Integrative computational models of latent behavioral and neural constructs in children: a longitudinal developmental big-data approach

儿童潜在行为和神经结构的综合计算模型：纵向发展大数据方法

• 批准号: 10425350
• 负责人: VINOD MENON
• 金额: $ 78.31万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-26 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10425350
• 关键词: Adolescent; Attention deficit hyperactivity disorder; Behavioral; Behavioral Model; Belief; Big Data; Brain; Child; Clinical; Cognition; Cognitive; Computer Models; Development; Dimensions; Early treatment; Goals; Impairment; Learning; Link; Measures; Mental disorders; Methods; Mission; Modeling; Monitor; National Institute of Mental Health; Performance; Process; Psychopathology; Reaction Time; Reproducibility; Research; Research Domain Criteria; Risk; Sampling; Schizophrenia; Stimulus; Substance abuse problem; Symptoms; Time; Update; autism spectrum disorder; base; behavior measurement; behavioral construct; biomarker development; brain behavior; cognitive development; cognitive process; cognitive system; cohort; early detection biomarkers; expectation; externalizing behavior; flexibility; innovation; insight; neural circuit; neurodevelopment; novel; relating to nervous system; trait

Project Abstract Impairments in cognitive systems that regulate the ability to adaptively engage with and respond to changing stimuli and goals are a hallmark of psychopathology. Identifying the underlying cognitive and neural factors that drive dysfunctional behavioral dynamics is a primary goal for psychiatric research. However conventional methods are unable to reveal latent constructs that govern these dynamic processes. Novel computational approaches are required to reveal latent behavioral dynamics and traits associated with psychopathology, and their neural circuit basis, within the Research Domain Criteria (RDoC) framework. Most, if not all, psychiatric disorders have a neurodevelopmental origin and are associated with atypical maturation of cognitive brain networks. Cognition is a dynamic process, which relies on flexible inhibitory control, goal-directed beliefs that impact moment-to-moment expectation, and the capacity to learn and adapt from prior decisions. Developing dynamic latent behavioral models of cognition is significant in the context of psychopathology, because deficits in inhibitory control, performance monitoring and belief updating are implicated in multiple psychiatric disorders including ADHD, autism, and schizophrenia. Our overarching goal is to develop and validate Hierarchical Latent Variable Dynamics (HLVD), a novel integrative computational approach for discovering robust latent behavioral constructs and their neural circuit bases. The proposed studies will leverage the longitudinal Adolescent Behavioral and Cognitive Development (ABCD) study, which has generated unprecedented amounts of “Big Data” (N>5,000) for charting cognitive and brain development in children and adolescents over time. Crucially, HLVD will be used to identify and validate novel latent constructs of behavioral dynamics that are expected to be significant dimensional predictors of externalizing symptoms and developmental psychopathology. The proposed studies will significantly enhance our understanding of RDoC constructs and provide new insights into latent behavioral dynamics and traits associated with psychopathology in the developing brain. Our studies are highly relevant to the mission of the NIMH initiative RFA-MH-19-242, which seeks to accelerate research on neurodevelopment and trajectories of risk for mental illness. Our innovative approach will ultimately aid in the development of biomarkers for early detection and treatment of psychiatric disorders.

项目摘要