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 br...
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Online Access: | https://doi.org/10.1002/brb3.1942 |
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doaj-fa7deb9e05624f778920c1ef3b1375f92021-10-07T11:31:04ZengWileyBrain and Behavior2162-32792021-01-01111n/an/a10.1002/brb3.1942A whole‐brain modeling approach to identify individual and group variations in functional connectivityYi Zhao0Brian S. Caffo1Bingkai Wang2Chiang‐Shan R. Li3Xi Luo4Department of Biostatistics Indiana University School of Medicine Indianapolis IN USADepartment of Biostatistics Johns Hopkins Bloomberg School of Public Health The University of Texas Health Science Center at Houston Houston TX USADepartment of Biostatistics Johns Hopkins Bloomberg School of Public Health The University of Texas Health Science Center at Houston Houston TX USADepartment of Psychiatry Yale School of Medicine The University of Texas Health Science Center at Houston Houston TX USADepartment of Biostatistics and Data Science The University of Texas Health Science Center at Houston Houston TX USAAbstract 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 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.https://doi.org/10.1002/brb3.1942 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi Zhao Brian S. Caffo Bingkai Wang Chiang‐Shan R. Li Xi Luo |
spellingShingle |
Yi Zhao Brian S. Caffo Bingkai Wang Chiang‐Shan R. Li Xi Luo A whole‐brain modeling approach to identify individual and group variations in functional connectivity Brain and Behavior |
author_facet |
Yi Zhao Brian S. Caffo Bingkai Wang Chiang‐Shan R. Li Xi Luo |
author_sort |
Yi Zhao |
title |
A whole‐brain modeling approach to identify individual and group variations in functional connectivity |
title_short |
A whole‐brain modeling approach to identify individual and group variations in functional connectivity |
title_full |
A whole‐brain modeling approach to identify individual and group variations in functional connectivity |
title_fullStr |
A whole‐brain modeling approach to identify individual and group variations in functional connectivity |
title_full_unstemmed |
A whole‐brain modeling approach to identify individual and group variations in functional connectivity |
title_sort |
whole‐brain modeling approach to identify individual and group variations in functional connectivity |
publisher |
Wiley |
series |
Brain and Behavior |
issn |
2162-3279 |
publishDate |
2021-01-01 |
description |
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 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. |
url |
https://doi.org/10.1002/brb3.1942 |
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