A unified estimation framework for state-related changes in effective brain connectivity
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 br...
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2017
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| author | Samdin, Siti Balqis Ting, Chee Ming Ombao, Hernando Shaikh Salleh, Sheikh Hussain |
| author_facet | Samdin, Siti Balqis Ting, Chee Ming Ombao, Hernando Shaikh Salleh, Sheikh Hussain |
| author_sort | Samdin, Siti Balqis |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T19:57:43Z |
| format | Article |
| id | utm.eprints-66523 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:57:43Z |
| publishDate | 2017 |
| publisher | IEEE |
| record_format | dspace |
| spelling | utm.eprints-665232017-10-03T13:50:50Z http://eprints.utm.my/66523/ A unified estimation framework for state-related changes in effective brain connectivity Samdin, Siti Balqis Ting, Chee Ming Ombao, Hernando Shaikh Salleh, Sheikh Hussain R Medicine 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. IEEE 2017-01-04 Article PeerReviewed Samdin, Siti Balqis and Ting, Chee Ming and Ombao, Hernando and Shaikh Salleh, Sheikh Hussain (2017) A unified estimation framework for state-related changes in effective brain connectivity. IEEE Transactions on Biomedical Engineering, 64 (4). pp. 844-858. ISSN 0018-9294 http://dx.doi.org/10.1109/TBME.2016.2580738 DOI:10.1109/TBME.2016.2580738 |
| spellingShingle | R Medicine Samdin, Siti Balqis Ting, Chee Ming Ombao, Hernando Shaikh Salleh, Sheikh Hussain A unified estimation framework for state-related changes in effective brain connectivity |
| title | A unified estimation framework for state-related changes in effective brain connectivity |
| title_full | A unified estimation framework for state-related changes in effective brain connectivity |
| title_fullStr | A unified estimation framework for state-related changes in effective brain connectivity |
| title_full_unstemmed | A unified estimation framework for state-related changes in effective brain connectivity |
| title_short | A unified estimation framework for state-related changes in effective brain connectivity |
| title_sort | unified estimation framework for state related changes in effective brain connectivity |
| topic | R Medicine |
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