A whole-brain modeling approach to identify individual and group variations in functional connectivity.

ABSTRACT: Resting-state functional connectivity is an important and widely used measure of individual and group differences. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a modeling approach that regresses whole-brain functional connectivity on covariates. Our approach is a mesoscale approach that enables identification of brain subnetworks. These subnetworks are composite of spatially independent components discovered by a dimension reduction approach (such as whole-brain group ICA) and covariate-related projections determined by the covariate-assisted principal regression, a recently introduced covariance matrix regression method. We demonstra

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC7821576 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature