PROJECT SUMMARY Schizophrenia (SCZ) is a heterogeneous brain disorder typically characterized by delusions, hallucinations, and functional decline, with a typical first onset in late adolescence and early adulthood. Genetic, neuropathological, and neuroimaging studies have suggested a role of neuroinflammation in the etiology of SCZ, which is evident early in the course of illness. This suggests neuroinflammation may represent a SCZ risk marker and therefore facilitate early recognition and future drug development to improve outcomes. In vivo imaging methods for estimating neuroinflammation have been limited by radiation exposure, specificity, and cost. Our proposal aims to validate a novel non-invasive, new magnetic resonance imaging (MRI) technique called Diffusion Basis Spectrum Imaging (DBSI) to identify neuroinflammation in SCZ. DBSI can simultaneously detect and quantify neuroinflammation (increased cellularity) and white matter alterations (axonal injury/loss and demyelination) and has been previously validated in multiple sclerosis and Alzheimer's disease, but not in SCZ. We propose to test the overarching hypothesis that DBSI will identify neuroinflammation in histological samples from SCZ patients. To achieve this objective, we will obtain postmortem brain samples of 18–30-year-old SCZ patients and matched controls (n=20) from the NIH Neurobiobank and investigate the relationship of the DBSI cellularity subcomponent with tissue reactivity for the microglial marker, CD163, and the complement marker, C4 (Aim 1). We hypothesize a strong linear relationship between DBSI cellularity and selected gray and white matter regions. In addition, we will use DBSI in vivo to characterize the brains of 18–30-year-old SCZ patients and controls (n=30) and identify group differences in DBSI subcomponents (Aim 2). We hypothesize greater DBSI cellularity in SCZ brains compared to controls. In completing this work, we expect to identify non-invasive neuroinflammation and white matter integrity markers for SCZ. In the long term, this information would be used to improve the identification of those at risk for developing psychosis and facilitate the testing of new treatments.