Leveraging fMRI non-stationarity for deep learning classifier training and feature detection to improve schizophrenia diagnosis

A neurobiologically-based diagnosis with superior reliability in place of clinical interview-based diagnosis is a primary goal in psychiatry. Dynamic functional connectomes (dFCs) identified using change-point detection applied to functional magnetic resonance imaging (fMRI) data was used to train graph convolutional network (GCN) models to classify persons with psychiatric diagnoses from healthy controls. We examined four samples, adolescent-onset schizophrenia (AOS), adult schizophrenia, major depressive disorder, bipolar disorder, each with healthy controls (HC) for resting state fMRI (rs-fMRI) and working memory task for AOS. Classification accuracy was as high as 89.2% (sensitivity=0.90; specificity=0.88) for adult schizophrenia. The GCNs were further examined to understand which nodes and edges contributed highly to the classification using Class Activation Mapping (CAM) and Integrated Gradients (IG), respectively. CAM and IG analysis were convergent between adult schizophrenia and AOS which included default mode network regions, cerebellum, and sensory regions for rs-fMRI. For working memory, Brodmann area 10 and dorsolateral prefrontal cortex contributed the most towards AOS classification. Applied in a clinical context, post-test probability of accurate classification was 93% for adult-onset schizophrenia using rs-fMRI with a positive test suggesting clinical usefulness of our model. Our results suggest that a combination of deep-learning models and explanatory algorithms can markedly improve diagnostic reliability, offer approaches to objective diagnostic approach, and provide a neurobiological basis for the diagnosis by identifying regions and edges in the networks.