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

This paper introduces a modeling approach that regresses whole ‐brain functional connectivity on covariates. Our approach enables the identification of brain subnetworks, which 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 c ovariate‐assisted principal regression, a recently introduced covariance matrix regression method. Applying to the Human Connectome Project data, the results show that the approach enjoys improved statistical power in detecting interaction effects of sex and alcohol on whole‐brain functional con nectivity, and in identifying the brain areas contributing significantly to the covariate‐related differences in functional connectivity. AbstractResting ‐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 pa per introduces a modeling approach that regresses whole‐brain functional connectivity on covariates. Our approach is amesoscale 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 deter...
Source: Brain and Behavior - Category: Neurology Authors: Tags: Original Research Source Type: research