http://www.nitrc.org/forum/forum.php?forum_id=6972 <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=28065836">Related Articles</a></td></tr></table> <p><b>Learning Effective Connectivity from fMRI using Autoregressive hidden Markov model with missing data.</b></p> <p>J Neurosci Methods. 2017 Jan 05;:</p> <p>Authors: Dang S, Chaudhury S, Lall B, Roy PK</p> <p>Abstract<br/> BACKGROUND: Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity.<br/> NEW METHOD: The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence.<br/> RESULTS: The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs.<br/> CONTROLS: The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method.<br/> COMPARISON: The proposed architecture leads to reliable estimates of EC than the existing latent models.<br/> CONCLUSIONS: This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process.<br/> </p><p>PMID: 28065836 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Wed, 06 May 2026 2:14:16 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator