug to gather money, to travel to Libya, when I got to Libya I continued selling drugs to survive, even after returning to Nigeria, I still deal on drugs even now, just for me to survive” (FGD-08, male respondents).

Respondents arrested for possession of illegal drugs during their migration

In the study population of 60 migrant returnees involved in drug business, only 28% of them have had been arrested or detained by law enforcement agent (in Libya) due to their illegal action on drug business. Among this population, 28% have had experience of arrest, 12% were females. Information gathered from the focus group discussions revealed that some of the irregular migrants involved in drug business bribe some law enforcement agents to escape legal prosecution.

Irregular migration along the Central Mediterranean route is increasingly dangerous for migrants ( International Organization for Migration 2016 a). This study revealed the prevalence of drug use among migrant returnees. Irregular migrants, according to Fazel, Wheeler, and Danesh, usually witnessed or personally experienced pre and post-migration stress and trauma, including experience of frustration ( Fazel et al., 2005 ), as revealed in the findings of this study. As such, the above factors make them vulnerable to drugs use, and it is no surprising that the prevalence of drug use is high among this population. The challenges and other process of psychological change that follow the experience in irregular migration may lead migrants to have engaged in drug use ( Berry, 1997 ). In this study, drug use was predominant among the age group 26 – 30, accounting for 24.9% among the respondents. The study also depicts high percentage (89.51%) of non-treatment seekers among migrants who are problematic drug users in the country of migration.

There is a dearth of literature on irregular migrants and drug use, precisely in Africa, but a growing body of research, predominantly conducted among vulnerable populations of those with problematic drug use has found non-treatment seeking to be high among this population. According to the 2014 United States National Survey on Drug Use and Health, people may be reluctant to seek treatment for drug use because of denial of their drug use disorder, societal stigma, and time constraints ( Substance Abuse and Mental Health Services Administration 2019 ). In United States, about 85% of those with problematic drug use have not received treatment. In this study, the illegal status of the respondents also poses a challenge in seeking treatment in the country, of which they do not have legal position. The present reintegration programmes or the family reunion has not contributed to reduction in drug use among the migrant returnees. Of the 147 (38.5%) respondents among the study population that use drugs on their return to Nigeria, 136 (93%) continue to use drugs after undergoing reintegration programmes or after three months reunion with their family. Only 11 (7%) have stopped using drugs. It was observed that the present reintegration programmes by government, non-governmental, intergovernmental and international agencies that have been provided for these migrant returnees lacks psychosocial and behavioural therapy component that addresses the issue of drugs and addiction among the migrant returnees. Rather, the reintegration was mainly on business and entrepreneurship skill training and economic empowerment.

Although, some studies have established that employment can reduce the likelihood of drug use or aid recovery ( Montoya, 2004 ). It is also believed that the family also plays a role in the recovery process ( Substance Abuse and mental Health Administration 2015 ). The result of this study showed that the current reintegration program or family reunion has not reduced drug use among these migrant returnees. Research, as conducted by Anikeeva et al. (2010 ) and Gushulak et al. (2011 ) , found that migrant health decreases over time to a range of factors, including reintegration challenges and barriers to health service use, which emphasizes the importance of maintaining contact with arrived migrants to monitor changes in drug use during the early post-migration period. Migrants, particularly those who have passed through ill-treatment and abuse, commonly experience social and economic inequality, marginalization and discrimination ( Fozdar and Hartley, 2014 , Capps et al., 2015 ). Many of the migrant returnees in this study reported being stigmatized and discriminated against; factors that are important determinants of health ( Frohlich and Potvin, 2008 , Steel et al., 2009 ), and may contribute to feelings of stress and hopelessness which may, in turn, contribute to drug use problem. For instance, the result of this study showed that 85.3% of the migrant returnees have not been involved in any treatment programme since their return to Nigeria. However, 14.7% that seek treatment were male. Societal stigma, among other factors, could contribute to non-treatment seeking upon return of these migrants. Stigma has also been expressed as a factor in non-treatment seeking by studies of Gorman (2014 ), Appel et al. (2004 ), and Mojtabai et al. (2014 ).

The Central Mediterranean route, through the Sahara desert, has been observed in this study as drug trafficking route. From the result of the analysis, the migrant returnees get into drug trafficking/peddling through the activities of the migrant smugglers, transit countries citizens and human traffickers which was also reported by Simon (2017 ). Similar findings was also observed in the study of Slack and Whiteford 21 and Triandafyllidou et al. (2012 ), who associate migrant smuggling networks with illicit activities such as kidnapping and migrant participation in drug trafficking. However, the above authors also highlighted the migrants’ agency and noted that these networks do not resemble Mafia organizations. Migrant returnees most time get involved in trafficking or peddling as well as other related crimes during their migration, as this was done for surviving during the journey. Specifically, it was done to raise money to complete their irregular migration as some get into drug trafficking due to compulsion by human traffickers, migrant smugglers or citizens of transit countries. This is in line with the findings of Simon in his work titled ‘From victims of trafficking to felons: Migrant smugglers recruited by Mexican cartels’ ( Simon, 2017 ).

The current study compliments the findings of past researchers who worked on UNODC research paper titled ‘The role of organized crime in the smuggling of migrant from west Africa to the European Union ( UNODC 2011 ), as they concluded that fostering of socio-economic development (such as vocational business training) in the countries experiencing irregular migration such as Nigeria would help to further reduce demand for smuggling service. This study has identified that migrant returnees are presently facing employment challenges which is the main reasons why they are illegally migrating to other countries and most being involved in drug trafficking, and failure to secure jobs can keep up the trend of further irregular migration, drug peddling and other crimes among these return migrants if care is not taken.

In summary, Alcohol (43.2%), marijuana (33.8%) and hashish (24.6%) constituted most of the drugs used by respondents on immigration. The percentage that used drugs in immigration was 61.3%, and 38.5% used drugs on their return to Nigeria. Considering the prevalence of 14.4% drug use in Nigerian based on 2017 National Household Survey ( UNODC 2018 ). Drugs mostly trafficked were Marijuana (76.7%), Hashish (50%) and Tramadol (43.3%). The study revealed that 15.7% of the respondents engaged in drug trafficking during their migration, and 28% of the respondents that trafficked drugs had previous experience of arrest or detention by law enforcement agent.

It is recommended that providing access to treatment and addressing the underlying factors which lead to drug use is essential. Therefore, evidence-based responses to drug use should be provided to returned migrants on immediate return to include health screening, psychosocial support and medication-assisted therapies for drug use dependence and withdrawal. It will be very essential for drug use services to be integrated with mental health services for trauma-informed care. The existing reintegration programmes for the migrant returnees should be reviewed to incorporate psychosocial and behavioural therapy component that addresses drug use problem and involvement of their families to ensure sustainable reintegration. Sensitization and advocacy at the grassroots level involving communities and schools would help in creating awareness and educate most especially the young people on the dangers of irregular migration and human trafficking, with emphasis on predisposing factors leading to drug use and penalties for the offence on drug trafficking. The emphasis is to promote regular migration. Supporting legal entry into any country ensures safety, worthy livelihood and entitled health benefits or coverage to migrants. Priority should focus on improving border securities and cooperation with member states and international border control to reduce security threat and corruption, and adopting sustainable measures to amend the lapses in border checks such as bribery and smuggling.

Disclosure was a challenge observed among the female respondents. Most female respondents were reluctant to disclose their migration experience, most especially those involved in prostitution. Perhaps the trauma or stigma could be the reason and owing to the sensitive nature of this research. Efforts were made to generate pool of data from the female using a female interviewer in most of the difficult encounters.

Conclusions

The prevalence of drug use in irregular migration among the study population was 61.3% which draws attention to the need to understand the epidemiology of drug use among irregular migrant populations, particularly among persons who fall victim to deception, coercion, human trafficking and migrant smuggling. Experience of migration stress, trauma and frustration were among the factors that contribute to drug use among the study population. Non-treatment seeking, both in the migration countries and in the return to Nigeria, was high among those with problematic drug use. The existing migrant returnees’ reintegration programmes lack components of psychosocial and behavioural therapy that address drug use problem. However, stereotype stigma as a result of involvement in irregular migration and drug use has pose negative effects among this community. The study also discovered that some of the migrants get into drug business primarily to raise money for survival, while some were compelled into the business. The major influencers through whom the migrants were introduced to drugs were the migrant smugglers, human traffickers and friends within the migrants’ social group. On this basis, there is need to draw attention of the findings, with important implications for public health and social reintegration.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The study was supported by a grant from UNODC Academic Research NGAV16 project under European Union funded project entitled, “Response to Drugs and Related Organized Crime in Nigeria.”

Acknowledgments

The author would like to acknowledge Osita Osemene for assisting in the recruitment of the respondents, and to Simeon Olaoye, Quadri Ajibade and Grace Iwuagwu for assisting in the data collection and analysis.

• [1] International Organization for Migration . International Migration Law Series No. 25. 2011. Glossary on migration. [ Google Scholar ]
• [2]International Organization for Migration, Glossary on migration, Geneva, Switzerland, 2004.
• [3] UNCRI . 2004. Trafficking of Nigerian Girls to Italy. Turin [ Google Scholar ]
• [4] UNODC . 2006. Organized Crime and Irregular Migration from Africa to Europe. [ Google Scholar ]
• [5] UNODC . United Nation Office on Drug and Crime; Vienna: 2011. The Role of Organized Crime in the Smuggling of Migrants from West Africa to the European Union. [ Google Scholar ]
• [6] García V., Laura L. labor migration, drug trafficking organizations, and drug use: major challenges for transnational communities in Mexico. Urban Anthropol. Stud. Cult. Syst. World Econ. Dev. 2009; Jun; 38 (2–4):303–344. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [7] Borges G., Rafful C., Benjet C., Tancredi D.J., Saito N., Aguilar-Gaxiola S. Mexican immigration to the US and alcohol and drug use opportunities: does it make a difference in alcohol and/or drug use? Drug Alcohol Depend. 2012; 125 (Suppl 1):S4–11. doi: 10.1016/j.drugalcdep.2012.05.007. PMID: 22658285 . [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• [8] International Centre for Migration Policy Development . Paper Prepared For the Dialogue on Mediterranean Transit Migration. 2004. Irregular transit migration in the Mediterranean: some facts, figures, and insights. Vienna [ Google Scholar ]
• [9] Horyniak D., Melo J.S., Farrell R.M., Ojeda V.D., Strathdee S.A. Epidemiology of substance use among forced migrants, a global systematic review. PLoS One. 2016; 11 (7) doi: 10.1371/journal.pone.0159134. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• [10] Kennedy S., Kidd M.P., McDonald J.T., Biddle N. The healthy immigrant effect: patterns and evidence from four countries. J. Int. Migr. Integr. 2015; 16 :317–332. [ Google Scholar ]
• [11] Fozdar F., Hartley L. Civic and ethno belonging among recent refugees to Australia. J. Refug. Stud. 2014; 27 :126–144. [ Google Scholar ]
• [12] Capps R., Newland K., Fratzke S., Groves S., Auclair G., Fix M. Migration Policy Institute; Washington DC, USA: 2015. The Integration Outcomes of U.S. refugees: Successes and Challenges. [ Google Scholar ]
• [13] Frohlich K.L., Potvin L. Transcending the known in public health practice: the inequality paradox: the population approach and vulnerable populations. Am. J. Public Health. 2008; 98 :216–221. doi: 10.2105/AJPH.2007.114777. PMID: 18172133 . [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• [14] Steel Z., Chey T., Silove D., Marnane C., Bryant R.A., Ommeren M.V. Association of torture and other potentially traumatic events with mental health outcomes among populations exposed to mass conflict and displacement: a systematic review and meta-analysis. J. Am. Med. Assoc. 2009; 302 :537–549. doi: 10.1001/jama.2009.1132. PMID: 19654388 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• [15] Anikeeva O., Bi P., Hiller J.E., Ryan P., Roder D., Han G.S. The health status of migrants in Australia: a review. Asia Pac. J. Public Health. 2010; 22 :159–193. doi: 10.1177/1010539509358193. [PubMed] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• [16] Gushulak B.D., Pottie K., Roberts J.H., Torres S., DesMeules M. Migration and health in Canada: health in the global village. CMAJ. 2011; 183 :952–958. doi: 10.1503/cmaj.090287. [PMC free article] [PubMed] [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• [17] Gorman A. 2014. Barriers Remain Despite Health Law's Push to Expand Access to Substance Abuse Treatment. http://khn.org/news/substance-abuse-treatment-access-health-law/ (accessed 27 August 2018) [ Google Scholar ]
• [18] Mojtabai R., Chen L., Kaufmann C., Crum R. Comparing barriers to mental health treatment and substance use disorder treatment among individuals with comorbid major depression and substance use disorders. J. Subst. Abuse Treat. 2014; 46 (2):268–273. doi: 10.1016/j.jsat.2013.07.012. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• [19] International Organization for Migration . 2016. Assessing the Risks of Migration Along the Central and Eastern Mediterranean Routes: Iraq and Nigeria as Case Study. Geneva [ Google Scholar ]
• [20]International Organization for Migration, Missing Migrants Project, 2016. https://missingmigrants.iom.int/ (accessed 1 September 2018).
• [21] Slack J., Whiteford S. Viajes violentos: la transformación de la migración clandestina hacia Sonora y Arizona. Norteamérica. 2010; 5 (2):79–107. [ Google Scholar ]
• [22] Triandafyllidou A., Maroukis T., Smuggling Migrant. Palgrave MacMillan; London: 2012. Irregular Migration from Asia and Africa to Europe. [ Google Scholar ]
• [23] García V. Meeting a bi-national research challenge: substance abuse among transnational Mexican farmworkers in the United States. J. Rural Health. 2007; 23 (Fall):61–67. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• [24]W.C. Cook, Mexico's drug cartels, CRS Report for Congress, L34215, Washington DC, 2007.
• [25] Carling J. IOM Migration Research Series No 23. IOM; Geneva: 2014. Migration, human smuggling and trafficking from Nigeria to Europe. [ Google Scholar ]
• [26] International Organization for Migration . Presentation of the EU-IOM Initiative for Migration Governance and integration in Nigeria at the meeting of the working group on reintegration, Abuja, 12th April. 2018. [ Google Scholar ]
• [27] Fazel M., Wheeler J., Danesh J. Prevalence of serious mental disorder in 7000 refugees resettled in western countries: a systematic review. Lancet. 2005; 365 :1309–1314. [PubMed] [ PubMed ] [ Google Scholar ]
• [28] Berry J.W. Immigration, acculturation and adaptation. Appl. Psychol. 1997; 46 :5–68. doi: 10.1111/j.1464-0597.1997.tb01087.x. [ CrossRef ] [ Google Scholar ]
• [29]Substance Abuse and Mental Health Services Administration, Results from the 2014 national survey on drug use and health: detailed tables. http://www.samhsa.gov/data/sites/default/files/NSDUH-DetTabs2014/NSDUH-DetTabs2014.htm (accessed 13 February 2019).
• [30] Montoya I. The University of Houston, Texas, National Institutes of Health (NIH) study; 2004. Employment Reduces the Chronic Drug Use of Female Welfare Recipients. http://www.uh.edu/news-events/archive/nr/2004/11nov/113004montoya.html (Accessed 20 March 2019) [ Google Scholar ]
• [31]Substance Abuse and mental Health Administration, Recovery and recovery support, USA, 2015. http://www.samhsa.gov/recovery (accessed 13 February 2019).
• [32] Appel P.W., Ellison A., Jansky H., Oldak R. Barriers to enrollment in drug abuse treatment and suggestions for reducing them: opinions of drug injecting street outreach clients and other system stakeholders. Am. J. Drug Alcohol Abuse. 2004; 30 (1) doi: 10.1081/ADA-120029870. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• [33] Simon P. From victims of trafficking to felons: migrant smugglers recruited by Mexican cartels, Mexico. Estud. Front. 2017; 18 (37):41–60. doi: 10.21670/ref.2017.37.a03. [ CrossRef ] [ Google Scholar ]
• [34] UNODC . Vol. 2017. 2018. p. 12. (National Household Survey On Drug Use and Health in Nigeria). [ Google Scholar ]

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International Drug Trafficking by Erik D. Fritsvold LAST REVIEWED: 29 June 2015 LAST MODIFIED: 29 June 2015 DOI: 10.1093/obo/9780195396607-0126

The global illegal drug trade is massive in scope with sweeping consequences for the developed and developing world. With an estimated annual value of between $300 billion and $400 billion, international drug trafficking dwarfs the value of many staple legal commodities in the global economy. This massive underground criminal marketplace further destabilizes regions rife with unrest, provides revenue for entrenched organized criminal groups, undermines cornerstone institutions, corrupts law enforcement, infiltrates the financial sector, and further complicates issues of national security. Recent decades have dramatically reconfigured international drug trafficking. While the United States remains the largest consumer of illicit drugs, recent years have witnessed a modest shift away from punitive prohibition policies; several states have relaxed marijuana laws, and there has been a modest deescalation of penalties at the federal level. The last decade has also witnessed significant increases in cocaine use in Europe that has further established this region as a hub in the global market for illicit drugs. In addition, the primary source of the world’s opium has largely relocated from the Golden Triangle in Southeast Asia to Afghanistan and the Golden Crescent; this shift has had profound impacts on large-scale conflicts and the political economy of the region. While cocaine production continues to fluctuate, Colombia, Peru, Bolivia, and other traditional coca-cultivating countries in South America continue to produce the lion’s share of the world’s cocaine. Colombia remains a leader in the production of cocaine, although the power of the Colombian drug cartels has decreased in recent decades. Originally subcontracted to smuggle drugs into the United States by Colombian cartels, Mexican drug trafficking organizations have emerged as, potentially, the dominant force in the drug economy of the new millennium. These deep-rooted criminal groups established control over longstanding smuggling routes into the United States. Given the scope and mutually constitutive nature of international drug trafficking with global social, economic, and political dynamics, an empirically based understanding of this issue is imperative. Gathering reliable information about an inherently criminal, inherently clandestine industry is challenging. This article seeks to tackle this task by compiling a global, interdisciplinary, and methodologically diverse series of resources. Policy reports from various organizations provide macro-level quantitative data. Historical accounts reveal the social, political, and economic embeddedness of the drug trade. Ethnographies and case studies of drug-dealing groups and peasant farmers provide vivid qualitative data on the lived experience of the underground economy. More specifically, these resources focus on a series of subtopics, including the political economy of illegal drugs, transnational organized crime, money laundering, corruption, violence, and alternative policy. In combination, the following resources attempt to rely on empirical evidence and science in an area that has sometimes been dominated by bombastic claims and fear-driven politics.

International drug trafficking is a broad and multifaceted issue. Accordingly, many of the resources in this section provide encompassing overviews of drug manufacturing, drug distribution, and drug use. United Nations Office on Drugs and Crime 2009 provides a tremendously inclusive and empirically driven history of drug use and drug control worldwide. United Nations Office on Drugs and Crime 2014 presents a near-comprehensive examination of contemporary illegal drug use worldwide. These two reports are excellent starting points for understanding this topic. Bureau for International Narcotics and Law Enforcement Affairs 2014 provides a more technical examination of illegal drug trafficking in eighty-nine individual countries. International Narcotics Control Board 2013 adds an examination of the global market for the legal scientific or medicinal use of drugs. National Drug Intelligence Center 2011 describes how organized criminal groups within the United States cooperate with transnational organized criminal groups to traffic drugs into the lucrative US market. United Nations Office on Drugs and Crime 2011 is a detailed examination of the global market in cocaine. Each of the reports in this section is empirically rich, searchable, clearly written, and visually interesting; they are excellent, user-friendly resources for the study of international drug trafficking.

Bureau for International Narcotics and Law Enforcement Affairs. 2014. International narcotics control strategy report . 2 Vols. Washington, DC: US Department of State.

In combination, this two-part, 537-page report is more inclusive but also more technical compared to more succinct reports from the United Nations and US Department of Justice. The highlight of this report is a very usable, alphabetized executive summary of illegal drug issues in eighty-nine individual countries. For most countries, information is presented regarding drug manufacturing, distribution, antidrug policies, law enforcement efforts, and relevant laws and treaties.

International Narcotics Control Board. 2013. Report of the International Narcotics Control Board . New York: United Nations.

The International Narcotics Control Board oversees the legal availability of drugs for legitimate use in science and medicine. Under this umbrella, it also analyzes the illegal global trafficking of drugs. The highlights of this annual report include an informative section on drug and corruption and a very user-friendly regional breakdown of the production and distribution of illegal drugs worldwide. This report is rich and reflects a longitudinal understanding of these complex issues, as evident by this marking the forty-fifth annual report from the International Narcotics Control Board.

National Drug Intelligence Center. 2011. National drug threat assessment . Washington, DC: US Department of Justice.

This evidence-driven report details drug use and availability in the United States and accompanying individual and societal consequences. The international production and distribution of drugs are presented, highlighting the linkages between international drug trafficking organizations (DTOs) and prison and street gangs within the United States. Additional notable sections include smuggling methods into the United States; movement of individual illegal drugs within the United States; prescription drug diversion; and money laundering in the United States, Mexico, and worldwide.

United Nations Office on Drugs and Crime. 2009. A century of international drug control . Vienna: United Nations.

A cornerstone resource; potentially the definitive history of worldwide drug use and control efforts over the past century. Beginning with the Chinese opium pandemic in the early 1900s, “the international drug control system is one of the oldest consensus-based multilateral systems in existence” (p. 13). Highlights include rich and usable data; timelines; historical evidence on drug use, production, imports, exports; and legal responses from the international lawmaking community.

United Nations Office on Drugs and Crime. 2011. The transatlantic cocaine market . Vienna: United Nations.

This is a user-friendly and detailed report on the international cocaine market. Cocaine production, distribution routes, usage data, and financial data are presented using accessible language and visually interesting charts and maps. Report highlights how the market, with a peak value in the 1990s driven by high use rates in the United States, has become more dispersed internationally and less valuable over time.

United Nations Office on Drugs and Crime. 2014. World drug report 2014 . Vienna: United Nations.

This report is produced annually by the UNODC. This is an outstanding, user-friendly resource for research and teaching on global drug use and drug markets (and searchable if downloaded as a PDF file). The colorful maps and charts are empirically rich and visually interesting. The report is a detailed analysis of contemporary international markets for heroin, cocaine, and amphetamines and a near-comprehensive examination of illegal drug use, and attendant public health consequences worldwide. The complete series is available online .

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Drug Trafficking Research Paper

This sample drugs research paper on drug trafficking features: 4700 words (approx. 15 pages) and a bibliography with 26 sources. Browse other research paper examples for more inspiration. If you need a thorough research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our writing service for professional assistance. We offer high-quality assignments for reasonable rates.

In an organic sense, drugs are chemicals. When people ingest drugs, they pass through their body to their brain where they interfere with the neurotransmitters that transfer signals across synapses changing the messages that the brain sends to the body and thereby affecting the way the body works. In strictly economic terms, however, drugs are a commodity. That is, they are a product that is exchanged between people. Some drugs in some nations are socially and legally considered to have value for one reason or another and are deemed acceptable for use by some or all people under some or all circumstances. These are produced, distributed, and consumed under authority of law and sanctioned by less formal social norms for commercial purposes and personal use by approved individuals in approved circumstances. Others drugs are not acceptable for any purpose or any person under any circumstance. Today and for some time now, the production, distribution, and consumption of those drugs that are not authorized by law or sanctioned by social norms have been considered a serious problem for individuals, communities, and nations. There are very real public health concerns about drugs, all drugs but in particular illicit drugs or licit drugs used in illicit ways, and what they do to individuals and their minds and bodies. And there are very real public safety concerns about the impact of drugs on people and their communities and nations, especially with regard to the consequences of the commercial transactions involving those drugs that are not recognized by law. So it is not surprising that for the last century or so, there has been considerable attention among social scientists and public policymakers to questions and concerns about the trafficking and marketing of illicit drugs and their relationship to crime and violence.

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Get 10% off with 24start discount code, understanding drug trafficking and illicit drug markets.

As a commodity drugs need to be produced and distributed in order to reach consumers. Among those drugs that are illegal, there are differences in how and where they are produced, and therefore, how and at what scale they are distributed. For example, heroin may be sold to consumers in nations such as the United States as powder but it starts as an agricultural product cultivated in other parts of the world and requires a manufacturing process to convert the plant product into the form preferred by consumers and a distribution process that crosses national boundaries. Methamphetamine, on the other hand, can be produced by cooking the required but easily available chemicals at a fixed location with enough equipment to be called a laboratory, or simply by shaking the chemicals together in a 2-L bottle while sitting in the back of a moving car. Cocaine starts as a plant grown in places like South America, but by the time it reaches consumers in the United States, it is in the form of a powder manufactured in a laboratory setting or as little rocks known as crack that are made almost anywhere from a small amount of the powder for small quantity sales. So a broad commercial perspective is helpful for a general understanding of the trafficking and trade in illicit drugs.

Industrial economists distinguish industry from markets. Whereas industry refers to groupings of individual businesses that share common techniques and processes for the production of a certain commodity, markets refer to groupings of the consumers of the product of that industry (Andrews 1949). When the product is illicit drugs, whatever specific drug it is, the markets of consumers for those drugs are inextricably linked to the production and distribution techniques and processes and the organization and operation of the illicit drug industry. For example, people who purchase heroin from a local dealer in their own community are nonetheless connected to the individuals and organizations that are involved in the international, national, and regional agricultural, manufacturing, and trafficking operations that are necessary to produce heroin and bring it to market.

The illicit drug industry and its related markets have been studied from a number of perspectives. Looking at drugs as a commercial enterprise, economists have studied the illicit drug industry for one or more drugs and their various markets in terms of things like price and purity (e.g., Caulkins 2005; Caulkins and Reuter 1998; Rhodes et al. 2000). Public health researchers have focused on the impact of drug trafficking and markets on communities in terms of issues related to things like drug-related morbidity and mortality and drug treatment and prevention (e.g., Curtis et al. 1995; Sommers et al. 2006). Criminologists have studied the illicit drug industry and markets in terms of issues related to public safety, criminal activity, and violence (e.g., Goldstein et al. 1992; Weisburd and Mazerolle 2000). Overall the questions raised by this body of research ask how is the industry and how are the markets organized and how do they operate? How large are they in terms of production and in terms of economic value? Specifically in terms of trafficking, given that the product is illegal, how does it get from producers to consumers? And what is the impact of the trafficking and marketing of illicit drugs on people, communities, and nations?

Drug Trafficking

From the founding of the United States as a nation through to the late 1800s, it was common for home remedies to be freely available to citizens for dealing with personal problems of pain and illness. David Musto, a historian of drugs in America, called the late nineteenth century a period of “wide availability and unrestrained advertising” (1991, p. 42). During this period, the production and distribution of home remedies was not regulated by government, and many of these remedies contained palliative substances such as opium or stimulating substances such as cocaine (Inciardi 2007). Then in the early twentieth century, concerns for health and safety problems believed to be associated with their use resulted in the passage in 1906 by the United States Congress of the Pure Food and Drug Act, which imposed quality and packaging standards on all food and drug products (Inciardi 2007). Following that in 1914, the Harrison Narcotics Act was passed and in 1937 the Marijuana Tax Act, and together they gave the federal government some measure of regulatory control over the production and distribution of drugs (Musto 1999). Unfortunately while these government actions through taxation regulated the production and distribution of drugs that were earlier found in popular home remedies, they did not address the consumer demand for those drugs. So the illicit drug industry and markets filled the void to meet the demand.

As the illicit drug industry grew, the government of the United States increasingly became concerned about the impact of these drugs on public health and safety. In the middle of the twentieth century, there was not sufficient scientific evidence to know what that impact really was, but there were strong beliefs and opinions and those were used to support the direction of public policy toward illicit drug use and users and trafficking and markets. In 1971, President Richard Nixon formally declared war on drugs (Inciardi 2007). With his declaration, attention shifted from drug users to drug trafficking, and in 1973, Nixon created the Drug Enforcement Administration (DEA) as a component of the United States Department of Justice specifically charged with fighting the war on drug trafficking. Later President Ronald Reagan reaffirmed the war on drug trafficking, and then President George H.W. Bush with Congress passed the Anti-Drug Abuse Act of 1988 thereby forming the Office of National Drug Control Policy (ONDCP) as a policy agency to lead the national drug strategy. With ONDCP, the United States might finally have a Drug Czar, but the focus on drug trafficking as the problem and drugs as the enemy started with Nixon’s declaration of war and his formation of the DEA to fight that war.

While the United States was intensifying its war effort against drugs and drug traffickers, not all nations followed suit. Many others favored a policy of harm reduction emphasizing efforts to manage the harm to drug consumers and their communities from drug use rather than trying to control the supply of drugs through trafficking (Inciardi and Harrison 2000). For example, in the Netherlands, drug policy historically has favored protecting the health of users and reducing their health risks by focusing on programs of prevention and treatment (van Laar et al. 2011). Given a tradition known as “gedoogbeleid” that favors discretion when dealing with drugs and drug users, since the late twentieth century, the Dutch have formally and systematically not enforced laws involving small quantities of cannabis and have even established guidelines allowing certain retail establishments to sell cannabis to consumers without fear of prosecution as long as they adhere to established guidelines, such as not advertising and not selling to minors (MacCoun and Reuter 1997). Under international pressure in recent years, the Dutch government has placed greater limits on how these establishments can operate and who they can serve, but the tradition and policy allow the trade to continue. This does not mean that the government of the Netherlands favors unregulated trafficking or trade in illicit drugs. A recent report issued by the Netherlands National Drug Monitor to the European Monitoring Centre for Drugs and Drug Addiction (van Laar et al. 2011) notes that along with addressing the need to prevent drug use, the needs of drug users for treatment and rehabilitation, the reduction of harms faced by drug users, and lessening any disturbances to public safety they might cause in their communities, combatting drug production and trafficking is one of its main objectives. But the report continues, the “primary aim of Dutch drug policy is focused on health protection and health risk reduction” (van Laar et al. 2011, p. 15). Similarly, in the United Kingdom, a harm reduction approach to drugs has been in place going back to a report issued in 1926, The Report of the Departmental Committee on Morphine and Heroin (Bennett 1988).

While sovereign nations appropriately and naturally each have their own policy toward drugs, drug users, and drug trafficking, there has been international cooperation going back to 1909 when 13 nations gathered in Shanghai for the International Opium Commission (Musto 1991). At that meeting, which focused on opium and opiates, no binding decisions were made and no treaties signed. But other meetings followed. In 1911 there was a meeting of 12 nations in Hague resulting in an agreement for each nation to enact legislation to control narcotics trade and to fund educational programs. Then starting in 1961, there was a series of three international Conventions held under the auspices of the United Nations. Through the first two, all previous multinational treaties that had been negotiated from 1912 to 1953 were consolidated, and controls over 100 different substances considered to be narcotic drugs were tightened, and through the third in 1988, a focus was set on international drug trafficking (United Nations 1988).

Despite national and international efforts to control or manage it, today the illicit drug industry is well established on a global scale with active local markets and large and profitable national and international corporate-type manufacturers and distributors. Systems and procedures are in place to move drugs that are produced in one part of the world to other parts of the world where consumers are waiting to purchase and use them. With the focus on drug trafficking, the question then is what is the nature and scope of international trafficking? What is the impact of the traffic in illicit drugs on both producer and consumer nations and on their citizens?

Since the industry and the markets operate outside of the law, there are no official records of how much is produced or how much is sold or purchased. There are no official records of costs related to production or distribution of any illicit drug product, and no official records of how many consumers there are or what consumers have paid or are willing to pay for their drugs. So statisticians, economists, and other social scientists use available data sources to try to calculate the scope and scale of the illicit drug industry as a whole, and for particular drug industries and markets, such as heroin or cocaine. They use these data to derive estimates of drug consumption and estimates of the supply of drugs. For consumption estimates, they sometimes use data from surveys of samples of people who tell an interviewer about their experience using drugs, or not. For example, in the United States, estimates are derived using data from the National Survey of Drug Use and Health (NSDUH), an annual nationwide household survey produced for the Federal government by the Substance Abuse and Mental Health Services Administration (SAMHSA) and administered to a random sample of 70,000 respondents age 12 and older asking them about their use of and experience with various illicit drugs. Estimates of consumer demand in the United States also come from the Treatment Episode Data Set (TEDS) similarly maintained by SAMHSA, though in this case, counting the drug treatment and using experiences of about 1.5 million people annually admitted to drug treatment.

In addition to survey data, estimates of drug consumption and supply are also derived from available official record data from Federal, state, and local government agencies that have operational involvement in some way or other with acknowledged users of various illicit drugs. These agencies include, for example, law enforcement agencies that arrest drug users and dealers or traffickers, and treatment service providers that work with drug users to try to help them stop using drugs. Law enforcement records, for example, have been used to derive estimates of the supply of drugs based on a variety of law enforcement activities including crimes known to the police and arrests, but also things like the amount of particular drugs seized by various local, regional, and national law enforcement agencies. In the United States, the agency record data for supply estimates comes from national aggregate counts of crime and agency counts of arrests from sources like the Uniform Crime Reports (UCR), an annual national count of crimes known to the police and arrests made by the police. More directly focused on the actual supply of drugs known to be in the country, another estimate comes from data on drugs seized and analyzed in laboratories by law enforcement for the DEA System to Retrieve Information from Drug Evidence (STRIDE) program (NDIC 2011), or the tactical intelligence collected by the El Paso Intelligence Center (EPIC), established by the DEA in 1974 for the collection and dissemination of information related to drug trafficking particularly along the United States border with Mexico.

In summary, what is known about the scope and scale of drug trafficking or even drug using is limited. No official records of activity in the illicit drug industry or drug markets are maintained. All estimates are indirect, derived from proxy measures. They are based on an accounting of the activity of individuals and agencies that in some way work with people who are involved in or with the drug industry. So there is some uncertainty about what is known or believed to be known about the illicit drug industry and drug trafficking.

Illicit Retail Drug Markets

Similarly there are no official records to support what is known or believed to be known about local drug markets and the retail trade in illicit drugs. However, there has been considerable research conducted over the last few decades on the organization and operation of illicit retail markets at the local level (National Institute of Justice 2003; National Research Council 2010, 2001). There are several ethnographic studies of markets in particular cities, and there are a number of studies that provide sociological, geographical, and economic analyses of market organization and operation. Important findings of these studies include the identification of differentiated roles among buyers and sellers of illicit drugs, the characteristics of social relationships and of structural forms in different local markets, and the variation among patterns of distribution and consumption across places like neighborhoods and by the different types of drugs being transacted.

An illicit retail drug market can be defined as the set of people, facilities, and procedures through which illicit drugs are transferred from sellers or dealers to buyers or users (National Research Council 2001, p. 160). As such they are economic enterprises and therefore operate in response to the forces of supply and demand. But unlike legitimate commercial enterprises, illicit drug markets participants are regularly faced with the an odd mix of rapid turnover and overlapping roles among sellers and buyers, a broad range of product price and quality in a limited geographic area, and an absence of any legitimate authority to settle disputes over things like market share or product quality. Consequently, the natural economic forces of supply and demand in such markets are tempered by the need of buyers and sellers to worry about things like the trustworthiness of the people they are dealing with and the fairness and security of those dealings. By definition in an illicit retail drug market, all transactions are criminal transactions. As a result every individual who participates as a buyer or seller in such a market risks a hostile encounter with law enforcement officers and criminal justice agencies. In addition they risk intimidation, coercion, or victimization by other buyers and sellers over market-related disputes or for just being there.

Drug Markets, Crime, And Violence

Estimates from official statistical data from surveys on drug using and the supply of illicit drugs known to law enforcement along with the evidence of a large body of social scientific research strongly suggest that there is a relationship between illicit drug trafficking and drug markets and crime and violence. For the most part, the research on the connection between drugs and crime and violence has focused on drug use, but there are also studies that directly link crime and violence with drug trafficking and drug markets. In an early study of homicide rates in the United States during the twentieth century, for example, criminologist Margaret Zahn found that the rate of homicide varied over time in relation to the establishment of markets for illicit products with rates of homicide being at their highest in the 1920s and 1930s, during the control of alcohol under Prohibition, and in the 1960s through 1970s, during periods of disruption in the heroin and cocaine markets (Zahn 1980). More recently, studies have found higher rates of crime and violence during periods of disruption in organization and operation of particular illicit retail drug markets. For example, in the late 1980s when crack cocaine markets were newly emerging in urban areas, researchers found that most of the crime and violence associated with those markets involved things like disputes between dealers over territory or disputes between sellers and buyers over the quality of the product sold (Brownstein 1996).

The focus of drug policy in the United States on drug trafficking has been linked over the years to a stated relationship between international drug trafficking and crime and violence. The National Drug Intelligence Center (NDIC) of the United States Department of Justice in its annual threat assessment has regularly reported on violence and crime related to transnational crime organizations in producer nations, notably in Latin America and Asia, and its impact in and on the United States (NDIC 2011). In recent years, concern has focused on the Transnational Criminal Organizations in Mexico with the NDIC reporting that seven such organizations in Mexico “control much of the production, transportation, and wholesale distribution of illicit drugs destined for and in the United States” and that among them “a dynamic struggle for control of the lucrative smuggling corridors leading into the United States [has resulted] in unprecedented levels of violence in Mexico” (NDIC 2011, p. 7).

Similarly crime and violence have been associated with domestic illicit retail drug markets. In the 1970s in Chicago, Patrick Hughes conducted a groundbreaking epidemiological study of heroin addicts and concluded, “… heroin dealers must have a reputation for violence, otherwise addicts and other deviants would simply take their drugs and their money” (1977, p. 31). Not only did he observe that heroin dealers would use violence in the conduct of their business but also that other participants in the market were at risk of being the perpetrators or victims of crime and violence themselves through confrontations with others including drug users, drunks, and gang members (Hughes 1977, p. 31). A body of research conducted since that time provides additional though inconclusive support for the notion that while drug markets more often than not are peaceful, there is an observable relationship between the organization and operation of illicit retail drug markets and criminal violence (see Reuter 2009).

In 1985, sociologist Paul Goldstein conceptualized the ways that drugs and violence might be related (Goldstein 1985). He suggested that violence might be a consequence of drug ingestion, a response by an addicted drug user needing drugs and having to use force to get them, or the product of the normal organization and operation of the illicit drug trade. The latter he called systemic and suggested it could include things like disputes between drug dealers over claims to territory or among dealers and buyers over the quality of the product being sold. Just about the time that Goldstein was writing and talking about his tripartite framework, the level of violence and crime began to rise dramatically in United States cities concurrent with the introduction of crack cocaine. Public policymakers, criminal justice and law enforcement practitioners, and the news media around the country became alarmed and responded in the belief that the increasing crime and violence could be linked to the use of crack cocaine in city neighborhoods (Brownstein 1996). But studies using the tripartite framework provided preliminary evidence that in fact the growing crime and violence had more to do with market dynamics than with user behavior (Goldstein et al. 1992).

