Machine learning fMRI classifier delineates subgroups of schizophrenia patients.

Schizophr Res. 2014 Nov 11;160(1-3):196-200

Authors: Bleich-Cohen M, Jamshy S, Sharon H, Weizman R, Intrator N, Poyurovsky M, Hendler T

Abstract
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).
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.
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.
CONCLUSIONS: The results indicate that this novel SBFE approach can successfully delineate between symptom dimensions in the context of complex psychiatric morbidity.

PMID: 25464921 [PubMed - as supplied by publisher]