Neurophysiological subtyping of schizophrenia reveals a reproducible phenotype with temporoparietal circuit disruptions.

Abstract

Background: Heterogeneity in schizophrenia (SCZ) remains a major obstacle to the development of effective treatment.

Aims: This study aimed to identify reproducible neurobiological subtypes of SCZ using source-localised, high-density resting-state electroencephalography (EEG) functional connectivity patterns.

Methods: We analysed high-density resting-state EEG data from a discovery dataset of 86 patients with SCZ and an independent replication dataset of 89 patients. We applied a sparse k-means clustering algorithm to power envelope correlations, computed across five frequency bands and two resting conditions in the source space. Subtype stability was rigorously assessed using split-half validation and cross-cohort replication.

Results: The analysis consistently identified two reproducible SCZ subtypes. One subtype was characterised by significant temporoparietal disconnection compared with healthy controls, while the second subtype exhibited a relatively preserved functional connectivity pattern. These subtypes were stable within the cohort and successfully replicated in the independent dataset. Crucially, connectivity features significantly predicted symptom severity, but this association was specific to the more disconnected subtype.

Conclusions: Source-space EEG connectivity enables the identification of robust, reproducible, and mechanistically distinct subtypes of SCZ. These findings highlight underlying neurobiological heterogeneity that is not captured by standard clinical assessments and suggest that these subtypes may provide a basis for future biomarker development and patient stratification studies.