Dynamical brain connectivity estimation using GARCH models: An application to personality neuroscience

摘要: It has recently become evident that the functional connectome of human brain is a dynamical entity whose time evolution carries important information underpinning physiological function as well its disease-related aberrations. While simple sliding window approaches have had some success in estimating connectivity MRI (fMRI) context, these methods suffer from limitations related to arbitrary choice length and limited resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models been employed generate covariance which can be applied fMRI. Here, we employ GARCH-based method (dynamic correlation - DCC) estimate Human Connectome Project (HCP) dataset study how dynamic behaviors personality described by five-factor model. Openness, trait curiosity creativity, only associated with significant differences amount time-variability (but not absolute median connectivity) several inter-network connections brain. The DCC offers novel extract aid elucidating neurophysiological phenomena conventional static estimates are insensitive.