http://www.nitrc.org/forum/forum.php?forum_id=4603 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Machine learning fMRI classifier delineates subgroups of schizophrenia patients.</b></p> <p>Schizophr Res. 2014 Nov 11;160(1-3):196-200</p> <p>Authors: Bleich-Cohen M, Jamshy S, Sharon H, Weizman R, Intrator N, Poyurovsky M, Hendler T</p> <p>Abstract<br/> BACKGROUND: The search for a validated neuroimaging-based brain marker in psychiatry has thus far been fraught with both clinical and methodological difficulties. The present study aimed to apply a novel data-driven machine-learning approach to functional Magnetic Resonance Imaging (fMRI) data obtained during a cognitive task in order to delineate the neural mechanisms involved in two schizophrenia subgroups: schizophrenia patients with and without Obsessive-Compulsive Disorder (OCD).<br/> METHODS: 16 schizophrenia patients with OCD ("schizo-obsessive"), 17 pure schizophrenia patients, and 20 healthy controls underwent fMRI while performing a working memory task. A whole brain search for activation clusters of cognitive load was performed using a recently developed data-driven multi-voxel pattern analysis (MVPA) approach, termed Searchlight Based Feature Extraction (SBFE), and which yields a robust fMRI-based classifier.<br/> RESULTS: The SBFE successfully classified the two schizophrenia groups with 91% accuracy based on activations in the right intraparietal sulcus (r-IPS), which further correlated with reduced symptom severity among schizo-obsessive patients.<br/> CONCLUSIONS: The results indicate that this novel SBFE approach can successfully delineate between symptom dimensions in the context of complex psychiatric morbidity.<br/> </p><p>PMID: 25464921 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Fri, 01 May 2026 22:46:17 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator