Identification of Brain Circuit Markers for Psychosis in Alzheimer's Disease by Leveraging Big Data and Machine Learning

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Psychotic symptoms are among the most common and persistent neuropsychiatric symptoms in Alzheimer’s disease, affecting over 50% of patients with Alzheimer’s disease. Yet, the etiology of psychosis in Alzheimer’s disease is still poorly understood and systematic investigations have been hampered by small samples and lack of reproducible findings. Critically, robust biomarkers are needed to understand the origins/progression of psychosis in Alzheimer’s disease, and to identify targets for treatment. Newly available human neurobiological data offer an unprecedented opportunity for developing robust and predictive biomarkers for psychosis in Alzheimer’s disease. The overarching goal of our proposal is to identify robust and predictive biomarkers for psychosis in Alzheimer’s disease using a novel data-driven computational framework. Specifically, we will use a transformative “Big Data” science approach combining exciting recent advances in deep learning and our recent work on quantitative dynamic brain circuit analyses with a wealth of newly available large-scale open- source brain imaging and phenotypic data from multiple consortia, as well as data we have acquired at Stanford University. To achieve these goals, we propose four aims. In Aim 1, we will develop and validate a novel data-driven computational framework for identifying neurobiological features that distinguish between groups, leveraging recent advances in deep learning and brain circuit dynamics. In Aim 2, we will identify neurobiological features that distinguish idiopathic psychosis (schizophrenia) from neurotypical controls, using our validated computational framework and “Big” data from schizophrenia. In Aim 3, we will identify neurobiological features that distinguish Alzheimer’s disease patients with psychosis from Alzheimer’s disease patients without psychosis, using our validated computational framework and data from Alzheimer’s disease and schizophrenia. In Aim 4, we will identify neurobiological features that predict onset of psychosis in Alzheimer’s disease. The proposed studies are highly synergistic with the goals of the PAR-20-159, which aims to “enhance knowledge of mechanisms associated with neuropsychiatric symptoms in persons with Alzheimer’s disease”. Through the successful completion of the work described here, the proposed studies will transform our understanding of brain circuit mechanisms underlying psychosis in Alzheimer’s disease, and crucially, provide a new computational framework for improved mechanistic understanding of other neuropsychiatric symptoms in Alzheimer’s disease. Ultimately, these advances will lead to better diagnosis and more effective treatments for neuropsychiatric symptoms in Alzheimer’s disease and, more broadly, advance precision medicine.