TY - JOUR
T1 - A unified estimation framework for state-related changes in effective brain connectivity
AU - Samdin, S. Balqis
AU - Ting, Chee Ming
AU - Ombao, Hernando
AU - Salleh, Sh Hussain
N1 - Funding Information:
This work was supported by the Universiti Teknologi Malaysia and the Ministry of Higher Education, Malaysia under Grant R.J130000.7845.4L840 and Grant R.J130000.7809.4F668. The work of H. Ombao was supported in part by the US National Science Foundation (DMS and SES).
Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/4
Y1 - 2017/4
N2 - Objective: This paper addresses the critical problem of estimating time-evolving effective brain connectivity. Current approaches based on sliding window analysis or time-varying coefficient models do not simultaneously capture both slow and abrupt changes in causal interactions between different brain regions. Methods: To overcome these limitations, we develop a unified framework based on a switching vector autoregressive (SVAR) model. Here, the dynamic connectivity regimes are uniquely characterized by distinct vector autoregressive (VAR) processes and allowed to switch between quasi-stationary brain states. The state evolution and the associated directed dependencies are defined by a Markov process and the SVAR parameters. We develop a three-stage estimation algorithm for the SVAR model: 1) feature extraction using time-varying VAR (TV-VAR) coefficients, 2) preliminary regime identification via clustering of the TV-VAR coefficients, 3) refined regime segmentation by Kalman smoothing and parameter estimation via expectation-maximization algorithm under a state-space formulation, using initial estimates from the previous two stages. Results: The proposed framework is adaptive to state-related changes and gives reliable estimates of effective connectivity. Simulation results show that our method provides accurate regime change-point detection and connectivity estimates. In real applications to brain signals, the approach was able to capture directed connectivity state changes in functional magnetic resonance imaging data linked with changes in stimulus conditions, and in epileptic electroencephalograms, differentiating ictal from nonictal periods. Conclusion: The proposed framework accurately identifies state-dependent changes in brain network and provides estimates of connectivity strength and directionality. Significance: The proposed approach is useful in neuroscience studies that investigate the dynamics of underlying brain states.
AB - Objective: This paper addresses the critical problem of estimating time-evolving effective brain connectivity. Current approaches based on sliding window analysis or time-varying coefficient models do not simultaneously capture both slow and abrupt changes in causal interactions between different brain regions. Methods: To overcome these limitations, we develop a unified framework based on a switching vector autoregressive (SVAR) model. Here, the dynamic connectivity regimes are uniquely characterized by distinct vector autoregressive (VAR) processes and allowed to switch between quasi-stationary brain states. The state evolution and the associated directed dependencies are defined by a Markov process and the SVAR parameters. We develop a three-stage estimation algorithm for the SVAR model: 1) feature extraction using time-varying VAR (TV-VAR) coefficients, 2) preliminary regime identification via clustering of the TV-VAR coefficients, 3) refined regime segmentation by Kalman smoothing and parameter estimation via expectation-maximization algorithm under a state-space formulation, using initial estimates from the previous two stages. Results: The proposed framework is adaptive to state-related changes and gives reliable estimates of effective connectivity. Simulation results show that our method provides accurate regime change-point detection and connectivity estimates. In real applications to brain signals, the approach was able to capture directed connectivity state changes in functional magnetic resonance imaging data linked with changes in stimulus conditions, and in epileptic electroencephalograms, differentiating ictal from nonictal periods. Conclusion: The proposed framework accurately identifies state-dependent changes in brain network and provides estimates of connectivity strength and directionality. Significance: The proposed approach is useful in neuroscience studies that investigate the dynamics of underlying brain states.
KW - Dynamic brain connectivity
KW - regime-switching models
KW - state-space models
KW - vector autoregressive models
UR - http://www.scopus.com/inward/record.url?scp=85017617079&partnerID=8YFLogxK
U2 - 10.1109/TBME.2016.2580738
DO - 10.1109/TBME.2016.2580738
M3 - Article
C2 - 27323355
AN - SCOPUS:85017617079
SN - 0018-9294
VL - 64
SP - 844
EP - 858
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 4
ER -