Predictive Markers and Mechanisms of Persistent Psychotic-like Experiences in Children: An Adolescent Brain and Cognitive Development Study Analysis

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PROJECT TITLE Predictive Markers and Mechanisms of Persistent Psychotic-like Experiences in Children: An Adolescent Brain and Cognitive Development Study Analysis PROJECT SUMMARY/ABSTRACT My long-term career goal is to establish a highly influential and independent research program and become a broad spectrum leader and innovator in computational psychiatry. I therefore propose the following training goals: 1) To gain formalized training in computational psychiatry methods and statistics, including deep learning and structural equation modeling, 2) to obtain a deeper understanding of the field of developmental psychopathology, and 3) to acquire training in developing and sustaining an independent investigator position with his own laboratory. To meet the first goal, I will meet regularly with University of California Davis (UCD) faculty co-mentors Ian Davidson and Emilio Ferrer, attend relevant UCD classes, and complete a Research Plan with the following Specific Aims: 1) Using deep learning from Adolescent Brain and Cognitive Development (ABCD) Study data (demographic/clinical information, neurocognitive testing, neuroimaging data, and environmental metrics), predict child psychotic-like experience (PLE) distress scores the following year (i.e., baseline data predicting year one PQ-BC distress, year one data predicting year two distress, etc.), and 2) Using rules-based guidance from deep learning using explainable AI (XAI) algorithms, use sequential, structural equation modeling (SEM) of latent profiles (i.e., longitudinal trajectories, e.g., emerging, absent, remitting, persistent) to test hypotheses regarding the longitudinal mechanism(s) which may lead to persistent PLEs in the ABCD cohort. In Aim 1, XAI methods will be used identify the rules used by the deep learner to make decisions; in Aim 2, these rules will then be used to create latent constructs for modeling profiles of longitudinal trajectories. If r2>0.80 is consistently achieved for each iterative analysis in Aim 1, it suggests that ABCD instruments may predict PLE severity one year after measurement at a potentially clinically implementable level and help generate future hypotheses for personalized interventions aimed at reducing risk for persistent PLE distress. For Aim 2, insight will be gained with regard to how brain structure, brain function, neurocognitive ability, and environmental influences interact to influence the time course of PLE expression. To meet the second goal, I will receive mentoring including guided readings via monthly meetings with co-mentor Dr. Ellen Leibenluft, a world-renowned psychiatrist specializing in developmental research and Section Chief at the NIMH. I will also attend developmental classes, seminars, and workshops offered by UCD. To meet the third goal, I will attend weekly meetings with mentor Dr. Cameron Carter, from whom I will gain overarching career guidance. This will include learning how to create and oversee a laboratory, obtaining recommendations for society memberships, acquiring networking opportunities, securing grant sponsorships, and receiving RCR-related guidance. I will also attend mentoring and grantsmanship workshops offered by UCD, attend an off-campus workshop providing hands-on activities to provide exposure to fundamental management skills, complete a formalized UCD RCR plan, and gain mentoring experience by advising a research assistant and postdoc.