ArticleBrain and behavior2021
A whole-brain modeling approach to identify individual and group variations in functional connectivity.
Article in Brain and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Connectivity Regression.Biostatistics (Oxford, England) · 2025Article
- Drinking severity mediates the relationship between hypothalamic connectivity and rule-breaking/intrusive behavior differently in young women and men: an exploratory study.Quantitative imaging in medicine and surgery · 2024Article
- Longitudinal regression of covariance matrix outcomes.Biostatistics (Oxford, England) · 2024Article
- Beyond massive univariate tests: Covariance regression reveals complex patterns of functional connectivity related to attention-deficit/hyperactivity disorder, age, sex, and response control.Biological psychiatry global open science · 2022Article
- A whole-brain modeling approach to identify individual and group variations in functional connectivity.Brain and behavior · 2021Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
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.
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Registered trials
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