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Research Article

Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping

Affiliation Imaging Research Center and Departments of Psychology and Neurobiology, University of Texas, Austin, Texas, United States of America

Affiliation NASA Ames Research Center, Mountain View, California, United States of America

Affiliation Department of Electrical and Computer Engineering, University of Texas, Austin, Texas, United States of America

Affiliation Department of Psychology, Colorado University, Boulder, Colorado, United States of America

Russell A. Poldrack,
Jeanette A. Mumford,
Tom Schonberg,
Donald Kalar,
Bishal Barman,
Tal Yarkoni

Published: October 11, 2012
https://doi.org/10.1371/journal.pcbi.1002707
Reader Comments

Neuroimaging research has largely focused on the identification of associations between brain activation and specific mental functions. Here we show that data mining techniques applied to a large database of neuroimaging results can be used to identify the conceptual structure of mental functions and their mapping to brain systems. This analysis confirms many current ideas regarding the neural organization of cognition, but also provides some new insights into the roles of particular brain systems in mental function. We further show that the same methods can be used to identify the relations between mental disorders. Finally, we show that these two approaches can be combined to empirically identify novel relations between mental disorders and mental functions via their common involvement of particular brain networks. This approach has the potential to discover novel endophenotypes for neuropsychiatric disorders and to better characterize the structure of these disorders and the relations between them.

Author Summary

One of the major challenges of neuroscience research is to integrate the results of the large number of published research studies in order to better understand how psychological functions are mapped onto brain systems. In this research, we take advantage of a large database of neuroimaging studies, along with text mining methods, to extract information about the topics that are found in the brain imaging literature and their mapping onto reported brain activation data. We also show that this method can be used to identify new relations between psychological functions and mental disorders, through their shared brain activity patterns. This work provides a new way to discover the underlying structure that relates brain function and mental processes.

Citation: Poldrack RA, Mumford JA, Schonberg T, Kalar D, Barman B, Yarkoni T (2012) Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping. PLoS Comput Biol 8(10): e1002707. https://doi.org/10.1371/journal.pcbi.1002707

Editor: Olaf Sporns, Indiana University, United States of America

Received: May 14, 2012; Accepted: August 2, 2012; Published: October 11, 2012

Copyright: © Poldrack et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by NIH grant RO1MH082795 (to RAP) and F32NR012081 (to TY) and by the Texas Emerging Technology Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Competing interests: The authors have declared that no competing interests exist.

Introduction

The search for clues regarding the underlying causes of mental disorders has led to the notion that these disorders may be best understood in terms of a set of underlying psychological and/or neural mechanisms that stand between genes and environment on the one hand and psychiatric diagnoses on the other hand. Such intermediate phenotypes, or “endophenotypes”, may provide the traction that has eluded research using diagnostic categories as primary phenotypes [1] , [2] . They may also provide the means to better understand the structure the underlying psychological dimensions that appear to underlie overlapping categories of mental disorders [3] , [4] .

The identification of endophenotypes requires an understanding the basic structure of mental functions and their associated brain networks. For more than 30 years, cognitive neuroscientists have used neuroimaging methods (including EEG/MEG, PET, and fMRI) in an attempt to address this question. This work has led to a large body of knowledge about associations between specific psychological processes or tasks and activity in brain regions or networks. However, this knowledge has not led to a commensurate improvement in our understanding of the basic mental operations that may be subserved by particular brain systems. Instead, diverse literatures often assign widely varying functions to the same networks. A prime example is the anterior cingulate cortex, which has been associated with such widespread functions as conflict monitoring, error processing, pain, and interoceptive awareness. In order to understand the unique functions that are subserved by brain regions or networks, a different approach is necessary; namely, we need to analyze data obtained across a broad range of mental domains and understand how these domains are organized with regard to neural function and structure.

The identification of basic operations can be understood statistically as a problem of latent structure identification; that is, what are the latent underlying mental functions and brain networks that give rise to to the broad range of observed behaviors and patterns of brain activity and neuropsychiatric disorders? The focus within cognitive neuroscience on establishing associations between activation and specific hypothesized processes has hindered the ability to identify such latent structures. However, within the fields of machine learning and text mining, a number of powerful approaches have been developed to estimate the latent structure that generates observed data, assuming that large enough datasets are available. In the present work, we take advantage of one class of such generative models to develop a new approach to identifying the underlying latent structure of mental processing and the associated brain functions, which we refer to as “topic mapping”. We examine the latent conceptual structure of the fMRI literature by mining the full text from a large text corpus comprising more than 5,800 articles from the neuroimaging literature, and model the relation between these topics and associated brain activation using automated methods for extracting activation coordinates from published papers. This analysis uncovers conceptual structure and activation patterns consistent with those observed in previous neuroimaging meta-analyses, which provides confirmation of the approach, while also providing some novel suggestions regarding structure/function relationships. We then use this approach to identify the topical structure of terms related neuropsychiatric diseases, and use multivariate methods to identify relations between these the mental and disorder domains based on common brain activation patterns. This approach provides an empirical means of discovering novel endophenotypes that may underlie mental disorders, as well providing new insights into the relations between diagnostic categories.

Within the fields of information retrieval and computer science, research into document retrieval has led to the development of a set of techniques for estimating the latent structure underlying a set of documents. Early work in this area treated documents as vectors in a high-dimensional space, and used matrix decomposition techniques such as singular value decomposition to identify the latent semantic structure of the documents [5] . More recently, researchers in this domain have developed approaches that are based on generative models of documents. One popular approach, known generically as “topic models” [6] , treats each document as a mixture of a small number of underlying “topics”, each of which is associated with a distribution over words. Generating a document via this model involves sampling a topic and then sampling over words within the chosen topic; using Bayesian estimation techniques, it is possible to invert this model and estimate the topic and word distributions given a set of documents. The particular topic modeling technique that we employ here, known as latent Dirichlet allocation (LDA: [7] ), has been shown to be highly effective at extracting the structure of large text corpuses. For example [8] , used this approach to characterize the topical structure of science by analyzing 10 years of abstracts from PNAS , showing that it was able to accurately extract the conceptual structure of this domain.

We characterized the latent structure of the cognitive neuroscience literature by applying latent Dirichlet allocation to a corpus of 5,809 articles (using an expanded version of the corpus developed in [9] ), which were selected on the basis of reporting fMRI activation in a standardized coordinate format. An overview of the entire data processing workflow is presented in Figure 1 . This technique estimates a number of underlying latent “topics” that generate the observed text, where each topic is defined by a distribution over words. The dimensionality (i.e., number of topics) is estimated using a cross-validation approach; the documents are randomly split into 8 sets, and for each set a topic model is trained on the remaining data and then used to estimate the empirical likelihood of the held-out documents [10] . Plots of the empirical likelihood of left-out documents as a function of the number of topics are shown in Figure 2 , and histograms of the number of documents per topic and number of topics per document are shown in figure 3 .

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https://doi.org/10.1371/journal.pcbi.1002707.g001

https://doi.org/10.1371/journal.pcbi.1002707.g002

https://doi.org/10.1371/journal.pcbi.1002707.g003

Initial application of LDA to the full-text corpus identified a number of topics that were related to mental function, but also many topics related to methodological or linguistic aspects of the documents. Because we were specifically interested in estimating the conceptual structure of mental processes, we examined each document in the corpus and identified each occurrence of any of the 605 terms (both single words and phrases) that are present as mental concepts in the Cognitive Atlas ( http://www.cognitiveatlas.org ); the topic model was then estimated using this limited word set (treating each word or phrase as a single-word token). The Cognitive Atlas is a curated collaborative ontology that aims to describe mental functions, and contains terms spanning across nearly all domains of psychological function [11] . The cross-validation analysis identified 130 as the optimal number of topics for this dataset. Examples of these topics are shown in Figure 4 , and the full list is presented in Table S1 . In large part these topics are consistent with the topics that are the focus of research in the cognitive neuroscience literature. The topics with the highest number of associated documents were those related to very common features of neuroimaging tasks such as movement (topic 20), emotion (topic 93), audition (topic 74), attention (topic 43), and working memory (topic 61). Each of these was associated with more than 400 documents in the corpus. At the other end of the spectrum were more focused topics that loaded on fewer than 200 documents, such as topic 121 (regret,surprise), topic 71 (narrative, discourse), and topic 108 (empathy, pain). The results of this analysis suggest that topic modeling applied to the limited term set of mental functions can successfully extract the conceptual structure of psychological processes at multiple levels within the current text corpus.

https://doi.org/10.1371/journal.pcbi.1002707.g004

In order to further examine the effects of topic dimensionality, we compared the results obtained across several values for the number of topics (10,50, 100, and 250). We chose the term “language” and identified all topics for each model in which that term occurred in the top five terms. We then examined the correlation in the loading vector across documents for each set of levels, in order to identify the hierarchical graph relating topics across levels (see Figure 5 ). This analysis showed that increasing the topic dimensionality resulted in finer-grained topics; for example, with 10 topics there was a single matching topic that included “meaning”, “reading”, and “comprehension”, whereas each of these was split into a separate set of topics in the 50-topic model, and further subdivided as the dimensionality increased. This suggests that although the cross validation resulted in a particular “best” dimensionality, in reality there is relevant information at many different levels which differs in grain size.

All topics with “language in their top 5 terms were first identified from the results for topic models fit to the data at 10, 50, 100, and 250 topics. At each level, each topic is linked to the topic at the previous level with which it had the highest correlation in its document loadings. The values on each edge reflect the correlation in the topic loading vector across documents between the two levels.

https://doi.org/10.1371/journal.pcbi.1002707.g005

Topic mapping

While concordance with the existing literature is reassuring, the true promise of this approach is in its ability to uncover novel associations between functions and activation, and the topic mapping analysis did in fact identify some unexpected associations, particularly when looking at negative associations. Two interesting examples are evident in Figure 4 . First, topic 61 was associated with the bilateral fronto-parietal network usually associated with working memory, but it also exhibited strong and focused negative association in the right amygdala; this means that the amygdala was significantly less likely to be activated in studies that loaded on this topic relative to those that did not. This is particularly interesting in light of further exploration of the literature using the PubBrain tool ( http://www.pubbrain.org ) which identified a number of studies that have noted amygdala activation in association with working memory tasks (cf. [13] ). Another example is topic 71 (associated with auditory processing) which was negatively associated with activation in a broad set of regions previously implicated in emotional function, such as orbitofrontal cortex, striatum, and amygdala. Whether such negative associations reflect truly negative relations in activation between these networks or reflect features of the tasks used in these domains remains to be determined, but such unexpected associations could suggest novel hypotheses about relations between specific brain networks. These are only two examples of potential novel discoveries using Topic Mapping.; future studies will be needed to systematically examine all possible new findings emerging from the usage of this tool.

Mapping the neural basis of neuropsychiatric disorders

Based on the results from the foregoing analyses, we then examined whether it was possible to obtain new insights about the organization of brain disorders using the topic mapping approach developed above. We estimated a set of topics using only terms related to brain disorders, based on a lexicon of mental disorders terms derived from the NIFSTD Dysfunction ontology [14] along with the DSM-IV. The optimal dimensionality of 60 based on cross-validation was found to produce multiple topics with exactly the same word distribution, so we used the largest number of topics yielding a unique set of word distributions across topics, which was 29 topics. Examples of these topics and the associated topic maps are presented in Figure 6 .

Topics are ordered in terms of the number of documents loading on the topic; color maps reflect the correlation coefficient between topic loading and activation across documents. The images are presented in radiological convention (i.e., left-right reversed).

https://doi.org/10.1371/journal.pcbi.1002707.g006

The results of this analysis are largely consistent with results from prior meta-analyses and known functional anatomy of the various disorders, but are novel in highlighting relations between some of the disorders. For example, Topic 7 demonstrates the relations between bipolar disorder, schizophrenia, and mood disorders, with activation centered on the medial prefrontal cortex, basal ganglia, and amygdala. Topic 8 highlights relations between obesity and eating disorders and drug abuse, with activation in the ventral striatum and ventromedial prefrontal cortex. Topic 14 demonstrates relations between a set of externalizing disorders (drug abuse, conduct disorder, alcoholism, antisocial personality disorder, and cannabis related disorder) with activation focused in the striatum, amygdala, orbitofrontal cortex, and dorsal prefrontal cortex. Conversely, Topic 25 demonstrates relations between a set of internalizing disorders (anxiety disorder, panic disorder, phobia, obsessive compulsive disorder, agoraphobia, and post traumatic stress disorder), with a very similar pattern of activation, though notably weaker in the striatum. One striking result of these analyses is the similarity of the patterns of brain activity associated with the mention of all of these different disorders. This could arise either from the fact that this particular set of limbic brain systems is the seat of all major psychiatric disorders, or the fact that these disorders are commonly mentioned in relation to tasks or cognitive domains that happen to preferentially engage these brain systems.

We further characterized the relations between different disorder concepts in their associated neural activations by clustering the disorder topics based on their associated brain activation patterns using hierarchical clustering. The results of this analysis are shown in Figure 7 . The results show the degree to which the neural patterns associated with the use of particular sets of mental disorder terms exhibit a consistent systematic structure. The clustering breaks into four large groups, comprising language disorders, mood/anxiety disorders and drug abuse, psychotic disorders, and autism and memory disorders. What is particularly interesting is that, although none of the topic maps associated with the term “schizophrenia” showed strong activation, the fact that they cluster together in this analysis suggests that they are nonetheless similar in the patterns of activation that are reported in the associated papers; however, this could also reflect the fact that a relatively small number of tasks is used in the literature, and thus any concordance could be driven by overlap of tasks that are commonly mentioned in the context of schizophrenia. Despite such limitations, these results provide further confirmation that the present analysis, while largely based on studies involving healthy adults, can nonetheless accurately characterize the neural basis of mental disorders as described in the literature.

Euclidean distance was used as the distance metric for clustering, and hierarchical clustering was performed using Ward's method. The colored blocks show the four major groupings obtained by cutting the tree at a height of 2.0. Abbreviations: APH: aphasia, DLX:dyslexia, SLI: specific language impairment, DA: drug abuse, AD:Alzheimer's disease, DEP:depressive disorder, MDD:major depressive disorder, ANX:anxiety disorder, PAN: panic disorder, BPD: bipolar disorder, CD: conduct disorder, GAM: gambling, MD: mood disorder, PD: Parkinson's disease, OCD: obsessive compulsive disorder, PHO: phobia, EAT: eating disorder, SZ: schizophrenia, OBE: obesity, COC: cocaine related disorder, PSY: psychotic disorder, PAR: paranoid disorder, SZTY: schizotypal personality disorder, TIC: tic disorder, ALC: alcoholism, ALX: alexia, ADD: attention deficit disorder, AMN: amnesia, AUT: autism, ASP: Asperger syndrome.

https://doi.org/10.1371/journal.pcbi.1002707.g007

Empirical discovery of endophenotypes

https://doi.org/10.1371/journal.pcbi.1002707.t001

The first canonical variate (#0) demonstrated associations between a number of both internalizing and externalizing disorders (anxiety, depression, obesity, gambling) which were centered around the involvement of emotional processes (such as mood and fear) and reward-related decision processes. Another canonical variate (#1) was focused on memory processes, and identified a cluster of disorders including classical memory disorders (amnesia and Alzheimer's disease) as well as schizophrenia. Another (#2) focused on language processes and was associated with activity in left prefrontal, temporal, and parietal regions.

The results of the CCA analysis provide a potential new window into the complex psychological and neural underpinnings of schizophrenia and its relation to other psychiatric disorders. Across different canonical variates, schizophrenia is related to mood and decision making processes (components 0 and 3), memory processes (component 5), and social perception (component 10). These could potentially relate to different aspects of schizophrenic symptomatology, such as the distinctions between positive versus negative symptoms or between cognitive versus affective impairments. Further, they provide novel potential targets for genetic association studies, which have struggled to identify meaningful and replicable associations between schizophrenic symptoms or endophenotypes and genetic polymorphisms (cf. [16] ).

We also performed CCA directly using topic-document loading vectors, in order to determine whether the results differed from CCA computed on neural loading vectors; the results are presented in Table 2 . The results of this analysis are quite concordant with the foregoing analyses based on activation patterns, but one noticeable difference between the two analyses is that the activation-based CCA analysis appeared to cluster disorders more broadly, whereas many of the components found in the text-based analysis had only a single disorder. This may reflect the fact that disorders are less neurally distinct than is suggested by what is written by authors, but could also reflect greater noise in the neural data; further work will be necessary to better understand the unique contributions of activation-based and text-based analyses.

https://doi.org/10.1371/journal.pcbi.1002707.t002

It is clear that neuroimaging can provide important evidence regarding the functional organization of the brain, but one of the most fundamental questions in cognitive neuroscience has been whether it can provide any new insights into psychological function [17] – [19] . The results presented here demonstrate how large databases of neuroimaging data can provide new insights into the structure of psychological processes, by laying bare their relations within a similarity space defined by neural function. The present results highlight the importance of “discovery science” approaches that take advantage of modern statistical techniques to characterize large, high-dimensional datasets (cf. [20] ). Just as the fields of molecular biology and genomics have been revolutionized by this approach [21] , we propose that the hypothesis-generating approach supported by data mining tools can serve as a powerful complement to more standard hypothesis-testing approaches [22] .

There is growing recognition that the diagnostic categories used in psychiatry are not reflective of sharp parallel biological distinctions; instead, a growing body of behavioral, genetic, and neuroimaging data suggest that these different disorders fall along a set of underlying continuous dimensions which likely relate to particular basic psychological processes [3] , [4] . The results presented here are consistent with that viewpoint, and further show how endophenotypes for groups of disorders can be empirically discovered via data mining, even if those disorders were not the primary aims of the studies being mined. This approach would likely be even more powerful using databases that were focused on imaging data from studies of patients. In addition, this approach has the potential to characterize the genetic architecture of these disorders through mining of genetic association data; unfortunately, genetic terms are not sufficiently frequent in the Neurosynth database to support robust mapping of relationships to genes, but future analyses using enhanced databases has the potential to discover additional relations between neurocognitive components and genetic contributions.

The present work is limited by several features of the data that were used in the analyses. The first limitation arises from the fact that we rely upon the presence of particular terms in the text, rather than on manual annotation of the relevance of those terms. Thus, obvious issues such as polysemy (e.g., the multiple senses of the term “working memory”) and negation can be problematic, though these issues could potentially be addressed using more powerful natural language processing. A second limitation arises from the meta-analytic nature of the activation data used in the analyses, which are reconstructed from a very sparse representation of the original data. A third limitation is that the activation maps are associated only with complete documents, not with specific terms within the document, and this coarseness undoubtedly adds a significant amount of noise to the modeling results. These limitations necessitate caution in drawing strong conclusions from the results reported here. At the same time, the concordance of many of the results with previous analyses using different datasets and analysis approaches suggests that these limitations have not greatly undermined the power of the technique. We propose that the approach outlined here is likely to be most useful for inspiring novel hypotheses rather than for confirming existing hypotheses, which means that any such results will be just the first step in a research program that must also include hypothesis-driven experimentation.

Another potential limitation of the present work is that the fact that a number of the parameters in the analyses were set arbitrarily. While the dimensionality of the topic models was determined using an automated method, there remain parameter settings (such as smoothness of the word and topic distributions) that must be chosen arbitrarily (in our case, we chose them based on previously published results). The results of the topic model are quite robust; for example, we saw very similar results when performing the topic models on the original set of 4,393 papers from the earlier paper by Yarkoni et al. compared to the results from the corpus of 5,809 papers. It is also evident from Figure 5 that there is strong continuity in topics across different dimensionalities, with single topics at lower dimensionalities splitting into multiple finer-grained topics at higher dimensionalities. We have chosen model parameters that appear to give sensible results relative to prior findings, but the possibility remains that different parameterizations or analysis approaches could lead to different outcomes; future research will need to explore this question in more detail. We would also note that some of these limitations may be offset by the fact that the analyses presented here are almost fully automated, which removes many possible opportunities for research bias to affect the results.

The present work follows and extends other recent work that has aimed to mine the relations between mental function and brain function using coordinate-based meta-analyses. Smith et al. [23] analyzed the BrainMap database (which is similar to the database used here, but is created via manual annotation and thus has lower coverage but greater specificity and accuracy than the Neurosynth database). This work showed that independent components analysis applied to the meta-analytic data was able to identify networks very similar to those observed in resting-state fMRI time series, and that these could be related to specific aspects of psychological function via the annotations in the BrainMap database. Laird et al [24] extended this by showing that behavioral functions could be clustered together based on these meta-analytic maps. The present work further extends those previous studies by showing that the structure of the psychological domain can be identified in an unsupervised manner using topic modeling across both cognitive function and mental disorder domains, and that these can further be used to identify potential endophenotypes that share common neural patterns across these two domains. Visual examination of the ICA components presented in the Smith and Laird papers shows substantial overlap with the topic maps identified in the present study. In future work, we hope to directly compare the topic mapping results with the maps identified in those papers, to further characterize the utility of each approach.

In summary, we have shown how large neuroimaging and text databases can be used to identify novel relations between brain, mind, and mental disorders. The approach developed here has the potential to enable new discoveries about the neural and cognitive bases of neuropsychiatric disorders, and to provide empirically-driven functional characterizations of patterns of brain activation. The results also highlight the importance of the availability of large open datasets in cognitive neuroscience to enable discovery-based science as a complement to hypothesis-driven research.

Materials and Methods

Code to implement all of the analyses reported here, along with all of the auxiliary files, are available at https://github.com/poldrack/LatentStructure .

Data extraction

The full text from the Neurosynth corpus was used for the text mining analyses. The sources of these data as well as the process for automated extraction of activation coordinates are described in detail in [9] .

Peak image creation

Synthetic activation peak images were created from the extracted activation coordinates by placing a sphere (10 mm radius) at each activation location, at 3 mm resolution using the MNI305 template. Activations detected to be in Talairach space were first converted to MNI305 coordinates using the Lancaster transform [25] .

Topic modeling

We ran two topic modeling analyses using limited sets of terms to obtain focused topics in specific domains. In the first, we used 605 mental concept terms from the Cognitive Atlas database mentioned previously. In the second, we used a set of 55 terms describing mental disorders; these were obtained by taking the NIFSTD Dysfunction ontology and removing all terms not relevant to psychiatric disorders, and then adding a set of missing terms that described additional disorders listed in the DSM-IV. In each case, we processed the full text corpus and created restricted documents containing only terms that were present in the respective term list (along with synonyms, which were mapped back to the base term), and then performed topic modeling on those restricted documents. The median number of terms per document after filtering was 127 for cognitive terms and 3 for disease terms.

For each dataset, the optimal number of topics was determined by performing a grid search across a range of dimensionality values (from 10 to 250 in steps of 10). Each document set was split into 8 random sets of documents, and 8 separate models were trained, in each case leaving out one subset of documents. The empirical likelihood of the left-out documents was then estimated using an importance sampling method as implemented in MALLET [10] .

In order to identify the hierarchical relations between topics across different dimensionalities (as shown in Figure 5 ), the topic models from the first crossvalidation fold for each level (10, 50, 100, and 250 topics) were used; because 1/8 of the data were excluded as test data, these models were thus trained on a total of 5082 documents (using the same documents across all different dimensionalities). Hierarchical relations between levels were identified by computing the correlation between the document loading vectors for each lower-level topic and all higher-level topics, and then assigning the link according to the maximum correlation.

Disorder clustering

Disorders were clustered using hierarchical clustering (Ward's method) applied to the Euclidean distance matrix computed across voxels for the disorder-based topic maps (Pearson r values).

Canonical correlation analysis

Supporting Information

Complete list of topics identified through application of latent Dirichlet allocation to the text corpus filtered for Cognitive Atlas terms. The top 5 words shown for each topic are those which had the highest loading for that topic across documents. The number of documents that loaded on each topic is also listed.

https://doi.org/10.1371/journal.pcbi.1002707.s001

Complete list of topics identified through application of latent Dirichlet allocation to the text corpus filtered for mental disorder terms. The top 5 words shown for each topic are those which had the highest loading for that topic across documents. The number of documents that loaded on each topic is also listed.

https://doi.org/10.1371/journal.pcbi.1002707.s002

Acknowledgments

Thanks to Robert Bilder, Eliza Congdon, Steve Hanson, Oluwasanmi Koyejo, Jonathan Pillow, and Fred Sabb for helpful comments on a draft of this paper and to Daniela Witten for assistance with the R PMA package.

