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Activity Number: 630
Type: Invited
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: WNAR
Abstract #318201
Title: A Unified Modeling Framework for State-Related Changes in High-Dimensional Effective Brain Connectivity
Author(s): Hernando Ombao* and Yuxiao Wang and Chee-Ming Ting
Companies: University of California at Irvine and University of California at Irvine and Universiti Teknologi Malaysia
Keywords: Multivariate time series ; State-space model ; Switching models ; Vector autoregressive models ; Clustering

We develop a model for time-evolving effective connectivity and dynamic changes in causal interactions between many different brain regions. Our approach is a unified framework for reliable and adaptive estimation of state-related changes in effective connectivity, based on switching VAR (SVAR) models. Regimes are uniquely characterized by high dimensional VAR processes, which switch between a finite number of underlying quasi-stationary brain states. The evolution of states and the associated directed dependencies are defined by a Markov chain and the SVAR parameters. The algorithm has three stages: (1.) feature extraction using TV-VAR coefficients; (2.) preliminary regime identification, via clustering of the TV-VAR coefficients; (3.) refined regime segmentation by Kalman smoothing and SVAR parameter estimation via the expectation-maximization (EM) algorithm using the initial estimates from the first two stages. The proposed method was able to identify state-dependent directed connectivity changes via the switching of the VAR states in motor-task fMRI and epileptic seizure EEG data.

Authors who are presenting talks have a * after their name.

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