http://www.nitrc.org/forum/forum.php?forum_id=8474 <table border="0" width="100%"><tr><td align="left"/><td align="right"><a href="https://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&amp;cmd=Link&amp;LinkName=pubmed_pubmed&amp;from_uid=29610078">Related Articles</a></td></tr></table> <p><b>Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models.</b></p> <p>IEEE Trans Med Imaging. 2018 Apr;37(4):1011-1023</p> <p>Authors: Ting CM, Ombao H, Samdin SB, Salleh SH</p> <p>Abstract<br/> 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 -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.<br/> </p><p>PMID: 29610078 [PubMed - in process]</p> en-us Copyright 2000-2026 NITRC OSI Tue, 28 Apr 2026 12:37:38 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator