nimare neuroimaging meta analysis research environment

NiMARE: Neuroimaging Meta-Analysis Research Environment NiMARE is a Python package for neuroimaging meta-analyses. It makes conducting scary meta-analyses a dream! ... {NiMARE: Neuroimaging Meta-Analysis Research Environment}, journal = {Aperture Neuro}} # This is the Zenodo citation for version 0.0.11. @software ...

NiMARE: Neuroimaging Meta-Analysis Research Environment. A Python library for coordinate- and image-based meta-analysis. Currently, NiMARE implements a range of image- and coordinate-based meta-analytic algorithms, as well as several methods for advanced meta-analytic methods, like automated annotation and functional decoding.

NiMARE is a Python package for performing meta-analyses, and derivative analyses using meta-analytic data, of the neuroimaging literature. While meta-analytic packages exist which implement one or two algorithms each, NiMARE provides a standard syntax for performing a wide range of analyses and for interacting with databases of coordinates and ...

We present NiMARE (Neuroimaging Meta‑Analysis Research Environment; RRID:SCR_0173981), a Python library for neuroimaging meta‑analyses and metaanalysis‑related analyses. NiMARE is an open ...

Coordinate‑Based Meta‑Analysis (CBMA) is performed by creating a NiMARE Dataset with coordinate information stored in the Dataset.coordinates attribute, which is then used in a CBMA Estimator.

Neuroimaging Meta-Analysis Research Environment (NiMARE) is a Python package for neuroimaging meta-analysis. NiMARE is developed in conjunction with a number of other members of the neuroinformatics community. The primary developer within the lab for NiMARE is Julio Peraza. Lab alumni Taylor Salo and Katie Bottenhorn also continue to contribute.

We present NiMARE (Neuroimaging Meta-Analysis Research Environment; RRID:SCR_017398), a Python library for neuroimaging meta-analyses and meta-analysis-related analyses (Salo et al., 2022). NiMARE is an open source, collaboratively-developed package that implements a range of meta-analytic algorithms, including coordinate- and image-based meta ...

We present NiMARE (Neuroimaging Meta-Analysis Resea rch Environment; RRID:SCR_017398), a Python library fo r neuroimaging meta-analyses and meta-analysis-related analyses ( Salo et. al., 2022 ...

Introduction¶. We introduce NiMARE (Neuroimaging Meta-Analysis Research Environment; RRID:SCR_017398; Salo et al. []), a Python package for analyzing meta-analytic neuroimaging data.NiMARE is a new library developed as a component in a burgeoning open-source meta-analytic ecosystem for neuroimaging data, which currently includes Neurosynth, NeuroVault, NeuroQuery, and PyMARE.

NiMARE: Neuroimaging Meta-Analysis Research Environment. Jupyter Notebook JavaScript Python Submitted 15 November 2021 • Published 13 June 2022.

We present NiMARE (Neuroimaging Meta-Analysis Research Environment; RRID:SCR_017398), Python library for neuroimaging meta-analyses and meta-analysis-related analyses (Salo et al., 2022). NiMARE is an open source, collaboratively-developed package that implements. range of meta-analytic algorithms, including coordinate- and image-based meta ...

NiMARE (Neuroimaging Meta-Analysis Research Environment) is a Python package for coordinate-based and image-based meta-analysis of neuroimaging data. License. NiMARE is licensed under the terms of the MIT license. See the file 'LICENSE' for information on the history of this software, terms & conditions for usage, and a DISCLAIMER OF ALL ...

Meta-analytic functional decoding . Functional decoding performed with meta-analytic data, refers to methods which attempt to predict mental states from neuroimaging data using a large-scale meta-analytic database (Smith et al., 2009).Such analyses may also be referred to as "informal reverse inference" (Poldrack, 2011), "functional characterization analysis" (Bzdok, Laird, et al ...

NiMARE Overview. NiMARE is designed to be modular and object-oriented, with an interface that mimics popular Python libraries, including scikit-learn and nilearn. This standardized interface allows users to employ a wide range of meta-analytic algorithms without having to familiarize themselves with the idiosyncrasies of algorithm-specific tools.

Coordinate-based meta-analysis combines evidence from a collection of Neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally ...

NiMARE: Neuroimaging Meta-Analysis Research Environment; Edit on GitHub; Performing meta-analyses NiMARE implements a number of coordinate- and image-based meta-analysis algorithms in its meta module. In the examples below, we exhibit a range of meta-analyses that can be done with coordinates and/or images in NiMARE. ...

Example: KernelTransformers and CBMA Estimators . Estimators refer to the core meta-analytic algorithm. The Estimator classes take a kernel object as a parameter, and use that kernel to (1) transform the coordinates into modeled activation maps, (2) combine those modeled activation maps into a summary statistic map, (3) derive a transformation from summary statistic to z-score, and (4 ...

Image-based meta-analysis (IBMA) methods perform a meta-analysis directly on brain images (either whole-brain or partial) rather than on extracted peaks. On paper, IBMA is superior to CBMA in virtually all respects, as the availability of analysis-level parameter and variance estimates at all analyzed voxels allows researchers to use the full ...

In this meta-analysis of genome-wide association studies (GWAS) of blood NfL levels from eleven cohorts of European ancestry, we identify two genome-wide significant loci at 16p12 (UMOD) and 17q24 ...

Simulating data before you run your meta-analysis is a great way to test your assumptions and see how the meta-analysis would perform with simplified data import matplotlib.pyplot as plt from nilearn.plotting import plot_stat_map from nimare.correct import FDRCorrector from nimare.generate import create_coordinate_dataset from nimare.meta ...

Coordinate-based meta-analysis (CBMA) is currently the most popular method for neuroimaging meta-analysis, given that the majority of fMRI papers currently report their findings as peaks of statistically significant clusters in standard space and do not release unthresholded statistical maps. These peaks indicate where significant results were ...

Additionally, NiMARE contains fetching and conversion tools for a number of meta-analytic resources, including Neurosynth, NeuroQuery, NeuroVault, and, to a limited extent, BrainMap. In the examples below, we show what a Dataset can do and exhibit tools for working with data from external meta-analytic resources.