Activity Number:
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77
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #320641
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Title:
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Regime-Switching Dynamic Factor Models with Applications to Estimating Large-Scale Time-Varying Brain Connectivity
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Author(s):
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Chee-Ming Ting* and Hernando Ombao and S. Balqis Samdin and Sh-Hussain Salleh
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Companies:
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Universiti Teknologi Malaysia and University of California at Irvine and Universiti Teknologi Malaysia and Universiti Teknologi Malaysia
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Keywords:
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Regime-switching models ;
Dynamic Factor Models ;
Brain connectivity ;
fMRI
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Abstract:
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Recent studies on analyzing dynamic brain connectivity rely on sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes simultaneously. Emerging evidence suggests state-related changes in brain connectivity where dependence structure alternates between a finite number of latent states or regimes. Another challenge is inference of full-brain networks with large number of nodes. We employ a Markov-switching dynamic factor model in which the state-driven time-varying connectivity regimes of high-dimensional neural data are characterized by lower-dimensional common latent factors, following a regime-switching process. It enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We consider the switching VAR and SV model to quantity the directed and undirected connectivity. We propose a two-step estimation procedure: (1) extracting the factors using PCA and (2) estimating the switching models in a state-space form via ML method using Kalman filter and EM algorithm. The method is applied to indentify connectivity states in resting-state fMRI.
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Authors who are presenting talks have a * after their name.