Dynamic Functional Connectivity Change-Point Detection With Random Matrix Theory Inference - Kim, Jaehee; Jeong, Woorim; Chung, Chun Kee (Frontiers in Neuroscience, 2021)
Functional magnetic resonance imaging (fMRI) data sets are large and have complex dependence structure. They often include multiple sessions with temporal dependencies between the corresponding estimates of mean neural activity. Spatial correlations between brain activity measurements in different locations exist as well. Recent studies have shown that the functional connectivity (FC) varies according to time and location which should be incorporated into the model. Modeling this dynamic FC requires time-varying measures of spatial region of interest (ROI) sets. In this paper, we propose a method of detecting a change-point based on the maximum of eigenvalues via random matrix theory (RMT). From covariance matrices for FC of all ROI’s, the temporal change-point of FC is decided by an RMT approach. Simulation results show that our proposed method can detect meaningful FC change-points. We also illustrate the effectiveness of our FC detection approach by applying our method to epilepsy data where change-points detected are explained by the changes in memory capacity. Our study shows the possibility of RMT based approach in the dynamic functional connectivity (DFC) change-point problem and in studying the complex dynamic pattern of functional brain interactions.