http://www.nitrc.org/forum/forum.php?forum_id=5374 <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=26187550">Related Articles</a></td></tr></table> <p><b>Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.</b></p> <p>Psychiatry Res. 2015 Jul 5;</p> <p>Authors: Sato JR, Moll J, Green S, Deakin JF, Thomaz CE, Zahn R</p> <p>Abstract<br/> Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the high potential of our fMRI signature as a biomarker of MD vulnerability.<br/> </p><p>PMID: 26187550 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sun, 03 May 2026 16:25:06 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator