Data driven analysis of functional brain networks in fMRI for schizophrenia investigation

The purpose of this article is to present a methodology to identify the sources of activity in brain networks from functional magnetic resonance imaging (fMRI) data using the multiset canonical correlation analysis algorithm. The aim is to lay the foundations for a screening marker to be used as indicator of mental diseases. Group analysis blind source separation methods have proved reliable to extract the latent sources underlying the brain activities but currently there is no recognized biomarker for mental disorders. Recent studies have identified alterations in the so called default mode network (DMN) that are common to several neuropsychiatric disorders, including schizophrenia. In particular, here we account for the hypothesis that the alterations in the DMN activity can be effectively highlighted by analyzing the transient states between two different tasks. A set of fMRI data acquired from 18 subjects performing working memory tasks is investigated for such purpose. Subjects are patients affected by schizophrenia for one half and healthy control subjects for the other. Under these conditions, the proposed methodology provides high discrimination performances in terms of classification error, thereby providing promising results for a preliminary tool able to monitor the disease state or to perform a prescreening for patients at risk for schizophrenia.