Identifying, Validating, and Characterizing Biological Subtypes of PsychiatricDisorders Using a Novel Computational Framework - PROJECT SUMMARY Biologically-grounded data-driven stratification of disease has revolutionized most of modern medicine. Yet, psychiatric disorders—a major public health concern—are still defined strictly on the basis of subjective signs and symptoms. Newly available large-scale human neurobiological data offer an unprecedented opportunity for biologically-grounded data-driven stratification of psychiatric disorders. However, conventional data analytical procedures are ill-suited for such investigations, and new computational approaches are needed. The goal of this proposal is to develop and validate a novel computational framework for identifying, validating, and characterizing biological subtypes of psychiatric disorders, and to apply the framework to the study of autism. We aim to leverage newly available large-scale open-source human brain imaging, phenotypic and transcriptomic data from consortia and repositories around the world, as well as data we have acquired at Stanford University, combined with our recent work in brain circuit analysis and modeling methods and advances in machine learning. To achieve these goals, we propose three aims. In Aim 1, we will develop and validate novel computational methods to extract individual-level neural fingerprint, in particular brain circuit dynamics, using human brain imaging data. In Aim 2, we will develop analytical procedures to stratify psychiatric disorders using neural fingerprints and apply the procedures to identify and validate biological subtypes of autism. In Aim 3, we will perform integrative analysis to determine the transcriptomic signatures of the identified biological subtypes, using neural fingerprints and human brain gene expression data. The proposed work is highly relevant to the strategic plan of the NLM to accelerate discovery and advance health through data-driven research. Through the successful completion of the work described here, the proposed studies will add new knowledge to our current understanding of the etiology of autism and, crucially, provide a new computational framework for improved stratification of other psychiatric disorders. Ultimately, these advances will lead to better diagnosis and more effective treatments for psychiatric disorders and, more broadly, advance precision medicine.
