@article{30dda01ba19a4cdabc0ca9ce5cde1c8c,
title = "Machine learning fMRI classifier delineates subgroups of schizophrenia patients",
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.",
keywords = "MVPA, N-back, OCD, R-IPS, Schizo-obsessive, Searchlight",
author = "Maya Bleich-Cohen and Shahar Jamshy and Haggai Sharon and Ronit Weizman and Nathan Intrator and Michael Poyurovsky and Talma Hendler",
note = "Publisher Copyright: {\textcopyright} 2014 Elsevier B.V.",
year = "2014",
month = dec,
day = "1",
doi = "10.1016/j.schres.2014.10.033",
language = "אנגלית",
volume = "160",
pages = "196--200",
journal = "Schizophrenia Research",
issn = "0920-9964",
publisher = "Elsevier B.V.",
number = "1-3",
}