Conclusions And Future Research

In summary, there are limits to what is known and can be known about the illicit drug industry, drug trafficking, and drug markets. To some extent those limits result from the lack of official records available to be used for scientific or policy analyses. This problem limitation is not likely to be overcome in that the industry exists outside of the law. In addition, the research conducted in this area to date largely has followed the personal agenda of individual researchers and research teams rather than being guided by an organized and systematic program specifically designed to guide researchers and their studies in the direction of questions that need to be answered to adequately and effectively inform policy and practice. The unfortunate consequence has been that much of the policy that is made and many of the practices and programs that are initiated are guided by unfounded assumptions or political principles rather than by scientific evidence.

Nonetheless, there are some things that researchers have found that are convincing. The drug industry, with its consumer markets and the trafficking operations that comprise it, is highly organized and does operate not only on the local level but also on a global scale. Depending on the drug (and among illicit drugs and drugs used in illicit ways, there are many distinctions in what they do and how they are obtained and used), in some way or ways, there is a relationship between local retail market outlets for the product, large- and small-scale producers or manufacturers of the product, and the organized enterprises and wellestablished procedures and practices put into place to move the product from producers to consumers. And as all this happens outside of the law and legitimate authority, things do not always go well and sometimes the outcome is harmful and a threat to personal and public health and safety.

While there may be one or more studies that have addressed a particular question, it does not necessarily mean that the question has been adequately answered. So questions important for understanding, explaining, and addressing the institution of drug trafficking and any social and personal problems related to it remain open. As noted earlier, among the questions probably worth asking but certainly not all of the questions that need to be asked are the following: How is the industry and how are the markets organized, and how do they operate? How does drug trafficking work? How do different drugs go from producers to consumers? How much of all and different drugs are being produced, and how great is the demand for them? What is the value of illicit drug production worldwide, and what is the value of all drugs being consumed? What is the impact of the illicit drug industry on people, communities, and nations? Are there any positive outcomes? What are the harms? Besides scientific evidence, what if any ethical or moral principles need to be considered when making public decisions about policies and practices related to drugs and drug trafficking? What if any practical limitations are there that may restrict what can or cannot be done about drugs and drug trafficking?

What needs to be done now is not just a continuation of the current practice of individual researchers raising individual questions based on personal interest or public attention and designing studies. What needs to be done is the work by teams of researchers, policymakers, and practitioners to design and develop a broader research agenda, probably international even more than national, to identify and study relevant questions about the drug industry and markets and drug trafficking to then allow policy to be made and practices implemented that will lead to outcomes that benefit and do not harm people, communities, and nations.

Bibliography:

• Andrews PWS (1949) Manufacturing business. Macmillan, London
• Bennett T (1988) The British experience with heroin regulation. Law Contemp Probl 51:299–314
• Brownstein HH (1996) The rise and fall of a violent crime wave – crack cocaine and the social construction of a crime problem. Harrow & Heston, Guilderland
• Caulkins JP (2005) Price and purity analysis for illicit drug: data and conceptual issues. Heinz Research. Paper 25. http://repository.cmu.edu/heinzworks/25. Accessed 25 Oct 2011
• Caulkins JP, Reuter P (1998) What price data tell us about drug markets. J Drug Issues 28:593–612
• Curtis R, Friedman SR, Neaigus A, Jose B, Goldstein M, Ildefonso G (1995) Street-level drug markets: network structure and HIV risk. Soc Netw 17: 229–249
• Goldstein PJ (1985) The drugs/violence nexus: a tripartite conceptual framework. J Drug Issues 14: 493–506
• Goldstein PJ, Brownstein HH, Ryan PJ (1992) Drugrelated homicide in New York: 1984 and 1988. Crime Delinq 38:459–476
• Hughes PH (1977) Behind the wall of respect: community experiments in heroin addiction control. University of Chicago Press, Chicago
• Inciardi JA (2007) War on drugs IV: the continuing saga of the mysteries and miseries of intoxication, addiction, crime and public policy, 4th edn. Allyn & Bacon, Boston
• Inciardi JA, Harrison LD (eds) (2000) Harm reduction – national and international perspectives. Sage, Thousand Oaks
• MacCoun R, Reuter P (1997) Interpreting Dutch cannabis policy: reasoning by analogy in the legalization debate. Science 278:47–52
• Musto D (1991) Opium, cocaine, and marijuana in American history. Sci Am 265:40–47
• Musto D (1999) The American disease, 3rd edn. Oxford University Press, New York
• National Drug Intelligence Center (2011) National drug threat assessment 2011. No. 2011-Q0317-001. USDOJ, Washington, DC
• National Institute of Justice (2003) Toward a drugs and crime research agenda for the 21st century. Special report. USDOJ, Washington, DC
• National Research Council (2001) Informing America’s policy on illegal drugs: what we don’t know keeps hurting us. National Academy Press, Washington, DC, Commission on Behavioral and Social Sciences and Education
• National Research Council (2010) Understanding the demand for illegal drugs. National Academy Press, Washington, DC, Committee on Law and Justice
• Reuter P (2009) Systemic violence in drug markets. Crime Law Soc Chang 52:275–284
• Reuter P, MacCoun R, Murphy P (1990) Money from crime: a study of the economics of drug dealing in Washington, DC. Drug Policy Research Center, RAND, Santa Monica
• Rhodes W, Layne M, Johnston P, Hozik L (2000) What America’s users spend on illegal drugs 1988–1998. Office of National Drug Control Policy, Washington, DC
• Sommers I, Baskin D, Baskin-Sommers A (2006) Methamphetamine use among young adults: health and social consequences. Addict Behav 31: 1469–1476
• United Nations (UN) (1988) United Nations conference for the adoption of a convention against illicit traffic in narcotic drugs and psychotropic substances. http://www.unodc.org/pdf/convention_(1988_en.pdf. Accessed 5 June 2012
• van Laar M, Cruts G, van Gageldonk A, van OoyenHouben M, Croes E, Meijer R, Ketelaars T (2011) Report to the EMCDDA by the Reitox National Focal Point. Final version as approved by the Scientific Committee of the Netherlands National Drug Monitor (NDM) on the 22nd of December 2010. European Monitoring Centre for Drugs and Drug Addiction
• Weisburd D, Mazerolle LG (2000) Crime and disorder in drug hot spots: implications for theory and practice in policing. Police Q 3:331–349
• Zahn M (1980) Homicide in the twentieth century United States. In: Inciardi JA, Faupel CE (eds) History and crime. Sage, Beverly Hills

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2 Drug Trafficking

• Published: October 2020
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This chapter addresses drug trafficking in four sections: the nature and extent of the harm; the structure of drug trafficking (considered in terms of source, transit and market); regulation and control; and finally a discussion about drug trafficking as illicit business. Major drug types and supply routes are reviewed, to illustrate the scale of the problem regionally and globally. The usual routines of drug trafficking are discussed, including production, wholesale, kingpins and mules, and street-level dealing. The well-known challenges of interdiction are noted, amounting to the observed failure of the ‘war on drugs’. Finally, we look at empirical evidence that drug trafficking is considered by its protagonists in terms of business enterprise, and the ways this distances them from the harm it causes.

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United Nations

Office on drugs and crime, drug trafficking, introduction.

Drug trafficking is a global illicit trade involving the cultivation, manufacture, distribution and sale of substances which are subject to drug prohibition laws. UNODC is continuously monitoring and researching global illicit drug markets in order to gain a more comprehensive understanding of their dynamics. Drug trafficking is a key part of this research. Further information can be found in the yearly World Drug Report .

At current levels, world heroin consumption (340 tons) and seizures represent an annual flow of 430-450 tons of heroin into the global heroin market. Of that total, opium from Myanmar and the Lao People's Democratic Republic yields some 50 tons, while the rest, some 380 tons of heroin and morphine, is produced exclusively from Afghan opium. While approximately 5 tons are consumed and seized in Afghanistan, the remaining bulk of 375 tons is trafficked worldwide via routes flowing into and through the countries neighbouring Afghanistan.

The Balkan and northern routes are the main heroin trafficking corridors linking Afghanistan to the huge markets of the Russian Federation and Western Europe. The Balkan route traverses the Islamic Republic of Iran (often via Pakistan), Turkey, Greece and Bulgaria across South-East Europe to the Western European market, with an annual market value of some $20 billion. The northern route runs mainly through Tajikistan and Kyrgyzstan (or Uzbekistan or Turkmenistan) to Kazakhstan and the Russian Federation. The size of that market is estimated to total $13 billion per year.

Global heroin flows from Asian points of origin

Source: UNODC World Drug Report 2010

In 2008, global heroin seizures reached a record level of 73.7 metric tons. Most of the heroin was seized in the Near and Middle East and South-West Asia (39 per cent of the global total), South-East Europe (24 per cent) and Western and Central Europe (10 per cent). The global increase in heroin seizures over the period 2006-2008 was driven mainly by continued burgeoning seizures in the Islamic Republic of Iran and Turkey. In 2008, those two countries accounted for more than half of global heroin seizures and registered, for the third consecutive year, the highest and second highest seizures worldwide, respectively.

In 2007 and 2008, cocaine was used by some 16 to 17 million people worldwide, similar to the number of global opiate users. North America accounted for more than 40 per cent of global cocaine consumption (the total was estimated at around 470 tons), while the 27 European Union and four European Free Trade Association countries accounted for more than a quarter of total consumption. These two regions account for more than 80 per cent of the total value of the global cocaine market, which was estimated at $88 billion in 2008.

For the North American market, cocaine is typically transported from Colombia to Mexico or Central America by sea and then onwards by land to the United States and Canada. Cocaine is trafficked to Europe mostly by sea, often in container shipments. Colombia remains the main source of the cocaine found in Europe, but direct shipments from Peru and the Plurinational State of Bolivia are far more common than in the United States market.

Main global cocaine flows, 2008

Following a significant increase over the period 2002-2005, global cocaine seizure totals have recently followed a stable trend, amounting to 712 tons in 2007 and 711 tons in 2008. Seizures continued to be concentrated in the Americas and Europe. However, the transition from 2007 to 2008 brought about a geographical shift in seizures towards the source countries for cocaine. Seizures in South America accounted for 59 per cent of the global total for 2008, compared with 45 per cent in 2007.

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• Published: 05 August 2024

Rapid, biochemical tagging of cellular activity history in vivo

• Run Zhang 1 , 2   na1 ,
• Maribel Anguiano 2 , 3   na1 ,
• Isak K. Aarrestad 2 , 3 , 4 ,
• Sophia Lin 2 , 5 ,
• Joshua Chandra 2 , 3 ,
• Sruti S. Vadde 2 , 5 ,
• David E. Olson   ORCID: orcid.org/0000-0002-4517-0543 2 , 4 , 6 , 7 &
• Christina K. Kim   ORCID: orcid.org/0000-0002-1466-7098 2 , 4 , 5  

Nature Methods ( 2024 ) Cite this article

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• Molecular engineering
• Neuroscience

Intracellular calcium (Ca 2+ ) is ubiquitous to cell signaling across biology. While existing fluorescent sensors and reporters can detect activated cells with elevated Ca 2+ levels, these approaches require implants to deliver light to deep tissue, precluding their noninvasive use in freely behaving animals. Here we engineered an enzyme-catalyzed approach that rapidly and biochemically tags cells with elevated Ca 2+ in vivo. Ca 2+ -activated split-TurboID (CaST) labels activated cells within 10 min with an exogenously delivered biotin molecule. The enzymatic signal increases with Ca 2+ concentration and biotin labeling time, demonstrating that CaST is a time-gated integrator of total Ca 2+ activity. Furthermore, the CaST readout can be performed immediately after activity labeling, in contrast to transcriptional reporters that require hours to produce signal. These capabilities allowed us to apply CaST to tag prefrontal cortex neurons activated by psilocybin, and to correlate the CaST signal with psilocybin-induced head-twitch responses in untethered mice.

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Molecular recording of calcium signals via calcium-dependent proximity labeling

A luciferase prosubstrate and a red bioluminescent calcium indicator for imaging neuronal activity in mice

An optimized bioluminescent substrate for non-invasive imaging in the brain

Dynamic changes in intracellular ion concentrations allow cells to respond and adapt to their local environment, ultimately contributing to the normal physiological functioning of organisms. For example, neurons, the basic functional units of the brain, can be activated by various external stimuli or pharmacological compounds, leading to rapid fluctuations in intracellular Ca 2+ concentrations. Thus, activity among complex neural networks can be measured using cellular changes in Ca 2+ levels as a direct proxy for neuronal firing. Genetically encodable Ca 2+ indicators have transformed our ability to record neural activity in awake and behaving animals 1 , 2 , 3 , 4 . However, a major limitation of fluorescent sensors is that their readout is transient, and they generally require invasive methods to gain optical access to deep brain structures. This can make it challenging to couple the activity history of a given neuron with its numerous other cellular properties (for example, precise spatial localization, RNA expression or protein expression).

To overcome this issue, previous efforts have focused on designing orthogonal transcriptional reporters (FLARE 5 , FLiCRE 6 , Cal-Light 7 ) or fluorescent proteins (CaMPARI 8 ) that can stably mark activated cells undergoing high intracellular Ca 2+ levels. However, these approaches implement light-sensitive proteins that require blue 9 or ultraviolet 10 light to restrict the time window of activity labeling in cells. This requirement hampers their scalability in deep brain regions, or in body areas where fibers for light delivery cannot be implanted. Alternative stable tagging approaches include immediate early gene (IEG)-based transcriptional reporters (TRAP2 and tetTag 11 , 12 , 13 ), which utilize a drug injection instead of light to gate the activity labeling window. However, while IEG activity has been linked to neural activity in many cell types 14 , it is not nearly as universal a readout as Ca 2+ is. Furthermore, the slow onset of IEG expression limits the ability to immediately tag and identify neurons activated during a specific time window. This is compounded by the fact that all transcription-based activity reporters take several hours (~6–18 h 6 , 15 ) before sufficient levels of the reporter protein can be detected. Thus, there is a need for a strategy that enables noninvasive and rapid activity-dependent labeling of cells.

We designed an activity-dependent enzyme that can attach a small, biochemical handle to activated cells exhibiting high intracellular Ca 2+ . Our strategy was to reengineer and repurpose a proximity-labeling enzyme, split-TurboID 16 , to report increased intracellular Ca 2+ in living cells by tagging proteins with an exogenously delivered biotin molecule. Proximity-labeling enzymes such as split-TurboID 16 (and its predecessors, BioID 17 and TurboID 18 ) have been traditionally used to biotinylate nearby, statically present proteins for downstream enrichment and analysis over long periods (typically 4–7 days in vivo). But they have not been engineered to detect dynamic changes in intracellular ion concentrations. Our design, Ca 2+ -activated split-TurboID (CaST), enzymatically tags activated neurons within brief, user-defined time windows of exogenous biotin delivery. The biotinylated proteins can then immediately be read out using any existing method for biotin detection. Because the biotin molecule is permeable to both the cell and the blood–brain barrier 19 , 20 , this facilitates its application in living organisms.

Design and optimization of CaST

The basic CaST design tethers the Ca 2+ -binding protein calmodulin (CaM) and a CaM-binding synthetic peptide M13 variant to either inactive half of split-TurboID (Fig. 1a ). We postulated that under high cytosolic Ca 2+ concentrations, the CaM fragment would be recruited to M13, resulting in the reconstitution and activation of split-TurboID. Upon simultaneous biotin supplementation, the reconstituted split-TurboID would then biotinylate itself and nearby proteins 16 , in a Ca 2+ -dependent manner. High Ca 2+ alone should not result in signal, as the endogenous biotin levels are too low to result in substantial protein biotinylation; and exogenous biotin alone should not result in signal, as the split-TurboID fragments remain separated and inactive (Fig. 1b ). Thus, CaST can act as a coincidence detector of both exogenous biotin and high intracellular Ca 2+ .

a , AlphaFold2 (refs. 56 , 57 ) prediction of the protein structures for the two halves of CaST either in isolation (left; as expected in the absence of Ca 2+ ) or in complex (right; as expected in the presence of high Ca 2+ ). The two CaST components reversibly reconstitute in a Ca 2+ -dependent manner. The predicted biotin binding site is shown in blue. b , Schematic of CaST design for expression in HEK cells. The component with sTb(C)-M13-GFP is tethered to the membrane via the transmembrane domain of a CD4 cell membrane protein 58 , while the CaM-V5 epitope tag 59 -sTb(N) component is expressed throughout the cytosol. CaST only tags proteins when cells are treated with biotin and exhibit elevated intracellular Ca 2+ . c , Example confocal images of HEK cells transfected with both components of CaST and treated with biotin ± Ca 2+ for 30 min. Cells were washed, fixed and stained with anti-V5 and SA-647. The anti-V5 signal stains the CaM-sTb(N) component (left), while the GFP fluorescence shows the CD4-sTb(C)-M13 component (middle). Biotinylation of proteins is detected by SA-647 staining (right). Scale bar, 20 µm. d , HEK cells were transfected with CaST and treated with ±50 µM biotin and ±Ca 2+ (5 mM CaCl 2 and 1 µM ionomycin) for 30 min. Cells were then washed with Dulbecco’s phosphate buffered saline (DPBS), and whole-cell lysates were collected and analyzed using a western blot stained with streptavidin–horseradish peroxidase (SA–HRP) or anti-V5/HRP. ‘N’ indicates the expected size of the CaM-V5-sTb(N) fragment, while ‘C’ indicates the expected size of the CD4-sTb(C)-M13-GFP fragment. e , Quantification of biotinylated proteins present in western blot lanes of the experiment shown in d . Two independent biological replicates were quantified. The entire lane below the 75-kDa endogenously biotinylated bands was included in the quantification (sum of the total raw intensity pixel values). A line plot profile spanning the entire blot is shown in Extended Data Fig. 2 .

Source data

Initially, we tested various approaches to tether CaM and M13 to either of the split-TurboID fragments, sTb(N) and sTb(C). We transfected four different versions of the tool into human embryonic kidney (HEK) 293T cells, with different conformational arrangements and subcellular localizations of the proteins (Supplementary Fig. 1a ). We first treated the cells with a combination of biotin with or without Ca 2+ and an ionophore for 30 min, and then we fixed and stained the cells for biotinylated proteins using streptavidin conjugated to Alexa Fluor 647 (SA-647). We quantified both the green fluorescent protein (GFP) and SA-647 fluorescence for each cell and calculated their ratio (SA-647/GFP) to normalize for differences in expression levels across cells or experimental conditions. Importantly, all four versions had no SA-647 signal in the absence of exogenously delivered biotin. Of the four versions, we found that a membrane-tethered CD4-sTb(C)-M13-GFP with a cytosolic CaM-V5-sTb(N) resulted in the highest signal-to-background ratio (SBR) of biotin ± Ca 2+ tagging (Supplementary Fig. 1b ). We subsequently designated this optimized construct as our final CaST design (Fig. 1b ). We also showed that a 5:2 transfection ratio of the two CaST fragments (CD4-sTb(C)-M13-GFP to CaM-V5-sTb(N)) yielded the highest SBR of all ratios tested (Extended Data Fig. 1a–c ). We performed subsequent characterizations using this optimal transfection ratio.

Immunohistochemistry and confocal imaging showed the expression of both fragments of the tool in HEK cells, confirming that the low SA-647 signal in negative control conditions is not due to a lack of fragment coexpression (Fig. 1c ). As expected, purposefully omitting either fragment of CaST in the presence of biotin and Ca 2+ resulted in no biotinylation signal (Extended Data Fig. 1d ). Western blot analysis confirmed that CaST-transfected HEK239T cells treated with biotin and Ca 2+ drove biotinylation across an array of nearby proteins, compared to negative control conditions (Fig. 1d,e and Extended Data Fig. 2a–d ).

Characterization of CaST labeling in HEK cells

To further quantify the extent of biotin-dependent and Ca 2+ -dependent CaST labeling, we performed fluorescence imaging across multiple fields of view (FOVs) in CaST-transfected HEK cells treated with biotin ± Ca 2+ (Fig. 2a ). Single-cell analysis confirmed that the Ca 2+ -dependent increase in SA-647 labeling occurred across cells with varying GFP expression levels, showing that it is not due to differences in expression levels of the tool across conditions (Fig. 2b ). The distributions of normalized SA-647/GFP fluorescence also differed between cells treated with or without Ca 2+ (Fig. 2c ). These results demonstrate that CaST robustly detects elevated intracellular Ca 2+ levels in living cells.

a , Example images of HEK cells transfected with CaST and treated with 50 µM biotin ± Ca 2+ (5 mM CaCl 2 and 1 µM ionomycin) for 30 min. Top row shows SA-647 staining of biotinylated proteins. Bottom row shows CD4-sTb(C)-M13-GFP. b , Scatterplot of the mean SA-647 versus mean GFP fluorescence calculated for every GFP + cell detected across 11 FOVs treated with either biotin + Ca 2+ ( n  = 451 cells; two-tailed Pearson’s R  = 0.50, P  = 6.2 × 10 −30 ) or biotin − Ca 2+ ( n  = 473 cells; two-tailed Pearson’s R  = 0.69, P  = 2.6 × 10 −67 ). c , Violin plots showing the distributions of the mean SA-647/GFP fluorescence ratio per cell from data in b ( P  = 2.0 × 10 −85 , U  = 27,234, two-tailed Mann–Whitney U test). d , Schematic of the bi-cistronic CaST-IRES construct design. e , Example images of HEK cells transfected with CaST-IRES and treated with biotin ± Ca 2+ for 30 min, as in a . f , Scatterplot of the mean SA-647 versus mean GFP fluorescence calculated for each GFP + cell detected across 10 FOVs treated with either biotin + Ca 2+ ( n  = 293 cells; two-tailed Pearson’s R  = 0.72, P  = 1.5 × 10 −47 ) or biotin − Ca 2+ ( n  = 332 cells; two-tailed Pearson’s R  = 0.78, P  = 3.7 × 10 −69 ). g , Violin plots showing the distributions of the mean SA-647/GFP fluorescence ratio per cell from data in f ( P  = 3.1 × 10 − 77 , U  = 6,732, two-tailed Mann–Whitney U test). h , The FOV averages of the SA-647/GFP fluorescence ratio per cell from the non-IRES data shown in b ( n  = 11 FOVs per condition; P  = 1.1 × 10 − 5 , U  = 2, two-tailed Mann–Whitney U test), and the IRES data shown in f ( n  = 10 FOVs per condition; P  = 1.1 × 10 − 5 , U  = 0, two-tailed Mann–Whitney U test). i , j , ROC curves for distinguishing Ca 2+ -treated versus non-treated cell populations based on CaST non-IRES cells from c ( i ; AUC = 0.87, P  = 2.0 × 10 − 85 , Wilson/Brown method) and CaST-IRES-transfected cells from g ( j ; AUC = 0.93, P  = 3.1 × 10 − 77 , Wilson/Brown method). All scale bars, 300 µm. **** P  < 0.0001.

To optimize CaST delivery, we concatenated its two fragments into a bi-cistronic vector containing either a porcine teschovirus 2A peptide (P2A) coding sequence or an internal ribosome entry site (IRES). P2A and IRES are well-established strategies for coexpressing multiple proteins from a single promoter 21 , 22 , ensuring that each cell expresses both fragments of CaST. We found that both CaST-P2A and CaST-IRES exhibited higher SA-647/GFP labeling with biotin and Ca 2+ compared to with biotin alone; but the IRES version resulted in a higher biotin ± Ca 2+ SBR (5-fold, compared to 2.7-fold; Supplementary Fig. 2 ). Studies have reported that the IRES motif lowers the expression level of the second component relative to the first 23 ; this may explain why this strategy performs better than P2A, given our data showing an optimal transfection ratio of 5:2 for the two separate components of CaST (Extended Data Fig. 1a ). For these reasons, we chose to further characterize the CaST-IRES version (Fig. 2d–g ).

Western blot analysis confirmed that CaST-IRES cells exhibited elevated biotinylation signal compared to negative control conditions (Extended Data Fig. 2e ). In addition, immunofluorescence analysis showed that in comparison to the non-IRES version, CaST-IRES resulted in a larger calcium-dependent fold-change in both the mean SA-647/GFP cell ratio and the mean SA-647 cell fluorescence (likely due to more controlled protein expression levels of the two components; Fig. 2h and Extended Data Fig. 3a,b ). We also performed receiver operating characteristic (ROC) analyses of the SA-647/GFP ratios to evaluate CaST’s ability to discriminate between individual Ca 2+ -treated and non-treated cells. We determined an area under the curve (AUC) of 0.87 for non-IRES CaST, and an AUC of 0.93 for CaST-IRES, indicating that both versions can robustly distinguish activated versus non-activated cells (Fig. 2i,j ).

Temporal resolution, Ca 2+ sensitivity and time integration of CaST

One important requisite of our design is that both the Ca 2+ -sensing and split-TurboID reconstitution are reversible. This is so that enzymes activated during high Ca 2+ before the desired biotin labeling window can split back into inactive fragments once the cytosolic Ca 2+ returns to resting levels. To test for the reversibility of CaST, we treated HEK cells expressing CaST-IRES with Ca 2+ for 30 min, washed the cells over the course of 10 min and then delivered biotin for 30 min. We directly compared this condition to CaST-IRES cells treated with biotin alone, or with biotin and Ca 2+ . Cells treated with biotin after removal of Ca 2+ exhibited no biotinylation, similar to the negative control (Fig. 3a,b ). This demonstrates that CaST has a temporal resolution for detecting intracellular Ca 2+ on the order of 10 min (meaning it can ignore Ca 2+ events that occur 10 min before the desired biotin labeling window).

a , To test the split enzyme’s reversibility, HEK cells were transfected with CaST-IRES and treated with biotin alone for 30 min (top), with Ca 2+ for 30 min followed by a 10-min wash and then biotin for 30 min (middle), or with biotin + Ca 2+ simultaneously for 30 min (bottom). Example images are shown for all three conditions with SA-647 staining of biotin and GFP expression of CaST-IRES. Scale bar, 300 µm. b , The FOV averages of the SA-647/GFP fluorescence ratio per cell for the three conditions shown in a ( n  = 8 FOVs per condition; biotin − Ca 2+ versus Ca 2+ /wash/biotin: P  = 0.75; biotin − Ca 2+ versus biotin + Ca 2+ : P  = 4.9 × 10 −8 ; Ca 2+ /wash/biotin versus biotin + Ca 2+ : P  = 1.3 × 10 −8 ; Tukey’s post hoc multiple-comparison’s test following a one-way analysis of variance (ANOVA), F 2,21  = 56.37, P  = 3.6 × 10 −9 ). c , Example FOVs of HEK cells transfected with CaST-IRES and treated with biotin and increasing concentrations of CaCl 2 (and 1 µM ionomycin). d , The FOV averages of the SA-647/GFP fluorescence ratio per cell for the CaCl 2 concentrations shown in c ( n  = 7 FOVs per condition; 0 mM versus 2.5 mM: P  = 7.6 × 10 −4 ; 0 mM versus 5 mM: P  = 8.8 × 10 −7 ; 0 mM versus 7.5 mM: P  = 5.5 × 10 −10 ; 0 mM versus 10 mM: P  = 5.4 × 10 −12 ; Tukey’s post hoc multiple-comparison’s test following a one-way ANOVA, F 4,30  = 44.07, P  = 3.8 × 10 −12 ). The FOV average SA-647/GFP ratios were linearly correlated with CaCl 2 concentration (two-tailed Pearson’s correlation coefficient R  = 0.99, P  = 0.001). e , Example FOVs of HEK cells transfected with CaST-IRES and treated with 50 µM biotin ± Ca 2+ (5 mM CaCl 2 and 1 µM ionomycin) for different durations. f , The mean FOV averages of the SA-647/GFP fluorescence ratio per cell for the different stimulation times shown in e ( n  = 10 FOVs per condition). The untreated condition is shown on the left. Data are plotted as the mean ± s.e.m. All scale bars, 300 µm. *** P  < 0.001, **** P  < 0.0001. NS, not significant.

Next, we asked whether the CaST signal is correlated to the levels of intracellular Ca 2+ present in cells. We conducted a Ca 2+ titration experiment in which we treated cells with increasing concentrations of calcium chloride (CaCl 2 ) in the medium (along with 1 µM ionomycin and biotin) for 30 min. Our results demonstrated a monotonically increasing SA-647/GFP signal from 2.5 mM to 10 mM CaCl 2 , with a linear correlation to Ca 2+ concentration over this range (Fig. 3c,d ).

We also conducted temporal integration experiments to evaluate the labeling of CaST over different time exposures to a fixed Ca 2+ concentration. We treated CaST-IRES cells with biotin ± Ca 2+ for increasing durations of time. We found that a 10-min stimulation period is sufficient to induce elevated SA-647/GFP labeling over background (Fig. 3e,f ). The amount of labeling increased with longer stimulation times, saturating around 1 h (Extended Data Fig. 3c,d ). These results show that CaST acts as an integrator of Ca 2+ , both in terms of detecting increasing Ca 2+ concentration, and increasing duration of Ca 2+ exposure.

Direct comparison of CaST to existing technologies

Compared to existing Ca 2+ -dependent integrators, CaST has two major advantages: it is noninvasive (using biotin instead of light), and it acts on rapid timescales (tags cells within minutes rather than hours). We directly compared the performance of CaST against an existing light-dependent and Ca 2+ -dependent integrator, FLiCRE 6 . FLiCRE works via a protease-mediated release of a non-native transcription factor (for example, Gal4) in the presence of blue light and Ca 2+ . This released transcription factor then enters the nucleus to drive expression of a modular reporter gene, such as a fluorescent protein (Extended Data Fig. 4a ). While it has fast labeling kinetics (it can detect 30 s of blue light and elevated Ca 2+ ), its readout kinetics are slow, requiring transcription and translation of the reporter gene that can take hours to accumulate. Here we transfected HEK cells overnight with either CaST-IRES or FLiCRE and treated cells for 15 min with either biotin ± Ca 2+ or light ± Ca 2+ , respectively. We then fixed cells immediately or 2, 4, 6 or 8 h after the stimulation, and imaged the reporter expression in each case (SA-647 for CaST, and UAS::mCherry for FLiCRE; Fig. 4a,b and Extended Data Fig. 4b,c ). Across all time points measured, we observed an increase in CaST SA-647 labeling in conditions treated with biotin and Ca 2+ compared to with biotin alone; however, an increase in FLiCRE UAS::mCherry reporter expression was only apparent starting 6 h after the light and Ca 2+ stimulation (Fig. 4c,d ). We calculated the biotin ± Ca 2+ and light ± Ca 2+ SBRs by normalizing the SA-647 or UAS::mCherry expression against an expression marker for each respective tool (GFP; Fig. 4e,f and Extended Data Fig. 4d–g ). Note that for the cells treated with biotin and Ca 2+ , the decrease in the SA-647/GFP ratio over the 8 h is due to both a decrease in SA-647 signal (protein turnover) and an increase in CaST GFP expression (Extended Data Fig. 4d,e ).

a , b , Schematics of CaST ( a ) and FLiCRE ( b ) as AND logic gates, and the experimental paradigms used to test the time course of labeling detection after biotin + Ca 2+ (CaST) and light + Ca 2+ (FLiCRE) stimulation. Cells were transfected with either CaST-IRES or FLiCRE (Extended Data Fig. 4a ) components. c , For CaST, the FOV average of the SA-647 cell fluorescence was calculated following a variable delay period after biotin ± Ca 2+ stimulation ( n  = 12 FOVs for conditions with 0, 4, 6 and 8 h delay; n  = 11 FOVs for conditions with 2 h delay; 0 h: P  = 1.0 × 10 −30 ; 2 h: P  = 3.0 × 10 −36 ; 4 h: P  = 2.4 × 10 −35 ; 6 h: P  = 3.0 × 10 −24 ; 8 h: P  = 1.4 × 10 −10 ; Sidak’s post hoc multiple-comparison’s test following a two-way ANOVA, F 4,108  = 25.94, P  = 4.5 × 10 −15 ). d , For FLiCRE, the FOV average of the UAS::mCherry cell fluorescence was calculated following a variable delay period after light ± Ca 2+ stimulation ( n  = 11 FOVs for conditions with 0 h delay; n  = 12 FOVs for conditions with 2, 4, 6 and 8 h delay; 6 h: P  = 4.6 × 10 −5 ; 8 h: P  = 2.4 × 10 −28 ; Sidak’s post hoc multiple-comparison’s test following a two-way ANOVA, F 4,108  = 46.46, P  = 1.2 × 10 −22 ). e , f , The ±Ca 2+ SBR of normalized reporter expression is shown for both CaST ( e ) and FLiCRE ( f ). For CaST, the SA-647 fluorescence was divided by the GFP fluorescence ( n  = 12 FOVs for conditions with 0, 4, 6 and 8 h delay; n  = 11 FOVs for conditions with 2 h delay). For FLiCRE, the UAS::mCherry fluorescence was divided by the GFP fluorescence ( n  = 11 FOVs for conditions with 0 h delay; n  = 12 FOVs for conditions with 2, 4, 6 and 8 h delay). Data are plotted as the mean ± s.e.m. in c and d . **** P  < 0.0001.

To ensure the use of optimal labeling parameters for evaluating FLiCRE, we also stimulated cells at higher expression levels of each tool (48 h after transfection). We observed an increase in nonspecific background labeling for both CaST-IRES and FLiCRE, even immediately after stimulation (Supplementary Fig. 3a,b ). Nonetheless, we were still able to observe a difference between biotin ± Ca 2+ groups immediately after stimulation with CaST-IRES (Supplementary Fig. 3c–e ). In contrast, FLiCRE showed no differences between light ± Ca 2+ groups, even at 8 h and 24 h after stimulation, due to nonspecific background activation caused by high expression levels (Supplementary Fig. 3f–h ). Previous studies have also reported that the performance of FLiCRE 6 and related protease-dependent tools 24 can suffer at high expression levels. These findings demonstrate both the immediacy of labeling, and robustness at various expression levels, of CaST compared to protease-driven transcriptional tools such as FLiCRE.

Application of CaST in cultured neurons

We then asked whether CaST could detect elevated intracellular Ca 2+ in cultured rat hippocampal neurons. We expressed the two-component version of CaST using adeno-associated viruses (AAVs) to obtain the maximal expression levels, and stimulated neurons using potassium chloride (KCl) in the presence of biotin for 30 min. Immunofluorescence imaging confirmed that CaST robustly tagged activated neurons treated with biotin and KCl, compared to negative control conditions (Fig. 5a,b and Extended Data Fig. 5a ). Stimulation for only 10 min was also sufficient to drive elevated CaST labeling (Fig. 5c,d ). Quantitative analysis of individual GFP + neurons expressing CaST showed that ~35% of GFP + neurons exhibited strong SA-647 labeling in the 10-min biotin and KCl condition (thresholded as described in Extended Data Fig. 5b ), compared to ~10% labeled in the biotin-alone condition (Fig. 5e ). In neurons stimulated for 30 min, ~65% of GFP + neurons exhibited strong SA-647 labeling, compared to ~10% labeled in the biotin-alone condition (Fig. 5e and Extended Data Fig. 5b,c ). ROC analysis showed that under the 30-min condition, CaST could distinguish KCl-stimulated versus unstimulated neurons with an AUC of 0.91 (Extended Data Fig. 5d ). DRAQ7 staining showed no apparent cytotoxicity before stimulation in neurons transduced with CaST (Extended Data Fig. 5e,f ).

a , Example FOVs of cultured rat hippocampal neurons infected with AAV2/1-Synapsin-CD4-sTb(C)-M13-GFP and AAV2/1-Synapsin-CaM-sTb(N) viruses and stimulated with ±biotin and ±KCl for 30 min. b , The FOV averages of the SA-647/GFP fluorescence ratio per cell for the data shown in a ( n  = 6 FOVs per condition; −biotin −KCl versus +biotin +KCl: P  = 1.8 × 10 −12 ; −biotin +KCl versus +biotin +KCl: P  = 3.0 × 10 −11 ; +biotin −KCl versus +biotin +KCl: P  = 1.4 × 10 −10 ; −biotin −KCl versus +biotin −KCl: P  = 0.013; Sidak’s post hoc multiple-comparison’s test following a two-way ANOVA, F 1,20  = 59.43, P  = 2.1 × 10 −7 ). c , Example FOVs of rat hippocampal neurons infected with CaST as in a but stimulated with biotin ± KCl for only 10 min. d , The FOV averages of the SA-647/GFP fluorescence ratio per cell for the data shown in c ( n  = 8 FOVs per condition; P  = 0.015, U  = 9, two-tailed Mann–Whitney U test). e , Fraction of all GFP + neurons that are also SA-647 + (defined as having an SA-647 fluorescence value greater than the 90th percentile of neurons in the biotin − KCl group). Data are quantified for the 10-min labeling experiment shown in c ( n  = 8 FOVs per condition; P  = 0.003, U  = 5, two-tailed Mann–Whitney U test) and for a replicated 30-min labeling experiment shown in Extended Data Fig. 5b,c ( n  = 6 FOVs per condition; P  = 0.002, U  = 0, two-tailed Mann–Whitney U test). Data are plotted as the mean ± s.e.m. f , Example FOVs of rat hippocampal neurons infected with CaST as in a and treated with 50 µM biotin and 10 µM DA, 10 µM DOI or 30 mM KCl for 30 min. g , The FOV averages of the SA-647/GFP fluorescence ratio per cell for the conditions shown in f ( n  = 12 FOVs per condition; −KCl versus DA: P  = 0.547; −KCl versus DOI: P  = 0.008; −KCl versus KCl: P  = 6.5 × 10 −4 , Tukey’s post hoc multiple-comparison’s test following a one-way ANOVA, F 3,44  = 7.373, P  = 4.2 × 10 −4 ). All scale bars, 300 µm. * P  < 0.05, * *P  < 0.01, *** P  < 0.001, **** P  < 0.0001.

Next, we explored the relationship between intracellular Ca 2+ changes and CaST labeling using the real-time fluorescent calcium indicator RCaMP2 (ref. 4 ). We co-infected RCaMP2 and CaST AAVs in neurons, and mildly stimulated them using a media change. We found that neurons exhibiting greater Ca 2+ changes in response to stimulation (quantified by the RCaMP2 mean d F/F peak height) also displayed stronger SA-647/GFP CaST labeling (Extended Data Fig. 6 ). This demonstrates on a cell-by-cell basis that CaST labeling is correlated to intracellular Ca 2+ level changes. To additionally demonstrate the specificity of CaST labeling, we used a red-shifted optogenetic cation channel, bReaChES 25 , to achieve spatially targeted neuron stimulation using orange light. We selectively stimulated neurons within a narrow ~1-mm width slit through the bottom of the culture dish. We observed an increased SA-647/GFP cell ratio only within the subregion of the FOV exposed to orange light, while KCl stimulation resulted in uniform CaST biotinylation across the dish (Extended Data Fig. 7 ).

Lastly, we investigated the potential for CaST to detect elevated intracellular Ca 2+ in response to physiological stimuli, such as exposure to neuromodulators or pharmacological agents. We treated neurons with biotin and either 10 µM dopamine (DA) or 10 µM 2,5-dimethoxy-4-iodoamphetamine (DOI; a serotonin 5-HT type 2A/C receptor agonist), for 30 min. Both DA receptors and 5-HT 2A receptors are known to be expressed in rat hippocampal neurons 26 , 27 , 28 . DA receptors are either G i - or G s -protein-coupled receptors, while 5-HT 2A receptors are G q -protein-coupled receptors 29 , 30 . Whereas activation of G i /G s -protein signaling primarily modulates cAMP with variable effects on intracellular Ca 2+ levels, G q -protein signaling should directly elevate intracellular Ca 2+ levels 31 , 32 . We found that DA treatment did not increase SA-647 labeling relative to a biotin-alone vehicle control; however, biotin and DOI drove an increase in SA-647 labeling (Fig. 5f,g ). RCaMP2 imaging in neurons treated under experimentally matched conditions confirmed that both DOI and KCl (but not DA) induce an increase in Ca 2+ activity (Extended Data Fig. 8 ). These results demonstrate that CaST can be applied to reveal differential responses to pharmacological compounds among a population of neurons containing multiple neuronal subtypes and expressing different receptors.

Identifying psilocybin-activated neurons during head twitch

We next applied CaST in vivo to record cellular activity in behavioral contexts that have previously been challenging to record from with existing methods. Psychedelics are an emerging class of therapeutics that are known to promote neuroplasticity in the prefrontal cortex 33 , 34 , 35 and produce positive behavioral adaptations in animal models of neuropsychiatric disorders 36 , 37 , 38 , 39 . These compounds also drive hallucinations in humans; therefore, a major area of research is to understand whether the hallucinogenic and therapeutic aspects of psychoplastogens can be decoupled 40 . The main hallucinogenic behavioral correlate of psychedelic drugs in animal models is the head-twitch response (HTR) 41 , 42 —a rhythmic rotational head movement that is tightly coupled to 5-HT 2A receptor activation and correlated with hallucinogenic potential in humans 42 . Critically, the HTR measurement requires free movement of the animal’s head, precluding its measurement in head-fixed rodents under a microscope during cellular-resolution neuronal recordings. We posited that CaST could be applied to directly correlate cellular neuronal activity with the psychedelic-induced HTR in vivo.

We used CaST to measure how psilocybin, a potent and therapeutically relevant psychedelic, modulates population activity in the medial prefrontal cortex (mPFC) of untethered mice during the simultaneous measurement of the HTR. Notably, there have been conflicting reports as to whether 5-HT 2A receptor agonists increase 43 , 44 , 45 or decrease 46 , 47 population neuronal activity in the cortex. This could be in part due to the different sensitivities in the previous recording methods used.

We expressed CaST viruses under a pan-neuronal synapsin promoter in the mPFC to identify what percentage of neurons are activated during acute psilocybin injection. Mice expressing CaST received a single intraperitoneal (i.p.) injection of biotin and saline, or biotin and psilocybin. We also recorded video of the mice to quantify the number of head twitches displayed following the drug treatment. One hour later, we euthanized the mice, and stained mPFC slices with SA-647 (Fig. 6a ). We first asked how psilocybin modulated mPFC neuronal activity using CaST as the readout. Immunohistochemistry showed an increase in SA-647 labeling in mice treated with psilocybin, compared to control mice (Fig. 6b ). Image quantification showed that individual CaST-expressing GFP + neurons in psilocybin-treated mice exhibited increased SA-647 fluorescence compared to neurons in saline-treated mice (Fig. 6c ). The normalized SA-647/GFP cell ratio averaged across FOVs was also higher in psilocybin-treated versus saline-treated mice (Fig. 6d ). CaST identified that ~70% of GFP + neurons in the mPFC exhibited strong SA-647 labeling following psilocybin treatment (Fig. 6e ). Mice not expressing CaST, but injected with biotin and psilocybin, did not exhibit SA-647 labeling (Extended Data Fig. 9a,b ).

a , Schematic for using CaST to tag psilocybin-activated neurons during HTR measurement. b , Example mPFC images of SA-647 and CaST GFP fluorescence, for mice injected with biotin + saline, or biotin + psilocybin. c , Mean SA-647 versus GFP fluorescence for each GFP + neuron detected in biotin + saline-injected mice ( n  = 218 neurons from 3 mice) or biotin + psilocybin-injected mice ( n  = 220 neurons from 3 mice). The horizontal dashed line indicates the 90th percentile threshold value of all SA-647 neurons in the biotin + saline group. d , FOV averages of the SA-647/GFP fluorescence ratios from c ( n  = 8 FOVs pooled from 3 mice in both conditions; P  = 6.2 × 10 −4 , U  = 38, two-tailed Mann–Whitney U test). e , Fraction of all GFP + neurons that are SA-647 + (thresholded using the dashed line in c ; n  = 8 FOVs pooled from 3 mice in both conditions; P  = 1.6 × 10 −4 , U  = 36, two-tailed Mann–Whitney U test). f , Cell masks of SA-647 + mPFC neurons identified during HTR measurements. FOVs with the same number of HTRs were taken from the same mice, but from independent CaST injections on opposite hemispheres. g , Number of HTRs versus the number of SA-647 + neurons per mm 2 for data shown in f ( n  = 6 FOVs from independent CaST injections; two-tailed Pearson’s correlation coefficient R  = 0.85, P  = 0.03). h , Number of HTRs versus the mean cell SA-647/GFP ratio for data shown in f . i , Example mPFC images of CaST GFP, SA-647 staining and c-Fos staining in mice treated with biotin + saline, or biotin + psilocybin. j – l , Number of c-Fos + neurons per mm 2 ( j ), SA-647 + neurons per mm 2 ( k ) or SA-647 + divided by GFP + neurons per mm 2 per FOV ( l ), in mice injected with biotin + saline versus biotin + psilocybin ( n  = 5 mice per condition; P  = 0.42, U  = 8, two-tailed Mann–Whitney U test ( j ); P  = 0.0079, U  = 0, two-tailed Mann–Whitney U test ( k ); P  = 0.0079, U  = 0, two-tailed Mann–Whitney U test ( l )). All scale bars, 50 µm. Data are plotted as the mean ± s.e.m. in e and j – l . ** P  < 0.01, *** P  < 0.001. Psilo., psilocybin.

No prior reports have measured Ca 2+ activity in the mPFC following psilocybin. Thus, to validate our finding that psilocybin activated a large fraction of mPFC neurons using CaST, we performed two-photon (2P) scanning microscopy in head-fixed mice. We injected mice in the mPFC with an AAV encoding the Ca 2+ indicator GCaMP6f and implanted a 1.0-mm-diameter gradient-index (GRIN) lens to optically access neurons ~2.5 mm deep in the brain (Extended Data Fig. 9c ). We conducted 2P imaging in mice immediately after administering saline or psilocybin (Extended Data Fig. 9d–f ). Similarly to our findings with CaST, 2P imaging identified a substantial population of neurons in the mPFC activated by psilocybin (Extended Data Fig. 9g,h ).

While the 2P GRIN lens imaging validated our CaST findings, this methodology inflicts damage to the surrounding tissue, and it also requires head-fixation to record activity. It is impossible to measure the HTR during head-fixed 2P imaging, and this behavior may also be hampered using miniaturized head-mounted microscopes 48 . Indeed, studies have thus far only quantified the HTR during bulk fiber photometry recordings 49 , which lacks cellular resolution. Here we were able to simultaneously record the HTR during CaST labeling in mice injected with either saline or psilocybin. We then measured the amount of neuronal activity induced in the mPFC as a function of the HTR observed (Fig. 6f ). The number of SA-647 + neurons showed a positive correlation with the number of HTRs measured during the recordings (Fig. 6g ). The mean SA-647/GFP fluorescence ratio of all neurons was also higher in mice exhibiting psilocybin-induced HTRs (Fig. 6h ).

To compare these results to the current best-in-class, activity-dependent antibody staining method compatible with untethered mice, we repeated our CaST experiment while staining for c-Fos. We found that c-Fos staining did not show a relative increase in labeling in the mPFC following psilocybin injection, due to high background staining in this region even with a saline injection (Fig. 6i,j and Extended Data Fig. 10a–d ). As previously reported 45 , c-Fos staining showed increased labeling in the somatosensory cortex of psilocybin-treated mice (Extended Data Fig. 10e,f ). This result confirms that there were no issues with our c-Fos staining protocol and suggests that a major limitation of c-Fos is its variability across brain regions. In contrast, CaST showed an increased number of SA-647 + neurons in mice treated with psilocybin compared to saline control, even when normalizing for the total number of CaST-expressing GFP + cells in each FOV (Fig. 6k,l ). Altogether, these results establish CaST as a sensitive technology for rapid and noninvasive tagging of activated neurons in untethered mice—providing a powerful and complementary strategy to existing molecular tools for recording and identifying activated neurons in vivo.

Here we demonstrate an enzymatic approach for stably tagging activated cells with a biocompatible handle both in vitro and in vivo. Our method, CaST, acts as a Ca 2+ -dependent ligase, labeling itself and nearby proteins with a biotin tag in living cells. Endogenous biotin levels in cells and in the brain are low enough that CaST requires exogenously delivered biotin to robustly tag proteins. Thus, CaST acts as a time-gated integrator of Ca 2+ concentration, driving the accumulation of biotinylated proteins during a specified labeling period. Importantly, CaST reconstitution is reversible, and following a period of Ca 2+ activation, it can be reset to its inactivated state within 10 min. Due to its tight temporal resolution and Ca 2+ sensitivity, CaST could robustly detect mPFC neurons activated by psilocybin in vivo during the simultaneous measurement of the HTR (which could not be detected using c-Fos labeling).

CaST reported that psilocybin activates a large fraction of neurons in the prelimbic mPFC in mice. This was corroborated by our own secondary validation studies using single-cell Ca 2+ imaging. A previous study in rats using in vivo electrophysiology reported that another 5-HT 2A receptor agonist, DOI, primarily inhibits mPFC population activity in rats 46 . It is possible that in vivo electrophysiology recordings may be biased toward more highly active neurons at baseline (missing more silent neurons that are activated by psychedelics); or that DOI and psilocybin have different effects on population activity in mPFC. Another recent study using c-Fos labeling following psilocybin injection (1 mg per kg body weight) reported that they could detect elevated c-Fos labeling in the mPFC only when using one of the two different imaging modalities that they tested 45 ; in addition, our own studies here showed that we could not detect a difference in mPFC c-Fos labeling following psilocybin injection. This suggests that the neuronal activity changes driven by psilocybin in the mPFC are challenging to tag using IEG-based approaches and are better detected using Ca 2+ -based approaches such as CaST.

In contrast to existing light-gated methods for Ca 2+ -dependent labeling 5 , 6 , 8 , a major advantage of CaST is that it is essentially noninvasive, requiring only the brief i.p. injection of a biotin solution in mice, which can rapidly cross the blood–brain barrier. This is particularly advantageous in deep tissue, where light must be delivered through fiber implantation, limiting the recording area and the range of tool usage. It could also be advantageous in other areas of the body, such as the pancreas or in the spinal cord, where it is not possible to deliver blue light. As TurboID is already applicable for cell-type-specific biotin proximity labeling across the entire brain 50 , it is feasible that CaST can also be scaled as a brain-wide activity-dependent labeling tool in the future, with the use of PhP.eB 51 viruses or transgenic lines. Although we did not observe overt cytotoxicity in neurons expressing CaST (nor has it been reported for transgenic TurboID mice 50 ), additional assays of neuron physiology and cell health should be performed if expressing CaST for prolonged periods of time in the brain.

In addition, because the biotin molecule is directly attached to already expressed proteins, this enables the immediate readout of activated neurons after the labeling period with CaST. This contrasts with transcriptional-based reporters, including both Ca 2+ - and IEG-dependent systems, which require multiple hours before activated cells can be identified by the reporter RNA or protein. The immediacy of labeling, along with the scalability of CaST, would be particularly useful for registering cellular activity history with other spatial molecular imaging modalities, such as MERFISH 52 or STARmap 53 for in situ transcriptomics, or MALDI-IHC 54 for spatial proteomics. Because CaST affords a stable readout of neuron activation following tissue fixation, one could apply spatial omics approaches to identify acutely induced changes in gene expression or protein localization caused specifically in this subset of activated neurons. The spatial distribution of fluorescently stained biotinylated proteins could be imaged and registered to the in situ omics data, or a streptavidin–oligonucleotide bound to biotinylated proteins could be detected during in situ sequencing 55 .

However, despite the advantages of CaST, it is important to note its limitations in comparison to existing tools. For example, some users may still require the fast labeling windows afforded by light-gated and calcium-gated tools, to tag neurons activated during acute behaviors that cannot be isolated during the relatively broad biotin labeling period. In addition, we note that the biotin labeling is an ‘end-point experiment’ where cells must be fixed and stained for imaging or collected and lysed for protein analysis. Although biotinylated cells can be subjected to subsequent downstream molecular or chemical analysis, proximity labeling itself does not enable further manipulation or genetic access to biotin-tagged neurons. Consequently, CaST is intended to complement, rather than replace, existing tools that can activate induced transcription factors and drive the expression of proteins such as opsins for downstream neuronal manipulation. Finally, there are several considerations for using CaST in vivo, including the relative time window of tagging based on the biotin injection, the strength of the behavioral stimulus required to induce strong enough CaST labeling and the need for negative control animals to determine the appropriate thresholds for detecting true CaST signal (Supplementary Note 1 ).

Finally, although here we primarily highlight CaST as an enzymatic-based activity integrator, it is worth noting that it could also be used for Ca 2+ -dependent proximity labeling by analyzing biotinylated proteins. Thus, future studies could also enrich CaST-tagged proteins for analysis with mass spectrometry to examine activity-dependent differences in subcellular protein expression among unique neuronal cell types.

All constructs used or developed in this study are listed in Supplementary Table 1 . For all constructs, the vectors were double digested with restriction enzymes (New England BioLabs; NEB) following the standard digest protocols. PCR fragments were amplified using Q5 polymerase (M0494S, NEB). Both vectors and PCR fragments were purified using gel electrophoresis and gel extraction (28706, Qiagen) and were ligated using Gibson assembly (E2611S, NEB). Ligated plasmids were introduced into NEB Stable Competent Escherichia coli (C3040H, NEB) via a heat shock following the manufacturer’s transformation protocol. Plasmids were amplified using NEB 5-alpha Competent E. coli (C2987H, NEB) and Plasmid Miniprep Kit (27106, Qiagen).

AlphaFold2 protein structure predictions

To generate AlphaFold2 (ref. 57 ) predicted structures of CaST in Fig. 1 , ColabFold 56 (v1.5.5) was used with default settings. To generate the predicted structures of the two halves of CaST in isolation, each half’s amino acid sequence was entered individually, to predict each structure separately. To generate the predicted structure of the two halves of CaST in complex, the two amino acid sequences were entered, separated by a ‘:’ to specify inter-protein chain breaks for modeling complexes (for example, heterodimers). Predicted structures were recolored using Pymol (v2.5.2).

Mammalian cell culture and transfection

HEK293T cells (CRL-3216, American Type Culture Collection; no additional verification performed) were cultured as a monolayer in DMEM (D5796-500ML, Sigma-Aldrich), supplemented with 10% FBS (F1051-500ML, Sigma-Aldrich) and 1% (vol/vol) penicillin–streptomycin (15070063, 5,000 U ml −1 , Life Technologies; Complete DMEM). Cells were cultured in 100-mm tissue-culture-treated dishes (353003, Falcon) and maintained in the cell culture incubator at 37 °C with humidified 5% CO 2 and subcultured when they reached 80–90% confluence using trypsin (T2610-100ML, Sigma-Aldrich).

For immunofluorescence experiments, cells were plated in 48-well plates pretreated with 50 mg ml −1 human fibronectin (FC010-10MG, Millipore) and incubated for 24 h. Cells at ~80% confluency were transfected with DNA plasmids using polyethylenimine ‘Max’ according to the manufacturer’s manual (PEI Max; 24765-1, Polysciences). For CaST experiments, cells were transfected with 50 ng CD4-sTurboID (C)-M13-GFP, 20 ng CaM-V5-sTurboID (N) and 0.8 µl PEI Max per well. For CaST-IRES experiments, cells were transfected with 50 ng CD4-sTurboID (C)-M13-GFP-IRES-CaM-V5-sTurboID (N) and 0.8 µl PEI Max per well. Cells were incubated at 37 °C overnight for 15–16 h before stimulation.

For confocal experiments, cells were plated in 35-mm glass-bottom dishes (D35-14-1-N, Cellvis) pretreated with 50 mg ml −1 human fibronectin and incubated for 24 h. Cells at ~80% confluency were transfected with DNA plasmids using PEI Max according to the manufacturer’s manual. For CaST experiments, cells were transfected with 250 ng CD4-sTurboID (C)-M13-GFP, 125 ng CaM-V5-sTurboID (N) and 8 µl PEI Max per well. Cells were incubated at 37 °C overnight for 15–16 h before stimulation.

HEK293T cell CaST/CaST-IRES experiments

CaST or CaST-IRES HEK cells were stimulated 15–16 h following transfection. Stimulation master mixes were made by adding CaCl 2 (21115-100 ML, Sigma-Aldrich), ionomycin (I3909-1ML, Sigma-Aldrich) and biotin (B4639, Sigma-Aldrich; dissolved in dimethylsulfoxide as 100 mM stock) to complete DMEM. The stimulation mixtures were then added to the cell, and the cells were incubated at 37 °C for 30 min for CaST/CaST-IRES labeling. For +Ca 2+ conditions, we used a final concentration of 5 mM Ca 2+ and 1 µM ionomycin. For +biotin conditions, we used a final concentration of 50 µM biotin. After incubation for the indicated time, the solution was removed in each well. Cells were washed once with warm Complete DMEM and twice with warm DPBS (D8537-500ML, Sigma-Aldrich). Cells were subsequently fixed with 4% (vol/vol) paraformaldehyde (PFA; sc-281692, Santa Cruz Biotechnology) in DPBS at room temperature for 10 min followed by two washes with DPBS. After that, cells were permeabilized with ice-cold methanol at −20 °C for 8 min followed by two washes with DPBS at room temperature. Cells were incubated with blocking solution (1% wt/vol BSA; BP1600-100, Fisher Scientific, in DPBS) for 45 min at room temperature; followed by a 1-h incubation in primary antibody solution at room temperature (1:1,000 dilution mouse anti-v5; R96025, Invitrogen). Cells were washed twice with DPBS and incubated with secondary antibody (1:1,000 dilution donkey anti-mouse 568; ab175472, Abcam) and SA-647 (1:5,000 dilution; S32357, Invitrogen) for 30 min at room temperature. Cells were then washed three times with DPBS and imaged by epifluorescence microscopy (‘Immunofluorescence imaging’).

For CaST variable transfection ratio experiments, cells at ~80% confluency were transfected with different ratios of CD4-sTurboID (C)-M13-GFP and CaM-V5-sTurboID (N) using PEI Max, according to the manufacturer’s manual. Single-fragment CaST experiments were performed by transfecting cells with only one CaST fragment (either 50 ng CD4-sTb(C)-M13-GFP or 20 ng CaM-V5-sTb(N)) or both CaST fragments as positive control. Reversibility experiments were performed by treating the cells with 5 mM Ca 2+ and 1 µM ionomycin without biotin at 37 °C for 30 min followed by two DPBS washes to wash away Ca 2+ . Then, the cells were treated with 50 µM biotin without Ca 2+ for another 30 min. Ca 2+ titration experiments were performed by treating the cells with different CaCl 2 concentrations ranging from 0 to 10 mM and 1 µM ionomycin at 37 °C for 30 min. Time integration experiments were performed by treating the cells with 5 mM Ca 2+ and 1 µM ionomycin with or without biotin at 37 °C for diverse durations ranging from 10 to 240 min. Stimulation, fixation and staining were performed as described above.

HEK293T FLiCRE/CaST-IRES comparison experiment

Transfection of HEK293T cells with FLiCRE was performed following published protocols 6 . In detail, cells were transfected with FLiCRE constructs using PEI Max (20 ng UAS-mCherry, 30 ng CaM-TEVp, 50 ng CD4-MKII-LOV-TEVcs-Gal4 and 0.8 µl PEI Max per well in 48-well plates) at ~80% confluency and were immediately wrapped in foil and incubated overnight for 8–9 h. Transfection of CaST-IRES was performed as described above. FLiCRE-expressing cells were treated with continuous blue light ± Ca 2+ (6 mM Ca 2+ and 1 µM ionomycin) for 15 min at 37 °C. CaST-IRES cells were treated with biotin ± Ca 2+ with a final concentration of 5 mM Ca 2+ , 1 µM ionomycin and 50 µM biotin and incubated at 37 °C for 15 min. After stimulation, cells were washed twice with warm DPBS and incubated with DPBS at 37 °C for 10 min. After the washes, the DPBS was removed and replaced with 200 µl Complete DMEM for further incubation. The cells were fixed immediately or 2, 4, 6 or 8 h after the stimulation. Staining was performed after all the cells were fixed following the methods described above. The biotinylated proteins (SA-647, CaST-IRES) and expression of the reporter (UAS::mCherry, FLiCRE) were imaged by fluorescence microscopy.

For extended post-transfection incubation, cells were transfected with CaST-IRES or FLiCRE constructs as described above and were incubated at 37 °C for 48 h before stimulation. Cells were stimulated, washed and incubated as described above. The cells were fixed immediately, 8 h or 24 h after the stimulation. Staining was performed after all the cells were fixed following the methods described above. The biotinylated proteins (SA-647, CaST-IRES) and expression of the reporter (UAS::mCherry, FLiCRE) were imaged by fluorescence microscopy.

Western blot analysis of CaST

HEK293T cells expressing CaST or CaST-IRES were stimulated and labeled with biotin as described above in six-well plates and were subsequently washed three times with 1 ml DPBS. Cells were then detached from the well by gently pipetting 1 ml of ice-cold DPBS onto the cells and were pelleted by centrifugation at 300 g at 4 °C for 3 min. The resulting supernatant was removed, and the pellet was lysed on ice for 10 min using 100 μl RIPA lysis buffer (50 mM Tris pH 8, 150 mM NaCl, 0.1% SDS, 0.5% sodium deoxycholate and 1% Triton X-100) supplemented with protease inhibitor cocktail (P8849, Sigma-Aldrich) and 1 mM phenylmethylsulfonyl fluoride (82021, G-Biosciences). The cell lysates were clarified by centrifugation at 13,000 g at 4 °C for 10 min. The supernatants were collected and mixed with 4× protein loading buffer. The resulting samples were boiled at 95 °C for 10 min before separation on a 10% precast SDS–PAGE gel. Separated proteins on SDS–PAGE gels were transferred to a nitrocellulose membrane in transfer buffer on ice. The membrane was removed after the transfer and incubated in 5 ml Ponceau stain (0.1% wt/vol Ponceau in 5% acetic acid/water) for 5 min. The Ponceau stain was then removed with deionized water and then rinsed with TBST (Tris-buffered saline, 0.1% Tween 20). The membrane was then blocked in 5% (wt/vol) nonfat dry milk in TBST at room temperature for 1 h and washed three times with TBST for 5 min each. To detect biotinylated proteins, the membrane was incubated in 10 ml of 3% BSA/TBST (wt/vol) with 2 µl streptavidin–HRP (1:5,000 dilution; S911, Invitrogen) for 1 h at room temperature. The blot was washed three times with TBST for 5 min and was developed with Clarity Max Western ECL Substrate (Bio-Rad) for 1 min before imaging. To detect the V5 tag, the membrane was incubated in 10 ml of 3% BSA/TBST (wt/vol) with 1 µl mouse anti-V5 primary antibody (1:10,000 dilution; R96025, Invitrogen) for 1 h at room temperature. The blot was washed three times with TBST for 5 min and was incubated with 10 ml of 3% BSA/TBST (wt/vol) with 1 µl anti-mouse-HRP secondary antibody (1:10,000 dilution; 170-6516, Bio-Rad) for 30 min at room temperature. The blot was washed three times with TBST for 5 min and was developed with Clarity Max Western ECL Substrate (Bio-Rad) for 1 min before imaging. The blots were imaged on a Bio-Rad Chemi-Doc XR gel imager. The raw western blot images were quantified using Fiji/ImageJ v2.9.0 (the exact parameters used for each blot are shown in the relevant figure legends).

AAV1/AAV2 virus production, concentration and titration

The production and concentration processes for AAV viruses were conducted following a previously reported method 5 , 60 . In detail, HEK293T cells at ~80% confluency in three T-150 flasks were transfected with 5.2 µg AAV vector, 4.35 µg AAV1 plasmid, 4.35 µg AAV2 plasmid, 10.4 µg DF6 AAV helper plasmid and 130 µl PEI Max solution per construct and were incubated for 48 h. After the incubation, the cell culture conditioned media (supernatant) from the T-150 flasks were collected and filtered through a filter with a 0.45-µm pore size (9914-2504, Cytiva) for cultured neuron infection. The remaining HEK293T cells in the flasks were lifted using a cell scraper and were pelleted at 800 g for 10 min for making the purified and concentrated virus. Pelleted cells were then resuspended using 20 ml of 100 mM TBS (100 mM NaCl, 20 mM Tris, pH 8.0). The surfactant, 10% sodium deoxycholate (D5670-25G, Sigma-Aldrich), was then added to the resuspended pellet to a final concentration 0.5%, along with benzonase nuclease (E1014-5KU, Sigma-Aldrich) to a final concentration of 50 units per ml and incubated for 1 h at 37 °C. Cell debris were removed by centrifugation at 2,500 g for 15 min and the supernatant was harvested. The clarified cell lysate was loaded to a pre-equilibrated HiTrap heparin column (GE17-0406-01, Cytiva) followed by a 10 ml 100 mM TBS wash using a peristaltic pump, and then successive washes of 1 ml of 200 mM TBS (200 mM NaCl, 20 mM Tris, pH 8.0), and 1 ml of 300 mM TBS (300 mM NaCl, 20 mM Tris, pH 8.0), using a syringe. The bound virus in the column was eluted using a sequence of 1.5 ml 400 mM TBS, 3 ml 450 mM TBS and 1.5 ml 500 mM TBS (all with 20 mM Tris, pH 8.0). Eluted viruses were then concentrated by centrifugation at 2,000 g for 2 min using a 15-ml centrifugal unit with a 100,000-Da molecular-weight cutoff (UFC910024, Sigma-Aldrich) to a final volume of 500 µl. Further concentration of viruses was achieved using a 0.5 ml centrifugal unit (UFC510024, Sigma-Aldrich) to a final volume of 100–200 µl. Titration of the concentrated viruses was performed by AAVpro Titration Kit (for Real-Time PCR version 2; 6233, Takara Bio) following the manufacturer’s protocol. The standard curve was prepared using the positive control solution provided by Takara with serial dilution for absolute quantification. The titer of each viral sample was calculated in reference to the standard curve.

Primary neuronal culture and infection

One day before neuron dissociation, 24-well plates and 35-mm glass-bottom dishes (D35-14-1-N, Cellvis) were coated with 0.1 mg ml −1 poly- d -lysine (P6407-5MG, Millipore) dissolved in 1× borate buffer (28341, Thermo Scientific) overnight at room temperature. Plates and dishes were washed four times with sterile deionized water and dried in the tissue culture hood on the day of neuron dissociation. Embryonic day 18 rat hippocampal tissue (SDEHP, Brain Bits) was dissociated with papain (PAP, Brain Bits) and DNase I (07469, StemCell Technologies) following the manufacturer’s instructions for neuron dissociation. Dissociated neurons were plated in poly- d -lysine-coated plates and dishes and cultured in complete Neurobasal Plus medium (A3582901, Life Technologies) supplemented with 0.01% (vol/vol) gentamicin (15710064, Life Technologies), 0.75% (vol/vol) GlutaMAX (35050061, Life Technologies), 2% (vol/vol) B27 Plus (A3582801, Life Technologie) and 5% (vol/vol) FBS (F1051-500ML, Sigma-Aldrich) at 37 °C, 5% CO 2 . The entire culture medium of each well and dish was removed 24 h after the plating and replaced with complete Neurobasal Plus medium supplemented with 0.01% (vol/vol) gentamicin, 0.75% (vol/vol) GlutaMAX, 2% (vol/vol) B27 Plus to stop glial growth. Subsequently, ~50% of the medium in each well was replaced every 3–4 days. At days in vitro (DIV) 6, neurons were infected with a mixture of crude supernatant of CaST-encoded AAV1/2 viruses by replacing half of the culture media. The plate and dishes were incubated for another ~2 weeks in the incubator before stimulation.

Primary neuron CaST experiments

Rat hippocampal neurons were dissociated, plated and infected as described above. At DIV 17–22 (~2 weeks after infection), neurons were ready for stimulation. Neuron stimulation reagents including KCl (P3911-25G, Sigma-Aldrich), DOI (D101, Sigma-Aldrich) and DA (H8502, Sigma-Aldrich) were used to introduce neuron firing. A portion of the culture media from each well was taken out and saved in microcentrifuge tubes. The stimulation mixtures were generated by adding the neuron stimulation reagent and biotin solution to the saved culture media to the desired concentration. The stimulation mixtures were then added back to each well and the neurons were incubated at 37 °C for 30 min for CaST labeling. For +KCl conditions, we used a final concentration of 30 mM KCl. For +DOI conditions, we used 10 µM of DOI as the final concentration. For +DA conditions, we used a final concentration of 10 µM DA. For +biotin conditions, we used a final concentration of 50 μM biotin. Neurons were then fixed and stained as described above for HEK293T cell experiments. Time-series experiments were performed by treating the neurons with or without 30 mM KCl for 10 min or 30 min at 37 °C. Neurons were then fixed and stained as described above for HEK293T cell experiments before imaging.

Immunofluorescence imaging

Cells and neurons were imaged immediately after fixation and staining. Fluorescence images were taken with Keyence BZ-X810 fluorescence microscope (acquisition software v1.1.2) with an 80 W metal halide lamp as the fluorescence light source and a PlanApo ×10 air objective lens (NA 0.45). Expression of CaST was visualized by GFP using a 470/40-nm excitation filter and a 525/50-nm emission filter, and Alexa Fluor 568 using a 545/25-nm excitation filter and a 605/70-nm emission filter. Biotinylated proteins were labeled by SA-647 and visualized using a 620/60-nm excitation filter and a 700/75-nm emission filter. Images were analyzed by custom scripts in Fiji/ImageJ v2.9.0 and MATLAB vR2020b (‘Analysis of CaST immunofluorescence’).

Confocal imaging was performed using a Carl Zeiss LSM 800 confocal microscope equipped with 488-, 561- and 640-nm lasers, and a ×63 oil immersion objective (NA 1.4). For high-resolution confocal imaging, 35-mm glass-bottom dishes with a 14-mm micro-well and cover glass with thickness no. 1.5 (0.16 mm–0.19 mm) were used. Fluorescence images were collected with a 512 × 512-pixel resolution and with a pixel dwell time of 1.03 μs per pixel. Images were acquired using a PMT detector and emission filter ranges of 450–575 nm, 450–640 nm and 645–700 nm for EGFP, Alexa Fluor 568 and Alexa Fluor 647 detection, respectively, for best signal. All images were collected and processed using ZEN software v2.3 (Carl Zeiss).

Analysis of CaST immunofluorescence

For CaST fluorescence characterization, we analyzed all cells in the FOV that expressed CaST (assessed using the GFP channel). To accomplish this, the GFP images across all conditions of a given experiment (calcium-treated and non-treated) were pseudo combined into one super FOV, so that the exact same cell-detection threshold was applied equally to all images (using the ‘cell-segm’ automated thresholding script 61 ). This ensured no bias in the detection of GFP + cells across conditions. The cell masks were then applied to the original GFP and SA-647 images, so that the GFP and SA-647 cell fluorescence could be calculated for cells belonging to an individual FOV.

To ensure reproducibility, typically 6–12 FOVs were imaged for a given experimental condition. We reported both the raw GFP and raw SA-647 fluorescence of all cells pooled across all FOVs in a scatter plot. These raw scatter plots illustrate that the SA-647 labeling in calcium-treated cells is higher than the SA-647 labeling observed in untreated cells across generally all GFP expression levels of the tool (the entire x axis).

To make a quantitative comparison of this data that matched the standards previously reported for evaluating similar tools (such as FLARE 5 , FLiCRE 6 and Cal-Light 7 ), we calculated the mean SA-647/GFP cell ratios found for each FOV. This analysis can normalize for any difference in expression levels of the tool across cells and conditions. Matching the standards of these previously published works, the background SA-647 autofluorescence in the epifluorescence images was subtracted from every cell for each FOV before taking the ratio to the GFP fluorescence values (autofluorescence was calculated as the mean pixel value of the entire image, excluding pixels that corresponded to cell masks). See Supplementary Fig. 2 for an example of each step of this analysis pipeline, along with appropriate summary data.

Neuron viability analysis

Neuron viability assays were performed using DRAQ7 dye (D15105, Invitrogen). Rat hippocampal neurons were dissociated, plated as described above and were infected with crude supernatant AAV1/2 viruses for either the complete CaST, or only one CaST fragment (CD4-sTb(C)-M13-GFP) as a DRAQ7-negative control, at DIV 6. At DIV 19, neurons were stained with DRAQ7 solution with a final concentration of 3 µM for 10 min at 37 °C, protected from light. After staining, neurons were washed with DPBS, fixed with 4% PFA, and imaged in DPBS. For the DRAQ7-positive control, neurons expressing complete CaST constructs were fixed with 4% PFA and were permeabilized with methanol for 8 min at −20 °C at DIV 19. Neurons were then incubated with DRAQ7 dye solution for 10 min at 37 °C, protected from light and were washed and imaged using fluorescence microscopy.

Calcium imaging in cultured neurons

Rat hippocampal neurons were dissociated, plated as described above and were infected with a mixture of crude supernatant of CaST-encoded and RCaMP2-encoded AAV1/AAV2 viruses by replacing half of the culture media at DIV 6. The plate and dishes were incubated for another ~2 weeks in the incubator before stimulation. For simultaneous RCaMP2 recording and CaST labeling characterization, mild neuronal stimulation was introduced by replacing half of the volume of the neuron culture media, and biotin was also introduced at a final concentration of 50 µM for CaST labeling. Calcium activity of each RCaMP2-positive neuron was continuously recorded for 5 min after stimulation using the Keyence microscope. Neurons were treated with biotin for a total of 30 min at room temperature. After stimulation, neurons were washed, fixed and stained as described above. The same FOV recorded during RCaMP2 imaging was then reidentified, and images showing CaST expression and labeling were captured.

For calcium imaging during drug treatment, neurons were infected with RCaMP2-encoded AAV1/2 virus at DIV 6. The neurons were incubated for another ~2 weeks in the incubator before stimulation. KCl, DA and DOI stimulations were induced as described above. Calcium activity of each RCaMP2-positive neuron was continuously recorded for 1 min before and 1 min after stimulation using the Keyence microscope.

The mean RCaMP2 FOV during the recording was input to Cellpose 62 (v2.2.3) to identify masks corresponding to individual neurons. Pixels corresponding to individual cell masks were then analyzed in Fiji to obtain the fluorescence time series of all neurons in the FOV, and values were imported into MATLAB vR2020b for further analysis. The time series of each neuron was reported either as d F/F (Extended Data Fig. 6 ), or as a z -score relative to the baseline recording (Extended Data Fig. 8 ). d F/F was calculated as ( F t  −  F b )/ F b , where F b is the 2nd percentile of each cell’s fluorescence time series. z -scores were calculated in MATLAB using the ‘zscore’ function. To quantify the mean RCaMP2 activity following mild stimulation, the ‘findpeaks’ function was used in MATLAB, and the detected peak heights were averaged for each neuron’s activity trace (Extended Data Fig. 6 ). To quantify the mean peak height following the drug-induced baseline increase following calcium influx, the maximum of each cell’s calcium trace during the post-stimulation period was reported (Extended Data Fig. 8 ).

Optogenetic stimulation for CaST labeling in cultured neurons

Rat hippocampal neurons were dissociated and plated as described above and were infected with a mixture of crude supernatant of CaST- and bReaChES-encoded AAV1/AAV2 viruses by replacing half of the culture media at DIV 6 and were immediately wrapped in foil. At DIV 18, dishes with CaST and bReaChES coexpressing rat hippocampal neurons were covered on the bottom with black tape to block the orange stimulation light (M595F2, Thorlabs, 5.67 mW per mm 2 ). A ~1-mm slit was left open allowing spatially targeted bReaChES stimulation. Biotin was introduced at a final concentration of 50 µM for CaST labeling. Orange light was cycled every 6.5 s with 2 s on and 4.5 s off. The total stimulation was 30 min long, using 5-ms-long pulses delivered at 20 Hz during the ‘on’ cycle. Glutamate receptor antagonists APV and NBQX were added at a final concentration of 50 µM and 20 µM, respectively, at the time of light stimulation to reduce synchronized neuron firing across the entire dish. After stimulation, neurons were washed, fixed and stained as described above. Images were taken using the Keyence BZ-X810 with tile scan mode.

Mouse animal models

All experimental and surgical protocols were approved by the University of California, Davis, Institutional Animal Care and Use Committee. For CaST experiments, 5–7-week-old male and female wild-type C57BL/6J (Jackson Laboratory Strain, 000664) mice were used. Mice were maintained on a 12-h reverse light–dark cycle (lights on at 21:00) at 22 °C and 40–60% humidity, group-housed with same-sex cage mates and given ad libitum access to food and water.

Mouse stereotaxic surgeries

Briefly, mice were maintained under anesthesia with 1.5–2% isoflurane and placed in a stereotaxic apparatus (RWD) on a heating pad. The fur on the top of the skull was removed and antiseptic iodine and 70% alcohol were used in alternation to clean the scalp. Sterile ocular lubricant (Dechra) was administered to the eyes of the mice to protect them from drying out. A midline scalp incision was made, and 0.1% hydrogen peroxide was applied to the skull. A craniotomy was made above the injection site. Virus was then injected into the targeted region using a 33-gauge beveled needle (WPI) and a 10 μl Hamilton syringe controlled by an injection pump (WPI). For all surgeries, 1,000 nl of virus was injected into the targeted mPFC brain region (coordinates: ML, ±0.5; AP, +1.98; DV, −2.25) at a rate of 150 nl min − 1 .

For 2P imaging surgeries, 1,000 nl of AAV5-CaMKIIa-GCaMP6f was injected into mPFC (diluted 1:1 in DPBS; Addgene, 100834-AAV5, 2.2 × 10 12 viral genomes per ml titer). A 1-mm diameter GRIN lens (Inscopix) was then implanted above the mPFC (ML, ± 0.5; AP, +1.98; DV, −2.05). Implants and custom stainless-steel headplates were secured to the skull using a dental adhesive and cement system (Pentron, C&B metabond). For CaST surgeries, 500 nl of a 1:1 ratio of the two homemade CaST viruses was injected into the mPFC (AAV2/1-Syn-CD4-sTb(C)-M13-GFP, 4.85 × 10 9 copies per ml; AAV2/1-Syn-CaM-V5-sTb(N), 4.79 × 10 9 copies per ml). Once the surgery was completed, the incision was closed with tissue adhesive (GLUture). Mice received a dose of 3.25 mg per kg body weight EthiqaXR for pain recovery and were revived in a new, clean cage placed on a heating pad.

