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BrainStat: A toolbox for brain-wide statistics and multimodal feature associations

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Title
BrainStat: A toolbox for brain-wide statistics and multimodal feature associations
Author(s)
Larivière, S.; Bayrak, Ş.; Vos, de Wael R.; Benkarim, O.; Herholz, P.; Rodriguez-Cruces, R.; Paquola, C.; Seok-Jun Hong; Misic, B.; Evans, A.C.; Valk, S.L.; Bernhardt, B.C.
Publication Date
2023-02
Journal
NeuroImage, v.266
Publisher
Academic Press Inc.
Abstract
Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation. © 2022
URI
https://pr.ibs.re.kr/handle/8788114/13035
DOI
10.1016/j.neuroimage.2022.119807
ISSN
1053-8119
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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