http://www.nitrc.org/forum/forum.php?forum_id=4625 <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=25488236">Related Articles</a></td></tr></table> <p><b>Findings in resting-state fMRI by differences from K-means clustering.</b></p> <p>Stud Health Technol Inform. 2014;207:300-10</p> <p>Authors: Chyzhyk D, Graña M</p> <p>Abstract<br/> Resting state fMRI has growing number of studies with diverse aims, always centered on some kind of functional connectivity biomarker obtained from correlation regarding seed regions, or by analytical decomposition of the signal towards the localization of the spatial distribution of functional connectivity patterns. In general, studies are computationally costly and very sensitive to noise and preprocessing of data. In this paper we consider clustering by K-means as a exploratory procedure which can provide some results with little computational effort, due to efficient implementations that are readily available. We demonstrate the approach on a dataset of schizophrenia patients, finding differences between patients with and without auditory hallucinations. <br/> </p><p>PMID: 25488236 [PubMed - in process]</p> en-us Copyright 2000-2026 NITRC OSI Fri, 07 Aug 2026 11:30:49 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator