Estimating dynamic connectivity states in fMRI using regime-switching factor models
We consider the challenges in estimating the state-related changes in brain connectivity networks with a large number of nodes. Existing studies use the sliding-window analysis or time-varying coefficient models, which are unable to capture both smooth and abrupt changes simultaneously, and rely on...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers Inc.
2018
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| author | Ting, Chee Ming Ombao, Hernando Samdin, S. Balqis Salleh, Sh. Hussain |
| author_facet | Ting, Chee Ming Ombao, Hernando Samdin, S. Balqis Salleh, Sh. Hussain |
| author_sort | Ting, Chee Ming |
| collection | ePrints |
| description | We consider the challenges in estimating the state-related changes in brain connectivity networks with a large number of nodes. Existing studies use the sliding-window analysis or time-varying coefficient models, which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model, which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors. We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity. The model enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR model, and 3) constructing high-dimensional state connectivity metrics based on the subspace estimates. Simulation results show that our estimator outperforms K -means clustering of time-windowed coefficients, providing more accurate estimate of time-evolving connectivity. It achieves percentage of reduction in mean squared error by 60% when the network dimension is comparable to the sample size. When applied to the resting-state fMRI data, our method successfully identifies modular organization in the resting-statenetworksin consistencywith other studies. It further reveals changes in brain states with variations across subjects and distinct large-scale directed connectivity patterns across states. |
| first_indexed | 2024-03-05T20:36:32Z |
| format | Article |
| id | utm.eprints-85647 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:36:32Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | utm.eprints-856472020-07-07T05:16:13Z http://eprints.utm.my/85647/ Estimating dynamic connectivity states in fMRI using regime-switching factor models Ting, Chee Ming Ombao, Hernando Samdin, S. Balqis Salleh, Sh. Hussain Q Science (General) We consider the challenges in estimating the state-related changes in brain connectivity networks with a large number of nodes. Existing studies use the sliding-window analysis or time-varying coefficient models, which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model, which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors. We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity. The model enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR model, and 3) constructing high-dimensional state connectivity metrics based on the subspace estimates. Simulation results show that our estimator outperforms K -means clustering of time-windowed coefficients, providing more accurate estimate of time-evolving connectivity. It achieves percentage of reduction in mean squared error by 60% when the network dimension is comparable to the sample size. When applied to the resting-state fMRI data, our method successfully identifies modular organization in the resting-statenetworksin consistencywith other studies. It further reveals changes in brain states with variations across subjects and distinct large-scale directed connectivity patterns across states. Institute of Electrical and Electronics Engineers Inc. 2018-04 Article PeerReviewed Ting, Chee Ming and Ombao, Hernando and Samdin, S. Balqis and Salleh, Sh. Hussain (2018) Estimating dynamic connectivity states in fMRI using regime-switching factor models. IEEE Transactions on Medical Imaging, 37 (4). pp. 1011-1023. ISSN 0278-0062 http://dx.doi.org/10.1109/TMI.2017.2780185 |
| spellingShingle | Q Science (General) Ting, Chee Ming Ombao, Hernando Samdin, S. Balqis Salleh, Sh. Hussain Estimating dynamic connectivity states in fMRI using regime-switching factor models |
| title | Estimating dynamic connectivity states in fMRI using regime-switching factor models |
| title_full | Estimating dynamic connectivity states in fMRI using regime-switching factor models |
| title_fullStr | Estimating dynamic connectivity states in fMRI using regime-switching factor models |
| title_full_unstemmed | Estimating dynamic connectivity states in fMRI using regime-switching factor models |
| title_short | Estimating dynamic connectivity states in fMRI using regime-switching factor models |
| title_sort | estimating dynamic connectivity states in fmri using regime switching factor models |
| topic | Q Science (General) |
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