Author Contributions

Conceived and designed the experiments: RAP TS DK BB TY. Performed the experiments: RAP TY. Analyzed the data: RAP JAM TY. Contributed reagents/materials/analysis tools: RAP JAM DK BB TY. Wrote the paper: RAP JAM TS TY.

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Google Scholar
6. Steyvers M, Griffiths T (2007) Probabilistic topic models. In: Landauer T, McNamara D, Dennis S, Kintsch W, editors. Latent Semantic Analysis: A Road to Meaning.
10. Wallach HM, Murray I, Salakhutdinov R, Mimno D (2009) Evaluation methods for topic models. In: Proceedings of the 26th Annual International Conference on Machine Learning. New York, NY, USA: ACM, ICML 2009. pp. 1105–1112. Available: http://doi.acm.org/10.1145/1553374.1553515 .
26. McCallum AK (2002) Mallet: A machine learning for language toolkit. Available: http://mallet.cs.umass.edu .

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Brief, daily meditation enhances attention, memory, mood, and emotional regulation in non-experienced meditators

Affiliations.

1 New York University, Center for Neural Science, 4 Washington Place, Room 809, New York, NY 10003, United States; Virginia Tech Carilion Research Institute, Center for Transformative Research on Health Behaviors, 1 Riverside Circle, Suite 104G, Roanoke, VA 24016, United States. Electronic address: [email protected].
2 New York University, Center for Neural Science, 4 Washington Place, Room 809, New York, NY 10003, United States.
3 New York University, Center for Neural Science, 4 Washington Place, Room 809, New York, NY 10003, United States. Electronic address: [email protected].
PMID: 30153464
DOI: 10.1016/j.bbr.2018.08.023

Meditation is an ancient practice that cultivates a calm yet focused mind; however, little is known about how short, practical meditation practices affect cognitive functioning in meditation-naïve populations. To address this question, we randomized subjects (ages of 18-45) who were non-experienced meditators into either a 13-min daily guided meditation session or a 13-min daily podcast listening session (control group) for a total duration of 8 weeks. We examined the effects of the daily meditation practice relative to podcast listening on mood, prefrontal and hippocampal functioning, baseline cortisol levels, and emotional regulation using the Trier Social Stress Test (TSST). Compared to our control group, we found that 8 but not 4 weeks of brief, daily meditation decreased negative mood state and enhanced attention, working memory, and recognition memory as well as decreased state anxiety scores on the TSST. Furthermore, we report that meditation-induced changes in emotional regulation are more strongly linked to improved affective state than improved cognition. This study not only suggests a lower limit for the duration of brief daily meditation needed to see significant benefits in non-experienced meditators, but suggests that even relatively short daily meditation practice can have similar behavioral effects as longer duration and higher-intensity mediation practices.

Keywords: Breathing; Cognition; Consciousness; Executive function; Mindfulness; Stress.

Published by Elsevier B.V.

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Researchers uncover how the human brain separates, stores, and retrieves memories

NIH-funded study identifies brain cells that form boundaries between discrete events.

Illustration of a brain with photographs.

Researchers have identified two types of cells in our brains that are involved in organizing discrete memories based on when they occurred. This finding improves our understanding of how the human brain forms memories and could have implications in memory disorders such as Alzheimer’s disease. The study was supported by the National Institutes of Health’s Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative and published in Nature Neuroscience .

“This work is transformative in how the researchers studied the way the human brain thinks,” said Jim Gnadt, Ph.D., program director at the National Institute of Neurological Disorders and Stroke and the NIH BRAIN Initiative. “It brings to human neuroscience an approach used previously in non-human primates and rodents by recording directly from neurons that are generating thoughts.”

This study, led by Ueli Rutishauser, Ph.D., professor of neurosurgery, neurology and biomedical sciences at Cedars-Sinai Medical Center in Los Angeles, started with a deceptively simple question: how does our brain form and organize memories? We live our awake lives as one continuous experience, but it is believed based on human behavior studies, that we store these life events as individual, distinct moments. What marks the beginning and end of a memory? This theory is referred to as “event segmentation,” and we know relatively little about how the process works in the human brain.

To study this, Rutishauser and his colleagues worked with 20 patients who were undergoing intracranial recording of brain activity to guide surgery for treatment of their drug-resistant epilepsy. They looked at how the patients’ brain activity was affected when shown film clips containing different types of “cognitive boundaries”—transitions thought to trigger changes in how a memory is stored and that mark the beginning and end of memory “files” in the brain.

The first type, referred to as a “soft boundary,” is a video containing a scene that then cuts to another scene that continues the same story. For example, a baseball game showing a pitch is thrown and, when the batter hits the ball, the camera cuts to a shot of the fielder making a play. In contrast, a “hard boundary” is a cut to a completely different story—imagine if the batted ball were immediately followed by a cut to a commercial.

Jie Zheng, Ph.D., postdoctoral fellow at Children’s Hospital Boston and first author of the study, explained the key difference between the two boundaries.

“Is this a new scene within the same story, or are we watching a completely different story? How much the narrative changes from one clip to the next determines the type of cognitive boundary,” said Zheng.

The researchers recorded the brain activity of participants as they watched the videos, and they noticed two distinct groups of cells that responded to different types of boundaries by increasing their activity. One group, called “boundary cells” became more active in response to either a soft or hard boundary. A second group, referred to as “event cells” responded only to hard boundaries. This led to the theory that the creation of a new memory occurs when there is a peak in the activity of both boundary and event cells, which is something that only occurs following a hard boundary.

One analogy to how memories might be stored and accessed in the brain is how photos are stored on your phone or computer. Often, photos are automatically grouped into events based on when and where they were taken and then later displayed to you as a key photo from that event. When you tap or click on that photo, you can drill down into that specific event.

“A boundary response can be thought of like creating a new photo event,” said Dr. Rutishauser. “As you build the memory, it’s like new photos are being added to that event. When a hard boundary occurs, that event is closed and a new one begins. Soft boundaries can be thought of to represent new images created within a single event.”

The researchers next looked at memory retrieval and how this process relates to the firing of boundary and event cells. They theorized that the brain uses boundary peaks as markers for “skimming” over past memories, much in the way the key photos are used to identify events. When the brain finds a firing pattern that looks familiar, it “opens” that event.

Two different memory tests designed to study this theory were used. In the first, the participants were shown a series of still images and were asked whether they were from a scene in the film clips they just watched. Study participants were more likely to remember images that occurred soon after a hard or soft boundary, which is when a new “photo” or “event” would have been created.

The second test involved showing pairs of images taken from film clips that they had just watched. The participants were then asked which of the two images had appeared first. It turned out that they had a much harder time choosing the correct image if the two occurred on different sides of a hard boundary, possibly because they had been placed in different “events.”

These findings provide a look into how the human brain creates, stores, and accesses memories. Because event segmentation is a process that can be affected in people living with memory disorders, these insights could be applied to the development of new therapies.

In the future, Dr. Rutishauser and his team plan to look at two possible avenues to develop therapies related to these findings. First, neurons that use the chemical dopamine, which are most-known for their role in reward mechanisms, may be activated by boundary and event cells, suggesting a possible target to help strengthen the formation of memories.

Second, one of the brain’s normal internal rhythms, known as the theta rhythm, has been connected to learning and memory. If event cells fired in time with that rhythm, the participants had an easier time remembering the order of the images that they were shown. Because deep brain stimulation can affect theta rhythms, this could be another avenue for treating patients with certain memory disorders.

This project was made possible by a multi-institutional consortium through the NIH BRAIN Initiative’s Research on Humans program. Institutions involved in this study were Cedars-Sinai Medical Center, Children’s Hospital Boston (site PI Gabriel Kreiman, Ph.D.), and Toronto Western Hospital (site PI Taufik Valiante, M.D., Ph.D.). The study was funded by the NIH BRAIN Initiative (NS103792, NS117839), the National Science Foundation, and Brain Canada.

The BRAIN Initiative ® is a registered trademark of the U.S. Department of Health and Human Services.

The NIH BRAIN Initiative is managed by 10 institutes whose missions and current research portfolios complement the goals of The BRAIN Initiative ® : National Center for Complementary and Integrative Health, National Eye Institute, National Institute on Aging, National Institute on Alcohol Abuse and Alcoholism, National Institute of Biomedical Imaging and Bioengineering, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute on Drug Abuse, National Institute on Deafness and other Communication Disorders, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke.

NINDS ( https://www.ninds.nih.gov ) is the nation’s leading funder of research on the brain and nervous system. The mission of NINDS is to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

NIH…Turning Discovery Into Health ®

Zheng J. et al. Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience. March 7, 2022. DOI: 10.1038/s41593-022-01020-w

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Published: 12 August 2021

Factors associated with brain ageing - a systematic review

Jo Wrigglesworth 1 ,
Phillip Ward 2 , 3 , 4 ,
Ian H. Harding 2 , 5 ,
Dinuli Nilaweera 1 ,
Zimu Wu 1 ,
Robyn L. Woods 1 &
Joanne Ryan ORCID: orcid.org/0000-0002-7039-6325 1

BMC Neurology volume 21 , Article number: 312 ( 2021 ) Cite this article

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Brain age is a biomarker that predicts chronological age using neuroimaging features. Deviations of this predicted age from chronological age is considered a sign of age-related brain changes, or commonly referred to as brain ageing. The aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors and diseases in adult populations, with brain ageing.

This systematic review was undertaken in accordance with the PRISMA guidelines. A systematic search of Embase and Medline was conducted to identify relevant articles using search terms relating to the prediction of age from neuroimaging data or brain ageing. The tables of two recent review papers on brain ageing were also examined to identify additional articles. Studies were limited to adult humans (aged 18 years and above), from clinical or general populations. Exposures and study design of all types were also considered eligible.

A systematic search identified 52 studies, which examined brain ageing in clinical and community dwelling adults (mean age between 21 to 78 years, ~ 37% were female). Most research came from studies of individuals diagnosed with schizophrenia or Alzheimer’s disease, or healthy populations that were assessed cognitively. From these studies, psychiatric and neurologic diseases were most commonly associated with accelerated brain ageing, though not all studies drew the same conclusions. Evidence for all other exposures is nascent, and relatively inconsistent. Heterogenous methodologies, or methods of outcome ascertainment, were partly accountable.

This systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing. Overall there is good evidence to suggest schizophrenia and Alzheimer’s disease are associated with accelerated brain ageing. Evidence for all other exposures was mixed or limited. This was mostly due to a lack of independent replication, and inconsistency across studies that were primarily cross sectional in nature. Future research efforts should focus on replicating current findings, using prospective datasets.

Trial registration

A copy of the review protocol can be accessed through PROSPERO, registration number CRD42020142817 .

Peer Review reports

Introduction

Ageing is a complex biological process characterised by an accumulation of molecular and cellular damages over the lifespan [ 1 , 2 , 3 ]. The body’s inability to repair this damage leads to a subsequent loss of physiological functions [ 1 ]. These include sensory, motor, and cognitive functions that, when impaired, impact quality of life [ 4 ]. Age is also a major risk factor for many life threatening diseases including cancer, cardiovascular disease, and neurodegenerative disorders [ 1 ]. The trajectory of ageing, however, varies within the population, and thus, chronological age is not always a reliable predictor of age-related risk. Genetic and environmental factors are diverse among the population, and have varied effects on ageing processes occurring within individual cells, and tissue types [ 2 ].

The brain is particularly sensitive to the effects of ageing, manifesting as changes in structure and cognitive function [ 5 , 6 , 7 , 8 ]. Neuroimaging technologies, including magnetic resonance imaging (MRI), have made it possible to monitor these changes in vivo. The most common changes associated with ageing are brain atrophy (i.e., loss of grey matter volume and cortical thinning) [ 9 , 10 , 11 , 12 ], a reduction in white matter integrity and volume, and abnormal functional connectivity [ 7 , 13 , 14 , 15 , 16 ]. When severe, these phenotypes can be considered a sign of accelerated ageing or an underlying disease process [ 5 , 6 ]. Though neuroimaging research has advanced our understanding of these processes, current group based analyses (i.e., mass univariate modelling that uses chronological age to predict neuroimaging features), cannot account for the diversity of individual ageing trajectories [ 17 ].

Among these developments are efforts focused on identifying individual biomarkers of age-related brain changes [ 18 ]. So-called 'brain age' algorithms use neuroimaging features to capture the changes in the brain that commonly occur with age [ 18 ]. Typically, this requires training a multivariate statistical model to learn normative patterns of brain ageing, before being applied to predict individual brain ages in a group of interest. The difference between predicted biological and actual chronological age signifies a deviation from the normal ageing trajectory, and has the potential to identify individuals with disease, monitor treatment effects, or identify lifestyle factors that are beneficial or detrimental to brain health [ 18 , 19 , 20 ].

A recent literature review summarised different methods that use brain volume to define brain age [ 20 ]; whilst another provided a more comprehensive overview of all methodologies currently being applied in the field, including developmental and animal studies [ 21 ]. However, to date, no systematic review has summarised age-related brain changes (referred to as ‘brain ageing’), defined solely by the deviation of estimated brain age from chronological age, in human adult populations. Thus, the aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors, and diseases in adult populations, with brain ageing.

Protocol and registration

This systematic review was undertaken in accordance with the PRISMA guidelines ( http://www.prisma-statement.org ) - the 2009 checklist is provided in Additional File 1 [ 22 ]. In compliance with these guidelines, a record of this protocol can be accessed through PROSPERO via the following registration number CRD42020142817.

Eligibility criteria

This systematic review included studies investigating brain ageing in adult humans (mean age 18 years and above), from community or clinical populations. Studies measured exposures of all types, including genetic, health, and lifestyle factors, and the outcome was brain ageing. All study designs (cohort and case-control) were eligible, with brain ageing measured either at the same time as the exposure (cross sectionally) or a later time-point (longitudinally). Papers limited to evaluating the sensitivity of different methodologies (e.g. sample size) on brain ageing were not included.

Brain ageing

Estimates of brain age were considered eligible when chronological age was predicted from neuroimaging features, acquired from any imaging modality (e.g., MRI). Eligible studies were those which examined brain ageing as the difference between brain age and chronological age. Studies using alternative methods for calculating brain ageing, including the slope between chronological age and brain age [ 23 ]; or the group differences in models of brain features as a function of age [ 24 ], were excluded.

Information sources and search strategy

A systematic search of Embase via Ovid (1974 to present) and Ovid MEDLINE was conducted to identify relevant articles, using search terms relating to the prediction of age from neuroimaging data or brain ageing: (BrainAge.mp. OR Neuroanatomical adj3 age.mp. OR brain age.mp. OR age adj3 estimat*.mp. AND Imaging.mp) OR (BrainAge.mp. OR Neuroanatomical adj3 ag*.mp. OR age adj3 estimat*.mp OR brain ag*.mp. OR BrainAGE adj3 accelerat*.mp OR brain age gap.mp OR BrainPAD.mp OR Brain adj1 predict*.mp AND imaging.mp. AND chronological age.mp. AND accelerat* adj3 ag*.mp). No yearly limit was set, however searches were limited to studies only including human participants, and articles published in English. The tables of two recent review papers on brain ageing [ 20 , 21 ] were also examined to identify additional articles.

Study selection

Following the initial search, duplicate articles were removed by one reviewer (JW). Article abstracts and titles were screened independently by three reviewers (JW, DN, ZW), followed by a full text review of the eligible texts. In the case of discordance, a fourth reviewer (JR) was involved to provide a final verdict.

Data extraction

For each included study, the following information was extracted onto a standardised data extraction form: Study characteristics (i.e., name, country and design); Participant characteristics (i.e., sample size, mean age and/or range, number of female participants); neuroimaging features used for brain age prediction (i.e., modality, protocol, and features) and statistical methodologies (i.e., algorithm, and cross validation, and adjustment for age bias); and exposures (e.g., cognitive function, disease type). Main findings and details of any adjustments for confounders were also extracted.

Data synthesis/summary measures

A narrative synthesis of the main brain ageing findings is provided, and grouped according to the type of exposure. Findings are summarised quantitatively in tables with effect sizes (when available), regardless of statistical significance. Effect sizes of all types are reported, and include correlations; differences in mean brain ageing (including Cohens D/Eta squared); 95% confidence intervals (when p -value was not available), and beta values (both un/standardized) from regression models. Authors considered brain ageing methodologies, and/or participant characteristics too heterogenous to conduct a meta-analysis.

Risk of bias

Included articles were assessed for risk of bias using a modified version of the Joanna Briggs Institute Critical Appraisal Checklist for Randomized Control Trial, Case-Study or Cohort study, as appropriate [ 25 ]. This assessment was merely a tool for determining the quality of information extracted from each article, rather than a means for excluding papers. This was completed by three reviewers (JW, ZW, DN), independently. Any discrepancies were discussed and resolved through consensus.

An initial search of Medline and Embase resulted in 2514 articles, and an additional three papers were identified from prior reviews on brain ageing (Fig. 1 ) [ 20 , 21 ]. After removing duplicates, the titles and abstracts of 1896 articles were screened, and 1637 papers excluded. Two hundred and fifty-nine papers underwent a full text review. From these papers, a further 207 articles were removed as they did not meet the eligibility criteria (ineligible article type; sample of children/adolescents only; or ineligible calculation of age prediction). A total of 52 papers were thus included in this systematic review.

PRISMA flow diagram outlining results from the initial search, and subsequent screening for article eligibility

Participant characteristics

Studies investigated brain ageing in samples ranging in size (between 5 to 31,227 participants), and age (mean age between 21 to 78 years). One study compared one male with Prader-Willi syndrome to a small sample of 95 healthy controls (approximately 39% were male) [ 26 ]. Four studies included children, and/or adolescents as well as adults, but fit the inclusion criteria given that the mean age of the sample was 18 years or older [ 27 , 28 , 29 , 30 ]. All but two studies included both men and women, with the percentage of women ranging from 4.4 to 89.1%. Five of these studies, however, did not report the number of men or women [ 30 , 31 , 32 , 33 , 34 ]. Of the two remaining studies, one involved military serving male twins [ 35 ], and a second focused on brain ageing in post-menopausal women [ 36 ].

Twenty-nine studies sub-sampled participants from a larger cohort study, nine were case-controls [ 26 , 30 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. Of the remaining 10 case-control studies, eight had sampled participants from registries, hospitals (i.e., both in and outpatient services) or treatment clinics, university research institutes, or the local community [ 29 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], while two were unclear [ 51 , 52 ]. The Early Stages of Schizophrenia study [ 38 , 41 ], the UK Biobank [ 19 , 32 , 53 , 54 ] and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [ 33 , 55 , 56 , 57 , 58 ] were cohorts sampled on more than one occasion. Thirteen studies included prospective data [ 28 , 30 , 31 , 33 , 35 , 36 , 47 , 56 , 58 , 59 , 60 , 61 , 62 ].

One study estimated brain age for participants who were a part of a randomised control trial [ 63 ]. Six studies pooled data from multiple studies [ 26 , 30 , 60 , 64 , 65 , 66 ]; while three studies involved more than one type of study design [ 30 , 41 , 47 ].

Summary of brain ageing findings

Brain ageing was investigated in relation to a number of exposures. These are summarised in the following text and tables, and are grouped according to the type of exposure. ‘Accelerated’ and ‘decelerated’ are terms commonly used to describe the direction of brain ageing (i.e., accelerated defines greater age-related changes to the brain; while decelerated suggests fewer changes) and thus will be used in the subsequent text. Similarly, in longitudinal studies, the ‘rate’ is conventionally used to define a change in brain age, but can be calculated by either regressing time on brain age, or dividing change in brain age by the time interval between the imaging acquisitions. Thus, while rate will be used throughout the following text, methods will be defined in tables accordingly.

In tables, brain ageing (i.e., brain age – chronological age) was abbreviated as the “brain age gap (GAP)”, and used to summarise results. Though conceptually the same, two studies subtracted brain age from chronological age, and thus, “CA-BA” is used to report these results [ 27 , 64 ]. When studies involve a common brain age framework (i.e., was referenced by more than one study), terms specific to this framework will be used. These include the “Brain age gap estimate (BrainAGE) score” [ 55 ], “Predicted age difference (PAD) score” [ 51 ], and “Brain ageing (BA) score” [ 67 ], and are specific to these referenced authors.

Psychiatric disorders

Thirteen studies investigated brain ageing in psychiatric disorders [ 27 , 30 , 32 , 34 , 37 , 38 , 41 , 44 , 49 , 50 , 60 , 66 ], eight focused on schizophrenia (SZ) [ 27 , 30 , 32 , 34 , 38 , 41 , 49 , 66 ] (Table 1 ). All studies report accelerated brain ageing in SZ (ranging between 2.3 and 7.8 years), though the majority included samples less than 100 participants. Of these studies, six found accelerated brain ageing to be significantly different to healthy controls [ 32 , 34 , 38 , 41 , 49 , 66 ]; while two made no statistical comparison between groups [ 27 , 30 ]. Five studies also included patients with bipolar disorder. Four of these found brain ageing to be comparable to healthy controls [ 32 , 34 , 41 , 49 ]. The fifth study only reported accelerated brain ageing, and made no statistical comparison to a control group [ 27 ].

Fewer studies investigated other psychiatric disorders. There were four studies involving patients with major depression (MD), but with mixed findings. Specifically, two found accelerated brain ageing in MDs, that was significantly different to controls [ 43 , 66 ]; a second study, involving fewer cases, found no difference between MDs and controls [ 44 ], and a third reported decelerated brain ageing but made no statistical comparison to a control group [ 27 ]. A fifth study analysed associations in a relatively large sample of community dwelling middle-aged adults, and reported a positive correlation between depression scores and brain ageing [ 60 ].

Neurological disease

A total of 18 studies investigated brain ageing in relation to neurological diseases, the most common being mild cognitive impairment (MCI), Alzheimer’s Disease (AD) and epilepsy (Table 2 ). Four of the five studies included a small group of AD participants (ranging between 27 to 76 in size), and reported a significantly higher accelerated brain ageing (ranging between 5.36 and 10 years, at baseline) relative to healthy controls – three sampled participants from the ADNI [ 33 , 55 , 56 ]. The fifth study observed decelerated brain ageing, but using a larger sample of participants with dementia (including AD), and did not statistically compare these findings to a healthy control group [ 27 ]. Two studies also included prospective data from the ADNI study, and reported a significantly higher accelerated brain ageing at follow-up, and a greater rate of brain ageing in ADs, relative to healthy controls or participants with stable MCI [ 33 , 56 ]. All measures of brain ageing (baseline, follow-up and the rate) were significantly higher when participants progressed from MCI to AD, relative to stable MCI and healthy controls [ 33 , 56 ]. An additional study that also sampled participants from the ADNI study reported a significantly higher accelerated brain ageing (i.e., measured at baseline only) in participants progressing from MCIs onto AD sooner than later, relative to individuals with a stable MCI, or had progressed onto AD at a later stage [ 58 ].

Beyond looking specifically at diagnostic categories of dementia, four studies also correlated brain ageing with cognitive scores. These studies used similar cognitive measures (Mini-Mental State Examination (MMSE) [ 78 , 79 ], Clinical Dementia Rating (CDR)/CDR-sub of boxes [ 75 ] or Alzheimer’s Disease Assessment Scale (ADAS) [ 72 , 73 , 74 ]) but reported mixed results [ 33 , 56 , 58 , 68 ]. Of the three studies including participants from the ADNI, one observed a significant correlation between brain ageing and each of the CDR, ADAS, and MMSE at both baseline and follow up, when pooling healthy controls with diagnostic groups [ 33 ]. A second study only included those with MCI, and observed a correlation with CDR and ADAS at baseline that increased at each follow up; correlations with MMSE were observed only at follow up [ 58 ]. A third study reported the strongest correlations in individuals with AD was between brain ageing and MMSE, and in progressive MCI with ADAS [ 56 ]. When pooling healthy controls with diagnostic groups, an alternative fourth study also observed a correlation with the CDR, ADAS, MMSE, [ 68 ].

Four studies investigated brain ageing in relation to various types of epilepsy [ 40 , 45 , 52 , 69 ]. Specifically, two studies focused on small groups (ranging between 17 to 104) of participants with temporal lobe epilepsy, and report accelerated brain ageing [ 45 , 69 ]. However, one was a case-control study that observed a significant difference to healthy controls, but only when seizures were localised to the right hemisphere [ 45 ], while the second, slightly larger cohort study had not statistically compared these findings to healthy controls [ 69 ]. The two-remaining case-control studies investigated brain ageing in patients with other forms of epilepsy. One compared brain ageing in medical refractory epilepsy (MRE) (~ 50% of the patients experienced seizures in the temporal lobe) to newly diagnosed focal epilepsy (NDE), and reported significant accelerated brain ageing in MREs only, as NDEs were comparable to healthy controls [ 40 ]. The second reported accelerated brain ageing in all participants with epilepsy (i.e., focal and generalised), including neuropsychiatric conditions with episodes that resemble epileptic seizures (i.e., psychogenic nonepileptic seizures), except those with extra-temporal lobe focal epilepsy, had a significantly higher accelerated brain ageing than healthy controls [ 52 , 80 ].

