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A Unified Modeling Framework for State-Related Changes in High Dimensional Effective Brain Connectivity Ombao, Hernando


In this talk, we consider the challenge in modeling time-evolving effective connectivity, the dynamic changes in causal interactions between many different brain regions. Effective connectivity is traditionally assumed constant and modeled using stationary vector autoregressive (VAR) models. However, recent studies which focused on the undirected dynamic connectivity using sliding-window analysis or time-varying (TV) coefficient models fail to capture simultaneously both slow and abrupt changes. We present a unified framework for reliable and adaptive estimation of state-related changes in effective connectivity, based on switching VAR (SVAR) models. Under this model, the dynamic connectivity regimes are uniquely characterized by distinct high dimensional VAR processes, which switch between a finite number of underlying quasistationary brain states. The evolution of states and the associated directed dependencies are defined by a Markov chain and the SVAR parameters. Our algorithm has three stages: (Stage 1.) Feature extraction using TV-VAR coefficients which we estimate with different types of penalties; (Stage 2.) Preliminary regime identification, via clustering of the TV-VAR coefficients; (Stage 3.) Following the initial estimates from the first two stages, refined regime segmentation is accomplished by Kalman smoothing and SVAR parameter estimation via the expectation-maximization (EM) algorithm under a state-space formulation. Simulation results show accurate regime change detection and connectivity estimates by the SVAR approach. When applied to real motor-task fMRI and epileptic seizure EEG data, the proposed method was able to identify statedependent directed connectivity changes via the switching of the VAR states. This is in collaboration with Yuxiao Wang (UC Irvine) and Chee-Ming Ting (Univ Teknologi Malaysia)

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