A new time-varying graphical model for learning dynamic brain networks of task fMRI

Thomas M. Menino Convention & Exhibition Center 

The problem of time-varying graphical modeling arises in the analysis of multivariate functional data, where repeated measurements of multiple correlated variables are collected over a sequence of time points. We propose a novel functional approach for estimating time-varying graphical models to characterize dynamic brain network patterns from single-subject fMRI data. The proposed method employs kernel-weighted penalties based on the Kullback–Leibler (KL) divergence to capture local temporal information while enforcing structural smoothness across graphs. In addition, the method incorporates task-design information in fMRI studies, enhancing its ability to detect rapidly evolving network structures. Simulation studies and analyses of task-based fMRI data demonstrate that the proposed approach effectively integrates information from both local and global temporal neighborhoods, resulting in improved temporal resolution and more accurate inference of dynamic brain networks.

time-varying graphical model

functional data

functional connectivity