Reviews: Time-dependent spatially varying graphical models, with application to brain fMRI data analysis

This paper studied the graphical structure in spatiotemporal data through transferring the time series data into an additive model, by assuming stationary temporal correlation structure and time-varying undirected Gaussian graphical model for spatial correlation structure. With the assumption that the spatial correlations change smoothly with time, they proposed estimators for both spatial and temporal structures based on kernel method and GLasso approach. The statistical convergence property of the estimators was provided under certain assumptions. The approach presented good performance in both simulation and fMRI data application studies. This paper is overall clearly written, with solid theoretical support and interesting application.