Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms

Abstract

Neuroimaging: Brain connectivity pattern predicts symptom severity Brain network analyses from functional magnetic resonance imaging (fMRI) data may help diagnose schizophrenia and predict symptom severity. Detecting neuroimaging patterns requires large-scale analysis across multiple data sets. Mina Gheiratmand and colleagues from the University of Alberta, along with researchers at the IBM T.J. Watson Research Center analyzed brain imaging data from the Function Biomedical Informatics Research Network, a study designed to test the reproducibility of brain scan results taken on different fMRI machines from people with schizophrenia and schizoaffective disorders, as well as healthy controls. They studied brain networks at different levels of resolution from data gathered while study participants conducted a common auditory test. The researchers showed that they could discriminate between patients with schizophrenia and controls with 74% accuracy across multiple neuroimaging sites using the strength of connection in a functional network. They observed the most robust and discriminative connectivity differences between the thalamus and primary motor and sensory cortices as well as between the precuneus and other brain regions. Moreover, they could determine symptom severity based on the connectivity changes involving these areas. This new approach towards finding objective, reliable neuroimaging biomarkers for schizophrenia and its severity could be used for diagnosis and to assess disease progression and therapeutic efficacy.