http://www.nitrc.org/forum/forum.php?forum_id=4299 <table border="0" width="100%"><tr><td align="left"/><td align="right"><a href="http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&amp;cmd=Link&amp;LinkName=pubmed_pubmed&amp;from_uid=25178438">Related Articles</a></td></tr></table> <p><b>Sparse dictionary learning of resting state fMRI networks.</b></p> <p>Int Workshop Pattern Recognit Neuroimaging. 2012 Jul 2;:73-76</p> <p>Authors: Eavani H, Filipovych R, Davatzikos C, Satterthwaite TD, Gur RE, Gur RC</p> <p>Abstract<br/> Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.<br/> </p><p>PMID: 25178438 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Fri, 01 May 2026 8:24:59 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator