Subtyping schizophrenia based on symptomatology and cognition using a data driven approach

Abstract

Schizophrenia is a highly heterogeneous disorder, not only in its phenomenology but in its clinical course. This limits the usefulness of the diagnosis as a basis for both research and clinical management. Methods of reducing this heterogeneity may inform the diagnostic classification. With this in mind, we performed k-means clustering with symptom and cognitive measures to generate groups in a machine-driven way. We found that our data was best organised in three clusters: high cognitive performance, high positive symptomatology, low positive symptomatology. We hypothesized that these clusters represented biological categories, which we tested by comparing these groups in terms of brain vol..