Semi-supervised clustering for neuro-subtyping of autism spectrum disorder

This is the first study to conduct neuro-subtyping of autism spectrum disorder with a semi-supervised clustering method HYDRA. With the use of functional connectivity data from a large cohort of ASD, ABIDE, a multi-scale dimension-reduction method OPNNMF was first conducted to get a more robust and representable feature space with reduced dimensions. A three-layer procedure was conducted to obtain the optimal clustering in terms of better validity and reliability indices resulting two distinct clusters. By comparing with unsupervised clustering, the semi-supervised method showed more distinct connectivity patterns between clusters. Heterogenous brain-behavior relationships under various brain networks were observed across clusters indicating potential usage of ASD neuro-subtyping to detect reliable neuro-biomarkers assisting precise diagnosis and treatment in the future.