Functional brain networks and schizophrenia analysis with fMRI by Multiset Canonical Correlation Analysis

In this work the multivariate technique called Multiset Canonical Correlation Analysis (M-CCA) is applied to study a group of functional Magnetic Resonance Imaging (fMRI) datasets acquired during a set of working memory (WM) tasks. The examined subjects are a small group of schizophrenic patients and an equal number of controls (healthy subjects). The purpose of the paper is to show that M-CCA is able to identify specific areas of the brain network that can be related with the sources of activity during the WM task. It is also shown that the different degree of activation between the two groups allows to discriminate between Controls and Patients and then provides a promising way for a pre-screening classification of successive dataset. This result can contribute, in future, to populate a database of specific brain activation areas of mental disorders.