Universiti Teknologi Malaysia Institutional Repository

Estimating dynamic connectivity states in fMRI using regime-switching factor models

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

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Official URL: http://dx.doi.org/10.1109/TMI.2017.2780185

Abstract

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.

Item Type:Article
Uncontrolled Keywords:factor analysis, fMRI
Subjects:Q Science > Q Science (General)
Divisions:Biosciences and Medical Engineering
ID Code:85647
Deposited By: Widya Wahid
Deposited On:07 Jul 2020 13:16
Last Modified:07 Jul 2020 13:16

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