Abstract:

It is difficult to distinguish schizophrenia (SZ) and bipolar disorder with psychosis (BPP) due to their overlapping symptoms. Indeed, there has been evidence supporting different subtypes within them. Data-driven clustering approaches are commonly used to explore biologically meaningful biotypes using neuroimaging features. However, previous studies typically consider pair-wise subject relationships. Here, we propose a hypergraph clustering method to explore biotypes. Our method extracts high-order features via hyperedges sampling, measures similarity and then regroups subjects using community detection. We applied it to identify biotypes of 100 BPP and 100 SZ patients using brain functional connectivity estimated from resting-state fMRI data, and compared with solutions from K-means and normalized cut (Ncut). Two reliable biotypes were identified and had greater differences in functional connectivity than groups determined by clinical diagnosis. Our method also outperformed K-means and Ncut for the clustering ability and computation efficiency. In summary, the proposed method is promising for developing biotypes, targeting accurate clinical diagnosis for psychosis.