PROJECT SUMMARY The overall objective of this project is to develop novel approaches to examine the dynamics of brain chemistry and establish correlations between molecular mechanisms and differences in behavior, especially related to cocaine use. Previous studies have established that cocaine use alters both dopamine release and neuronal structure, contributing to compulsive drug-seeking behavior. Yet no study has comprehensively analyzed the brain metabolome to discover additional changes in neurochemistry related to cocaine use. This knowledge gap is primarily due to the poor resolution and sensitivity of current liquid one-dimensional chromatography-mass spectrometry (1D-LC-MS) methods to analyze the diverse chemical composition present in the small sample volumes of brain dialysate. This work will develop novel ultra-high resolution instrumentation and computational methods to test our overall hypothesis that cocaine use causes both short-term and long-term metabolomic alterations in the brain. We further hypothesize that these alterations are differentially expressed based on behavioral phenotype and sex. In Aim 1, we will develop an in vivo metabolomic profile of the nucleus accumbens, a brain region related to motivation and reward, by integrating miniaturized column and stationary phase particle technology into a novel comprehensive two-dimensional LC-MS platform. The information obtained in Aim 1 will provide a valuable resource for both our future aims and neurochemistry research. In Aim 2, we will discover the temporal and differential impacts of cocaine intake on the brain metabolome using selectively bred high-responder (bHR) and low-responder (bLR) rats, an animal model for drug-seeking behavior. Use of fast, ultra-high resolution capillary 1D-LC-MS and computational algorithms will efficiently discover temporal differences in the metabolome of bHRs/bLRs and males/females. Aim 3 will characterize the differential impact related to acquisition of cocaine self-administration on the brain metabolome using bHRs, bLRs, and outbred rats trained to self-administer cocaine. Machine learning will demonstrate our ability to predict compulsive drug-seeking behavior based on metabolomic differences. The information obtained from Aims 2-3 will reveal new metabolomic pathways associated with psychostimulant use, ultimately providing new targets for early medical intervention. This F32 proposal will advance my training at the intersection of analytical chemistry, metabolomics, and neuroscience at the research-rich environment of the University of Michigan. Included in the training plan are the development of technical skills like microdialysis, animal models, and hypothesis-driven research design and professional skills like mentorship, teaching pedagogy, scientific communication, and grant preparation. Ultimately, the work outlined in this proposal will strengthen my transition to an independent research career as a tenure-track professor.