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: 10631143
• 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 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10631143
• 关键词: Acceleration; 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; neural circuit; neurodevelopment; novel; 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 认知系统的损害，这些系统调节能力适应和反应变化的能力 刺激和目标是心理病理学的标志。确定潜在的认知和神经因素 驱动功能失调的行为动态是精神病研究的主要目标。但是传统 方法无法揭示控制这些动态过程的潜在构造。新颖的计算 需要方法来揭示与心理病理学相关的潜在行为动态和特征 在研究领域标准（RDOC）框架内，它们的神经回路基础。大多数（如果不是全部）精神病学 疾病具有神经发育起源，与认知大脑的非典型成熟有关 网络。认知是一个动态过程，它依赖于灵活的抑制性控制，目标指导的信念 这会影响到时刻的期望，以及从先前的决定中学习和适应的能力。 在心理病理学的背景下，发展动态的潜在认知行为模型很重要， 因为在抑制性控制中定义，性能监视和相信更新与多个有关 精神病患者，包括多动症，自闭症和精神分裂症。我们的总体目标是发展和 验证分层潜在变量动力学（HLVD），这是一种新型的集成计算方法 发现强大的潜在行为构建体及其神经电路基础。拟议的研究将 利用纵向青少年行为和认知发展（ABCD）研究 生成了前所未有的“大数据”（n> 5,000），用于绘制认知和大脑的发展 随着时间的流逝，儿童和青少年。至关重要的是，HLVD将用于识别和验证新型潜在构建体 行为动态的预期是外在符号的重要维度预测指标 和发展心理病理学。拟议的研究将大大增强我们对 RDOC结构并提供有关与与之相关的潜在行为动态和特征的新见解 发育中的大脑的心理病理学。我们的研究与NIMH启动的使命高度相关 RFA-MH-19-242，旨在加快有关神经发育和精神风险轨迹的研究 我们的创新方法最终将有助于开发生物标志物，以供早期检测和 精神疾病的治疗。

项目成果

Developmental Maturation of Causal Signaling Hubs in Voluntary Control of Saccades and Their Functional Controllability.

• DOI: 10.1093/cercor/bhab514
• 发表时间: 2022-01
• 期刊: Cerebral cortex
• 影响因子: 3.7
• 作者: Yuan Zhang;S. Ryali;Weidong Cai;Kaustubh Supekar;R. Pasumarthy;A. Padmanabhan;Beatriz Luna;V. Menon