Mapping Neurochemical Signatures onto Brain Structure for Neurotransmitter-Informed Discrimination of Schizophrenia Patients from Healthy Controls
Abstract
BACKGROUND: Schizophrenia (SCZ) is associated with widespread gray matter volume (GMV) reductions, yet the underlying mechanisms driving these alterations remain unclear. This study investigates whether SCZ-related GMV changes co-localize with normative neurotransmitter (NT) density maps derived from healthy individuals. We further examine the diagnostic utility and transdiagnostic relevance of these NT-informed structural patterns within a machine learning (ML) framework.
METHODS: Structural imaging from 445 SCZ patients and 414 matched healthy controls (HC) were used to train two ML models: (1) an NT-informed model based on spatial cortical and subcortical correlations between GMV and 25 normative NT maps, and (2) an 'NT-unaware' control model using 50 principal components of GMV. Both models were tested for transdiagnostic generalizability in major depression (MDD), bipolar disorder (BD), borderline personality disorder (BPD), and attention-deficit/hyperactivity disorder (ADHD) to determine whether NT-informed and NT-uninformed morphometric patterns reflect psychosis-specific mechanisms. Associations with accelerated brain aging, clinical symptoms, and psychotropic medication exposure were evaluated. Individual feature importance profiles from the NT model were used for clustering to identify potential neurochemical subtypes of SCZ.
RESULTS: The NT model had a higher sensitivity, but a lower specificity compared to the control model in distinguishing SCZ from HC…
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