A whole‐brain modeling approach to identify individual and group variations in functional connectivity. Issue 1 (18th November 2020)

• Record Type: Journal Article
• Authors: Zhao, Yi; Caffo, Brian S.; Wang, Bingkai; Li, Chiang‐Shan R.; Luo, Xi
• Is Part Of: Brain and behavior. Volume 11:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-18
• Subjects: Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.1942
• Languages: English
• ISSNs: 2162-3279

Abstract

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 meso scale 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 demonstrate the efficacy of this approach using a resting‐state fMRI dataset of a medium‐sized cohort of subjects obtained from the Human Connectome Project. The results suggest that the approach may improve statistical power in detecting interaction effects of gender and alcohol on whole‐brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate‐related differences in functional connectivity. Abstract : 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 covariate‐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 connectivity, and in identifying the brain areas contributing significantly to the covariate‐related differences in functional connectivity.