Fewer studies analysed the effects of stroke [ 59 , 71 ], traumatic brain injury (TBI) [ 27 , 51 , 70 ], multiple sclerosis (MS) [ 28 , 47 ], or Parkinson’s disease on brain ageing [ 48 ]. Three studies analysed brain ageing in TBI patients, but report mixed results. Specifically, two smaller sample studies found significantly higher accelerated brain ageing in TBI patients relative to healthy controls [ 51 , 70 ]; a third reported decelerated brain ageing for a large cohort of TBI patients, but did not statistically compare findings to other diagnostic groups [ 27 ]. The two former studies also investigated time since TBI, but only one found a significant positive correlation with the time since TBI [ 51 , 70 ].

Of the remaining studies, two reported greater cross-sectional estimates of accelerated brain ageing for patients with MS relative to healthy controls [ 28 , 47 ]. Longitudinal assessments by one of these two studies resulted in a higher annual rate of accelerated brain ageing in a large pooled sample of MS and clinically isolated syndrome patients (i.e., individuals with a greater likelihood of MS), relative to healthy controls [ 28 , 81 ]; the second did not compare findings to healthy controls, but also observed an annual accelerated rate of brain ageing when using a much smaller sample of MS patients [ 47 ]. In stroke patients, one randomised control study found no correlation between regional or global estimates of brain ageing with cognitive function [ 71 ], while a second prospective cohort study found a significantly higher brain ageing than healthy controls, despite features used to estimate brain age [ 59 ]. For the latter study, however, the direction of brain ageing (i.e., accelerated/decelerated) varied between models, for both patients and controls [ 59 ]. From this study, the rate of brain ageing was also comparable between patients and healthy controls, though no statistics were reported [ 59 ].

Health, physical and biological markers

Fourteen studies investigated brain ageing in relation to diseases without a primary neurological presentation (Human Immunodeficiency Virus (HIV) and type II diabetes), markers of health (e.g., biological and physical), hormones, medications, chronic pain, or mortality risk (Table 3 ) [ 19 , 36 , 38 , 39 , 53 , 54 , 57 , 60 , 61 , 62 , 63 , 66 , 82 , 83 ]. Most commonly reported were associations with body mass index (BMI) [ 38 , 53 , 57 , 66 ]. Of the four studies investigating BMI, two involved community dwelling, initially healthy older adults from the ADNI cohort study or the UK Biobank, while the other two studies sampled young adult patients with SZ [ 38 , 53 , 57 , 66 ]. The two former studies both reported a positive correlation with BMI, however, the larger cohort study observed this association when predicting age for both genders, or females only [ 53 ]; while the second, smaller sample study reported this effect in males only when defined ageing for the total sample [ 53 , 57 ]. A significant positive association with BMI was also reported in SZ patients. However, one study found this effect to be independent to an SZ diagnosis (i.e., main effects of BMI and SZ on brain ageing were evident, but no significant BMI-by-SZ interaction); while the second only observed an association for a smaller group of patients with a recent onset of SZ [ 38 ].

Three small cohort studies (≤162 participants) analysed the effects of HIV [ 39 , 62 , 82 ]. Regardless of model and feature type, all studies reported accelerated brain ageing in HIV positive patients (ranging between 1.17 and 5.87 years). For two studies, this brain ageing was significantly higher than HIV-negative controls [ 39 , 82 ]; while a third study’s findings were relative only to the model (i.e., a null hypothesis that predicted minus chronological age equals zero) [ 62 ]. Associations between brain ageing and HIV clinical characteristics (e.g., years since diagnosis, cell counts (CD4)) were also investigated. One study reported an association between higher brain ageing and prior Acquired Immuno-Deficiency Syndrome status [ 62 ] whilst another with viral loading [ 39 ]. In contrast, a third observed no significant association with any of the clinical or health factors (all p > 0.10) [ 82 ].

Two studies considered the influence of female sex hormones, however, one in the context of pregnancy, while the other during a normal menstrual cycle [ 36 , 61 ]. Both studies relied on small sample sizes of young adult women (≤14 participants). Neither study found significant correlations between brain ageing and progesterone [ 36 , 61 ] but one reported a significant negative correlation with estradiol (i.e., measured at time point two, when it was most elevated) [ 61 ].

Environmental and lifestyle factors

Seven eligible studies investigated environmental influences on brain ageing with the most common being smoking and alcohol consumption (Table 4 ) [ 53 , 54 , 60 ]. Two of the three studies involved a large sample of participants from the UK Biobank, and report a positive association between brain ageing (estimated using different algorithms) and alcohol intake, however, the second also observed a correlation when estimating brain age for females only [ 53 , 54 ]. Both studies also reported a significant positive correlation with smoking [ 53 , 54 ]. A third independent study also reported a significant, positive association with smoking, and alcohol, but for fewer community dwelling adults [ 60 ]. Meditation practitioners, and amateur/professional musicians were reported to have a significantly lower brain ageing than controls, but were each analysed by one study [ 29 , 42 ]. Similarly, one study found a higher education, or a greater flight of stairs climbed, to be significantly associated with decelerated brain ageing [ 64 ].

Genetic influences

Five studies investigated genetic influences on brain ageing (Table 5 ). Two studies reported no significant difference in brain ageing due to Apolipoprotein E (APOE) e4 carrier status in older adults [ 33 , 84 ]. One, however, used prospective data from the ADNI study, and found a significantly higher rate of accelerated ageing in APOE e4 carriers [ 33 ]. Both study samples, however, involved a limited number of participants (≤101 participants), and thus may be under-powered.

One genome wide association study using data from the UK Biobank, found and replicated a significant association between brain ageing and two genetic variants - one spanning many genes, including MAPT , which encodes for the tau protein (i.e., considered to play a prominent role in Frontotemporal dementia, and other neurodegenerative disorders) [ 85 , 86 ]; the second is near the TREK-1 gene, that has been reported (in mice) to play a role in memory impairment, cerebral ischemia, and blood brain barrier dysfunction [ 87 , 88 , 89 ].

Other factors in ageing populations

Ten studies analysed brain ageing in relation to gender, race, cognitive function, and other measures of biological ageing (i.e., DNA methylation age, telomeres, physical and biological markers of health, and facial ageing) [ 19 , 31 ], most investigated was cognitive function (Table 6 ). Six out of seven studies reported a significant association between brain ageing and cognitive function across different domains, most consistent were psychomotor and executive function [ 31 , 32 , 53 , 54 , 90 ]. The remaining seventh study observed no correlation with working memory, and was the only study to measure cognition via a functional MRI based-task [ 84 ].

Three studies analysed brain ageing using a large sample of participants from the UK Biobank (ranging between 12,378 to 19,000 participants), and reported a significant positive association with a single measure of psychomotor and executive function (i.e., as per the UK Biobank’s Trail Making Task (TMT) B), despite applying different brain age algorithms [ 32 , 53 , 54 ]. However, only one of these three studies reported a significant positive association with all measures from the TMT (TMT-A, −B, and TMT minus B), but included fewer participants [ 32 ]. Two of these studies also observed a significant association with complex (i.e., Symbol Digit Substitution Test (DSST)) and simple psychomotor functions (i.e., Reaction time test), while the third had not included these two neuropsychological tests [ 54 ]. One additional study, measured brain ageing in three independent cohorts, and reported a significant association with psychomotor and executive function [ 90 ]. This same study also reported a significant association for two of the three cohorts that had used the same measure of executive function (i.e., TMT-B minus A) [ 90 ]. A fourth study, using longitudinal data (participants were assessed during childhood, and at 45 years of age), found a significant negative association with all measures of adult cognitive function and decline, including psychomotor function (i.e., as per the Wechsler Adult Intelligence Scale-IV, and DSST) [ 31 , 93 , 94 ].

Three studies investigated gender [ 19 , 53 , 65 ]. One study reported decelerated brain ageing for female participants that was significantly lower than the accelerated brain ageing in males [ 19 ]. Regardless of whether brain age was trained on males or females only, a second, larger cohort study consistently found decelerated brain ageing in females, that was significantly different to the accelerated brain ageing observed in males [ 65 ]. In contrast to these findings, a third study, involving fewer participants (108 and 76 females and males, respectively) estimated non-linear brain age, and found brain ageing in females to be 0.7 years higher than males, though the direction, and significance of this finding remains unclear [ 53 ].

Two studies analysed associations with alternative measures of biological ageing [ 31 , 54 ]. By combining various biological and physical markers (e.g., blood pressure, total cholesterol), Elliot et al. (2019) [ 31 ] calculated the pace of ageing and found a significant positive association between this and brain ageing. This same study also reported a significant positive association with subjective measures of facial ageing (i.e., defined by a panel of 8 independent raters) [ 31 ]. In contrast, Cole et al. (2020a) [ 54 ] found no significant relationship between brain ageing and DNA methylation age (i.e., ‘epigenetic clock’) or telomere length.

Risk of bias assessment

Details regarding the risk of bias assessment are given in the Additional File 2 : Tables S1 to 3. The 35 cohort studies had an overall low risk of bias. The most pertinent sources of potential bias were unclear recruitment and inclusion criteria, not applying or being clear on the methods used to validate brain ageing (the majority of these studies had referenced the validated, Franke et al. (2010) model [ 55 ]), and not adjusting for potential confounders. Two, however, had controlled for age or white matter hyperintensities during the development of the brain age model [ 69 , 91 ]. Three studies included multiple datasets with more than one study design (i.e., cohort and case-control) but had a similar, low level of bias [ 30 , 41 , 47 ]. Of the 18 studies with a case-control design, overall they had higher risk of bias than cohort studies, with the controls not often being comparable to cases (i.e., by confounders, primarily age and sex), and did not identify participants using the same criteria. Further, the method used to measure the exposure/s of interest differed between cases and controls. Only one RCT study design was included and was considered to be of a high quality [ 63 ].

This systematic review identified 52 studies which examined the association between genetic, lifestyle, health factors and disease, and brain ageing (age-related changes of the brain defined by the deviation of neuroimaging predicted brain age relative to chronological age). Studies were grouped according to exposure types, with some covering more than one. The majority of evidence on brain ageing came from populations diagnosed with certain forms of mental health or neurological disorders, or cognitive function in normal ageing populations. Evidence regarding the association with lifestyle or environmental, and genetic factors was sparse. Most studies investigated brain ageing in smaller sub-samples of participants drawn from a larger cohort study (34 had one or more samples with less than 100 people) and thus were limited in their statistical sensitivity. Further, some cohorts were a common source of participants for certain exposure types across multiple studies. Inconsistencies were evident for some exposure groups, but were partly attributed to the heterogeneity in study methodologies (i.e., either through design or participant characteristics) or methods of outcome ascertainment.

SZ was the most commonly studied of all exposures, and was consistently shown to be associated with more rapid brain ageing by studies with a relatively low to moderate risk of bias [ 27 , 30 , 32 , 34 , 41 , 49 , 66 ]. This is despite methodological differences between studies in terms of the neuroimaging features used to calculate brain ageing, such as cerebral perfusion [ 27 ], brain volume and/or density [ 30 , 38 , 41 , 49 , 66 ] and combinations of cortical thickness, fractional anisotropy, and cognitive performance scores [ 34 ]. This corroborates the neuroimaging literature, whereby brain changes overlap those observed in healthy ageing (reductions in brain volume, ventricular enlargement, and cortical thinning) [ 95 , 96 , 97 , 98 ]. However, concluding mechanisms still vary among studies (i.e., SZ is causative, or the consequence of accelerated ageing), and, in some accounts, limited by the cross-sectional design [ 27 , 32 , 34 , 38 , 41 , 49 ]. Effect sizes vary among studies, and brain ageing in SZ was not always compared to healthy controls. Further, healthy controls deviated from the normal ageing trajectory for some studies, and thus effects may also reflect innate model biases, or the effects of other exposures on brain age prediction.

Evidence of more rapid brain ageing in AD compared to healthy controls was also relatively consistent. Brain atrophy (i.e., the loss of tissue volume) is common with age and is more severe in AD [ 9 ]. These findings of accelerated brain ageing corroborate evidence from neuroimaging studies [ 99 , 100 ], and findings relating to other ageing biomarkers measured in brain tissue [ 101 ]. The positive association between brain ageing and disease symptom severity, and the progression from MCI to AD, provides further evidence that AD is directly linked with brain ageing [ 33 , 56 , 58 , 68 ]. Findings from two prospective studies also correspond with imaging studies that reported a greater rate of brain atrophy (2% per year for GM volume) in AD patients [ 33 , 56 , 102 ]. An important limitation however, is that all studies of AD used data collected from the ADNI study, and thus, even if the final sample was different between studies, they cannot be considered as entirely independent [ 33 , 56 ]. Further, the studies only provide a global measure of brain ageing, and thus cannot inform on regional differences in ageing that have been extensively reported in the literature [ 99 , 103 , 104 ].

Evidence across other exposures was relatively inconsistent, in particular with regards to gender and BMI [ 53 , 57 ]. Heterogeneity in brain age methodologies and participant characteristics are the likely cause of such discrepancies. For example, when investigating gender, two studies reporting preserved ageing in women both used linear models to estimate brain ageing, while the third used a non-linear algorithm, and reported preserved ageing in men. Though this evidence corroborates neuroimaging findings, the literature primarily relates to regional differences (which contrasts the whole brain estimates used by these two eligible studies), and is also relatively inconsistent [ 65 , 105 , 106 , 107 , 108 , 109 , 110 , 111 ]. Further, all three studies had not accounted for potential confounding effects of other environmental exposures, that are specific to certain genders (e.g., education or occupation) and may explain discrepancies between studies, as they have also been associated with altered brain phenotypes [ 105 ]. This is a similar limitation when interpreting associations between BMI and brain ageing. BMI is routinely used as a measure of obesity, which is considered to have adverse effects on the brain, and cognitive function in both elderly and SZ populations [ 112 , 113 , 114 , 115 , 116 ]. However, it is attributed to a number of environmental factors (e.g., socioeconomic status, lower physical activity) that may act as confounders in these studies [ 117 ]. Study designs and participants varied greatly when investigating BMI as an exposure of brain ageing. Specifically, two of the four studies involved a cohort of older community dwelling participants [ 53 , 57 ], while the remaining two were case-control studies investigating obesity in young adult populations with SZ [ 38 , 66 ]. Correlations were only reported by three of four studies investigating BMI, and show little to no relationship with brain ageing. Further, due to the cross-sectional nature of all studies on gender, and BMI, cause and effect relationships could not be determined.

Some studies investigated a number of lifestyle factors, and reported an association between education, physical activity and music with declines in brain ageing [ 29 , 42 , 64 ], while smoking and alcohol consumption were associated with accelerated ageing of the brain [ 37 , 53 , 54 , 60 ]. This corroborates the literature, whereby positive lifestyle factors, like physical activity, are associated with preserved structural and functional integrity [ 118 , 119 , 120 ], and a reduced risk for AD [ 121 ], while smoking and alcohol are found to exacerbate a decline in brain phenotypes [ 122 , 123 ]. Though this seems promising, the amount of evidence regarding brain ageing is still sparse. Further, studies are cross sectional, and thus temporal and causal relationships cannot be determined. Some studies were also underpowered, while others have limited generalisability (i.e., sampled data from the same cohort study).

Studies used a number of methods to calculate brain ageing. Most common was the framework proposed by Franke et al. (2010) [ 55 ] which utilises a relevance vector regression to estimate age from brain volume [ 29 , 33 , 36 , 38 , 41 , 42 , 44 , 49 , 50 , 55 , 56 , 57 , 58 , 60 , 61 , 84 ]. A large number of studies alternatively used the framework developed by Cole et al. (2015) [ 51 ], and thus the second most commonly used algorithm was the gaussian processes regression, primarily when estimating age from brain volume [ 19 , 26 , 28 , 40 , 46 , 51 , 62 , 82 , 83 ]. Considering the contribution by Franke and Cole to the field of brain ageing, the popularity of these frameworks is not surprising. Despite recommendations [ 124 ], few studies used multimodal approaches to estimate brain age, which may reflect the popularity of these single modal models; though the need for multiple acquisitions, and greater burden to elderly participants, may have also played a role [ 34 , 53 , 54 , 67 , 69 , 125 ]. Despite a rising interest in deep learning [ 126 , 127 ], only one study used a convolutional neural network to calculate brain ageing [ 32 ].

Strengths and limitations of review

This systematic review was conducted in accordance with PRISMA guidelines ( http://www.prisma-statement.org ) [ 22 ]. To ensure all relevant publications were included, a systematic search of the brain ageing literature was undertaken, and directed by a registered eligibility criterion, and involved databases and additional literature reviews [ 20 , 21 ]. Including general and clinical populations increased the coverage exposure types, and thus findings will be of interest to a greater array of research fields. This was also achieved by the inclusion of all neuroimaging modalities and feature types, and reduces any bias towards brain age frameworks that are developed from specific phenotypes (e.g., brain volume, as per Franke et al. (2010) [ 55 ] & Cole et al. (2015) [ 51 ]).

There are limitations to this systematic review that should be addressed. Considering the contribution of conference papers to machine learning research, the removal of this literary source may have reduced the number of identified papers, and thus influenced the conclusions for this systematic review. The accuracy and generalisability of age prediction were not reported, nor were details regarding the training sample.

Further directions

This systematic review identified a number of gaps in the brain ageing literature that should be addressed through future research efforts. So far, supervised machine learning is the most popular approach to define brain ageing, particularly when using brain volume as a feature. Comparatively few studies have pursued deep learning approaches to estimating brain ageing. Though they are computationally intensive, there are many benefits that could overcome limitations imposed by other machine learning algorithms, such as the ability to use raw neuroimaging data as input [ 126 , 127 ]. Clinically, this an appealing option as it is more time efficient (i.e., as no pre-processing is required), and requires little computational engineering [ 127 , 128 ]. Like deep learning, few studies used multimodal approaches for estimating brain age. Though there are challenges in acquiring, and combining multiple data types; features of various brain phenotypes (obtained from various modalities) could be more informative, and thus may be a more comprehensive approach to investigating brain ageing [ 125 , 129 ].

Few studies used prospective data, and thus could not investigate cause and effect relationships. Longitudinal studies will help overcome this limitation, and will address questions regarding whether brain age is a biomarker of ageing or disease, thus meeting a key criterion proposed by The American Federation for Ageing Research (i.e., biomarkers must monitor ageing processes, not disease) [ 130 ].

The evidence regarding the effects of environmental and lifestyle factors on brain ageing is sparse. Identifying interventions and treatments that are brain preserving, and thus slow the ageing process, is useful knowledge for the ever-growing ageing population, and has many clinical implications, like reducing the strain on age care facilities.

Results regarding brain ageing and gender or BMI were inconsistent. Heterogenous brain ageing methodologies, study designs, and participant characteristics were identified as the likely cause. Thus, to confirm whether findings reflect a true ageing effect, future studies should focus their efforts on replicating these methods, and sampling from populations that are characteristically similar. Information on whether brain ageing is sensitive to gender, or BMI, could help inform certain populations at risk, and be used to prevent poor health outcomes.

Finally, only two eligible studies compared, or combined, brain ageing to alternative ageing biomarkers [ 19 , 31 ]. It remains unclear whether ageing is tissue specific, or a systematic process, and thus additional knowledge from studies comparing brain ageing with other ageing biomarkers could help resolve this question.

This systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing, the most common being schizophrenia, followed by Alzheimer’s disease. Overall, there is good evidence to suggest schizophrenia is associated with accelerated brain ageing, but limited, or mixed evidence for all other exposures examined. In most cases this was due to a lack of independent replication and consistency across multiple studies that were primarily cross sectional in nature. Thus, future research efforts should focus on replicating current findings, using prospective datasets, to further clarify exposures that may have age preserving, or accelerating properties.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Abbreviations

Alzheimer’s disease

Alzheimer’s disease assessment scale

Alzheimer’s disease; neuroimaging initiative

Apolipoprotein E

Body mass index

Clinical dementia rating

Human immunodeficiency virus

Mild cognitive impairment

Major depression

Mini-mental state examination

Medical refractory epilepsy

Magnetic resonance imaging

Multiple sclerosis

Newly diagnosed focal epilepsy

Symbol digit substitution test

Schizophrenia

Trail making task

Traumatic brain injury

Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–217. https://doi.org/10.1016/j.cell.2013.05.039 .

Article CAS PubMed PubMed Central Google Scholar

Kirkwood TB. Understanding the odd science of aging. Cell. 2005;120(4):437–47. https://doi.org/10.1016/j.cell.2005.01.027 .

Article CAS PubMed Google Scholar

Kirkwood TB. A systematic look at an old problem. Nature. 2008;451(7179):644–7. https://doi.org/10.1038/451644a .

Organization WH. Active ageing: a policy framework, vol. 2015. Geneva: World Health Organization; 2002. WHO/NMP/NPH/02.8.

Google Scholar

Anderton BH. Changes in the ageing brain in health and disease. Philos Trans R Soc Lond Ser B Biol Sci. 1997;352(1363):1781–92. https://doi.org/10.1098/rstb.1997.0162 .

Article CAS Google Scholar

Anderton BH. Ageing of the brain. Mech Ageing Dev. 2002;123(7):811–7. https://doi.org/10.1016/S0047-6374(01)00426-2 .

Damoiseaux JS. Effects of aging on functional and structural brain connectivity. Neuroimage. 2017;160:32–40. https://doi.org/10.1016/j.neuroimage.2017.01.077 .

Article PubMed Google Scholar

Ferreira LK, Busatto GF. Resting-state functional connectivity in normal brain aging. Neurosci Biobehav Rev. 2013;37(3):384–400. https://doi.org/10.1016/j.neubiorev.2013.01.017 .

Grajauskas LA, Siu W, Medvedev G, Guo H, D'Arcy RCN, Song X. MRI-based evaluation of structural degeneration in the ageing brain: pathophysiology and assessment. Ageing Res Rev. 2019;49:67–82. https://doi.org/10.1016/j.arr.2018.11.004 .

Fjell AM, Westlye LT, Grydeland H, Amlien I, Espeseth T, Reinvang I, et al. Accelerating cortical thinning: unique to dementia or universal in aging? Cereb Cortex. 2014;24(4):919–34. https://doi.org/10.1093/cercor/bhs379 .

Fotenos AF, Snyder AZ, Girton LE, Morris JC, Buckner RL. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology. 2005;64(6):1032–9. https://doi.org/10.1212/01.WNL.0000154530.72969.11 .

Storsve AB, Fjell AM, Tamnes CK, Westlye LT, Overbye K, Aasland HW, et al. Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. J Neurosci. 2014;34(25):8488–98. https://doi.org/10.1523/JNEUROSCI.0391-14.2014 .

Bennett IJ, Madden DJ. Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience. 2014;276:187–205. https://doi.org/10.1016/j.neuroscience.2013.11.026 .

Bonifazi P, Erramuzpe A, Diez I, Gabilondo I, Boisgontier MP, Pauwels L, et al. Structure-function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging. Hum Brain Mapp. 2018;39(12):4663–77. https://doi.org/10.1002/hbm.24312 .

Article PubMed PubMed Central Google Scholar

Fjell AM, Walhovd KB. Structural brain changes in aging: courses, causes and cognitive consequences. Rev Neurosci. 2010;21(3):187–221. https://doi.org/10.1515/revneuro.2010.21.3.187 .

Gunning-Dixon FM, Brickman AM, Cheng JC, Alexopoulos GS. Aging of cerebral white matter: a review of MRI findings. Int J Geriatr Psychiatry. 2009;24(2):109–17. https://doi.org/10.1002/gps.2087 .

Vieira S, Pinaya WH, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neurosci Biobehav Rev. 2017;74(Pt A):58–75.

Cole JH, Marioni RE, Harris SE, Deary IJ. Brain age and other bodily 'ages': implications for neuropsychiatry. Mol Psychiatry. 2019;24(2):266–81. https://doi.org/10.1038/s41380-018-0098-1 .

Cole JH, Ritchie SJ, Bastin ME, Valdes Hernandez MC, Munoz Maniega S, Royle N, et al. Brain age predicts mortality. Mol Psychiatry. 2018;23(5):1385–92. https://doi.org/10.1038/mp.2017.62 .

Franke K, Gaser C. Ten years of BrainAGE as a neuroimaging biomarker of brain aging: what insights have we gained? Front Neurol. 2019;10:789. https://doi.org/10.3389/fneur.2019.00789 .

Cole JH, Franke K, Cherbuin N. Quantification of the biological age of the brain using neuroimaging. In: Biomarkers of human aging. Cham: Springer International Publishing; 2019. p. 293–328. https://doi.org/10.1007/978-3-030-24970-0_19 .

Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097 .

Adluru N, Korponay CH, Norton DL, Goldman RI, Davidson RJ. BrainAGE and regional volumetric analysis of a Buddhist monk: a longitudinal MRI case study. Neurocase. 2020;26(2):79–90. https://doi.org/10.1080/13554794.2020.1731553 .

Ronan L, Alexander-Bloch AF, Wagstyl K, Farooqi S, Brayne C, Tyler LK, et al. Obesity associated with increased brain age from midlife. Neurobiol Aging. 2016;47:63–70. https://doi.org/10.1016/j.neurobiolaging.2016.07.010 .

Institute JB. Joanna Briggs institute reviewers’ manual: 2014 edition. Australia: The Joanna Briggs Institute; 2014.

Azor AM, Cole JH, Holland AJ, Dumba M, Patel MC, Sadlon A, et al. Increased brain age in adults with Prader-Willi syndrome. Neuroimage Clin. 2019;21:101664. https://doi.org/10.1016/j.nicl.2019.101664 .

Amen DG, Egan S, Meysami S, Raji CA, George N. Patterns of regional cerebral blood flow as a function of age throughout the lifespan. J Alzheimers Dis. 2018;65(4):1087–92. https://doi.org/10.3233/JAD-180598 .

Cole JH, Raffel J, Friede T, Eshaghi A, Brownlee WJ, Chard D, et al. Longitudinal assessment of multiple sclerosis with the brain-age paradigm. Ann Neurol. 2020b;14:93–105.

Rogenmoser L, Kernbach J, Schlaug G, Gaser C. Keeping brains young with making music. Brain Struct Funct. 2018;223(1):297–305. https://doi.org/10.1007/s00429-017-1491-2 .

Schnack HG, Van Haren NEM, Nieuwenhuis M, Pol HEH, Cahn W, Kahn RS. Accelerated brain aging in schizophrenia: a longitudinal pattern recognition study. Am J Psychiatry. 2016;173(6):607–16. https://doi.org/10.1176/appi.ajp.2015.15070922 .

Elliott ML, Belsky DW, Knodt AR, Ireland D, Melzer TR, Poulton R, et al. Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort. Mol Psychiatry. 2019. Online ahead of print.

Jonsson BA, Bjornsdottir G, Thorgeirsson TE, Ellingsen LM, Walters GB, Gudbjartsson DF, et al. Brain age prediction using deep learning uncovers associated sequence variants. Nat Commun. 2019;10(1):5409. https://doi.org/10.1038/s41467-019-13163-9 .

Lowe LC, Gaser C, Franke K. The effect of the APOE genotype on individual BrainAGE in normal aging, Mild cognitive impairment, and Alzheimer's Disease. PLoS One. 2016;11(7):e0157514.

Shahab S, Mulsant BH, Levesque ML, Calarco N, Nazeri A, Wheeler AL, et al. Brain structure, cognition, and brain age in schizophrenia, bipolar disorder, and healthy controls. Neuropsychopharmacology. 2019;44(5):898–906. https://doi.org/10.1038/s41386-018-0298-z .

Hatton SN, Franz CE, Elman JA, Panizzon MS, Hagler DJ Jr, Fennema-Notestine C, et al. Negative fateful life events in midlife and advanced predicted brain aging. Neurobiol Aging. 2018;67:1–9. https://doi.org/10.1016/j.neurobiolaging.2018.03.004 .

Luders E, Gingnell M, Poromaa IS, Engman J, Kurth F, Gaser C. Potential brain age reversal after pregnancy: younger brains at 4-6Weeks postpartum. Neuroscience. 2018;386:309–14. https://doi.org/10.1016/j.neuroscience.2018.07.006 .

Guggenmos M, Schmack K, Sekutowicz M, Garbusow M, Sebold M, Sommer C, et al. Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence. Transl Psychiatry. 2017;7(12):1279. https://doi.org/10.1038/s41398-017-0037-y .

Kolenic M, Franke K, Hlinka J, Matejka M, Capkova J, Pausova Z, et al. Obesity, dyslipidemia and brain age in first-episode psychosis. J Psychiatr Res. 2018;99:151–8. https://doi.org/10.1016/j.jpsychires.2018.02.012 .

Kuhn T, Kaufmann T, Doan NT, Westlye LT, Jones J, Nunez RA, et al. An augmented aging process in brain white matter in HIV. Hum Brain Mapp. 2018;39(6):2532–40. https://doi.org/10.1002/hbm.24019 .

Pardoe HR, Cole JH, Blackmon K, Thesen T, Kuzniecky R. Structural brain changes in medically refractory focal epilepsy resemble premature brain aging. Epilepsy Res. 2017;133:28–32. https://doi.org/10.1016/j.eplepsyres.2017.03.007 .

Hajek T, Franke K, Kolenic M, Capkova J, Matejka M, Propper L, et al. Brain age in early stages of bipolar disorders or schizophrenia. Schizophr Bull. 2019;45(1):190–8. https://doi.org/10.1093/schbul/sbx172 .

Luders E, Cherbuin N, Gaser C. Estimating brain age using high-resolution pattern recognition: younger brains in long-term meditation practitioners. Neuroimage. 2016;134:508–13. https://doi.org/10.1016/j.neuroimage.2016.04.007 .

Han LKM, Dinga R, Hahn T, Ching CRK, Eyler LT, Aftanas L, et al. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Mol Psychiatry. 2020;1–16. https://doi.org/10.1038/s41380-020-0754-0 .

Besteher B, Gaser C, Nenadic I. Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging. Psychiatry Res Neuroimaging. 2019;290:1–4. https://doi.org/10.1016/j.pscychresns.2019.06.001 .

Chen CL, Shih YC, Liou HH, Hsu YC, Lin FH, Tseng WYI. Premature white matter aging in patients with right mesial temporal lobe epilepsy: A machine learning approach based on diffusion MRI data. NeuroImage Clin. 2019;24:102033.

Cole JH, Annus T, Wilson LR, Remtulla R, Hong YT, Fryer TD, et al. Brain-predicted age in Down syndrome is associated with beta amyloid deposition and cognitive decline. Neurobiol Aging. 2017c;56:41–9. https://doi.org/10.1016/j.neurobiolaging.2017.04.006 .

Hogestol EA, Kaufmann T, Nygaard GO, Beyer MK, Sowa P, Nordvik JE, et al. Cross-sectional and longitudinal MRI brain scans reveal accelerated brain aging in multiple sclerosis. Front Neurol. 2019;10:450.

Moeller JR, Eidelberg D. Divergent expression of regional metabolic topographies in Parkinson's disease and normal ageing. Brain. 1997;1:2197–206.

Nenadic I, Dietzek M, Langbein K, Sauer H, Gaser C. BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder. Psychiatry Res. 2017;266:86–9. https://doi.org/10.1016/j.pscychresns.2017.05.006 .

Article Google Scholar

Van Gestel H, Franke K, Petite J, Slaney C, Garnham J, Helmick C, et al. Brain age in bipolar disorders: effects of lithium treatment. Aust N Z J Psychiatry. 2019;53(12):1179–88.

Cole JH, Leech R, Sharp DJ. Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann Neurol. 2015;77(4):571–81. https://doi.org/10.1002/ana.24367 .

Sone D, Beheshti I, Maikusa N, Ota M, Kimura Y, Sato N, et al. Neuroimaging-based brain-age prediction in diverse forms of epilepsy: a signature of psychosis and beyond. Mol Psychiatry. 2019;26(3):825–34.

Smith SM, Vidaurre D, Alfaro-Almagro F, Nichols TE, Miller KL. Estimation of brain age delta from brain imaging. NeuroImage. 2019;12:528–39.

Cole JH. Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors. Neurobiol Aging. 2020a;92:34–42. https://doi.org/10.1016/j.neurobiolaging.2020.03.014 .

Franke K, Ziegler G, Kloppel S, Gaser C. Alzheimer's disease neuroimaging I. estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage. 2010;50(3):883–92. https://doi.org/10.1016/j.neuroimage.2010.01.005 .

Franke K, Gaser C. Longitudinal changes in individual BrainAGE in healthy aging, mild cognitive impairment, and Alzheimer’s disease. GeroPsych J Gerontopsychol Geriatr Psychiatry. 2012;25(4):235–45. https://doi.org/10.1024/1662-9647/a000074 .

Franke K, Ristow M, Gaser C. Gender-specific impact of personal health parameters on individual brain aging in cognitively unimpaired elderly subjects. Front Aging Neurosci. 2014;6:94.

Gaser C, Franke K, Kloppel S, Koutsouleris N, Sauer H. BrainAGE in mild cognitive impaired patients: predicting the conversion to alzheimer's disease. PLoS One. 2013;8(6):e67346.

Egorova N, Liem F, Hachinski V, Brodtmann A. Predicted brain age after stroke. Front Aging Neurosci. 2019;11:348.

Franke K, Gaser C, Manor B, Novak V. Advanced BrainAGE in older adults with type 2 diabetes mellitus. Front Aging Neurosci. 2013;5:90.

Franke K, Hagemann G, Schleussner E, Gaser C. Changes of individual BrainAGE during the course of the menstrual cycle. Neuroimage. 2015;115:1–6. https://doi.org/10.1016/j.neuroimage.2015.04.036 .

Underwood J, Cole JH, Leech R, Sharp DJ, Winston A. Group C. multivariate pattern analysis of volumetric neuroimaging data and its relationship with cognitive function in treated HIV disease. J Acquir Immune Defic Syndr. 2018;78(4):429–36. https://doi.org/10.1097/QAI.0000000000001687 .

Le TT, Kuplicki R, Yeh HW, Aupperle RL, Khalsa SS, Simmons WK, et al. Effect of ibuprofen on BrainAGE: a randomized, placebo-controlled, dose-response exploratory study. Biolog Psychiatry. 2018;3(10):836–43.

Steffener J, Habeck C, O'Shea D, Razlighi Q, Bherer L, Stern Y. Differences between chronological and brain age are related to education and self-reported physical activity. Neurobiol Aging. 2016;40:138–44. https://doi.org/10.1016/j.neurobiolaging.2016.01.014 .

Goyal MS, Blazey TM, Su Y, Couture LE, Durbin TJ, Bateman RJ, et al. Persistent metabolic youth in the aging female brain. Proc Natl Acad Sci U S A. 2019;116(8):3251–5. https://doi.org/10.1073/pnas.1815917116 .

Koutsouleris N, Davatzikos C, Borgwardt S, Gaser C, Bottlender R, Frodl T, et al. Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophr Bull. 2014;40(5):1140–53. https://doi.org/10.1093/schbul/sbt142 .

Liem F, Varoquaux G, Kynast J, Beyer F, Kharabian Masouleh S, Huntenburg JM, et al. Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage. 2017;148:179–88. https://doi.org/10.1016/j.neuroimage.2016.11.005 .

Beheshti I, Maikusa N, Matsuda H. The association between "brain-age score" (BAS) and traditional neuropsychological screening tools in Alzheimer's disease. Brain Behav. 2018;8(8):e01020. https://doi.org/10.1002/brb3.1020 .

Hwang G, Hermann B, Nair VA, Conant LL, Dabbs K, Mathis J, et al. Brain aging in temporal lobe epilepsy: Chronological, structural, and functional. NeuroImage Clin. 2020;25:102183.

Savjani RR, Taylor BA, Acion L, Wilde EA, Jorge RE. Accelerated changes in cortical thickness measurements with age in military service members with traumatic brain injury. J Neurotrauma. 2017;34(22):3107–16. https://doi.org/10.1089/neu.2017.5022 .

Richard G, Kolskar K, Ulrichsen KM, Kaufmann T, Alnaes D, Sanders AM, et al. Brain age prediction in stroke patients: Highly reliable but limited sensitivity to cognitive performance and response to cognitive training. NeuroImage Clin. 2020;25:102159.

Mohs RC. The Alzheimer's disease assessment scale. Int Psychogeriatr. 1996;8(2):195–203. https://doi.org/10.1017/S1041610296002578 .

Mohs RC, Cohen L. Alzheimer's disease assessment scale (ADAS). Psychopharmacol Bull. 1988;24(4):627–8.

CAS PubMed Google Scholar

Mohs RC, Rosen WG, Davis KL. The Alzheimer's disease assessment scale: an instrument for assessing treatment efficacy. Psychopharmacol Bull. 1983;19(3):448–50.

Morris JC. The clinical dementia rating (CDR): current version and scoring rules. Neurology. 1993;43(11):2412–4. https://doi.org/10.1212/wnl.43.11.2412-a .

Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444–52. https://doi.org/10.1212/WNL.33.11.1444 .

Fillenbaum GG, Smyer MA. The development, validity, and reliability of the OARS multidimensional functional assessment questionnaire. J Gerontol. 1981;36(4):428–34. https://doi.org/10.1093/geronj/36.4.428 .

Cockrell JR, Folstein MF. Mini-mental state examination (MMSE). Psychopharmacol Bull. 1988;24(4):689–92.

Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98. https://doi.org/10.1016/0022-3956(75)90026-6 .

Kanemoto K, LaFrance WC Jr, Duncan R, Gigineishvili D, Park SP, Tadokoro Y, et al. PNES around the world: where we are now and how we can close the diagnosis and treatment gaps-an ILAE PNES task force report. Epilepsia Open. 2017;2(3):307–16. https://doi.org/10.1002/epi4.12060 .

Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162–73. https://doi.org/10.1016/S1474-4422(17)30470-2 .

Cole JH, Underwood J, Caan MWA, De Francesco D, Van Zoest RA, Leech R, et al. Increased brain-predicted aging in treated HIV disease. Neurology. 2017b;88(14):1349–57. https://doi.org/10.1212/WNL.0000000000003790 .

Cruz-Almeida Y, Fillingim RB, Riley JL, Woods AJ, Porges E, Cohen R, et al. Chronic pain is associated with a brain aging biomarker in community-dwelling older adults. Pain. 2019;160(5):1119–30. https://doi.org/10.1097/j.pain.0000000000001491 .

Scheller E, Schumacher LV, Peter J, Lahr J, Wehrle J, Kaller CP, et al. Brain aging and APOE epsilon4 interact to reveal potential neuronal compensation in healthy older adults. Front Aging Neurosci. 2018;10:74.

Frost B, Gotz J, Feany MB. Connecting the dots between tau dysfunction and neurodegeneration. Trends Cell Biol. 2015;25(1):46–53. https://doi.org/10.1016/j.tcb.2014.07.005 .

Hedl TJ, San Gil R, Cheng F, Rayner SL, Davidson JM, De Luca A, et al. Proteomics approaches for biomarker and drug target discovery in ALS and FTD. Front Neurosci. 2019;13:548. https://doi.org/10.3389/fnins.2019.00548 .

Bittner S, Ruck T, Fernandez-Orth J, Meuth SG. TREK-king the blood-brain-barrier. J NeuroImmune Pharmacol. 2014;9(3):293–301. https://doi.org/10.1007/s11481-014-9530-8 .

Cai Y, Peng Z, Guo H, Wang F, Zeng Y. TREK-1 pathway mediates isoflurane-induced memory impairment in middle-aged mice. Neurobiol Learn Mem. 2017;145:199–204. https://doi.org/10.1016/j.nlm.2017.10.012 .

Wang W, Liu D, Xiao Q, Cai J, Feng N, Xu S, et al. Lig4-4 selectively inhibits TREK-1 and plays potent neuroprotective roles in vitro and in rat MCAO model. Neurosci Lett. 2018;671:93–8. https://doi.org/10.1016/j.neulet.2018.02.015 .

Boyle R, Jollans L, Rueda-Delgado LM, Rizzo R, Yener GG, McMorrow JP, et al. Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain Imaging Behavior. 2020;15(1):327–45.

McDonough IM. Beta-amyloid and Cortical thickness reveal racial disparities in preclinical Alzheimer's disease. Neuroimage: Clinical. 2017;16:659–67. https://doi.org/10.1016/j.nicl.2017.09.014 .

Reitan RM. The relation of the trail making test to organic brain damage. J Consult Psychol. 1955;19(5):393–4. https://doi.org/10.1037/h0044509 .

Salthouse TA. What do adult age differences in the digit symbol substitution test reflect? J Gerontol. 1992;47(3):P121–8. https://doi.org/10.1093/geronj/47.3.P121 .

Wechsler D. Wechsler adult intelligence scale–Fourth Edition (WAIS–IV). San Antonio, TX: NCS Pearson. 2008;22(498):1.

Curra A, Pierelli F, Gasbarrone R, Mannarelli D, Nofroni I, Matone V, et al. The ventricular system enlarges abnormally in the seventies, earlier in men, and first in the frontal horn: a study based on more than 3,000 scans. Front Aging Neurosci. 2019;11:294. https://doi.org/10.3389/fnagi.2019.00294 .

Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RS, Busa E, et al. Thinning of the cerebral cortex in aging. Cereb Cortex. 2004;14(7):721–30. https://doi.org/10.1093/cercor/bhh032 .

Steen RG, Mull C, McClure R, Hamer RM, Lieberman JA. Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br J Psychiatry. 2006;188(6):510–8. https://doi.org/10.1192/bjp.188.6.510 .

van Haren NE, Schnack HG, Cahn W, van den Heuvel MP, Lepage C, Collins L, et al. Changes in cortical thickness during the course of illness in schizophrenia. Arch Gen Psychiatry. 2011;68(9):871–80. https://doi.org/10.1001/archgenpsychiatry.2011.88 .

Frisoni GB, Testa C, Zorzan A, Sabattoli F, Beltramello A, Soininen H, et al. Detection of grey matter loss in mild Alzheimer's disease with voxel based morphometry. J Neurol Neurosurg Psychiatry. 2002;73(6):657–64. https://doi.org/10.1136/jnnp.73.6.657 .

Jones DT, Machulda MM, Vemuri P, McDade E, Zeng G, Senjem M, et al. Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology. 2011;77(16):1524–31. https://doi.org/10.1212/WNL.0b013e318233b33d .

Cao K, Chen-Plotkin AS, Plotkin JB, Wang LS. Age-correlated gene expression in normal and neurodegenerative human brain tissues. PLoS ONE. 2010;5(9):e13098.

Anderson VM, Schott JM, Bartlett JW, Leung KK, Miller DH, Fox NC. Gray matter atrophy rate as a marker of disease progression in AD. Neurobiol Aging. 2012;33(7):1194–202. https://doi.org/10.1016/j.neurobiolaging.2010.11.001 .

Ashburner J, Csernansky JG, Davatzikos C, Fox NC, Frisoni GB, Thompson PM. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol. 2003;2(2):79–88. https://doi.org/10.1016/S1474-4422(03)00304-1 .

Driscoll I, Davatzikos C, An Y, Wu X, Shen D, Kraut M, et al. Longitudinal pattern of regional brain volume change differentiates normal aging from MCI. Neurology. 2009;72(22):1906–13. https://doi.org/10.1212/WNL.0b013e3181a82634 .

Malpetti M, Ballarini T, Presotto L, Garibotto V, Tettamanti M, Perani D, et al. Gender differences in healthy aging and Alzheimer's dementia: a (18) F-FDG-PET study of brain and cognitive reserve. Hum Brain Mapp. 2017;38(8):4212–27. https://doi.org/10.1002/hbm.23659 .

Takahashi R, Ishii K, Kakigi T, Yokoyama K. Gender and age differences in normal adult human brain: voxel-based morphometric study. Hum Brain Mapp. 2011;32(7):1050–8. https://doi.org/10.1002/hbm.21088 .

Wang Y, Xu Q, Luo J, Hu M, Zuo C. Effects of age and sex on subcortical volumes. Front Aging Neurosci. 2019;11:259. https://doi.org/10.3389/fnagi.2019.00259 .

Xu J, Kobayashi S, Yamaguchi S, Iijima K, Okada K, Yamashita K. Gender effects on age-related changes in brain structure. AJNR Am J Neuroradiol. 2000;21(1):112–8.

CAS PubMed PubMed Central Google Scholar

Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage. 2001;14(1 Pt 1):21–36. https://doi.org/10.1006/nimg.2001.0786 .

Jack CR Jr, Wiste HJ, Weigand SD, Knopman DS, Vemuri P, Mielke MM, et al. Age, sex, and APOE epsilon4 effects on memory, brain structure, and beta-amyloid across the adult life span. JAMA Neurol. 2015;72(5):511–9. https://doi.org/10.1001/jamaneurol.2014.4821 .

Zhang X, Liang M, Qin W, Wan B, Yu C, Ming D. Gender differences are encoded differently in the structure and function of the human brain revealed by multimodal MRI. Front Hum Neurosci. 2020;14:244. https://doi.org/10.3389/fnhum.2020.00244 .

Bora E, Akdede BB, Alptekin K. The relationship between cognitive impairment in schizophrenia and metabolic syndrome: a systematic review and meta-analysis. Psychol Med. 2017;47(6):1030–40. https://doi.org/10.1017/S0033291716003366 .

Gustafson D, Lissner L, Bengtsson C, Bjorkelund C, Skoog I. A 24-year follow-up of body mass index and cerebral atrophy. Neurology. 2004;63(10):1876–81. https://doi.org/10.1212/01.WNL.0000141850.47773.5F .

Jagust W, Harvey D, Mungas D, Haan M. Central obesity and the aging brain. Arch Neurol. 2005;62(10):1545–8. https://doi.org/10.1001/archneur.62.10.1545 .

Luckhoff HK, du Plessis S, Scheffler F, Phahladira L, Kilian S, Buckle C, et al. Fronto-limbic white matter fractional anisotropy and body mass index in first-episode schizophrenia spectrum disorder patients compared to healthy controls. Psychiatry Res Neuroimaging. 2020;305:111173. https://doi.org/10.1016/j.pscychresns.2020.111173 .

Raji CA, Ho AJ, Parikshak NN, Becker JT, Lopez OL, Kuller LH, et al. Brain structure and obesity. Hum Brain Mapp. 2010;31(3):353–64. https://doi.org/10.1002/hbm.20870 .

O'Brien PD, Hinder LM, Callaghan BC, Feldman EL. Neurological consequences of obesity. Lancet Neurol. 2017;16(6):465–77. https://doi.org/10.1016/S1474-4422(17)30084-4 .

Colcombe SJ, Erickson KI, Raz N, Webb AG, Cohen NJ, McAuley E, et al. Aerobic fitness reduces brain tissue loss in aging humans. J Gerontol A Biol Sci Med Sci. 2003;58(2):176–80.

Colcombe SJ, Kramer AF, Erickson KI, Scalf P, McAuley E, Cohen NJ, et al. Cardiovascular fitness, cortical plasticity, and aging. Proc Natl Acad Sci U S A. 2004;101(9):3316–21. https://doi.org/10.1073/pnas.0400266101 .

Erickson KI, Leckie RL, Weinstein AM. Physical activity, fitness, and gray matter volume. Neurobiol Aging. 2014;35(Suppl 2):S20–8. https://doi.org/10.1016/j.neurobiolaging.2014.03.034 .

Okonkwo OC, Schultz SA, Oh JM, Larson J, Edwards D, Cook D, et al. Physical activity attenuates age-related biomarker alterations in preclinical AD. Neurology. 2014;83(19):1753–60. https://doi.org/10.1212/WNL.0000000000000964 .

Mukamal KJ, Longstreth WT Jr, Mittleman MA, Crum RM, Siscovick DS. Alcohol consumption and subclinical findings on magnetic resonance imaging of the brain in older adults: the cardiovascular health study. Stroke. 2001;32(9):1939–46. https://doi.org/10.1161/hs0901.095723 .

Zhou S, Xiao D, Peng P, Wang SK, Liu Z, Qin HY, et al. Effect of smoking on resting-state functional connectivity in smokers: An fMRI study. Respirology. 2017;22(6):1118–24. https://doi.org/10.1111/resp.13048 .

Cole JH, Franke K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 2017;40(12):681–90. https://doi.org/10.1016/j.tins.2017.10.001 .

Uludag K, Roebroeck A. General overview on the merits of multimodal neuroimaging data fusion. Neuroimage. 2014;102(Pt 1):3–10. https://doi.org/10.1016/j.neuroimage.2014.05.018 .

