Neuroimaging-Based Subgroups in Schizophrenia: A Critical Appraisal of Clustering Studies

Efforts to define biologically grounded subtypes of schizophrenia have increasingly leveraged neuroimaging data and clustering algorithms. Such approaches aim to capture patient-level heterogeneity with potential clinical and mechanistic relevance. This review evaluates whether subtypes derived solely from structural neuroimaging data can be robustly identified and meaningfully linked to clinical variation. A systematic review was conducted of peer-reviewed studies published between January 2015 and December 2024 that applied data-driven clustering algorithms to neuroimaging data to identify patient-level subtypes in individuals with schizophrenia or related spectrum disorders. We excluded transdiagnostic studies, studies focused solely on case–control classification, studies that included variables beyond structural neuroimaging measures (for example, clinical or cognitive features) in the clustering input, and studies that performed feature-level clustering without assigning individual-level subtypes. Eighteen studies met inclusion criteria. The structural MRI features used as input and the clustering algorithms employed varied widely. Across studies, three broad neuroanatomical patterns were identified: subtypes with widespread abnormalities, those with regionally circumscribed abnormalities, and those with largely preserved profiles. However, the specific brain regions implicated within each subtype varied considerably between studies, and no subtype profile was consistently reproduced. Few studies reported associations between subtypes and clinical features. When such associations were detected, subtypes characterised by more widespread structural abnormalities tended to show higher symptom severity. Current evidence is insufficient to determine whether macroscale neuroimaging features can define subtypes of schizophrenia that are reproducible, biologically valid, and clinically meaningful. The subtypes reported to date may instead reflect continuous variation within the disorder rather than discrete, biologically distinct entities. Advancing the field will require larger, harmonised datasets, standardised analytic pipelines, and rigorous external and longitudinal validation.