2P Ca 2+ imaging in mice

2P Ca 2+ imaging was performed using a commercial microscope (2P+, Bruker) and a ×16, 0.8 NA objective (MRP07220, Nikon). A tunable infrared femtosecond pulse laser set to 920 nm (Coherent, Discovery TPC) was used for excitation, and fluorescence emission was collected using a GaAsP PMT (H10770PB-40, Hamamatsu). The excitation laser was directed by galvo scanners sampling 512 × 512 pixels. Each image was captured at 2 Hz. The imaging FOV was 448 × 448 μm (optical zoom of ×2.5). Data were collected using the PrairieView v5.6 software and analyzed using Suite2P 63 (v0.14.3) and custom MATLAB vR2020b scripts. Fluorescence values corresponding to each cell mask output from Suite2P were then averaged to create a fluorescence time series for each neuron. Each neuron’s trace was z -scored (‘zscore’ function in MATLAB) and smoothed using a 5-s sliding window (‘smooth’ function in MATLAB). We calculated the difference between each neuron’s 10-min psilocybin activity trace and 10-min saline activity trace (‘psilocybin minus saline’ activity trace). We then took the average of this difference and ranked cells according to this ‘Avg diff’ value. We considered ‘activated’ neurons to be those with an Avg diff z -score value > 0.05, and ‘inhibited’ neurons to be those with an Avg diff z -score value < −0.05.

CaST experiments in mice

Mice were handled and given USP-grade saline (0.9%) injections for three consecutive days before biotin labeling. After a week of viral expression, experimental mice were given two injections, one 24 mg per kg body weight i.p. injection of diluted biotin solution (B4639, Sigma-Aldrich; dissolved in dimethylsulfoxide as a 10 mM stock solution) and one 3 mg per kg body weight i.p. injection of psilocybin (synthesized as previously described 36 ) dissolved in saline. Control mice were injected with biotin solution and saline (5 ml per kg body weight, i.p.). Mice were placed in separated clean cages following the injection and were euthanized 1 h after i.p. injections. A subset of mice was video recorded for head-twitch data acquisition following injections. The number of HTRs during the first 20 min of video recording was quantified, manually scored by a blinded experimenter.

After biotin labeling, mice were perfused with ice-cold PBS, followed by 4% PFA, and brains were collected and stored in 4% PFA overnight at 4 °C. The next day, brains were switched out from PFA and stored in PBS until slicing. Next, 60-μm slices were collected from the mPFC and placed in wells with PBS and stored in 4 °C. Slices were washed with PBS-T for 2 min (3×), then blocked in 5% normal donkey serum (017-000-121, Jackson ImmunoResearch) and 0.3% Triton X-100 (T9284-100ML, Sigma-Aldrich; in PBS-T) for 1 h at room temperature. Slices were stained with SA-647 (1:1,000 dilution; S32357, Invitrogen) in 5% NDS/PBS-T for 1.5 h at room temperature. Slices were washed with PBS-T for 5 min at room temperature and then mounted with DAPI-Fluoromount-G (0100-20, SouthernBiotech) to adhere coverslips. Images at a magnification of ×40 were taken on the BZ-X810 Keyence fluorescence microscope and were analyzed as described above for ‘Analysis of CaST immunofluorescence’ .

c-Fos staining of mouse brain slices

Mice were injected with CaST and treated with saline or psilocybin as described above. Following perfusion of the brain and slicing, a subset of slices was saved for c-Fos-only staining. Slices were washed in PBS-T (1% Triton X-100 in PBS) for 2 h and placed on shaker at room temperature. Slices were blocked in 1% BSA/PBS-T for 1 h at room temperature, and then stained with 300 µl of rat anti-c-Fos primary antibody (226017, Synaptic Systems) at a 1:1,000 dilution in 1% BSA/PBS-T overnight on a shaker at room temperature. The next day, slices were washed with PBS-T for 20 min, three times, and placed on a shaker at room temperature. Slices were then stained with 300 µl of anti-rat-Alexa Fluor 568 secondary antibody (A78946, Invitrogen) at a 1:500 dilution in 1% BSA/PBS-T for 2 h on a shaker at room temperature. Slices were washed with PBS-T for 20 min, two times, and placed on a shaker at room temperature. Slices were mounted in DAPI-Fluoromount-G to adhere coverslips. For simultaneous CaST staining, the same procedure was followed, except SA-647 was also included during the secondary antibody incubation (1:1,000 dilution; S32357, Invitrogen). Images at a magnification of ×40 were taken on the BZ-X810 Keyence fluorescence microscope, and were analyzed using an automated cell-detection method in MATLAB (cell-segm) 61 for each channel individually.

Statistical analyses

Statistical analyses including Mann–Whitney U test, ordinary one-way ANOVA, two-way ANOVA, Pearson correlation analysis and Wilson/Brown ROC curve analysis were performed in GraphPad Prism v9.0 (GraphPad Software). The D’Agostino–Pearson test for normality was performed in Prism before using any parametric statistical tests. Significance was defined as a * P  < 0.05, ** P  < 0.01 and *** P  < 0.001 for the defined statistical test (NS, P  ≥ 0.05). All experiments performed in this study were independently replicated at least twice.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Plasmids and associated DNA sequences generated in this study are available on Addgene (catalog nos. 219779 – 219784 ; https://www.addgene.org/christina_kim/ ). There are no restrictions on data availability. Source data are provided with this paper.

Code availability

Custom MATLAB scripts used to analyze images are freely available under The MIT license at https://github.com/tinakimlab/CaST-analysis-scripts/ .

Grynkiewicz, G., Poenie, M. & Tsien, R. Y. A new generation of Ca 2+ indicators with greatly improved fluorescence properties. J. Biol. Chem. 260 , 3440–3450 (1985).

Article   CAS   PubMed   Google Scholar  

Tian, L. et al. Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat. Methods 6 , 875–881 (2009).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499 , 295–300 (2013).

Inoue, M. et al. Rational design of a high-affinity, fast, red calcium indicator R-CaMP2. Nat. Methods 12 , 64–70 (2015).

Wang, W. et al. A light- and calcium-gated transcription factor for imaging and manipulating activated neurons. Nat. Biotechnol. 35 , 864–871 (2017).

Kim, C. K. et al. A molecular calcium integrator reveals a striatal cell type driving aversion. Cell 183 , 2003–2019 (2020).

Lee, D., Hyun, J. H., Jung, K., Hannan, P. & Kwon, H.-B. A calcium- and light-gated switch to induce gene expression in activated neurons. Nat. Biotechnol. 35 , 858–863 (2017).

Fosque, B. F. et al. Labeling of active neural circuits in vivo with designed calcium integrators. Science 347 , 755–760 (2015).

Motta-Mena, L. B. et al. An optogenetic gene expression system with rapid activation and deactivation kinetics. Nat. Chem. Biol. 10 , 196–202 (2014).

McKinney, S. A., Murphy, C. S., Hazelwood, K. L., Davidson, M. W. & Looger, L. L. A bright and photostable photoconvertible fluorescent protein. Nat. Methods 6 , 131–133 (2009).

Allen, W. E. et al. Thirst-associated preoptic neurons encode an aversive motivational drive. Science 357 , 1149–1155 (2017).

Reijmers, L. G., Perkins, B. L., Matsuo, N. & Mayford, M. Localization of a stable neural correlate of associative memory. Science 317 , 1230–1233 (2007).

Liu, X. et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature 484 , 381–385 (2012).

Choi, J.-E., Kim, J. & Kim, J. Capturing activated neurons and synapses. Neurosci. Res. 152 , 25–34 (2020).

Article   PubMed   Google Scholar  

Gore, F. et al. Neural representations of unconditioned stimuli in basolateral amygdala mediate innate and learned responses. Cell 162 , 134–145 (2015).

Cho, K. F. et al. Split-TurboID enables contact-dependent proximity labeling in cells. Proc. Natl Acad. Sci. USA 117 , 12143–12154 (2020).

Roux, K. J., Kim, D. I., Raida, M. & Burke, B. A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J. Cell Biol. 196 , 801–810 (2012).

Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat. Biotechnol. 36 , 880–887 (2018).

Spector, R. & Mock, D. Biotin transport through the blood–brain barrier. J. Neurochem. 48 , 400–404 (1987).

Bongarzone, S. et al. Imaging biotin trafficking in vivo with positron emission tomography. J. Med. Chem. 63 , 8265–8275 (2020).

Kim, J. H. et al. High cleavage efficiency of a 2A peptide derived from porcine teschovirus-1 in human cell lines, zebrafish and mice. PLoS ONE 6 , e18556 (2011).

Mountford, P. S. & Smith, A. G. Internal ribosome entry sites and dicistronic RNAs in mammalian transgenesis. Trends Genet. 11 , 179–184 (1995).

Licursi, M., Christian, S., Pongnopparat, T. & Hirasawa, K. In vitro and in vivo comparison of viral and cellular internal ribosome entry sites for bicistronic vector expression. Gene Ther. 18 , 631–636 (2011).

Kim, C. K., Cho, K. F., Kim, M. W. & Ting, A. Y. Luciferase-LOV BRET enables versatile and specific transcriptional readout of cellular protein–protein interactions. eLife 8 , e43826 (2019).

Article   PubMed   PubMed Central   Google Scholar  

Kim, C. K. et al. Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain. Nat. Methods 13 , 325–328 (2016).

Gasbarri, A., Sulli, A. & Packard, M. G. The dopaminergic mesencephalic projections to the hippocampal formation in the rat. Prog. Neuropsychopharmacol. Biol. Psychiatry 21 , 1–22 (1997).

Yu, Q. et al. Genetic labeling reveals temporal and spatial expression pattern of D2 dopamine receptor in rat forebrain. Brain Struct. Funct. 224 , 1035–1049 (2019).

Bombardi, C. Neuronal localization of 5-HT 2A receptor immunoreactivity in the rat hippocampal region. Brain Res. Bull. 87 , 259–273 (2012).

Gurevich, E. V., Gainetdinov, R. R. & Gurevich, V. V. G protein-coupled receptor kinases as regulators of dopamine receptor functions. Pharmacol. Res. 111 , 1–16 (2016).

McCorvy, J. D. & Roth, B. L. Structure and function of serotonin G protein-coupled receptors. Pharmacol. Ther. 150 , 129–142 (2015).

Neve, K. A., Seamans, J. K. & Trantham-Davidson, H. Dopamine receptor signaling. J. Recept. Signal. Transduct. Res. 24 , 165–205 (2004).

Dhyani, V. et al. GPCR mediated control of calcium dynamics: a systems perspective. Cell. Signal. 74 , 109717 (2020).

Ly, C. et al. Psychedelics promote structural and functional neural plasticity. Cell Rep. 23 , 3170–3182 (2018).

Vargas, M. V. et al. Psychedelics promote neuroplasticity through the activation of intracellular 5-HT 2A receptors. Science 379 , 700–706 (2023).

Shao, L.-X. et al. Psilocybin induces rapid and persistent growth of dendritic spines in frontal cortex in vivo. Neuron 109 , 2535–2544 (2021).

Cameron, L. P. et al. 5-HT2ARs mediate therapeutic behavioral effects of psychedelic tryptamines. ACS Chem. Neurosci. 14 , 351–358 (2023).

Catlow, B. J., Song, S., Paredes, D. A., Kirstein, C. L. & Sanchez-Ramos, J. Effects of psilocybin on hippocampal neurogenesis and extinction of trace fear conditioning. Exp. Brain Res. 228 , 481–491 (2013).

Hibicke, M., Landry, A. N., Kramer, H. M., Talman, Z. K. & Nichols, C. D. Psychedelics, but not ketamine, produce persistent antidepressant-like effects in a rodent experimental system for the study of depression. ACS Chem. Neurosci. 11 , 864–871 (2020).

Hesselgrave, N., Troppoli, T. A., Wulff, A. B., Cole, A. B. & Thompson, S. M. Harnessing psilocybin: antidepressant-like behavioral and synaptic actions of psilocybin are independent of 5-HT2R activation in mice. Proc. Natl Acad. Sci. USA 118 , e2022489118 (2021).

Cameron, L. P. et al. A non-hallucinogenic psychedelic analogue with therapeutic potential. Nature 589 , 474–479 (2021).

Corne, S., Pickering, R. & Warner, B. A method for assessing the effects of drugs on the central actions of 5‐hydroxytryptamine. Br. J. Pharmacol. Chemother. 20 , 106–120 (1963).

Halberstadt, A. L. & Geyer, M. A. Characterization of the head-twitch response induced by hallucinogens in mice: detection of the behavior based on the dynamics of head movement. Psychopharmacology 227 , 727–739 (2013).

González-Maeso, J. et al. Hallucinogens recruit specific cortical 5-HT 2A receptor-mediated signaling pathways to affect behavior. Neuron 53 , 439–452 (2007).

Jefsen, O. H., Elfving, B., Wegener, G. & Müller, H. K. Transcriptional regulation in the rat prefrontal cortex and hippocampus after a single administration of psilocybin. J. Psychopharmacol. 35 , 483–493 (2021).

Davoudian, P. A., Shao, L.-X. & Kwan, A. C. Shared and distinct brain regions targeted for immediate early gene expression by ketamine and psilocybin. ACS Chem. Neurosci. 14 , 468–480 (2023).

Wood, J., Kim, Y. & Moghaddam, B. Disruption of prefrontal cortex large scale neuronal activity by different classes of psychotomimetic drugs. J. Neurosci. 32 , 3022–3031 (2012).

Michaiel, A. M., Parker, P. R. & Niell, C. M. A hallucinogenic serotonin-2A receptor agonist reduces visual response gain and alters temporal dynamics in mouse V1. Cell Rep. 26 , 3475–3483 (2019).

Ziv, Y. et al. Long-term dynamics of CA1 hippocampal place codes. Nat. Neurosci. 16 , 264 (2013).

Dong, C. et al. Psychedelic-inspired drug discovery using an engineered biosensor. Cell 184 , 2779–2792 (2021).

Rayaprolu, S. et al. Cell-type-specific biotin labeling in vivo resolves regional neuronal and astrocyte proteomic differences in mouse brain. Nat. Commun. 13 , 2927 (2022).

Chan, K. Y. et al. Engineered AAVs for efficient noninvasive gene delivery to the central and peripheral nervous systems. Nat. Neurosci. 20 , 1172–1179 (2017).

Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348 , aaa6090 (2015).

Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361 , eaat5691 (2018).

Lim, M. J. et al. MALDI HiPLEX-IHC: multiomic and multimodal imaging of targeted intact proteins in tissues. Front. Chem. 11 , 1182404 (2023).

Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14 , 865–868 (2017).

Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19 , 679–682 (2022).

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596 , 583–589 (2021).

Feinberg, E. H. et al. GFP reconstitution across synaptic partners (GRASP) defines cell contacts and synapses in living nervous systems. Neuron 57 , 353–363 (2008).

Hanke, T., Szawlowski, P. & Randall, R. E. Construction of solid matrix–antibody–antigen complexes containing simian immunodeficiency virus p27 using tag-specific monoclonal antibody and tag-linked antigen. J. Gen. Virol. 73 , 653–660 (1992).

Konermann, S. et al. Optical control of mammalian endogenous transcription and epigenetic states. Nature 500 , 472–476 (2013).

Hodneland, E., Kögel, T., Frei, D. M., Gerdes, H.-H. & Lundervold, A. CellSegm-a MATLAB toolbox for high-throughput 3D cell segmentation. Source Code Biol. Med. 8 , 16 (2013).

Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18 , 100–106 (2021).

Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv https://doi.org/10.1101/061507 (2016).

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Acknowledgements

We thank A. Y. Ting (Stanford University) for the generous gift of plasmids used as templates in this study. We thank J. Muir (UC Davis) for assistance with GRIN lens surgeries for in vivo mouse imaging. We thank H. T. Warren (UC Davis) and Winston L. Chow (UC Davis) for synthesizing psilocybin. This work was supported by a Burroughs Wellcome Fund Career Awards at the Scientific Interface (1019469, to C.K.K.), a NARSAD Young Investigator Grant (30238, to C.K.K.), the Searle Scholars Program (SSP-2022-107, to C.K.K.), the Arnold and Mabel Beckman Foundation (Beckman Young Investigator Award, to C.K.K.), the National Institutes of Health (DP2MH136588, to C.K.K., and R35GM148182, to D.E.O.) and the Boone Family Foundation (to D.E.O). R.Z. was supported by a National Science Foundation Research Traineeship (NeuralStorm, 2152260). M.A. was supported by a National Science Foundation Graduate Research Fellowship Program (000895154) and a National Institutes of Health T32 Training Program (T32 MH082174). J.C. was supported by a National Institutes of Health T32 Training Program (T32 MH112507).

Author information

These authors contributed equally: Run Zhang, Maribel Anguiano.

Authors and Affiliations

Biomedical Engineering Graduate Group, University of California, Davis, Davis, CA, USA

Center for Neuroscience, University of California, Davis, Davis, CA, USA

Run Zhang, Maribel Anguiano, Isak K. Aarrestad, Sophia Lin, Joshua Chandra, Sruti S. Vadde, David E. Olson & Christina K. Kim

Neuroscience Graduate Group, University of California, Davis, Davis, CA, USA

Maribel Anguiano, Isak K. Aarrestad & Joshua Chandra

Institute for Psychedelics and Neurotherapeutics, University of California, Davis, Davis, CA, USA

Isak K. Aarrestad, David E. Olson & Christina K. Kim

Department of Neurology, University of California, Davis, Sacramento, CA, USA

Sophia Lin, Sruti S. Vadde & Christina K. Kim

Department of Chemistry, University of California, Davis, Davis, CA, USA

David E. Olson

Department of Biochemistry and Molecular Medicine, University of California, Davis, Sacramento, CA, USA

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Contributions

R.Z. and C.K.K. conceived the tool design and conceptualized the project. R.Z. performed protein engineering, cultured HEK cell and neuron experiments, image analysis, and data quantification. M.A. performed and oversaw all western blot analyses and led the design and implementation of mouse in vivo CaST experiments, with assistance from I.K.A. I.K.A. analyzed CaST head-twitch data and assisted with CaST in vivo surgeries and labeling injections. S.L. performed western blot experiments and generated AAVs for CaST expression. J.C. performed GRIN lens surgeries for in vivo mouse imaging and collected 2P imaging data, with assistance from I.K.A. S.S.V. assisted with HEK cell assays characterizing the different versions of CaST. D.E.O. and I.K.A. provided psilocybin along with critical experimental design input for the in vivo GCaMP imaging and CaST labeling during psilocybin injection. R.Z. led the figure preparation with guidance from C.K.K. R.Z. and C.K.K. wrote the paper with contributions from all authors.

Corresponding author

Correspondence to Christina K. Kim .

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Competing interests.

D.E.O. is a cofounder of Delix Therapeutics and serves as the chief innovation officer and head of the scientific advisory board. The other authors declare no competing interests.

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Extended data

Extended data fig. 1 comparison of different cast transfection ratios of fragments..