Cole JH, Poudel RPK, Tsagkrasoulis D, Caan MWA, Steves C, Spector TD, et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage. 2017;163:115–24. https://doi.org/10.1016/j.neuroimage.2017.07.059 .

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539 .

Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, et al. Deep learning for neuroimaging: a validation study. Front Neurosci. 2014;8:229.

Zhang YD, Dong Z, Wang SH, Yu X, Yao X, Zhou Q, et al. Advances in multimodal data fusion in neuroimaging: overview, challenges, and novel orientation. Inf Fusion. 2020;64:149–87. https://doi.org/10.1016/j.inffus.2020.07.006 .

Johnson TE. Recent results: biomarkers of aging. Exp Gerontol. 2006;41(12):1243–6. https://doi.org/10.1016/j.exger.2006.09.006 .

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This work was supported by a Research Training Program stipend, awarded by Monash University and the Australian government to JW, DN, and ZW; the National Health and Medical Research Council (NHMRC) Fellowship (1106533 to IHH); and the NHMRC Dementia Research Leader Fellowship (1135727 to JR). Funders did not direct the conduction of this systematic review, nor the decision to publish these findings.

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Wrigglesworth, J., Ward, P., Harding, I.H. et al. Factors associated with brain ageing - a systematic review. BMC Neurol 21 , 312 (2021). https://doi.org/10.1186/s12883-021-02331-4

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What neuroscience tells us about the teenage brain

New research now turns an old assumption on its head, as psychologists seek to optimize social contexts and environments for developing minds

Vol. 53 No. 5 Print version: page 66

Cognition and the Brain

For years, the teenage brain was seen by researchers, policymakers, and the public as more of a burden than an asset. Adolescents were risk machines who lacked the decision-making powers of a fully developed prefrontal cortex—and liable to harm themselves and others as a result. That narrative is beginning to change.

There is growing recognition that what was previously seen as immaturity is actually a cognitive, behavioral, and neurological flexibility that allows teens to explore and adapt to their shifting inner and outer worlds.

Developmental cognitive neuroscientists are at the frontier of this new outlook, using updated methodology, larger and more diverse samples, and experimental tasks with real-world relevance to answer questions about adolescents in the context of society. They’re also supporting developmentally informed policy and practice on everything from mental health care to juvenile justice. “The adolescent brain was long portrayed as broken, immature, or contributing to problematic behaviors,” said Eva Telzer, PhD, an associate professor of psychology and director of the Developmental Social Neuroscience Lab at the University of North Carolina, Chapel Hill. “But in the last five years, there’s been a huge shift toward seeing the developing brain as malleable, flexible, and promoting many positive aspects of development in adolescence.”

Heightened sensitivity to rewards, for example, which is partly driven by increased activity in a part of the brain called the ventral striatum, has been implicated in behaviors such as substance use and unprotected sex among teens. But research now shows that in different settings, that same neural circuitry can also promote positive peer influence and behaviors, Telzer said, such as wearing a seat belt or joining a peaceful protest.

As the field of developmental neuroscience matures, so too do the questions researchers ask. Studies are increasingly considering the influence not just of peers but also of parents. Researchers are also looking closely at how social media use may affect young brains, as concerns mount about teens’ online activity. As a result, research on the teenage brain is finally starting to catch up with studies of other age groups, complete with the level of detail it deserves.

“The shift from childhood to adulthood is not a linear one. Adolescence is a time of wonderfully dynamic change in the brain,” said BJ Casey, PhD, a professor of psychology who directs the Fundamentals of the Adolescent Brain Lab at Yale University. “Too often, we’ve superimposed an adult model onto a developing brain, but now we’re starting to see more nuanced findings.”

Embracing new approaches

Adolescence—spanning from puberty until the mid-20s—describes the transitional period between childhood and adulthood, according to the National Academies of Sciences, Engineering, and Medicine . During this period, the brain grows and changes in a number of ways. Gray matter in the cerebral cortex tends to thin, while white matter that connects various regions of the brain generally increases in volume. Functional connections between regions, which researchers measure with brain scans that track oxygen usage in blood, also undergo widespread changes during adolescence.

Beyond that, things get a little more complicated—and recent replication efforts indicate that some findings considered fundamental to the field may not hold up in larger samples. For example, early research suggested that brain volume increases peaked earlier in adolescent girls (Lenroot, R. K., & Giedd, J. N., Brain and Cognition , Vol. 72, No. 1, 2010 ), but more recent studies of large, international samples have shown it’s not that simple. Instead, boys’ brains tend to change at similar rates regardless of variability in other brain metrics, while changes in girls’ brains can be predicted based on certain measurements, such as the thickness of the cortex (Mills, K. L., et al., NeuroImage , Vol. 242, 2021 ).

“This kind of finding is emblematic of a bigger shift in the field as to how we’re approaching our science, what techniques we use, and what information we consider valuable,” said Jennifer Pfeifer, PhD, a professor of psychology and director of the University of Oregon’s Developmental Social Neuroscience Lab . “It’s become clear that if we want to understand developmental processes within individuals, we need to use some different tactics.”

More sophisticated methodology is a big part of that shift, she added. Instead of merely comparing brain structure or activity between two age groups (12- and 18-year-olds, for instance), researchers are increasingly relying on a variety of experimental approaches that follow the same youth over time.

“Many people may not realize that our early insights about adolescent brain development were based on cross-sectional approaches, which can sometimes lead to the wrong conclusions,” said Pfeifer, who also codirects the National Scientific Council on Adolescence .

Now, researchers use other techniques, such as accelerated longitudinal designs, where participants are sampled a handful of times at a range of ages (starting at ages 12 to 15, for instance, and then annually for three years), which can paint a more comprehensive picture of neurodevelopment.

Large collaborative consortia, in particular the Adolescent Brain Cognitive Development (ABCD) Study, a decade-long effort that follows a nationally representative sample of nearly 12,000 teens, are also providing richer data that can power more rigorous studies of the developing brain. The ABCD Study shares its brain scans measuring neurological development, clinical tests of mental and physical health, and behavioral data on substance use, academic achievement, and more with researchers around the world; of about 250 papers published using the survey’s data so far, half were from investigators outside the consortium. Fittingly, one of the study’s early insights is that very large samples—with thousands of individual brain scans—are needed to detect reproducible differences between individuals at the whole-brain level (Marek, S., et al., Nature , Vol. 603, 2022 ).

“On the other hand, there is still a need for innovative, smaller-scale studies,” said Eveline Crone, PhD, a professor of neurocognitive developmental psychology and director of the Society, Youth, and Neuroscience Connected (SYNC) Lab at Erasmus University Rotterdam in the Netherlands. “If we only ran the big consortia, we would miss out on a lot of novelty in terms of our methods and research questions.”

Some questions—for instance, how adolescents’ brains respond, on average, to winning money for themselves, a family member, or a stranger—can be examined effectively in much smaller samples, pointing to the importance of a balance between large and small efforts ( Developmental Cognitive Neuroscience , Vol. 51, 2021 ). (Crone and her colleagues who conducted this research have found that teen brains show activation in the nucleus accumbens, part of the brain’s reward system, when achieving gains for themselves or their parents but not for strangers. Those findings point to the development of ingroup-outgroup distinctions during adolescence.) Like others in the field, Crone employs a mixed-methods approach, combining brain imaging with behavioral measures, youth panels, and large-scale surveys to contextualize development alongside behavior, relationships, and society.

Another major advance is the creation and use of “ecologically valid” experimental tasks, or those that more accurately mimic teens’ experiences outside the lab.

“If we really want to understand how the adolescent brain works, we need to use stimuli—things like social media and video games—that they actually care about,” said Jennifer Silk, PhD, a professor of psychology at the University of Pittsburgh who runs the Families, Emotions, Neuroscience, and Development Lab .

Silk and her colleagues have developed one such activity, called the Chatroom Interact Task, which simulates acceptance and rejection from peers. Teenage girls participating in the task are either “chosen” or “rejected” by other girls their age while undergoing an fMRI scan, which maps brain activity by measuring changes in blood flow and oxygen levels. Other tasks monitor teens while they use platforms similar to Instagram and Facebook, including how their brains respond to receiving “likes.”

Researchers are even collecting data that may redefine the meaning of “adolescence,” with an eye on the juvenile justice system.

“We’re expanding the age ranges we’re looking at because the field is recognizing that significant neurocognitive changes continue into the 20s,” said Casey. “Those changes have consequences with regard to decision-making,” and research in this area may ultimately inform more scientifically aligned approaches to reward, punishment, justice reform, and other areas.

Optimizing mental health

Teens are famous for their heightened emotional sensitivity, especially in social interactions. Researchers are starting to pin down brain circuitry linked to that sensitivity—and differentiate between cases where it’s an asset that helps teens reach emotional maturity versus a risk factor that may predict mental health problems (Casey, B. J., et al., Neuroscience Letters , Vol. 693, 2019 ).

Research by Silk, Telzer, Casey, and others has identified several areas of the brain that underlie emotional responses in teens, including the subgenual cingulate cortex, anterior insula, and amygdala. For example, teens who had more activity in those regions during the rejection phase of Silk’s Chatroom Interact Task, compared with the acceptance phase, were more likely to experience depression and suicidality down the line ( Journal of Clinical Child & Adolescent Psychology , online first publication, 2022 ; Child Psychiatry & Human Development , Vol. 51, 2020 ). “There seems to be some sensitivity to rejection in this brain network that’s related to the development of internalizing disorders,” Silk said.

Because mental health problems increase sharply during adolescence—affecting an estimated 1 in 4 teens—there’s an urgent need to determine who is at risk and what treatments may be most effective (Silva, S. A., et al., PLOS ONE , Vol. 15, No. 4, 2020 ).

In animal models, stressful experiences during adolescence appear to alter the development of emotion-focused regions such as the amygdala and hippocampus, as well as the prefrontal cortex (Eiland, L., & Romeo, R. D., Neuroscience , Vol. 249, 2013 ). Early findings from the ABCD Study have found different patterns of activation in the amygdala, anterior cingulate cortex, and other reward-associated brain regions among preteens with disruptive behavior disorders, as well as brain differences that may underlie attention-deficit/hyperactivity disorder (ADHD) (Hawes, S. W., et al., The American Journal of Psychiatry , Vol. 178, No. 4, 2021 ; Bernanke, J., et al., The Lancet Psychiatry , Vol. 9, No. 3, 2022 ).

Rather than searching for a drug or mechanism that can address the entirety of depression, anxiety, or ADHD, researchers are increasingly studying specific symptoms—anhedonia or inattention, for instance—as well as subtypes of various disorders and seeking solutions for each.

“We are now looking at specific behaviors for which we can identify a neural circuit, mechanisms, and sometimes even genes,” said Pradeep Bhide, PhD, a professor of developmental neuroscience and director of the Center for Brain Repair at the Florida State University College of Medicine. “That is a newer, better, and likely more successful approach to treating complex human psychiatric and developmental disorders.”

For example, adolescents tend to benefit less from fear extinction efforts than adults (Pattwell, S. S., et al., PNAS , Vol. 109, No. 40, 2012 ). According to Casey, this suggests that they may respond poorly to exposure therapy, a key component of cognitive behavioral therapy (CBT) for anxiety, which recruits the prefrontal cortex to reprogram fear memories. It may therefore be possible to optimize CBT to work better for adolescents by using strategies that bypass the prefrontal cortex, instead working to alter memories using other circuitry, including emotion- and memory-focused regions such as the hippocampus and amygdala ( Scientific Reports , Vol. 5, No. 8863, 2015 ; Nature Communications , Vol. 7, No. 11475, 2016 ). This process is often referred to as “memory reconsolidation” or “reconsolidation update.”

“Thus, there appear to be developmental windows in which we can optimize treatments in specific ways,” Casey said.

Parents and peers

When it comes to teens’ relationships, both the scientific community and the lay public have long embraced the assumption that adolescence triggers a shift away from parents and toward peers, particularly when it comes to risk-taking.

New findings are challenging that assumption, which was pervasive but difficult to test directly, Pfeifer said (Nelson, E. E., et al., Psychological Medicine , Vol. 35, No. 2, 2005 ). Early data from Project NeuroTeen, Telzer’s 5-year longitudinal study of how parent and peer relationships influence adolescent decision-making and development, show that teens shift their behavior to align with the risky choices of parents more than the risky choices of peers. This shift is supported by increased activation in regions of the brain related to reward, including the ventral striatum and ventromedial prefrontal cortex ( Journal of Research on Adolescence , Vol. 31, No. 1, 2021 ).

Silk, Amanda Morris, PhD, of Oklahoma State University, and their colleagues have started to document the synchrony between teens and their parents in real time, using a new simultaneous scanning technique to measure how one brain responds to another during an interaction. They have found that adolescent brain activity tends to mirror parent brain activity, especially in emotion-processing regions such as the amygdala and anterior insula ( Child Development , Vol. 92, No. 6, 2021 ).

“I think a lot of parents believe that it’s too late, that by adolescence, peers have all the power,” Silk said. “But this research is showing that parents shouldn’t give up, that they still do have the power to help their adolescents learn how to process and regulate their emotions.”

Pfeifer’s lab also recently explored the claim that changes in the brain during adolescence make teens more sensitive to social information related to acceptance by peers, but their findings did not clearly support that idea. Instead, they found that activity in regions such as the ventromedial prefrontal cortex—a brain area related to evaluation of the self—tended to peak during mid-adolescence, especially for information related to status (Cosme, D., et al., Developmental Cognitive Neuroscience , Vol. 54, 2022 ). These findings may suggest that “identity is an important source of value to adolescents, and this could be leveraged to promote healthy decision-making,” she said ( Child Development Perspectives , Vol. 12, No. 3, 2018 ).

Peer interactions are still important, of course, and they’re increasingly happening online. Parents, researchers, and policymakers have plenty of unanswered questions about how social media use may affect the developing brain. For example, do certain neural profiles among teens predict riskier online behavior, such as the tendency to compare one’s appearance and social status to others?

Silk and Cecile Ladouceur, PhD, of the University of Pittsburgh, have launched new research to bring more nuance to that conversation. They are collecting information about social media use from teens’ phones, along with fMRI data on their neurological responses to acceptance and rejection, for instance during the Chatroom Interact Task. Telzer has also launched a new effort, with Mitch Prinstein, PhD, APA’s chief science officer, to study whether brain development in regions responsible for reward, emotion, and cognitive control relates to how frequently teens check their social media apps.

“Undoubtedly there’s a link between teens’ social experiences online and the way their brains respond to the environment, but it’s something we’re slowly working to unpack,” Telzer said.

Challenging assumptions about teens

The malleability of the adolescent brain may make it vulnerable at times, but teen brains are also highly capable of prosocial growth under the right circumstances, Pfeifer said. Teens’ biological need for social connection, combined with their heightened sensitivity to rewards, likely underlies teen-led activism, for instance on climate change, racial justice, and gun control.

Research by Crone and others shows that the ventral striatum is linked to prosocial behavior, responding to rewards not just for oneself but also for others ( Nature Communications , Vol. 12, No. 313, 2021 ). Among teens serving time in youth detention centers, both the ability to spontaneously take the perspective of others and activity in the temporoparietal junction—an associated region of the brain—differed significantly from a control group. The temporoparietal junction is more malleable by environmental influence than other social brain regions, according to studies by Crone among twin populations. This suggests that interventions in perspective-taking, which target this area, may be helpful for justice-involved teens ( Social Cognitive and Affective Neuroscience , Vol. 9, No. 12, 2014 ).

Based on ongoing research by Casey and others about the trajectory of development in regions related to cognitive control, including the prefrontal cortex, APA has launched a task force to review new findings that may inform extending Roper v. Simmons , a Supreme Court decision that abolished the use of the death penalty for those under 18, to cover individuals into their early 20s.

Looking forward, researchers in the field emphasize the importance of continuing to challenge assumptions about adolescence—around risk-taking, emotionality, and more—to ensure that the science remains robust and can ultimately support interventions for healthy development.

“We’re not going to change adolescents’ brains, nor should we want to,” Telzer said. “What we can do is optimize what we know to create social contexts and environments that provide the most enriching experiences for them.”

Contact APA

Psychology topic: Teens

Groundbreaking Alzheimer’s therapy wipes out critical tangles in the brain

By StudyFinds Staff

Reviewed by Chris Melore

Research led by Dr. Leo James, Medical Research Council Laboratory of Molecular Biology

Sep 13, 2024

Vector of a hand erasing part of the human brain of a man

CAMBRIDGE, United Kingdom — Imagine a world where Alzheimer’s disease could be effectively treated, not just managed. We might be one step closer to that reality, thanks to an exciting breakthrough from scientists in the United Kingdom. They’ve developed a clever new approach that could revolutionize how we tackle this devastating illness.

To understand why this new research is so promising, let’s first break down what’s happening in an Alzheimer’s brain . Two main culprits cause problems: proteins called tau and amyloid . While we’ve made progress targeting amyloid clumps that form between brain cells, tau has been trickier to deal with.

Tau proteins normally act like scaffolding, helping to support and structure our brain cells. In Alzheimer’s, however, tau goes rogue. It misfolds and clumps together, forming tangles inside our neurons (brain cells). These tangles gum up the works, leading to cell death and cognitive decline.

Treating these tau tangles has been a real head-scratcher for scientists up until now. Antibody therapies, which work well for amyloid, struggle to get inside cells where tau hangs out. Other approaches that can reach tau inside cells end up being a bit like using a sledgehammer to crack a nut — they destroy all tau, even the healthy stuff our brains need.

Neurons (green) containing tau aggregates (red).

This is where the Cambridge scientists got creative. They tapped into a natural process our cells use to get rid of viruses . It involves a protein called TRIM21 , which acts like a quality control inspector in our cells. Normally, when an antibody-tagged virus sneaks into a cell, TRIM21 spots it and says, “Hey, this doesn’t belong here!” It then tags the virus for destruction in the cell’s “garbage disposal” (called the proteasome).

The researchers realized they could trick TRIM21 into targeting tau tangles instead of viruses. They created two clever therapies. The first, called RING-nanobody, combines a tau-seeking mini-antibody with a part of TRIM21 that activates the cellular garbage disposal. The second, named RING-Bait, is even sneakier. It’s a piece of tau protein linked to that same TRIM21 activator. It infiltrates the tau tangles like a Trojan horse, then calls in the clean-up crew from the inside.

The best part? Both therapies are super selective. They only destroy the problematic tau clumps, leaving healthy tau alone to do its important job. The team’s research is published in the journal Cell .

“Tau aggregates are tucked away inside brain cells and very difficult to degrade. Critically, these new TRIM21-based therapies can be delivered directly inside cells, where the majority of tau aggregates reside. We’ve found a way that not only degrades the tau aggregates, but leaves the healthy tau intact to do its job,” says Dr. Will McEwan, one of the study’s leaders, in a media release.

The gait of mice with tauopathy improved after treatment with RING-Bait (top, orange). Mice given the placebo retained poor gait (bottom, purple).

The team didn’t stop at just testing their idea in lab dishes. They used a harmless virus to deliver the RING-Bait therapy into the brains of mice with Alzheimer’s-like symptoms. The results were promising. Tau tangles in the mice’s brains decreased significantly , the progression of their symptoms slowed down, and the treated mice showed better movement and coordination.

“It was unknown whether specifically removing tau aggregates inside the cell would be enough to halt the progression of disease. It is encouraging that a RING-Bait approach reduces disease severity in our model systems, as this suggests that the selective removal of tau aggregates is a valid therapeutic approach,” notes Dr. Lauren Miller, another researcher on the project.

What’s really exciting is that this technique might not be limited to just Alzheimer’s. Other brain diseases like Parkinson’s and Huntington’s also involve problematic protein clumps. The RING-Bait therapy could potentially be adapted to target those as well.

“Neurodegenerative diseases can have tau proteins that misfold in many different ways, raising the possibility of needing a different treatment for every disease. A useful aspect of RING-Bait is because it is attached to a tau protein, it’s a universal Trojan horse that should be incorporated into different types of tau aggregates exactly like the cell’s own misfolding tau protein,” explains study co-leader Dr. Leo James.

Cells containing tau aggregates (green) before (left) and 13 hours after treatment with RING-nanobody (right)

While these results are incredibly promising, it’s important to remember that we’re still a long way from having a treatment ready for humans. The researchers need to figure out how to safely and effectively deliver the therapy throughout the much larger human brain.

“It’s important to stress that although we have shown it works in a mouse model, this is a long way from a therapeutic that could be used in humans. It would need to be determined that it is safe to use TRIM21-based therapies in the human brain and that the treatments are effective in both removing aggregates and improving the course of disease,” study team member Dr. Jonathan Benn cautions.

Despite the challenges ahead, this research represents a major step forward in our fight against Alzheimer’s and potentially other neurodegenerative diseases. By harnessing the power of our own cellular machinery, we might one day be able to outsmart these devastating illnesses. The road ahead may be long, but this innovative approach offers new hope for millions affected by these conditions worldwide.

Paper Summary

Methodology.

In this study, researchers used a technique called “RING-Bait technology” to target and degrade harmful tau protein aggregates, which are associated with neurodegenerative diseases like Alzheimer’s. They developed a fusion protein that combines a part of the tau protein with a specific enzyme that signals for the destruction of tau aggregates. This fusion protein gets incorporated into growing tau clusters, which then activates a process that tags these aggregates for degradation by the cell’s natural cleanup machinery. This method specifically targets the harmful, aggregated forms of tau without affecting the normal tau proteins that cells need.

Key Results

The results showed that the “RING-Bait” technology worked well in breaking down harmful tau protein clumps. When tested in cell cultures and in mice with tau buildup (a model for Alzheimer’s), the tau aggregates were significantly reduced. This decrease in tau clumps also led to better movement in mice, meaning the treatment might help improve brain function. Importantly, the treatment did not affect the healthy proteins needed by the cells, showing that it’s a targeted and safe approach.

Study Limitations

First, while the technology reduced tau aggregates and improved motor function in mice, the study remains unclear whether this would lead to similar improvements in humans. Additionally, the study did not measure how the treatment affects neuron survival over time, so more research is needed to determine long-term benefits. Another challenge is delivering the treatment effectively to the brain in humans since the method used in mice may not be suitable for clinical use.

Discussion & Takeaways

The study presents an exciting new approach to targeting harmful protein clumps in the brain, which are a hallmark of diseases like Alzheimer’s. The “RING-Bait” technology provides a precise way to remove only the toxic aggregates, leaving the healthy proteins intact. This method could potentially be adapted to treat other diseases caused by protein aggregation, making it a versatile tool. The next steps would be further refining this technology for human use and exploring its full therapeutic potential.

Funding & Disclosures

This research was supported by several organizations, including a Wellcome Trust Investigator Award and the UK Dementia Research Institute. The researchers have disclosed that they are listed as inventors on a patent related to the data in this paper, which suggests they may have a financial interest in the technology’s development. Additionally, various authors received support from other medical research councils and foundations, highlighting the collaborative nature of this project.

About StudyFinds Staff

StudyFinds sets out to find new research that speaks to mass audiences — without all the scientific jargon. The stories we publish are digestible, summarized versions of research that are intended to inform the reader as well as stir civil, educated debate. StudyFinds Staff articles are AI assisted, but always thoroughly reviewed and edited by a Study Finds staff member. Read our AI Policy for more information.

Our Editorial Process

StudyFinds publishes digestible, agenda-free, transparent research summaries that are intended to inform the reader as well as stir civil, educated debate. We do not agree nor disagree with any of the studies we post, rather, we encourage our readers to debate the veracity of the findings themselves. All articles published on StudyFinds are vetted by our editors prior to publication and include links back to the source or corresponding journal article, if possible.

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Chris Melore

Sophia Naughton

Associate Editor

Scientists take final key step toward a hepatitis C vaccine

September 17, 2024

Larval emergence is always from the abdomen and has been observed to occur between the second and third tergites (dorsolaterally) or laterally through a tear in the abdominal cuticle.

New wasp species bursts from flies like horrifying ‘Alien’ creatures

Election Day voting: Person holding "I Voted" sticker

Happiness may decide the 2024 election – and America’s current mood is scrambling expectations

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Scientists show how pregnancy changes the brain in innumerable ways

Neuroscientist Liz Chrastil got the unique chance to see how her brain changed while she was pregnant and share what she learned in a new study that offers the first detailed map of a woman's brain throughout gestation .

The transition to motherhood , researchers discovered, affects nearly every part of the brain .

Although the study looks at only one person, it kicks off a large, international research project that aims to scan the brains of hundreds of women and could one day provide clues about disorders like postpartum depression.

“It’s been a very long journey,” said Chrastil, co-author of the paper published Monday in Nature Neuroscience. “We did 26 scans before, during and after pregnancy” and found “some really remarkable things.”

More than 80% of the regions studied had reductions in the volume of gray matter, where thinking takes place. This is an average of about 4% of the brain — nearly identical to a reduction that happens during puberty. While less gray matter may sound bad, researchers said it probably isn't; it likely reflects the fine-tuning of networks of interconnected nerve cells called “neural circuits" to prepare for a new phase of life.

The team began following Chrastil — who works at the University of California, Irvine, and was 38 years old at the time — shortly before she became pregnant through in vitro fertilization.

During the pregnancy and for two years after she gave birth, they continued doing MRI brain scans and drawing blood to observe how her brain changed as sex hormones like estrogen ebbed and flowed. Some of the changes continued past pregnancy.

“Previous studies had taken snapshots of the brain before and after pregnancy, but we’ve never witnessed the brain in the midst of this metamorphosis,” said co-author Emily Jacobs of the University of California, Santa Barbara.

Unlike past studies, this one focused on many inner regions of the brain as well as the cerebral cortex, the outermost layer, said Joseph Lonstein, a professor of neuroscience and psychology at Michigan State University who was not involved in the research. It's “a good first step to understanding much more about whole-brain changes that could be possible in a woman across pregnancy and postpartum," he said.

Research in animals has linked some brain changes with qualities that could be helpful when caring for an infant. While the new study doesn’t address what the changes mean in terms of human behavior, Lonstein pointed out that it describes changes in brain areas involved in social cognition, or how people interact with others and understand their thoughts and feelings, for example.

The researchers have partners in Spain and are moving forward with the larger Maternal Brain Project, which is supported by the Ann S. Bowers Women’s Brain Health Initiative and the Chan Zuckerberg Initiative.

Eventually, they hope scientists can use data from a large number of women for things like predicting postpartum depression before it happens.

“There is so much about the neurobiology of pregnancy that we don’t understand yet, and it’s not because women are too complicated. It’s not because pregnancy is some Gordian knot,” Jacobs said. “It’s a byproduct of the fact that biomedical sciences have historically ignored women’s health.”

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

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Review Article
Published: 18 March 2015

The neuroscience of mindfulness meditation

Yi-Yuan Tang 1 , 2 na1 ,
Britta K. Hölzel 3 , 4 na1 &
Michael I. Posner 2

Nature Reviews Neuroscience volume 16 , pages 213–225 ( 2015 ) Cite this article

103k Accesses

1424 Citations

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Metrics details

Cognitive neuroscience

An Erratum to this article was published on 10 April 2015

It is proposed that the mechanism through which mindfulness meditation exerts its effects is a process of enhanced self-regulation, including attention control, emotion regulation and self-awareness.

Research on mindfulness meditation faces a number of important challenges in study design that limit the interpretation of existing studies.

A number of changes in brain structure have been related to mindfulness meditation.

Mindfulness practice enhances attention. The anterior cingulate cortex is the region associated with attention in which changes in activity and/or structure in response to mindfulness meditation are most consistently reported.

Mindfulness practice improves emotion regulation and reduces stress. Fronto-limbic networks involved in these processes show various patterns of engagement by mindfulness meditation.

Meditation practice has the potential to affect self-referential processing and improve present-moment awareness. The default mode networks — including the midline prefrontal cortex and posterior cingulate cortex, which support self-awareness — could be altered following mindfulness training.

Mindfulness meditation has potential for the treatment of clinical disorders and might facilitate the cultivation of a healthy mind and increased well-being.

Future research into mindfulness meditation should use randomized and actively controlled longitudinal studies with large sample sizes to validate previous findings.

The effects of mindfulness practice on neural structure and function need to be linked to behavioural performance, such as cognitive, affective and social functioning, in future research.

The complex mental state of mindfulness is likely to be supported by the large-scale brain networks; future work should take this into account rather than being restricted to activations in single brain areas.

Research over the past two decades broadly supports the claim that mindfulness meditation — practiced widely for the reduction of stress and promotion of health — exerts beneficial effects on physical and mental health, and cognitive performance. Recent neuroimaging studies have begun to uncover the brain areas and networks that mediate these positive effects. However, the underlying neural mechanisms remain unclear, and it is apparent that more methodologically rigorous studies are required if we are to gain a full understanding of the neuronal and molecular bases of the changes in the brain that accompany mindfulness meditation.

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Mindfulness meditation increases default mode, salience, and central executive network connectivity

Focused attention meditation changes the boundary and configuration of functional networks in the brain

Meta-analytic evidence that mindfulness training alters resting state default mode network connectivity

Ospina, M. B. et al. Meditation practices for health: state of the research. Evid. Rep. Technol. Assess. (Full Rep.) 155 , 1–263 (2007).

Google Scholar

Tang, Y.-Y. & Posner, M. I. Theory and method in mindfulness neuroscience. Soc. Cogn. Affect. Neurosci. 8 , 118–120 (2013).

Article PubMed Google Scholar

Hart, W. The Art of Living: Vipassana Meditation (Harper and Row, 1987).

Ivanovski, B. & Malhi, G. S. The psychological and neurophysiological concomitants of mindfulness forms of meditation. Acta Neuropsychiatr. 19 , 76–91 (2007).

Chiesa, A. & Malinowski, P. Mindfulness-based approaches: are they all the same? J. Clin. Psychol. 67 , 404–424 (2011).

Baer, R. A. Mindfulness training as a clinical intervention: a conceptual and empirical review. Clin. Psychol. Sci. Practice 10 , 125–143 (2003).

Article Google Scholar

Grossman, P. Defining mindfulness by how poorly I think I pay attention during everyday awareness and other intractable problems for psychology's (re)invention of mindfulness: comment on Brown et al . Psychol. Assess. 23 , 1034–1040 (2011).

Kabat-Zinn, J. Full Catastrophe Living: Using the Wisdom of Your Body and Mind to Face Stress, Pain, and Illness (Delta Trade Paperbacks, 1990).

Lutz, A., Slagter, H. A., Dunne, J. D. & Davidson, R. J. Attention regulation and monitoring in meditation. Trends Cogn. Sci. 12 , 163–169 (2008).

Article PubMed PubMed Central Google Scholar

Hölzel, B. K. et al. How does mindfulness meditation work? Proposing mechanisms of action from a conceptual and neural perspective. Perspect. Psychol. Sci. 6 , 537–559 (2011). A review of the mechanisms of meditation.

Tang, Y.Y., Rothbart, M. K. & Posner, M. I. Neural correlates of establishing, maintaining and switching brain states. Trends Cogn. Sci. 16 , 330–337 (2012). A review of the mechanisms of brain states associated with mental training.

Zeidan, F., Johnson, S. K., Diamond, B. J., David, Z. & Goolkasian, P. Mindfulness meditation improves cognition: evidence of brief mental training. Conscious. Cogn. 19 , 597–605 (2010).

Ding, X. et al. Short-term meditation modulates brain activity of insight evoked with solution cue. Soc. Cogn. Affect. Neurosci. 10 , 43–49 (2014).

Tang, Y. Y. et al. Short-term meditation training improves attention and self-regulation. Proc. Natl Acad. Sci. USA 104 , 17152–17156 (2007). The first longitudinal, randomized study to document that brief training improves executive attention, mood and immune function, and reduces levels of stress hormones.

Manna, A. et al. Neural correlates of focused attention and cognitive monitoring in meditation. Brain Res. Bull. 82 , 46–56 (2010).

Tomasino, B., Fregona, S., Skrap, M. & Fabbro, F. Meditation-related activations are modulated by the practices needed to obtain it and by the expertise: an ALE meta-analysis study. Front. Hum. Neurosci. 6 , 346 (2012).

PubMed Google Scholar

Fox, K. C. et al. Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners. Neurosci. Biobehav. Rev. 43 , 48–73 (2014). A review of structural alterations in the brain associated with meditation.

Brefczynski-Lewis, J. A., Lutz, A., Schaefer, H. S., Levinson, D. B. & Davidson, R. J. Neural correlates of attentional expertise in long-term meditation practitioners. Proc. Natl Acad. Sci. USA 104 , 11483–11488 (2007). One of the first cross-sectional studies to document the neural correlates of focused meditation.

Article CAS PubMed Google Scholar

Davidson, R. J. Empirical explorations of mindfulness: conceptual and methodological conundrums. Emotion 10 , 8–11 (2010).

MacCoon, D. G. et al. The validation of an active control intervention for Mindfulness Based Stress Reduction (MBSR). Behav. Res. Ther. 50 , 3–12 (2012). One of the first studies to validate the active control conditions in mindfulness training.

Rosenkranz, M. A. et al. A comparison of mindfulness-based stress reduction and an active control in modulation of neurogenic inflammation. Brain Behav. Immun. 27 , 174–184 (2013).

MacCoon, D. G., MacLean, K. A., Davidson, R. J., Saron, C. D. & Lutz, A. No sustained attention differences in a longitudinal randomized trial comparing mindfulness based stress reduction versus active control. PLoS ONE 9 , e97551 (2014).

Tang, Y. Y. et al. Central and autonomic nervous system interaction is altered by short-term meditation. Proc. Natl Acad. Sci. USA 106 , 8865–8870 (2009).

Erisman, S. M. & Roemer, L. The effects of experimentally induced mindfulness on emotional responding to film clips. Emotion 10 , 72–82 (2010).

Leiberg, S., Klimecki, O. & Singer, T. Short-term compassion training increases prosocial behaviour in a newly developed prosocial game. PLoS ONE 6 , e17798 (2011).

Article CAS PubMed PubMed Central Google Scholar

Hoge, E. A. et al. Randomized controlled trial of mindfulness meditation for generalized anxiety disorder: effects on anxiety and stress reactivity. J. Clin. Psychiatry 74 , 786–792 (2013).

Tang, Y. Y., Yang, L., Leve, L. D. & Harold, G. T. Improving executive function and its neurobiological mechanisms through a mindfulness-based intervention: advances within the field of developmental neuroscience. Child Dev. Perspect. 6 , 361–366 (2012).

PubMed PubMed Central Google Scholar

Zeidan, F., Johnson, S. K., Gordon, N. S. & Goolkasian, P. Effects of brief and sham mindfulness meditation on mood and cardiovascular variables. J. Altern. Complement. Med. 16 , 867–873 (2010).

Goldin, P., Ziv, M., Jazaieri, H., Hahn, K. & Gross, J. J. MBSR versus aerobic exercise in social anxiety: fMRI of emotion regulation of negative self-beliefs. Soc. Cogn. Affect. Neurosci. 8 , 65–72 (2013). One of the first randomized mindfulness studies to document the neural mechanisms in social anxiety.

Zeidan, F. et al. Brain mechanisms supporting the modulation of pain by mindfulness meditation. J. Neurosci. 31 , 5540–5548 (2011).

Hölzel, B. K. et al. Investigation of mindfulness meditation practitioners with voxel-based morphometry. Soc. Cogn. Affect. Neurosci. 3 , 55–61 (2008).

Lazar, S. W. et al. Meditation experience is associated with increased cortical thickness. Neuroreport 16 , 1893–1897 (2005). The first cross-sectional study to document that meditation is associated with structural changes in the brain.

Vestergaard-Poulsen, P. et al. Long-term meditation is associated with increased grey matter density in the brain stem. Neuroreport 20 , 170–174 (2009).

Pagnoni, G. & Cekic, M. Age effects on grey matter volume and attentional performance in Zen meditation. Neurobiol. Aging 28 , 1623–1627 (2007).

Grant, J. A., Courtemanche, J. & Rainville, P. A non-elaborative mental stance and decoupling of executive and pain-related cortices predicts low pain sensitivity in Zen meditators. Pain 152 , 150–156 (2010).

Grant, J. A. et al. Cortical thickness, mental absorption and meditative practice: possible implications for disorders of attention. Biol. Psychol. 92 , 275–281 (2013).

Fayed, N. et al. Brain changes in long-term zen meditators using proton magnetic resonance spectroscopy and diffusion tensor imaging: a controlled study. PLoS ONE 8 , e58476 (2013).

Tang, Y. Y. et al. Short-term meditation induces white matter changes in the anterior cingulate. Proc. Natl Acad. Sci. USA 107 , 15649–15652 (2010). The first longitudinal study to document that brief mindfulness training induces white-matter changes.

Tang, Y. Y., Lu, Q., Fan, M., Yang, Y. & Posner, M. I. Mechanisms of white matter changes induced by meditation. Proc. Natl Acad. Sci. USA 109 , 10570–10574 (2012).

Hölzel, B. K. et al. Mindfulness practice leads to increases in regional brain grey matter density. Psychiatry Res. 191 , 36–43 (2011).

Wells, R. E. et al. Meditation's impact on default mode network and hippocampus in mild cognitive impairment: a pilot study. Neurosci. Lett. 556 , 15–19 (2013).

Pickut, B. A. et al. Mindfulness based intervention in Parkinson's disease leads to structural brain changes on MRI: a randomized controlled longitudinal trial. Clin. Neurol. Neurosurg. 115 , 2419–2425 (2013).

Luders, E., Toga, A. W., Lepore, N. & Gaser, C. The underlying anatomical correlates of long-term meditation: larger hippocampal and frontal volumes of grey matter. Neuroimage 45 , 672–678 (2009).

Luders, E., Clark, K., Narr, K. L. & Toga, A. W. Enhanced brain connectivity in long-term meditation practitioners. Neuroimage 57 , 1308–1316 (2011).

Luders, E. et al. Bridging the hemispheres in meditation: thicker callosal regions and enhanced fractional anisotropy (FA) in long-term practitioners. Neuroimage 61 , 181–187 (2012).

Luders, E. et al. Global and regional alterations of hippocampal anatomy in long-term meditation practitioners. Hum. Brain Mapp. 34 , 3369–3375 (2013).

Singleton, O. et al. Change in brainstem grey matter concentration following a mindfulness-based intervention is correlated with improvement in psychological well-being. Front. Hum. Neurosci. 8 , 33 (2014).

Hölzel, B. K. et al. Stress reduction correlates with structural changes in the amygdala. Soc. Cogn. Affect. Neurosci. 5 , 11–17 (2010).

Luders, E., Kurth, F., Toga, A. W., Narr, K. L. & Gaser, C. Meditation effects within the hippocampal complex revealed by voxel-based morphometry and cytoarchitectonic probabilistic mapping. Front. Psychol. 4 , 398 (2013).

Luders, E. et al. The unique brain anatomy of meditation practitioners: alterations in cortical gyrification. Front. Hum. Neurosci. 6 , 34 (2012).

Grant, J. A., Courtemanche, J., Duerden, E. G., Duncan, G. H. & Rainville, P. Cortical thickness and pain sensitivity in zen meditators. Emotion 10 , 43–53 (2010).

Farb, N. A., Segal, Z. V. & Anderson, A. K. Mindfulness meditation training alters cortical representations of interoceptive attention. Soc. Cogn. Affect. Neurosci. 8 , 15–26 (2013).

Tang, Y.-Y. & Posner, M. I. Attention training and attention state training. Trends Cogn. Sci. 13 , 222–227 (2009).

Tang, Y. Y. & Posner, M. I. in Handbook of Mindfulness: Theory, Research, and Practice Ch. 5 (eds Brown, K. W., Creswell, J. D. & Ryan, R. M.) 81–89 (Guildford Press, 2014).

Posner, M. I. & Petersen, S. E. The attention system of the human brain. Annu. Rev. Neurosci. 13 , 25–42 (1990).

Petersen, S. E. & Posner, M. I. The attention system of the human brain: 20 years after. Annu. Rev. Neurosci. 35 , 73–89 (2012).

Fan, J., McCandliss, B. D., Sommer, T., Raz, A. & Posner, M. I. Testing the efficiency and independence of attentional networks. J. Cogn. Neurosci. 14 , 340–347 (2002).

Raz, A. & Buhle, J. Typologies of attentional networks. Nature Rev. Neurosci. 7 , 367–379 (2006).

Article CAS Google Scholar

Posner, M. I. & Rothbart, M. K. Research on attention networks as a model for the integration of psychological science. Annu. Rev. Psychol. 58 , 1–23 (2007).

Chiesa, A., Calati, R. & Serretti, A. Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clin. Psychol. Rev. 31 , 449–464 (2011).

Chan, D. & Woollacott, M. Effects of level of meditation experience on attentional focus: is the efficiency of executive or orientation networks improved? J. Altern. Complement. Med. 13 , 651–657 (2007).

Moore, A. & Malinowski, P. Meditation, mindfulness and cognitive flexibility. Conscious. Cogn. 18 , 176–186 (2009).

Wenk-Sormaz, H. Meditation can reduce habitual responding. Altern. Ther. Health Med. 11 , 42–58 (2005).

Slagter, H. A. et al. Mental training affects distribution of limited brain resources. PLoS Biol. 5 , e138 (2007).

Pashler, H. Overlapping mental operations in serial performance with preview. Q. J. Exp. Psychol. A. 47 , 161–191 (discussion 193–199, 201–205) (1994).

Posner, M. I. Measuring alertness. Ann. NY Acad. Sci. 1129 , 193–199 (2008).

Van Leeuwen, S., Willer, N. G. & Melloni, L. Age effects on attentional blink performance in meditation. Conscious. Cogn. 18 , 593–599 (2009).

Van den Hurk, P. A., Giommi, F., Gielen, S. C., Speckens, A. E. M. & Barendregt, H. P. Greater efficiency in attentional processing related to mindfulness meditation. Q. J. Exp. Psychol. (Hove) 63 , 1168–1180 (2010).

Anderson, N. D., Lau, M. A., Segal, Z. V. & Bishop, S. R. Mindfulness-based stress reduction and attentional control. Clin. Psychol. Psychother. 14 , 449–463 (2007).

Jha, A. P., Krompinger, J. & Baime, M. J. Mindfulness training modifies subsystems of attention. Cogn. Affect. Behav. Neurosci. 7 , 109–119 (2007).

MacLean, K. A. et al. Intensive meditation training improves perceptual discrimination and sustained attention. Psychol Sci , 21 , 829–839 (2010).

Tang, Y. Y. & Posner, M. I. Training brain networks and states. Trends Cogn. Sci. 18 , 345–350 (2014).

Tang, Y. Y., Tang, R. & Posner, M. I. Brief meditation training induces smoking reduction. Proc. Natl Acad. Sci. USA 110 , 13971–13975 (2013).

Cahn, B. R. & Polich, J. Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychol. Bull. 132 , 180–211 (2006).

Hölzel, B. K. et al. Differential engagement of anterior cingulate and adjacent medial frontal cortex in adept meditators and non-meditators. Neurosci. Lett. 421 , 16–21 (2007).

Van Veen, V. & Carter, C. S. The anterior cingulate as a conflict monitor: fMRI and ERP studies. Physiol. Behav. 77 , 477–482 (2002).

Posner, M. I., Sheese, B., Rothbart, M. & Tang, Y. Y. The anterior cingulate gyrus and the mechanism of self-regulation. Cogn. Affect. Behav. Neurosci. 7 , 391–395 (2007).

Tang, Y. Y. & Tang, R. Ventral-subgenual anterior cingulate cortex and self-transcendence. Front. Psychol. 4 , 1000 (2014).

Sridharan, D., Levitin, D. J. & Menon, V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Natl Acad. Sci. USA 105 , 12569–12574 (2008).

Gard, T. et al. Pain attenuation through mindfulness is associated with decreased cognitive control and increased sensory processing in the brain. Cereb. Cortex 22 , 2692–2702 (2012).

Allen, M. et al. Cognitive-affective neural plasticity following active-controlled mindfulness intervention. J. Neurosci. 32 , 15601–15610 (2012). One of the first studies to document the effects of mindfulness using active controls.

Goldin, P. R. & Gross, J. J. Effects of mindfulness-based stress reduction (MBSR) on emotion regulation in social anxiety disorder. Emotion 10 , 83–91 (2010).

Deckersbach, T., Hölzel, B. K., Eisner, L. R., Lazar, S. W. & Nierenberg, A. A. Mindfulness-Based Cognitive Therapy for Bipolar Disorder (Guildford Press, 2014).

Book Google Scholar

Passarotti, A. M., Sweeney, J. A. & Pavuluri, M. N. Emotion processing influences working memory circuits in pediatric bipolar disorder and attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 49 , 1064–1080 (2010).

Gross, J. J. in Handbook of Emotion Regulation 2nd edn (ed. Gross, J. J.) 3–20 (Guildford Press, 2014).

Ortner, C. N. M., Kilner, S. J. & Zelazo, P. D. Mindfulness meditation and reduced emotional interference on a cognitive task. Motiv. Emot. 31 , 271–283 (2007).

Goleman, D. J. & Schwartz, G. E. Meditation as an intervention in stress reactivity. J. Consult. Clin. Psychol. 44 , 456–466 (1976).

Robins, C. J., Keng, S.-L., Ekblad, A. G. & Brantley, J. G. Effects of mindfulness-based stress reduction on emotional experience and expression: a randomized controlled trial. J. Clin. Psychol. 68 , 117–131 (2012).

Chambers, R., Lo, B. C. Y. & Allen, N. B. The impact of intensive mindfulness training on attentional control, cognitive style, and affect. Cogn. Ther. Res. 32 , 303–322 (2008).

Ding, X., Tang, Y. Y., Tang, R. & Posner, M. I. Improving creativity performance by short-term meditation. Behav. Brain Funct. 10 , 9 (2014).

Jain, S. et al. A randomized controlled trial of mindfulness meditation versus relaxation training: effects on distress, positive states of mind, rumination, and distraction. Ann. Behav. Med. 33 , 11–21 (2007).

Desbordes, G. et al. Effects of mindful-attention and compassion meditation training on amygdala response to emotional stimuli in an ordinary, non-meditative state. Front. Hum. Neurosci. 6 , 292 (2012).

Lutz, J. et al. Mindfulness and emotion regulation — an fMRI study. Soc. Cogn. Affect. Neurosci. 9 , 776–785 (2014).

Taylor, V. A. et al. Impact of mindfulness on the neural responses to emotional pictures in experienced and beginner meditators. Neuroimage 57 , 1524–1533 (2011).

Westbrook, C. et al. Mindful attention reduces neural and self-reported cue-induced craving in smokers. Soc. Cogn. Affect. Neurosci. 8 , 73–84 (2013).

Hölzel, B. K. et al. Neural mechanisms of symptom improvements in generalized anxiety disorder following mindfulness training. Neuroimage Clin. 2 , 448–458 (2013). One of the first longitudinal, randomized mindfulness studies to document the neural mechanisms in generalized anxiety disorder.

Farb, N. A. S. et al. Attending to the present: mindfulness meditation reveals distinct neural modes of self-reference. Soc. Cogn. Affect. Neurosci. 2 , 313–322 (2007).

Teper, R., Segal, Z. V. & Inzlicht, M. Inside the mindful mind: how mindfulness enhances emotion regulation through improvements in executive control. Curr. Dir. Psychol. 22 , 449–454 (2013).

Chiesa, A., Serretti, A. & Jakobsen, J. C. Mindfulness: top-down or bottom-up emotion regulation strategy? Clin. Psychol. Rev. 33 , 82–96 (2013).

Malinowski, P. Neural mechanisms of attentional control in mindfulness meditation. Front. Hum. Neurosci. 7 , 8 (2013).

Jensen, C. G. et al. Mindfulness training affects attention — or is it attentional effort? J. Exp. Psychol. Gen. 141 , 106–123 (2012).

Posner, M. I., Rothbart, M. K., Reuda, M. R. & Tang, Y. Y. in Effortless Attention: A New Perspective in the Cognitive Science of Attention and Action (ed. Bruya, B.) 410–424 (MIT Press, 2010).

Banks, S. J., Eddy, K. T., Angstadt, M., Nathan, P. J. & Phan, K. L. Amygdala-frontal connectivity during emotion-regulation. Soc. Cogn. Affect Neurosci. 2 , 303–312 (2007).

Etkin, A., Egner, T., Peraza, D. M., Kandel, E. R. & Hirsch, J. Resolving emotional conflict: a role for the rostral anterior cingulate cortex in modulating activity in the amygdala. Neuron 51 , 871–882 (2006).

Kirk, U., Brown, K. W. & Downar, J. Adaptive neural reward processing during anticipation and receipt of monetary rewards in mindfulness meditators. Soc. Cogn. Affect. Neurosci. http://dx.doi.org/10.1093/scan/nsu112 (2014).

