Bridging structural MRI with cognitive function for individual level classification of early psychosis via deep learning

IntroductionRecent 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 of patients with EP from healthy individuals. MethodUnlike previous studies, we used sMRI as the direct input to perform EP classifications and cognitive estimations. The proposed deep learning model does not require time-consuming volumetric or surface based analysis and can provide additionally cognition predictions. Experiments were conducted on an in-house data set with 77 subjects and a public ABCD HCP-EP data set with 164 subjects. ResultsWe 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. DiscussionWe 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.

Automated summary

In this study, a multi-task deep learning framework was used to improve the classification accuracy of early psychosis. The framework combined structural magnetic resonance imaging (sMRI) with cognitive assessment, achieving a high accuracy rate.