Resting state connectivity signatures of obsessive compulsive symptoms

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PROJECT SUMMARY Obsessive-compulsive disorder (OCD) is a disabling illness that exhibits bimodal timing of onset, with up to half of cases beginning in childhood. Subclinical obsessive-compulsive symptoms (OCS) often precede the development of clinically significant OCD, though symptoms in some children remit naturally over time. However, the neural bases of OCS and their changes over development are poorly understood. Capitalizing on large, publicly available datasets and sophisticated computational methods, I propose to examine functional MRI signatures of OCS and their longitudinal trajectories in children and adolescents. Specifically, I will apply machine learning to publicly available data from the Adolescent Brain Cognitive Development (ABCD) study, a prospective community sample tracked longitudinally at 21 diverse sites across the United States, to reveal whole-brain functional MRI patterns that correspond to OCS severity. Baseline data is already available from approximately 12,000 children aged 9-10 whose families have committed to ongoing follow-up. I will then use data from the independent Healthy Brain Network (HBN) study, which includes data from approximately 2,500 children from the New York City area, to statistically and clinically validate these patterns (Aim 1). I will then combine baseline neuroimaging and clinical data with longitudinal follow-up clinical data from the ABCD study to examine neural signatures that predict subsequent OCS trajectories (Aim 2). Finally, I will leverage data collected from children with clinical-severity OCS (i.e., OCD) before and after gold-standard cognitive behavioral therapy at the New York State Psychiatric Institute (NYSPI) to identify pre-treatment predictors of response and remission (Exploratory Aim). Collectively, these aims will identify brain connectivity features that correspond to reliable patterns in which OCS co-vary, which could hint at common mechanisms underlying multiple symptoms and implicate specific circuits that can be targeted in future studies aimed at developing and testing novel treatments and prevention strategies. Furthermore, this research proposal integrates a detailed training plan that will bring me closer to my goal of becoming a physician-scientist focused on clinical and computational psychiatry. Supported by the resources of both Columbia University Irving Medical Center and the NYSPI, I will deepen my technical skills in MRI image processing, neuroimaging analysis, and machine learning, while also improving my scientific writing, oral presentation, and clinical skills.