Olendzki, A. Unlimiting Mind: The Radically Experiential Psychology of Buddhism (Wisdom Publications, 2010).

Sperduti, M., Martinelli, P. & Piolino, P. A neurocognitive model of meditation based on activation likelihood estimation (ALE) meta-analysis. Conscious. Cogn. 21 , 269–276 (2012).

Fresco, D. M. et al. Initial psychometric properties of the experiences questionnaire: validation of a self-report measure of decentering. Behav. Ther. 38 , 234–246 (2007).

Shapiro, S. L., Carlson, L. E., Astin, J. A. & Freedman, B. Mechanisms of mindfulness. J. Clin. Psychol. 62 , 373–386 (2006).

Josipovic, Z. Neural correlates of nondual awareness in meditation. Ann. NY Acad. Sci. 1307 , 9–18 (2014).

Kerr, C. E., Josyula, K. & Littenberg, R. Developing an observing attitude: an analysis of meditation diaries in an MBSR clinical trial. Clin. Psychol. Psychother. 18 , 80–93 (2011).

Dor-Ziderman, Y., Berkovich-Ohana, A., Glicksohn, J. & Goldstein, A. Mindfulness-induced selflessness: a MEG neurophenomenological study. Front. Hum. Neurosci. 7 , 582 (2013).

Emavardhana, T. & Tori, C. D. Changes in self-concept, ego defense mechanisms, and religiosity following seven-day Vipassana meditation retreats. J. Sci. Stud. Relig. 36 , 194–206 (1997).

Haimerl, C. J. & Valentine, E. R. The effect of contemplative practice on intrapersonal, interpersonal, and transpersonal dimensions of the self-concept. J. Transpers. Psychol. 33 , 37–52 (2001).

Sahdra, B. K., Shaver, P. R. & Brown, K. W. A scale to measure nonattachment: a Buddhist complement to Western research on attachment and adaptive functioning. J. Pers. Assess. 92 , 116–127 (2010).

Brewer, J. A. et al. Meditation experience is associated with differences in default mode network activity and connectivity. Proc. Natl Acad. Sci. USA 108 , 20254–20259 (2011). One of the first studies to document the alteration of the DMN by meditation.

Hasenkamp, W. & Barsalou, L. W. Effects of meditation experience on functional connectivity of distributed brain networks. Front. Hum. Neurosci. 6 , 38 (2012).

Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain's default network: anatomy, function, and relevance to disease. Ann. NY Acad. Sci. 1124 , 1–38 (2008).

Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98 , 676–682 (2001).

Northoff, G. et al. Self-referential processing in our brain: a meta-analysis of imaging studies on the self. Neuroimage 31 , 440–457 (2006).

Sajonz, B. et al. Delineating self-referential processing from episodic memory retrieval: common and dissociable networks. Neuroimage 50 , 1606–1617 (2010).

Buckner, R. L. & Carroll, D. C. Self-projection and the brain. Trends Cogn. Sci. 11 , 49–57 (2007).

Khalsa, S. S. et al. Interoceptive awareness in experienced meditators. Psychophysiology 45 , 671–677 (2008).

Nielsen, L. & Kaszniak, A. W. Awareness of subtle emotional feelings: a comparison of long-term meditators and nonmeditators. Emotion 6 , 392–405 (2006).

Sze, J. A., Gyurak, A., Yuan, J. W. & Levenson, R. W. Coherence between emotional experience and physiology: does body awareness training have an impact? Emotion 10 , 803–814 (2010).

Fox, K. C. R. et al. Meditation experience predicts introspective accuracy. PLoS ONE 7 , e45370 (2012).

Lutz, A., Brefczynski-Lewis, J., Johnstone, T. & Davidson, R. J. Regulation of the neural circuitry of emotion by compassion meditation: effects of meditative expertise. PLoS ONE 3 , e1897 (2008).

Craig, A. D. How do you feel — now? The anterior insula and human awareness. Nature Rev. Neurosci. 10 , 59–70 (2009).

Monti, D. A. et al. Changes in cerebral blood flow and anxiety associated with an 8-week mindfulness programme in women with breast cancer. Stress Health 28 , 397–407 (2012).

Grey, J. D., Milner, T. A. & McEwen, B. S. Dynamic plasticity: the role of glucocorticoids, brain-derived neurotrophic factor and other trophic factors. Neuroscience 239 , 214–227 (2013).

Creswell, J. D., Pacilio, L. E., Lindsay, E. K. & Brown, K. W. Brief mindfulness meditation training alters psychological and neuroendocrine responses to social evaluative stress. Psychoneuroendocrinology 44 , 1–12 (2014).

Tang, Y. Y., Tang, R., Jiang, C. & Posner, M. I. Short-term meditation intervention improves self-regulation and academic performance. J. Child Adolesc. Behav. 2 , 4 (2014).

Chiesa, A., Serretti, A. Mindfulness-based stress reduction for stress management in healthy people: a review and meta-analysis. J. Altern. Complement. Med. 15 , 593–600 (2009).

Jacobs, T. L. et al. Self-reported mindfulness and cortisol during a Shamatha meditation retreat. Health Psychol. 32 , 1104–1109 (2013).

Fan, Y., Tang, Y. Y. & Posner, M. I. Cortisol level modulated by integrative meditation in a dose-dependent fashion. Stress Health 30 , 65–70 (2013).

Fan, Y., Tang, Y. Y., Ma, Y. & Posner, M. I. Mucosal immunity modulated by integrative meditation in a dose-dependent fashion. J. Altern. Complement. Med. 16 , 151–155 (2010).

Kang, D. H. et al. The effect of meditation on brain structure: cortical thickness mapping and diffusion tensor imaging. Soc. Cogn. Affect. Neurosci. 8 , 27–33 (2013).

Bressler, S. L. & Menon, V. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14 , 277–290 (2010).

Menon, V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 15 , 483–506 (2011).

Xue, S., Tang, Y. Y. & Posner, M. I. Short-term meditation increases network efficiency of the anterior cingulate cortex. Neuroreport 22 , 570–574 (2011).

Gard, T. et al. Fluid intelligence and brain functional organization in aging yoga and meditation practitioners. Front. Aging Neurosci. 6 , 76 (2014).

Lane, R. D. & Wager, T. D. The new field of brain-body medicine: what have we learned and where are we headed? Neuroimage 47 , 1135–1140 (2009).

Garrison, K. M. et al. Real-time fMRI links subjective experience with brain activity during focused attention. Neuroimage 81 , 110–118 (2013).

LaConte, S. M. Decoding fMRI brain states in real-time. Neuroimage 56 , 440–454 (2011).

Zotev, V. et al. Self-regulation of amygdala activation using real-time fMRI neurofeedback. PLoS ONE 6 , e24522 (2011).

Haynes, J. D. & Rees, G. Decoding mental states from brain activity in humans. Nature Rev. Neurosci. 7 , 523–534 (2006).

van I. Jendoorn, M. H. et al. Gene-by-environment experiments: a new approach to finding the missing heritability. Nature Rev. Genet. 12 , 881 (2011).

Jung, Y. H. et al. Influence of brain-derived neurotrophic factor and catechol O-methyl transferase polymorphisms on effects of meditation on plasma catecholamines and stress. Stress 15 , 97–104 (2012).

Ding, X., Tang, Y. Y., Deng, Y., Tang, R. & Posner, M. I. Mood and personality predict improvement in creativity due to meditation training. Learn. Individ. Differ. 37 , 217–221 (2014).

Rothbart, M. K. Becoming Who We Are (Guilford Press, 2011).

Takahashi, T. et al. Changes in EEG and autonomic nervous activity during meditation and their association with personality traits. Int. J. Psychophysiol. 55 , 199–207 (2005).

Moffitt, T. E. et al. A gradient of childhood self-control predicts health, wealth, and public safety. Proc. Natl Acad. Sci. USA 108 , 2693–2698 (2011).

Hofmann, S. G., Sawyer, A. T., Witt, A. A. & Oh, D. The effect of mindfulness-based therapy on anxiety and depression: a meta-analytic review. J. Consult. Clin. Psychol. 78 , 169–183 (2010). A review of the effect of mindfulness-based therapy on anxiety and mood symptoms.

Bowen, S. et al. Relative efficacy of mindfulness-based relapse prevention, standard relapse prevention, and treatment as usual for substance use disorders: a randomized clinical trial. JAMA Psychiatry 71 , 547–556 (2014). One of the first longitudinal studies to document the effects of mindfulness on drug use and heavy drinking.

Schoenberg, P. L. A. et al. Effects of mindfulness-based cognitive therapy on neurophysiological correlates of performance monitoring in adult attention-deficit/hyperactivity disorder. Clin. Neurophysiol. 125 , 1407–1416 (2014).

Zeidan, F., Martucci, K. T., Kraft, R. A., McHaffie, J. G. & Coghill, R. C. Neural correlates of mindfulness meditation-related anxiety relief. Soc. Cogn. Affect. Neurosci. 9 , 751–759 (2014).

Desbordes, G. et al. Moving beyond mindfulness: defining equanimity as an outcome measure in meditation and contemplative research. Mindfulness http://dx.doi.org/10.1007/s12671-013-0269-8 (2014).

Smith, J. C. Alterations in brain and immune function produced by mindfulness meditation: three caveats. Psychosom. Med. 66 , 148–152 (2004).

Davidson, R. J., Kabat-Zinn, J. Response to Smith, J. C. Psychosom. Med. 66 , 148–152 (2004).

Lippelt, D. P., Hommel, B. & Colzato, L. S. Focused attention, open monitoring and loving kindness meditation: effects on attention, conflict monitoring, and creativity — a review. Front. Psychol. 5 , 1083 (2014).

Hasenkamp, W., Wilson-Mendenhall, C. D., Duncan, E. & Barsalou, L. W. Mind wandering and attention during focused meditation: a fine-grained temporal analysis of fluctuating cognitive states. Neuroimage 59 , 750–760 (2012). One of the first studies to document brain activity during different phases of focused-attention meditation.

Pagnoni, G. Dynamical properties of BOLD activity from the ventral posteromedial cortex associated with meditation and attentional skills. J. Neurosci. 32 , 5242–5249 (2012).

Öst, L. G. in Phobias: A Handbook of Theory, Research, and Treatment (ed. Davey, G. C. L.) 227–247 (John Wiley, 1997).

Milad, M. R. & Quirk, G. J. Fear extinction as a model for translational neuroscience: ten years of progress. Annu. Rev. Psychol. 63 , 129–151 (2012).

Milad, M. R. et al. Recall of fear extinction in humans activates the ventromedial prefrontal cortex and hippocampus in concert. Biol. Psychiatry. 62 , 446–454 (2007).

LeDoux, J. E. Emotion circuits in the brain. Annu. Rev. Neurosci. 23 , 155–184 (2000).

Davidson, R. J., Jackson, D. C. & Kalin, N. H. Emotion, plasticity, context, and regulation: perspectives from affective neuroscience. Psychol. Bull. 126 , 890–909 (2000).

Phelps, E. A., Delgado, M. R., Nearing, K. I. & LeDoux, J. E. Extinction learning in humans: role of the amygdala and vmPFC. Neuron 43 , 897–905 (2004).

Holt, D. J. et al. Extinction memory is impaired in schizophrenia. Biol. Psychiatry 65 , 455–463 (2009).

Milad, M. R. et al. Presence and acquired origin of reduced recall for fear extinction in PTSD: results of a twin study. J. Psychiatr. Res. 42 , 515–520 (2008).

McEwen, B. S. & Morrison, J. H. The brain on stress: vulnerability and plasticity of the prefrontal cortex over the life course. Neuron 79 , 16–29 (2013).

McEwen, B. S. & Gianaros, P. J. Stress- and allostasis-induced brain plasticity. Annu. Rev. Med. 62 , 431–445 (2011).

Liston, C., McEwen, B. S. & Casey, B. J. Psychossocial stress sreversibly disrupts prefrontal processing and attentional control. Proc. Natl Acad. Sci. USA 106 , 912–917 (2009).

Davidson, R. J. & McEwen, B. S. Social influences on neuroplasticity: stress and interventions to promote well-being. Nature Neurosci. 15 , 689–695 (2012).

McEwen, B. S. The brain on stress: toward an integrative approach to brain, body and behaviour. Perspect. Psychol. Sci. 8 , 673–675 (2013).

Thayer, J. F. & Lane, R. D. A model of neurovisceral integration in emotion regulation and dysregulation. J. Affective Disord. 61 , 201–216 (2000).

Creswell, J. D. in Handbook of Mindfulness: Theory, Research, and Practice Ch. 23 (eds Brown, K. W., Creswell, J. D. & Ryan, R. M.) (Guildford Press, 2014).

Ditto, B., Eclache, M. & Goldman, N. Short-term autonomic and cardiovascular effects of mindfulness body scan meditation. Ann. Behav. Med. 32 , 227–234 (2006).

Xiong, G. L. & Doraiswamy, P. M. Does meditation enhance cognition and brain plasticity? Ann. NY Acad. Sci. 1172 , 63–69 (2009).

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Acknowledgements

This work was supported by the US Office of Naval Research. We thank E. Luders for her contributions to an earlier version of this manuscript. We benefited from discussions with R. Davidson and A. Chiesa. We thank four anonymous reviewers for their constructive comments and R. Tang for manuscript preparation.

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Yi-Yuan Tang and Britta K. Hölzel: These authors contributed equally to this work.

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Department of Psychological Sciences, Texas Tech University, Lubbock, 79409, Texas, USA

Yi-Yuan Tang

Department of Psychology, University of Oregon, Eugene, 97403, Oregon, USA

Yi-Yuan Tang & Michael I. Posner

Department of Neuroradiology, Technical University of Munich, Munich, 81675, Germany

Britta K. Hölzel

Massachusetts General Hospital, Charlestown, 02129, Massachusetts, USA

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Powerpoint slide for fig. 1, powerpoint slide for table 1, powerpoint slide for table 2.

Study designs that compare data from one or more groups at several time points and that ideally include a (preferably active) control condition and random assignment to conditions.

Study designs that compare data from an experimental group with those from a control group at one point in time.

Studies that assess the co-variation between two variables: for example, co-variation of functional or structural properties of the brain and a behavioural variable, such as reported stress.

(BOLD contrasts). Signals that can be extracted with functional MRI and that reflect the change in the amount of deoxyhaemoglobin that is induced by changes in the activity of neurons and their synapses in a region of the brain. The signals thus reflect the activity in a local brain region.

(ASL). An MRI technique that is capable of measuring cerebral blood flow in vivo . It provides cerebral perfusion maps without requiring the administration of a contrast agent or the use of ionizing radiation because it uses magnetically labelled endogenous blood water as a freely diffusible tracer.

The reliable patterns of brain activity that involve the activation and/or connectivity of multiple large-scale brain networks.

A parameter in diffusion tensor imaging, which images brain structures by measuring the diffusion properties of water molecules. It provides information about the microstructural integrity of white matter.

Derived from the eigenvalues of the diffusion tensor, their underlying biophysical properties are associated with axonal density and myelination, respectively.

A technique for coordinate-based meta-analysis of neuroimaging data. It determines the convergence of foci reported from different experiments, weighted by the number of participants in each study.

A method of analysing functional MRI data that is capable of detecting and characterizing information represented in patterns of activity distributed within and across multiple regions of the brain. Unlike univariate approaches, which only identify magnitudes of activity in localized parts of the brain, this approach can monitor multiple areas at once.

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Tang, YY., Hölzel, B. & Posner, M. The neuroscience of mindfulness meditation. Nat Rev Neurosci 16 , 213–225 (2015). https://doi.org/10.1038/nrn3916

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Does Video Gaming Have Impacts on the Brain: Evidence from a Systematic Review

Denilson brilliant t..

1 Department of Biomedicine, Indonesia International Institute for Life Sciences (i3L), East Jakarta 13210, Indonesia

2 Smart Ageing Research Center (SARC), Tohoku University, Sendai 980-8575, Japan; pj.ca.ukohot@iur (R.N.); pj.ca.ukohot@atuyr (R.K.)

3 Department of Cognitive Health Science, Institute of Development, Aging and Cancer (IDAC), Tohoku University, Sendai 980-8575, Japan

Ryuta Kawashima

4 Department of Functional Brain Imaging, Institute of Development, Aging and Cancer (IDAC), Tohoku University, Sendai 980-8575, Japan

Video gaming, the experience of playing electronic games, has shown several benefits for human health. Recently, numerous video gaming studies showed beneficial effects on cognition and the brain. A systematic review of video gaming has been published. However, the previous systematic review has several differences to this systematic review. This systematic review evaluates the beneficial effects of video gaming on neuroplasticity specifically on intervention studies. Literature research was conducted from randomized controlled trials in PubMed and Google Scholar published after 2000. A systematic review was written instead of a meta-analytic review because of variations among participants, video games, and outcomes. Nine scientific articles were eligible for the review. Overall, the eligible articles showed fair quality according to Delphi Criteria. Video gaming affects the brain structure and function depending on how the game is played. The game genres examined were 3D adventure, first-person shooting (FPS), puzzle, rhythm dance, and strategy. The total training durations were 16–90 h. Results of this systematic review demonstrated that video gaming can be beneficial to the brain. However, the beneficial effects vary among video game types.

1. Introduction

Video gaming refers to the experience of playing electronic games, which vary from action to passive games, presenting a player with physical and mental challenges. The motivation to play video games might derive from the experience of autonomy or competing with others, which can explain why video gaming is pleasurable and addictive [ 1 ].

Video games can act as “teachers” depending on the game purpose [ 2 ]. Video gaming has varying effects depending on the game genre. For instance, an active video game can improve physical fitness [ 3 , 4 , 5 , 6 ], whereas social video games can improve social behavior [ 7 , 8 , 9 ]. The most interesting results show that playing video games can change cognition and the brain [ 10 , 11 , 12 , 13 ].

Earlier studies have demonstrated that playing video games can benefit cognition. Cross-sectional and longitudinal studies have demonstrated that the experience of video gaming is associated with better cognitive function, specifically in terms of visual attention and short-term memory [ 14 ], reaction time [ 15 ], and working memory [ 16 ]. Additionally, some randomized controlled studies show positive effects of video gaming interventions on cognition [ 17 , 18 ]. Recent meta-analytical studies have also supported the positive effects of video gaming on cognition [ 10 , 11 , 12 , 13 ]. These studies demonstrate that playing video games does provide cognitive benefits.

The effects of video gaming intervention are ever more widely discussed among scientists [ 13 ]. A review of the results and methodological quality of recently published intervention studies must be done. One systematic review of video gaming and neural correlates has been reported [ 19 ]. However, the technique of neuroimaging of the reviewed studies was not specific. This systematic review reviewed only magnetic resonance imaging (MRI) studies in contrast to the previous systematic review to focus on neuroplasticity effect. Neuroplasticity is capability of the brain that accommodates adaptation for learning, memorizing, and recovery purposes [ 19 ]. In normal adaptation, the brain is adapting to learn, remember, forget, and repair itself. Recent studies using MRI for brain imaging techniques have demonstrated neuroplasticity effects after an intervention, which include cognitive, exercise, and music training on the grey matter [ 20 , 21 , 22 , 23 , 24 ] and white matter [ 25 , 26 , 27 , 28 , 29 ]. However, the molecular mechanisms of the grey and white matter change remain inconclusive. The proposed mechanisms for the grey matter change are neurogenesis, gliogenesis, synaptogenesis, and angiogenesis, whereas those for white matter change are myelin modeling and formation, fiber organization, and angiogenesis [ 30 ]. Recent studies using MRI technique for brain imaging have demonstrated video gaming effects on neuroplasticity. Earlier imaging studies using cross-sectional and longitudinal methods have shown that playing video games affects the brain structure by changing the grey matter [ 31 , 32 , 33 ], white matter [ 34 , 35 ], and functional connectivity [ 36 , 37 , 38 , 39 ]. Additionally, a few intervention studies have demonstrated that playing video games changed brain structure and functions [ 40 , 41 , 42 , 43 ].

The earlier review also found a link between neural correlates of video gaming and cognitive function [ 19 ]. However, that review used both experimental and correlational studies and included non-healthy participants, which contrasts to this review. The differences between this and the previous review are presented in Table 1 . This review assesses only experimental studies conducted of healthy participants. Additionally, the cross-sectional and longitudinal studies merely showed an association between video gaming experiences and the brain, showing direct effects of playing video games in the brain is difficult. Therefore, this systematic review specifically examined intervention studies. This review is more specific as it reviews intervention and MRI studies on healthy participants. The purposes of this systematic review are therefore to evaluate the beneficial effects of video gaming and to assess the methodological quality of recent video gaming intervention studies.

Differences between previous review and current review.

Difference	Previous Review	Current Review
Type of reviewed studies	Experimental and correlational studies	Experimental studies only
Neuroimaging technique of reviewed studies	CT, fMRI, MEG, MRI, PET, SPECT, tDCS, EEG, and NIRS	fMRI and MRI only
Participants of reviewed studies	Healthy and addicted participant	Healthy participants Only

CT, computed tomography; fMRI, functional magnetic resonance imaging; MEG, magnetoencephalography MRI, magnetic resonance imaging; PET, positron emission tomography; SPECT, single photon emission computed tomography; tDCS, transcranial direct current stimulation; EEG, electroencephalography; NIRS, near-infrared spectroscopy.

2. Materials and Methods

2.1. search strategy.

This systematic review was designed in accordance with the PRISMA checklist [ 44 ] shown in Appendix Table A1 . A literature search was conducted using PubMed and Google Scholar to identify relevant studies. The keywords used for the literature search were combinations of “video game”, “video gaming”, “game”, “action video game”, “video game training”, “training”, “play”, “playing”, “MRI”, “cognitive”, “cognition”, “executive function”, and “randomized control trial”.

2.2. Inclusion and Exclusion Criteria

The primary inclusion criteria were randomized controlled trial study, video game interaction, and MRI/fMRI analysis. Studies that qualified with only one or two primary inclusions were not included. Review papers and experimental protocols were also not included. The secondary inclusion criteria were publishing after 2000 and published in English. Excluded were duration of less than 4 weeks or unspecified length intervention or combination intervention. Also excluded were studies of cognition-based games, and studies of participants with psychiatric, cognitive, neurological, and medical disorders.

2.3. Quality Assessment

Each of the quality studies was assessed using Delphi criteria [ 45 ] with several additional elements [ 46 ]: details of allocation methods, adequate descriptions of control and training groups, statistical comparisons between control and training groups, and dropout reports. The respective total scores (max = 12) are shown in Table 3. The quality assessment also includes assessment for risk of bias, which is shown in criteria numbers 1, 2, 5, 6, 7, 9, and 12.

2.4. Statistical Analysis

Instead of a meta-analysis study, a systematic review of the video game training/video gaming and the effects was conducted because of the variation in ranges of participant age, video game genre, control type, MRI and statistical analysis, and training outcomes. Therefore, the quality, inclusion and exclusion, control, treatment, game title, participants, training period, and MRI analysis and specification of the studies were recorded for the respective games.

The literature search made of the databases yielded 140 scientific articles. All scientific articles were screened based on inclusion and exclusion criteria. Of those 140 scientific articles, nine were eligible for the review [ 40 , 41 , 42 , 43 , 47 , 48 , 49 , 50 , 51 ]. Video gaming effects are listed in Table 2 .

Summary of beneficial effect of video gaming.

Author	Year	Participant Age	Game Genre	Control	Duration	Beneficial Effect
Gleich et al. [ ]	2017	18–36	3D adventure	passive	8 weeks	Increased activity in hippocampus
Gleich et al. [ ]	2017	18–36	3D adventure	passive	8 weeks	Decreased activity in DLPFC
Haier et al. [ ]	2009	12–15	puzzle	passive	3 months	Increased GM in several visual–spatial processing area
Haier et al. [ ]	2009	12–15	puzzle	passive	3 months	Decreased activity in frontal area
Kuhn et al. [ ]	2014	19–29	3D adventure	passive	8 weeks	Increased GM in hippocampal, DLPFC and cerebellum
Lee et al. [ ]	2012	18–30	strategy	active	8–10 weeks	Decreased activity in DLPFC
Lee et al. [ ]	2012	18–30	strategy	active	8–11 weeks	Non-significant activity difference
Lorenz et al. [ ]	2015	19–27	3D adventure	passive	8 weeks	Preserved activity in ventral striatum
Martinez et al. [ ]	2013	16–21	puzzle	passive	4 weeks	Functional connectivity change in multimodal integration system
Martinez et al. [ ]	2013	16–21	puzzle	passive	4 weeks	Functional connectivity change in higher-order executive processing
Roush [ ]	2013	50–65	rhythm dance	active	24 weeks	Increased activity in visuospatial working memory area
				active		Increased activity in emotional and attention area
				passive		Similar compared to active control-
West et al. [ ]	2017	55–75	3D adventure	active	24 weeks	Non-significant GM difference
				passive		Increased cognitive performance and short-term memory
				passive		Increased GM in hippocampus and cerebellum
West et al. [ ]	2018	18–29	FPS	active	8 weeks	Increased GM in hippocampus (spatial learner *)
						Increased GM in amygdala (response learner *)
						Decreased GM in hippocampus (response learner)

Duration was converted into weeks (1 month = 4 weeks); DLPFC, dorsolateral prefrontal cortex; GM, grey matter; FPS, first person shooting. * Participants were categorized based on how they played during the video gaming intervention.

