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Larivière, S.* ; Bayrak, Ş.* ; Vos de Wael, R.* ; Benkarim, O.* ; Herholz, P.* ; Rodríguez-Cruces, R.* ; Paquola, C.* ; Hong, S.J.* ; Misic, B.* ; Evans, A.C.* ; Valk, S.L.* ; Bernhardt, B.C.*

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

Neuroimage 266:119807 (2023)
Verlagsversion DOI
Open Access Gold
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Multivariate Analysis ; Neuroimaging ; Univariate Analysis
ISSN (print) / ISBN 1053-8119
e-ISSN 1095-9572
Zeitschrift NeuroImage - a Journal of Brain Function
Quellenangaben Band: 266, Heft: , Seiten: , Artikelnummer: 119807 Supplement: ,
Verlag Elsevier
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz AI - FZJ (HAI - FZJ)