A) HEK cells were transfected with different ratios of the CaST fragments indicated above in ng for 48-well plate and incubated overnight. Cells were treated with 50 µM biotin and ± Ca 2+ (5 mM CaCl 2 and 1 µM ionomycin) for 30 minutes. The ± Ca 2+ SBR and the FOV averages of the SA647/GFP fluorescence ratio per cell are shown. The ratio of 50:20 (CD4-sTb(C)-M13-GFP:CaM-V5-sTb(N)) had a significant 2.2-fold ± Ca 2+ SBR ( n  = 10 FOVs per condition; P  = 1.1 e-5 , U = 0, two-tailed Mann-Whitney U test). B,C) The FOV average of the SA-647 cell fluorescence ( B ) and the GFP cell fluorescence ( C ) was calculated for each transfection ratio (CD4-sTb(C)-M13-GFP:CaM-V5-sTb(N)) after biotin ± Ca 2+ stimulation ( n  = 10 FOVs per condition). Data are plotted as mean ± s.e.m. D) HEK cells were transfected with either the Cam-V5-sTb(N) fragment (top), the CD4-sTb(C)-M13-GFP fragment (middle), or both fragments of CaST together (bottom). Cells were treated with 50 µM biotin and Ca 2+ (5 mM CaCl 2 and 1 µM ionomycin) for 30 minutes, then washed, stained, and imaged. Data were replicated across 12 FOVs. Scale bar, 300 µm. **** P  < 0.0001.

Extended Data Fig. 2 Additional western blot analysis of CaST labeling.

A) Ponceau staining and raw image of the full SA-HRP blot from Fig. 1d . B) Biological replicate of experiment in panel A . C,D) Line plot profile of the entire lane of the raw blot shown in panels A and B (drawn vertically from the 250 kB to the 20 kB ladder line and centered horizontally within each lane). E) Additional biological replicate experiment of panels A , B , except using CaST-IRES. Cells were otherwise treated identically. *indicate bands showing endogenously biotinylated proteins. “N” indicates the expected size of the CaM-V5-sTb(N) fragment, while “C” indicates the expected size of the CD4-sTb(C)-M13-GFP fragment.

Extended Data Fig. 3 Additional characterization of CaST in HEK cells.

A) The FOV averages of the SA-647 fluorescence per cell from the non-IRES data shown in Fig. 2b , and the IRES data shown in Fig. 2f . The non-IRES version exhibited a Ca 2+ -dependent SBR of 2.3x ( n  = 11 FOVs per condition; P  = 1.1e-5, U = 2, two-tailed Mann-Whitney U test). The Ca 2+ -dependent SBR for the IRES version was 4.1x ( n  = 10 FOVs per condition; P  = 1.1e-5, U = 0, two-tailed Mann-Whitney U test). B) The FOV averages of the GFP cell fluorescence per cell from the non-IRES data shown in Fig. 2b ( n  = 11 FOVs per condition; P  = 0.562, U = 51, two-tailed Mann-Whitney U test), and the IRES data shown in Fig. 2f ( n  = 10 FOVs per condition; P  = 0.165, U = 31, two-tailed Mann-Whitney U test). C,D) The FOV average of the SA-647 ( C ) or GFP ( D ) cell fluorescence was calculated for each stimulation duration for data shown in Fig. 3e,f ( n  = 10 FOVs per condition). Cells were transfected with CaST-IRES. The untreated condition is shown on the left. Data are plotted as mean ± s.e.m. **** P  < 0.0001, ns, not significant.

Extended Data Fig. 4 Comparison of CaST to FLiCRE.

A) Schematic of FLiCRE as a light- and Ca 2+ -dependent transcriptional reporter. A TEV protease (TEVp) is tethered to GFP-CaM and expressed in the cytosol. A CD4-MKII-LOV-TEVcs-Gal4 fusion is expressed at the membrane. In the dark, the LOV protein cages the TEV cleavage site (TEVcs), protecting it from the TEVp. When there is high intracellular Ca 2+ , CaM-M13 interact to bring the TEVp nearby the TEVcs. However, only when blue light is simultaneously delivered, will the TEVcs become uncaged and available for cleavage. With both blue light and high intracellular Ca 2+ , the TEVp will cut the TEVcs, and the released Gal4 then enters the nucleus to drive expression of the UAS reporter gene. B,C) Example FOVs for CaST ( B ) and FLiCRE ( C ) experiments quantified in Fig. 4c–f . Scale bar, 300 µm. D,E) For CaST, the FOV average of the SA-647 cell fluorescence and the GFP cell fluorescence was calculated following a variable delay period after biotin + Ca 2+ ( D ) or biotin - Ca 2+ ( E ) stimulation ( n  = 12 FOVs for conditions with 0, 4, 6, 8 hr delay time after stimulation; n  = 11 FOVs for conditions with 2 hr delay time after stimulation). F,G) For FLiCRE, the FOV average of the UAS-mCherry cell fluorescence and the GFP cell fluorescence was calculated following a variable delay period after light + Ca 2+ ( D ) or light - Ca 2+ ( E ) stimulation ( n  = 11 FOVs for conditions with 0 hr delay time after stimulation; n  = 12 FOVs for conditions with 2, 4, 6, 8 hr delay time after stimulation). Data are plotted as mean ± s.e.m. in D – G .

Extended Data Fig. 5 Additional characterization of CaST in neurons.

A) Example confocal images of cultured rat hippocampal neurons infected with both components of CaST. Neurons were treated with 50 µM biotin ± 30 mM KCl for 30 minutes. They were then washed, fixed, and stained for SA-647. Scale bar, 20 µm. B) Scatter plot of the SA-647 versus GFP fluorescence calculated for each GFP+ neuron detected across FOVs treated with biotin - KCl ( n  = 53 neurons pooled from 8 FOVs) or biotin + KCl ( n  = 75 neurons pooled from 8 FOVs). Dashed line indicates the 90 th percentile of SA-647 fluorescence values of all neurons in the biotin - KCl group. C) The FOV averages of the SA-647/GFP fluorescence ratio per cell from the data shown in panel B ( n  = 8 FOVs per condition; P  = 1.6e-4, U = 0, two-tailed Mann-Whitney U test). D) ROC curve for distinguishing KCl-treated vs. non-treated neuron populations based on the SA-647/GFP ratios from panel B (AUC = 0.91, P  = 5.5e-15, Wilson/Brown’s method). E) Example FOV images of CaST expressing neuron viability experiment. Neurons expressing both fragments of CaST together (top) or the CD4-sTb(C)-M13-GFP fragment (middle) were stained with DRAQ7 at a final concentration of 3 µM at DIV 19 before fixation. Neurons expressing both fragments of CaST (bottom) were fixed, permeabilized, and stained with DRAQ7 at DIV 19. Scale bar, 300 µm. F) The FOV averages of the DRAQ7 fluorescence for the 3 conditions shown in panel D ( n  = 10 FOVs per condition; CaST versus CD4-sTb(C)-M13-GFP: P  = 0.9831; CaST versus CaST Fixed: P  = 8.0e-15; CD4-sTb(C)-M13-GFP versus CaST Fixed: P  = 8.0e-15, Tukey’s post-hoc multiple comparison’s test following a 1-way ANOVA, F 2,27  = 326.3, P  = 1.2e-19). *** P  < 0.001, **** P  < 0.0001, ns, not significant.

Extended Data Fig. 6 Simultaneous RCaMP2 imaging and CaST labeling in neurons.

A) Example FOV images of neurons co-infected with AAV2/1-Synapsin-RCaMP2 and AAV2/1-Synapsin-CaST viruses, following mild stimulation (50% media change) and 50 µM biotin treatment for 30 minutes. Post-hoc RCaMP2 and CaST labeling is shown for all identified cell masks during RCaMP2 imaging (shown as colored overlays to the left). Numbered arrows indicate locations from which traces were extracted for panel B . Scale bar, 100 µm. B) RCaMP2 dF/F fluorescence traces of example neurons from panel A , during a ~5-minute recording following treatment. Traces are colored according to their SA-647 cell fluorescence intensity value. C) Scatter plot showing a linear correlation between SA-647/GFP fluorescence ratio calculated for each GFP+ neuron detected, and the mean peak height during the RCaMP2 recording for each cell ( n  = 33 cells; two-tailed Pearson’s correlation coefficient R  = 0.37, P  = 0.035).

Extended Data Fig. 7 CaST specificity using targeted optogenetic stimulation.

A) Example fluorescence images for neurons co-infected with an excitatory opsin, AAV2/1-Synapsin-mCherry-P2A-bReaChES, and AAV2/1-Synapsin-CaST. 50 µM biotin and orange light was delivered for 30 minutes through a ~1 mm wide slit to the bottom of the culture dish. 50 µM APV and 20 µM NBQX were added at the time of light stimulation to reduce synchronized neuron firing. Scale bar, 1000 µm. B) Quantification of the mean SA-647 and GFP fluorescence intensity, averaged vertically across the entire FOV images shown in panel A . C) Example zoom-in images of the light-stimulated region in panel A showing neurons co-expressing bReaChES and CaST. Scale bar, 100 µm. D) Example SA-647 image for CaST-expressing neurons with whole dish KCl treatment as a non-spatial control. Scale bar, 1000 µm. E) Quantification of the mean SA-647 and GFP fluorescence intensity, averaged vertically across the entire FOV shown in panel D . F) Mean SA-647 and GFP fluorescence intensity, averaged vertically and binned across 1 mm horizontal sections to the left, middle, or right of the light stimulation gap (For SA-647, n  = 3 FOVs per condition; Left versus Middle: P  = 0.0281; Middle versus Right: P  = 0.0449, Šídák’s post-hoc multiple comparison’s test following a 2-way ANOVA, F 2,4  = 12.94, P  = 0.0179) (For GFP, n  = 3 FOVs per condition; Left versus Middle: P  = 0.1645; Middle versus Right: P  = 0.4352, Šídák’s post-hoc multiple comparison’s test following a 2-way ANOVA, F 2,4  = 3.536, P  = 0.1305). G) Same analysis as in panel F , except for a whole dish KCl stimulation non-spatial control (For SA-647, n  = 3 FOVs per condition; Left versus Middle: P  = 1.0; Middle versus Right: P  = 0.6731, Šídák’s post-hoc multiple comparison’s test following a 2-way ANOVA, F 2,4  = 0.9006, P  = 0.4754) (For GFP, n  = 3 FOVs per condition; Left versus Middle: P  = 0.7328; Middle versus Right: P  = 0.6682, Šídák’s post-hoc multiple comparison’s test following a 2-way ANOVA, F 2,4  = 2.446, P  = 0.2023). Data are plotted as mean ± s.e.m. in F and G . * P  < 0.05, ns, not significant.

Extended Data Fig. 8 RCaMP2 calcium imaging during various drug applications.

A) Average FOV images of neurons infected with AAV2/1-Synapsin-RCamp2, taken from the entire RCaMP2 baseline recording (“Pre”) or post-treatment recording (“Post”). Each recording was 1 minute long. Neurons were treated with 50 µM biotin and vehicle, 10 µM dopamine (DA), 10 µM DOI, or 30 mM KCl for 30 minutes. Scale bar, 300 µm. B) Top: average pre- and post-treatment fluorescence traces for all identified neurons in the FOV (Z-scored relative to the baseline “pre” period for each cell). Bottom: cell masks identified for each FOV shown in panel A . Data are plotted as mean ± s.e.m. with the shaded regions indicating the s.e.m. C) Peak neuron responses during the post-treatment recordings. Only DOI and KCl treatment drove a larger peak RCaMP2 response compared to the vehicle control ( n  = 69 cells for vehicle, 62 cells for DA, 104 cells for DOI, and 189 cells for KCl treatment; No KCl versus DOI: P  = 2.3e-13; No KCl versus KCl: P  = 2.3e-13, Tukey’s post-hoc multiple comparison’s test following a 1-way ANOVA, F 3,420  = 152.4, P  = 7.8e-67). **** P  < 0.0001.

Extended Data Fig. 9 Controls and validation for in vivo CaST labeling.

A) Schematic of control and experimental conditions. Both wildtype mice not expressing CaST, and wildtype mice injected with CaST in mPFC, were treated with 24 mg/kg biotin + 3 mg/kg psilocybin for 1 hour, and then sacrificed for histology. B) Example FOVs of wildtype mice not expressing CaST (-CaST) or expressing CaST (+CaST) that were injected with biotin and psilocybin. The -CaST control was replicated across two uninjected mice. C) Schematic for using real-time imaging to identify psilocybin-activated neurons in the mPFC. AAV5-CaMKIIa-GCaMP6f was injected into mPFC, and a 1 mm diameter GRIN lens was implanted. 4 weeks later, mice were imaged head-fixed under a 2 P microscope during an IP injection of 5 ml/kg saline or 3 mg/kg psilocybin. D) Background-subtracted mean images of the FOV during a 10-minute saline recording session (left), and a 10-minute psilocybin recording session (right). Active neurons are displayed as warmer pixel colors. Experiment was replicated in two mice. E) Mean 2 P FOV image of the combined saline and psilocybin recordings with all identified neuron masks shown as colored overlays. F) Example Z-scored fluorescence traces of neurons activated by psilocybin (magenta), unaffected by psilocybin (gray), or inhibited by psilocybin (blue), compared to the saline recording. Each recording was 10 minutes long. G) “Psilocybin minus Saline” activity traces were calculated by subtracting the baseline saline Z-scored trace from the psilocybin Z-scored trace for each neuron. The resulting 10-minute-long trace representing the difference is plotted for each neuron in the heatmap to the left, and the average difference for each neuron is plotted as the heatmap to the right labeled “Avg” ( N  = 254 cells from 2 mice). Neurons are plotted ranked by the highest to lowest average Z-score difference. The two horizontal bars represent the thresholds for defining “Activated” versus “Inhibited” neurons (>+0.05 = “Activated”, <−0.05 = “Inhibited”). H) The activity traces for the top 50 ranked “Activated” neurons from panel G are shown during the saline and psilocybin recordings (separated by a dashed vertical line). All scale bars, 50 µm.

Extended Data Fig. 10 cFos-only staining in psilocybin- versus saline-injected mice.

A,B) 2x (left) and 10x (right) images of mouse brain slices stained for cFos 1 hour after injection with saline vehicle ( A ) or 2 mg/kg psilocybin I.P. ( B ). White boxes on 2x images show location where the 10x images were taken in either the mPFC or SSC. C) An additional 4 example FOVs taken from different mice showing cFos staining in the mPFC after saline or psilocybin injection as described in panel A . D) Mean number of cFos+ neurons/mm 2 in the mPFC counted from mice injected with either saline or psilocybin ( n  = 5 mice each condition; P  = 0.73, U = 10.50, two-tailed Mann-Whitney U test). E) An additional 4 example FOVs taken from the same mice as in panel C , except showing cFos staining in the SSC after saline or psilocybin injection. F) Mean number of cFos+ neurons/mm 2 in the SSC counted from mice injected with either saline or psilocybin ( n  = 5 mice each condition; P  = 0.0079, U = 0, two-tailed Mann-Whitney U test). All scale bars, 50 µm. ** P  < 0.01, ns, not significant.

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Source data fig. 3, source data fig. 4, source data fig. 5, source data fig. 6, source data extended data fig. 1, source data extended data fig. 2.

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Zhang, R., Anguiano, M., Aarrestad, I.K. et al. Rapid, biochemical tagging of cellular activity history in vivo. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02375-7

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DOI : https://doi.org/10.1038/s41592-024-02375-7

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She said: “These drugs will affect their day-to-day functioning. And as they are at an age where their brain is developing, drugs can have a long-term effect on them.

“But it is not surprising they are taking drugs at a younger age, because social media gives them exposure to drugs and liberal attitudes.”

In July, Minister of State for Home Affairs and National Development Muhammad Faishal Ibrahim said there have been more cases of youth engaging in drug trafficking and abuse , and they were getting younger.

He said that in the first four months of 2024, 16 drug offenders who were below the age of 16 were arrested, compared with 24 such arrests in the whole of 2023.

Figures from the Central Narcotics Bureau (CNB) annual statistics report showed that there was a near 17 per cent increase in young drug abusers arrested in 2023 compared with the figure in 2022. 

Drug abusers below 30 form around 27 per cent of total drug abusers in Singapore. 

The most recent survey by the National Council Against Drug Abuse (NCADA) in 2023 revealed that 18 per cent of 3,000 youth aged 13 to 29 said they knew someone who had taken drugs. This was up from 10.6 per cent in 2019.

A CNB spokesman said the youngest person arrested for drug consumption between 2019 and 2023 was an 11-year-old Singaporean in 2020.

In July, a 14-year-old girl suspected of drug abuse was arrested in an islandwide operation.

Ms Gwen Ho, a caseworker with the Youth Enhanced Supervision scheme, where low-risk youth offenders are placed under the care of social service agencies, said some youth below the age of 16 take drugs out of curiosity.

She added that some have friends who know older youth with access to drugs, while others meet strangers online who introduce them to drugs.

Ms Ho recalls helping a 15-year-old girl through her rehabilitation programme in 2020 after she was arrested by CNB for using methamphetamine.

The girl had used drugs to cope with relationship issues and felt distant from her family.

When the teen’s parents found out about her drug abuse, she felt misunderstood by them. She ran away from home, but her siblings found her.

Ms Ho said the girl’s parents learnt to talk to her, not just focusing on her drug use but on her overall well-being.

Cope with problems

A study by the Institute of Mental Health in 2023 found that four in 10 drug abusers in Singapore started abusing drugs before they were 18 years old.

Ms Teo Kah Shun, a psychologist at the CNB Psychology Unit, said one of the reasons why youth below the age of 21 take drugs is to cope with problems.

They may also take drugs because they think it can improve their performance in mobile games, sports or their studies, or as a way to socialise.

And what she is observing worries her.

She said: “Compared with 10 years ago, youth today have access to social media platforms and many online sources. So, it’s hard to control where they get their information.”

Ms Teo added that she has cases where youth tell her they do in-depth research on drugs, giving them the misconception that the drugs are not harmful.

She said that online forums and shows such as Breaking Bad and Narcos have influenced youth to think that drug consumption is normal.

In a Forum letter in The Straits Times on Aug 7, NCADA council member Firdaus Daud replied to a letter by 19-year-old Lee Qin Yuan on how the young can learn to help others with drug problems.

Mr Firdaus said popular culture, social media and global trends have resulted in a growing misconception that drug use is normal and acceptable.

This has led to more young people trying drugs due to curiosity, peer influence, and underestimating the dangers of drug addiction until it is too late. 

This is compounded by the increasing momentum of international drug liberalisation and decriminalisation, which have made it challenging for Singapore to maintain its strict drug laws and policies. 

Aside from education and outreach programmes to warn young people of the harms, and reintegration initiatives to help them recover from drug use, the authorities are looking to police the online space.

In February, Minister of State for Home Affairs Sun Xueling said in Parliament that the Ministry of Home Affairs is working with online platforms to get them to comply with directions under the Online Criminal Harms Act within 24 hours.

If online platforms do not remove content allegedly used for criminal activities, including drug sales, within a stipulated timeline after being told by the authorities to do so, they would have committed an offence.

Sniffed glue at 11

Former drug addict Mohamed Ashyik, 40, tried glue sniffing at 11, used methamphetamine at 13, then got arrested at 16.

He went in and out of prison for various drug-related offences, and spent a total of 15 years behind bars.

Things changed nine years ago, after his eldest stepdaughter, who was 25 then, visited him at a drug rehabilitation centre.

He said: “She cried and said, ‘You broke our hearts, even after I tried to accept you as my father.’ Ever since then, I wanted to put a stop to all these (drug) activities.”

Mr Ashyik, who is now a safety officer, said: “If you keep on blaming your friends and surroundings for getting into drugs, you’ll never change.”

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• Central Narcotics Bureau
• Children and youth
• Drug consumption

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