17: Distributed Learning for Whole-Brain Functional Connectivity Analysis in Resting-State fMRI

Music City Center 

Resting-state fMRI (rfMRI) is a powerful tool for characterizing brain-related phenotypes, but current approaches are often limited in their ability to efficiently capture the heterogeneous nature of functional connectivity due to lack of robust, scalable statistical methods. In this paper, we propose a new distributed learning framework for modeling the voxel-level dependencies across the whole brain, thus avoiding the need to average voxel outcomes within each region of interest. In addition, our method addresses confounder heterogeneity by integrating subject-level covariates in the estimation, which allows for comparing functional connectivity across diverse populations. We demonstrate the effectiveness and scalability of our approach in handling large rfMRI outcomes through simulations. Finally, we apply the proposed framework to study the association between brain connectivity and autism spectrum disorder (ASD), uncovering connectivity patterns that may advance our understanding of ASD-related neural mechanisms.

rfMRI

functional connectivity,

oxel-level dependencies

distributed learning

ASD

whole-brain modeling 

Section on Statistics in Imaging