We excluded 121 articles: 46 were not MRI studies, 16 were not controlled studies, 38 were not intervention studies, 13 were review articles, and eight were miscellaneous, including study protocols, non-video gaming studies, and non-brain studies. Of 18 included scientific articles, nine were excluded. Of those nine excluded articles, two were cognitive-based game studies, three were shorter than 4 weeks in duration or were without a specified length intervention, two studies used a non-healthy participant treatment, and one was a combination intervention study. A screening flowchart is portrayed in Figure 1 .

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Flowchart of literature search.

3.1. Quality Assessment

The assessment methodology based on Delphi criteria [ 45 ] for the quality of eligible studies is presented in Table 3 . The quality scores assigned to the studies were 3–9 (mean = 6.10; S.D. = 1.69). Overall, the studies showed fair methodological quality according to the Delphi criteria. The highest quality score of the nine eligible articles was assigned to “Playing Super Mario 64 increases hippocampal grey matter in older adult” published by West et al. in 2017, which scored 9 of 12. The scores assigned for criteria 6 (blinded care provider) and 7 (blinded patient) were lowest because of unspecified information related to blinding for those criteria. Additionally, criteria 2 (concealed allocation) and 5 (blinding assessor) were low because only two articles specified that information. All articles met criteria 3 and 4 adequately.

Methodological quality of eligible studies.

Author	Year	Q1	Q2	Q3	Q4	Q5	Q8	Q9	Q10	Q11	Q12	Score
Gleich et al. [ ]	2017	1	0	1	1	0	0	0	1	1	1	6
Haier et al. [ ]	2009	1	0	1	1	0	0	0	1	1	0	5
Kuhn et al. [ ]	2014	1	0	1	1	0	0	0	1	1	0	5
Lee et al. [ ]	2012	0	0	1	1	0	0	1	1	1	1	6
Lorenz et al. [ ]	2015	1	0	1	1	0	1	0	1	1	1	7
Martinez et al. [ ]	2013	0	0	1	1	0	0	0	0	1	0	3
Roush [ ]	2013	1	1	1	1	1	0	1	1	0	0	7
West et al. [ ]	2017	1	1	1	1	0	1	1	1	1	1	9
West et al. [ ]	2018	0	0	1	1	1	1	1	1	0	1	7
Score		6	2	9	9	2	3	4	8	7	5

Q1, Random allocation; Q2, Concealed allocation; Q3, Similar baselines among groups; Q4, Eligibility specified; Q5, Blinded assessor outcome; Q6, Blinded care provider; Q7, Blinded patient; Q8, Intention-to-treat analysis; Q9, Detail of allocation method; Q10, Adequate description of each group; Q11, Statistical comparison between groups; Q12, Dropout report (1, specified; 0, unspecified).

3.2. Inclusion and Exclusion

Most studies included participants with little or no experience with gaming and excluded participants with psychiatric/mental, neurological, and medical illness. Four studies specified handedness of the participants and excluded participants with game training experience. The inclusion and exclusion criteria are presented in Table 4 .

Inclusion and exclusion criteria for eligible studies.

Author	Year	Inclusion			Exclusion
Author	Year	i1	i2	i3	e1	e2	e3	e4	e5
Gleich et al. [ ]	2017	1	0	0	1	1	1	1	1
Haier et al. [ ]	2009	1	0	1	1	1	1	0	0
Kuhn et al. [ ]	2014	1	0	0	1	1	1	1	1
Lee et al. [ ]	2012	1	1	0	1	1	0	1	0
Lorenz et al. [ ]	2015	1	1	0	1	0	0	1	1
Martinez et al. [ ]	2013	1	1	1	1	1	0	0	1
Roush [ ]	2013	0	0	1	0	0	1	0	0
West et al. [ ]	2017	1	1	0	1	1	1	1	0
West et al. [ ]	2018	1	0	0	1	1	1	0	0
total		8	4	3	8	7	6	5	4

i1, Little/no experience in video gaming; i2, Right-handed; i3, Sex-specific; e1, Psychiatric/mental illness; e2, Neurological illness; e3, Medical illness; e4, MRI contraindication; e5, experience in game training.

3.3. Control Group

Nine eligible studies were categorized as three types based on the control type. Two studies used active control, five studies used passive control, and two studies used both active and passive control. A summary of the control group is presented in Table 5 .

Control group examined eligible studies.

Control	Author	Year
Active control	Lee et al. [ ]	2012
Active control	West et al. [ ]	2018
Passive control	Gleich et al. [ ]	2017
	Haier et al. [ ]	2009
	Kuhn et al. [ ]	2014
	Lorenz et al. [ ]	2015
	Martinez et al. [ ]	2013
Active–passive control	Roush [ ]	2013
Active–passive control	West et al. [ ]	2017

3.4. Game Title and Genre

Of the nine eligible studies, four used the same 3D adventure game with different game platforms, which were “Super Mario 64” original and the DS version. One study used first-person shooting (FPS) shooting games with many different game titles: “Call of Duty” is one title. Two studies used puzzle games: “Tetris” and “Professor Layton and The Pandora’s Box.” One study used a rhythm dance game: Dance Revolution. One study used a strategy game: “Space Fortress.” Game genres are presented in Table 6 .

Genres and game titles of video gaming intervention.

Genre	Author	Year	Title
3D adventure	Gleich et al. [ ]	2017	Super Mario 64 DS
	Kuhn et al. [ ]	2014	Super Mario 64
	Lorenz et al. [ ]	2015	Super Mario 64 DS
	West et al. [ ]	2017	Super Mario 64
FPS	West et al. * [ ]	2018	Call of Duty
Puzzle	Haier et al. [ ]	2009	Tetris
Puzzle	Martinez et al. [ ]	2013	Professor Layton and The Pandora’s Box
Rhythm dance	Roush [ ]	2013	Dance Revolution
Strategy	Lee et al. [ ]	2012	Space Fortress

* West et al. used multiple games; other games are Call of Duty 2, 3, Black Ops, and World at War, Killzone 2 and 3, Battlefield 2, 3, and 4, Resistance 2 and Fall of Man, and Medal of Honor.

3.5. Participants and Sample Size

Among the nine studies, one study examined teenage participants, six studies included young adult participants, and two studies assessed older adult participants. Participant information is shown in Table 7 . Numbers of participants were 20–75 participants (mean = 43.67; S.D. = 15.63). Three studies examined female-only participants, whereas six others used male and female participants. Six studies with female and male participants had more female than male participants.

Participant details of eligible studies.

Category	Author	Year	Age			Sample Size	Ratio (%)		Detail
Category	Author	Year	Lowest	Highest	Range	Sample Size	Female	Male	Detail
Teenager	Haier et al. [ ]	2009	12	15	3	44	70.45	29.54	Training ( 24) Control ( 20)
Young adult	Gleich et al. [ ]	2017	18	36	18	26	100	0	Training ( 15)
	Gleich et al. [ ]	2017	18	36	18	26	100	0	Control ( 11)
	Kuhn et al. [ ]	2014	19	29	10	48	70.8	29.2	Training ( 23)
	Kuhn et al. [ ]	2014	19	29	10	48	70.8	29.2	Control ( 25)
	Lee et al. [ ]	2012	18	30	12	75	61.4	38.6	Training A ( 25)
									Training B ( 25)
									Control ( 25)
	Lorenz et al. [ ]	2015	19	27	8	50	72	28	Training ( 25
	Lorenz et al. [ ]	2015	19	27	8	50	72	28	Control ( 25)
	Martinez et al. [ ]	2013	16	21	5	20	100	0	Training ( 10)
	Martinez et al. [ ]	2013	16	21	5	20	100	0	Control ( 10)
	West et al. [ ]	2018	18	29	11	43	67.4	32.5	Action game ( 21)
	West et al. [ ]	2018	18	29	11	43	67.4	32.5	Non-action game ( 22)
Older adult	Roush [ ]	2013	50	65	15	39	100	0	Training ( 19)
									Active control ( 15)
									Passive control ( 5)
	West et al. [ ]	2017	55	75	20	48	66.7	33.3	Training ( 19)
									Active control ( 14)
									Passive control ( 15)

3.6. Training Period and Intensity

The training period was 4–24 weeks (mean = 11.49; S.D. = 6.88). One study by Lee et al. had two length periods and total hours because the study examined video game training of two types. The total training hours were 16–90 h (mean = 40.63; S.D. = 26.22), whereas the training intensity was 1.5–10.68 h/week (mean = 4.96; S.D. = 3.00). One study did not specify total training hours. Two studies did not specify the training intensity. The training periods and intensities are in Table 8 .

Periods and intensities of video gaming intervention.

Author	Year	Length (Week)	Total Hours	Average Intensity (h/Week)
Gleich et al. [ ]	2017	8	49.5	6.2
Haier et al. [ ]	2009	12	18	1.5
Kuhn et al. [ ]	2014	8	46.88	5.86
Lorenz et al. [ ]	2012	8	28	3.5
Lee et al. [ ]	2015	8–11 *	27	n/a
Martinez et al. [ ]	2013	4	16	4
Roush [ ]	2013	24	ns	n/a
West et al. [ ]	2017	24	72	3
West et al. [ ]	2018	8.4	90	10.68

The training length was converted into weeks (1 month = 4 weeks). ns, not specified; n/a, not available; * exact length is not available.

3.7. MRI Analysis and Specifications

Of nine eligible studies, one study used resting-state MRI analysis, three studies (excluding that by Haier et al. [ 40 ]) used structural MRI analysis, and five studies used task-based MRI analysis. A study by Haier et al. used MRI analyses of two types [ 40 ]. A summary of MRI analyses is presented in Table 9 . The related resting-state, structural, and task-based MRI specifications are presented in Table 10 , Table 11 and Table 12 respectively.

MRI analysis details of eligible studies.

MRI Analysis	Author	Year	Contrast	Statistical Tool	Statistical Method	Value
Resting	Martinez et al. [ ]	2013	(post- > pre-training) > (post>pre-control)	MATLAB; SPM8	TFCE uncorrected	<0.005
Structural	Haier et al. * [ ]	2009	(post>pre-training) > (post>pre-control)	MATLAB 7; SurfStat	FWE corrected	<0.005
	Kuhn et al. [ ]	2014	(post>pre-training) > (post>pre-control)	VBM8; SPM8	FWE corrected	<0.001
	West et al. [ ]	2017	(post>pre-training) > (post>pre-control)	Bpipe	Uncorrected	<0.0001
	West et al. [ ]	2018	(post>pre-training) > (post>pre-control)	Bpipe	Bonferroni corrected	<0.001
Task	Gleich et al. [ ]	2017	(post>pre-training) > (post>pre-control)	SPM12	Monte Carlo corrected	<0.05
	Haier et al. * [ ]	2009	(post>pre-training) > (post>pre-control)	SPM7	FDR corrected	<0.05
	Lee et al. [ ]	2012	(post>pre-training) > (post>pre-control)	FSL; FEAT	uncorrected	<0.01
	Lorenz et al. [ ]	2015	(post>pre-training) > (post>pre-control)	SPM8	Monte Carlo corrected	<0.05
	Roush [ ]	2013	post>pre-training	MATLAB 7; SPM8	uncorrected	=0.001

* Haier et al. conducted structural and task analyses. + Compared pre-training and post-training between groups without using contrast. TFCE, Threshold Free Cluster Enhancement; FEW, familywise error rate; FDR, false discovery rate.

Resting-State MRI specifications of eligible studies.

Author	Year	Resting State				Structural
Author	Year	Imaging	TR (s)	TE (ms)	Slice	Imaging	TR (s)	TE (ms)	Slice
]	2013	gradient-echo planar image	3	28.1	36	T1-weighted	0.92	4.2	158

Structural MRI specifications of eligible studies.

Author	Year	Imaging	TR (s)	TE (ms)
Kuhn et al. [ ]	2014	3D T1 weighted MPRAGE	2.5	4.77
West et al. [ ]	2017	3D gradient echo MPRAGE	2.3	2.91
West et al. [ ]	2018	3D gradient echo MPRAGE	2.3	2.91

Task-Based MRI specifications of eligible studies.

Author	Year	Task	BOLD				Structural
Author	Year	Task	Imaging	TR (s)	TE (ms)	Slice	Imaging	TR (s)	TE (ms)	Slice
Gleich et al. [ ]	2017	win–loss paradigm	T2 echo-planar image	2	30	36	T1-weighted	2.5	4.77	176
Haier et al. [ ]	2009	Tetris	Functional echo planar	2	29	ns	5-echo MPRAGE	2.53	1.64; 3.5; 5.36; 7.22; 9.08	ns
Lee et al. [ ]	2012	game control	fast echo-planar image	2	25	ns	T1-weighted MPRAGE	1.8	3.87	144
Lorenz et al. [ ]	2015	slot machine paradigm	T2 echo-planar image	2	30	36	T1-weighted MPRAGE	2.5	4.77	ns
Roush [ ]	2013	digit symbol substitution	fast echo-planar image	2	25	34	diffusion weighted image	ns	ns	ns

All analyses used 3 Tesla magnetic force; TR = repetition time; TE = echo time, ns = not specified.

4. Discussion

This literature review evaluated the effect of noncognitive-based video game intervention on the cognitive function of healthy people. Comparison of studies is difficult because of the heterogeneities of participant ages, beneficial effects, and durations. Comparisons are limited to studies sharing factors.

4.1. Participant Age

Video gaming intervention affects all age categories except for the children category. The exception derives from a lack of intervention studies using children as participants. The underlying reason for this exception is that the brain is still developing until age 10–12 [ 52 , 53 ]. Among the eligible studies were a study investigating adolescents [ 40 ], six studies investigating young adults [ 41 , 42 , 43 , 47 , 49 , 51 ] and two studies investigating older adults [ 48 , 50 ].

Differences among study purposes underlie the differences in participant age categories. The study by Haier et al. was intended to study adolescents because the category shows the most potential brain changes. The human brain is more sensitive to synaptic reorganization during the adolescent period [ 54 ]. Generally, grey matter decreases whereas white matter increases during the adolescent period [ 55 , 56 ]. By contrast, the cortical surface of the brain increases despite reduction of grey matter [ 55 , 57 ]. Six studies were investigating young adults with the intention of studying brain changes after the brain reaches maturity. The human brain reaches maturity during the young adult period [ 58 ]. Two studies were investigating older adults with the intention of combating difficulties caused by aging. The human brain shrinks as age increases [ 56 , 59 ], which almost invariably leads to declining cognitive function [ 59 , 60 ].

4.2. Beneficial Effects

Three beneficial outcomes were observed using MRI method: grey matter change [ 40 , 42 , 50 ], brain activity change [ 40 , 43 , 47 , 48 , 49 ], and functional connectivity change [ 41 ]. The affected brain area corresponds to how the respective games were played.

Four studies of 3D video gaming showed effects on the structure of hippocampus, dorsolateral prefrontal cortex (DLPFC), cerebellum [ 42 , 43 , 50 ], and DLPFC [ 43 ] and ventral striatum activity [ 49 ]. In this case, the hippocampus is used for memory [ 61 ] and scene recognition [ 62 ], whereas the DLPFC and cerebellum are used for working memory function for information manipulation and problem-solving processes [ 63 ]. The grey matter of the corresponding brain region has been shown to increase during training [ 20 , 64 ]. The increased grey matter of the hippocampus, DLPFC, and cerebellum are associated with better performance in reference and working memory [ 64 , 65 ].

The reduced activity of DLPFC found in the study by Gleich et al. corresponds to studies that showed reduced brain activity associated with brain training [ 66 , 67 , 68 , 69 ]. Decreased activity of the DLPFC after training is associated with efficiency in divergent thinking [ 70 ]. 3D video gaming also preserved reward systems by protecting the activity of the ventral striatum [ 71 ].

Two studies of puzzle gaming showed effects on the structure of the visual–spatial processing area, activity of the frontal area, and functional connectivity change. The increased grey matter of the visual–spatial area and decreased activity of the frontal area are similar to training-associated grey matter increase [ 20 , 64 ] and activity decrease [ 66 , 67 , 68 , 69 ]. In this case, visual–spatial processing and frontal area are used constantly for spatial prediction and problem-solving of Tetris. Functional connectivity of the multimodal integration and the higher-order executive system in the puzzle solving-based gaming of Professor Layton game corresponds to studies which demonstrated training-associated functional connectivity change [ 72 , 73 ]. Good functional connectivity implies better performance [ 73 ].

Strategy gaming affects the DLPFC activity, whereas rhythm gaming affects the activity of visuospatial working memory, emotional, and attention area. FPS gaming affects the structure of the hippocampus and amygdala. Decreased DLPFC activity is similar to training-associated activity decrease [ 66 , 67 , 68 , 69 ]. A study by Roush demonstrated increased activity of visuospatial working memory, emotion, and attention area, which might occur because of exercise and gaming in the Dance Revolution game. Results suggest that positive activations indicate altered functional areas by complex exercise [ 48 ]. The increased grey matter of the hippocampus and amygdala are similar to the training-associated grey matter increase [ 20 , 64 ]. The hippocampus is used for 3D navigation purposes in the FPS world [ 61 ], whereas the amygdala is used to stay alert during gaming [ 74 ].

4.3. Duration

Change of the brain structure and function was observed after 16 h of video gaming. The total durations of video gaming were 16–90 h. However, the gaming intensity must be noted because the gaming intensity varied: 1.5–10.68 h per week. The different intensities might affect the change of cognitive function. Cognitive intervention studies demonstrated intensity effects on the cortical thickness of the brain [ 75 , 76 ]. A similar effect might be observed in video gaming studies. More studies must be conducted to resolve how the intensity can be expected to affect cognitive function.

4.4. Criteria

Almost all studies used inclusion criteria “little/no experience with video games.” The criterion was used to reduce the factor of gaming-related experience on the effects of video gaming. Some of the studies also used specific handedness and specific sex of participants to reduce the variation of brain effects. Expertise and sex are shown to affect brain activity and structure [ 77 , 78 , 79 , 80 ]. The exclusion criterion of “MRI contraindication” is used for participant safety for the MRI protocol, whereas exclusion criteria of “psychiatric/mental illness”, “neurological illness”, and “medical illness” are used to standardize the participants.

4.5. Limitations and Recommendations

Some concern might be raised about the quality of methodology, assessed using Delphi criteria [ 45 ]. The quality was 3–9 (mean = 6.10; S.D. = 1.69). Low quality in most papers resulted from unspecified information corresponding to the criteria. Quality improvements for the studies must be performed related to the low quality of methodology. Allocation concealment, assessor blinding, care provider blinding, participant blinding, intention-to-treat analysis, and allocation method details must be improved in future studies.

Another concern is blinding and control. This type of study differs from medical studies in which patients can be blinded easily. In studies of these types, the participants were tasked to do either training as an active control group or to do nothing as a passive control group. The participants can expect something from the task. The expectation might affect the outcomes of the studies [ 81 , 82 , 83 ]. Additionally, the waiting-list control group might overestimate the outcome of training [ 84 ].

Considering the sample size, which was 20–75 (mean = 43.67; S.D. = 15.63), the studies must be upscaled to emphasize video gaming effects. There are four phases of clinical trials that start from the early stage and small-scale phase 1 to late stage and large-scale phase 3 and end in post-marketing observation phase 4. These four phases are used for drug clinical trials, according to the food and drug administration (FDA) [ 85 ]. Phase 1 has the purpose of revealing the safety of treatment with around 20–100 participants. Phase 2 has the purpose of elucidating the efficacy of the treatment with up to several hundred participants. Phase 3 has the purpose of revealing both efficacy and safety among 300–3000 participants. The final phase 4 has the purpose of finding unprecedented adverse effects of treatment after marketing. However, because medical studies and video gaming intervention studies differ in terms of experimental methods, slight modifications can be done for adaptation to video gaming studies.

Several unresolved issues persist in relation to video gaming intervention. First, no studies assessed chronic/long-term video gaming. The participants might lose their motivation to play the same game over a long time, which might affect the study outcomes [ 86 ]. Second, meta-analyses could not be done because the game genres are heterogeneous. To ensure homogeneity of the study, stricter criteria must be set. However, this step would engender a third limitation. Third, randomized controlled trial video gaming studies that use MRI analysis are few. More studies must be conducted to assess the effects of video gaming. Fourth, the eligible studies lacked cognitive tests to validate the cognitive change effects for training. Studies of video gaming intervention should also include a cognitive test to ascertain the relation between cognitive function and brain change.

5. Conclusions

The systematic review has several conclusions related to beneficial effects of noncognitive-based video games. First, noncognitive-based video gaming can be used in all age categories as a means to improve the brain. However, effects on children remain unclear. Second, noncognitive-based video gaming affects both structural and functional aspects of the brain. Third, video gaming effects were observed after a minimum of 16 h of training. Fourth, some methodology criteria must be improved for better methodological quality. In conclusion, acute video gaming of a minimum of 16 h is beneficial for brain function and structure. However, video gaming effects on the brain area vary depending on the video game type.

Acknowledgments

We would like to thank all our other colleagues in IDAC, Tohoku University for their support.

PRISMA Checklist of the literature review.

Section/Topic	#	Checklist Item	Reported on Page #

Title	1	Identify the report as a systematic review, meta-analysis, or both.	1

Structured summary	2	Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number.	1

Rationale	3	Describe the rationale for the review in the context of what is already known.	1, 2
Objectives	4	Provide an explicit statement of questions being addressed related to participants, interventions, comparisons, outcomes, and study design (PICOS).	2

Protocol and registration	5	Indicate if a review protocol exists, if and where it is accessible (e.g., Web address), and if available, provide registration information including registration number.	2
Eligibility criteria	6	Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.	2
Information sources	7	Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.	2
Search	8	Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.	2
Study selection	9	State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and if applicable, included in the meta-analysis).	3
Data collection process	10	Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.	3
Data items	11	List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.	3
Risk of bias in individual studies	12	Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.	2
Summary measures	13	State the principal summary measures (e.g., risk ratio, difference in means).	-
Synthesis of results	14	Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I ) for each meta-analysis.	-
Risk of bias across studies	15	Specify any assessment of risk of bias that might affect the cumulative evidence (e.g., publication bias, selective reporting within studies).	-
Additional analyses	16	Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.	-

Study selection	17	Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.	3,5
Study characteristics	18	For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.	5-11
Risk of bias within studies	19	Present data on risk of bias of each study, and if available, any outcome level assessment (see item 12).	5,6
Results of individual studies	20	For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.	4
Synthesis of results	21	Present results of each meta-analysis done, including confidence intervals and measures of consistency.	-
Risk of bias across studies	22	Present results of any assessment of risk of bias across studies (see Item 15).	-
Additional analysis	23	Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]).	-

Summary of evidence	24	Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).	12,13
Limitations	25	Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).	13
Conclusions	26	Provide a general interpretation of the results in the context of other evidence, and implications for future research.	14

Funding	27	Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review.	14

For more information, visit: www.prisma-statement.org .

Author Contributions

D.B.T., R.N., and R.K. designed the systematic review. D.B.T. and R.N. searched and selected the papers. D.B.T. and R.N. wrote the manuscript with R.K. All authors read and approved the final manuscript. D.B.T. and R.N. contributed equally to this work.

Study is supported by JSPS KAKENHI Grant Number 17H06046 (Grant-in-Aid for Scientific Research on Innovative Areas) and 16KT0002 (Grant-in-Aid for Scientific Research (B)).

Conflicts of Interest

None of the other authors has any conflict of interest to declare. Funding sources are not involved in the study design, collection, analysis, interpretation of data, or writing of the study report.

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Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping

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Introduction

Topic mapping

Mapping the neural basis of neuropsychiatric disorders

Empirical discovery of endophenotypes

Materials and Methods

Data extraction

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Canonical correlation analysis

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Factors associated with brain ageing - a systematic review

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