BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
05 03 2020
05 03 2020
Historique:
received:
28
09
2019
accepted:
24
01
2020
entrez:
7
3
2020
pubmed:
7
3
2020
medline:
16
6
2021
Statut:
epublish
Résumé
Understanding how cognitive functions emerge from brain structure depends on quantifying how discrete regions are integrated within the broader cortical landscape. Recent work established that macroscale brain organization and function can be described in a compact manner with multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. Here, we present BrainSpace, a Python/Matlab toolbox for (i) the identification of gradients, (ii) their alignment, and (iii) their visualization. Our toolbox furthermore allows for controlled association studies between gradients with other brain-level features, adjusted with respect to null models that account for spatial autocorrelation. Validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.
Identifiants
pubmed: 32139786
doi: 10.1038/s42003-020-0794-7
pii: 10.1038/s42003-020-0794-7
pmc: PMC7058611
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
103Subventions
Organisme : CIHR
ID : FDN-154298
Pays : Canada
Organisme : NIMH NIH HHS
ID : U54 MH091657
Pays : United States
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