Time varying brain connectivity modeling using FMRI signals

Inferring brain connectivity networks has been increasingly important for understanding brain functioning. It is suggested that brain is inherently non-stationary and the dynamic patterns of brain networks may provide deeper insights into brain function. However, the majority of current models assume that brain connectivity networks have time invariant structures, neglecting the variability in brain interactions over time. To investigate time varying brain connectivity networks, a stick time varying model is presented in this paper. Simulation results demonstrate that the proposed method could improve the accuracy in estimating time-dependent connectivity patterns. It is also applied to real fMRI data set for studying time-varying resting-state brain connectivity networks.