TY  - JOUR
AU  - Vos de Wael, Reinder
AU  - Benkarim, Oualid
AU  - Paquola, Casey
AU  - Lariviere, Sara
AU  - Royer, Jessica
AU  - Tavakol, Shahin
AU  - Xu, Ting
AU  - Hong, Seok-Jun
AU  - Langs, Georg
AU  - Valk, Sofie
AU  - Misic, Bratislav
AU  - Milham, Michael
AU  - Margulies, Daniel
AU  - Smallwood, Jonathan
AU  - Bernhardt, Boris C.
TI  - BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets
JO  - Communications biology
VL  - 3
IS  - 1
SN  - 2399-3642
CY  - London
PB  - Springer Nature
M1  - FZJ-2020-04610
SP  - 103
PY  - 2020
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:32139786
UR  - <Go to ISI:>//WOS:000519705500007
DO  - DOI:10.1038/s42003-020-0794-7
UR  - https://juser.fz-juelich.de/record/888032
ER  -