Bridging Structural MRI with Cognitive Function for Individual Level Classification of Early Psychosis via Deep Learning

Abstract

ABSTRACTRecent efforts have been made to apply machine learning and deep learning approaches to the automated classification of schizophrenia using structural magnetic resonance imaging (sMRI) at the individual level. However, these approaches are less accurate on early psychosis (EP) since there are mild structural brain changes at early stage. As cognitive impairments is one main feature in psychosis, in this study we apply a multi-task deep learning framework using sMRI with inclusion of cognitive assessment to facilitate the classification EP patients from healthy individuals. Unlike previous studies, we used sMRI as the direct input to perform EP classifications and cognitive estimations. The proposed model does not require time-consuming volumetric or surface based analysis and can provide additionally cognition predictions. Extensive experiments were conducted on a sMRI data set with a total of 77 subjects (38 EP patients and 39 healthy controls), and we achieved 74.9±4.3% five-fold cross-validated accuracy and an area under the curve of 71.1±4.1% on EP classification with the inclusion of cognitive estimations. We reveal the feasibility of automated cognitive estimation using sMRI by deep learning models, and also demonstrate the implicit adoption of cognitive measures as additional information to facilitate EP classifications from healthy controls.