Transcriptional and Neurochemical Signatures of Cerebral Blood Flow Alterations in Individuals With Schizophrenia or at Clinical High Risk for Psychosis

Background: The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behavior. Understanding relationships across these scales may be fundamental to advancing understanding of brain function in health and disease. Recent neuroimaging research has shown that functional brain alterations that are associated with schizophrenia spectrum disorders (SSDs) are already present in young adults at clinical high risk for psychosis (CHR-P), but the cellular and molecular determinants of these alterations remain unclear. Methods: Here, we used regional cerebral blood flow (rCBF) data from 425 individuals (122 with an SSD compared with 116 healthy control participants [HCs] and 129 individuals at CHR-P compared with 58 HCs) and applied a novel pipeline to integrate brainwide rCBF case-control maps with publicly available transcriptomic data (17,205 gene maps) and neurotransmitter atlases (19 maps) from 1074 healthy volunteers. Results: We identified significant correlations between astrocyte, oligodendrocyte, oligodendrocyte precursor cell, and vascular leptomeningeal cell gene modules for both SSD and CHR-P rCBF phenotypes. Additionally, endothelial cell genes were correlated in SSD, and microglia in CHR-P. Receptor distribution significantly predicted case-control rCBF differences, with dominance analysis highlighting dopamine (D₁, D₂, dopamine transporter), acetylcholine (VAChT, M₁), gamma-aminobutyric acid A (GABA_A), and glutamate (NMDA) receptors as key predictors for SSD (R²_adjusted = 0.58, false discovery rate [FDR]–corrected p <.05) and CHR-P (R²_adjusted = 0.6, p_FDR <.05) rCBF phenotypes. These associations were primarily localized in subcortical regions and implicate cell types involved in stress response and inflammation, alongside specific neuroreceptor systems, in shared and distinct rCBF phenotypes in psychosis. Conclusions: Our findings underscore the value of integrating multiscale data as a promising hypothesis-generating approach toward decoding biological pathways involved in neuroimaging-based psychosis phenotypes, potentially guiding novel interventions.