A Transdiagnostic Biotype Detection Method for Schizophrenia and Autism Spectrum Disorder Based on Graph Kernel

Abstract

Psychiatric diagnoses based on clinical manifestations are prone to be inaccurate. Schizophrenia (SZ) and autism spectrum disorder (ASD) were historically considered as the same disorder, and they still have many overlaps of clinical symptoms in the current standard. Therefore, there is an urgent need to explore the potential biotypes for them using neuroimaging measures such as brain functional connectivity (FC). However, previous studies have not effectively leveraged FC in detecting biotypes. Considering that graph theory helps reveal the topological information in FC, in this paper, we propose a graph kernel-based clustering method to explore transdiagnostic biotypes using FC estimated from functional magnetic resonance imaging (fMRI) data. In our method, frequent subnetworks are identified from the whole-brain FCs of all subjects, and then the graph kernel similarity is computed to measure the relationship between subjects for clustering. Based on fMRI data of 137 SZ and 150 ASD subjects, we obtained meaningful biotypes using our method, which shows significant differences between the identified biotypes in FC. In brief, our graph kernel-based clustering method is promising for transdiagnostic biotype detection.