http://www.nitrc.org/forum/forum.php?forum_id=7836 <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=28924878">Related Articles</a></td></tr></table> <p><b>Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI.</b></p> <p>J Digit Imaging. 2017 Sep 18;:</p> <p>Authors: Bae Y, Kumarasamy K, Ali IM, Korfiatis P, Akkus Z, Erickson BJ</p> <p>Abstract<br/> Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and local parameters of functional connectivity were extracted for classification. We found decreased global and local network connectivity in subjects with schizophrenia, particularly in the anterior right cingulate cortex, the superior right temporal region, and the inferior left parietal region as compared to healthy subjects. Using support vector machine and 10-fold cross-validation, nine features reached 92.1% prediction accuracy, respectively. Our results suggest that there are significant differences between control and schizophrenic subjects based on regional brain activity detected with fMRI.<br/> </p><p>PMID: 28924878 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sat, 02 May 2026 19:44:47 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator