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
基本信息
- 批准号:10631143
- 负责人:
- 金额:$ 78.31万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AccelerationAdolescentAttention deficit hyperactivity disorderBehavioralBehavioral ModelBeliefBig DataBrainChildClinicalCognitionCognitiveComputer ModelsDevelopmentDimensionsEarly treatmentGoalsImpairmentLearningLinkMeasuresMental disordersMethodsMissionModelingMonitorNational Institute of Mental HealthPerformanceProcessPsychopathologyReaction TimeReproducibilityResearchResearch Domain CriteriaRiskSamplingSchizophreniaStimulusSubstance abuse problemSymptomsTimeUpdateautism spectrum disorderbasebehavior measurementbehavioral constructbiomarker developmentbrain behaviorcognitive developmentcognitive processcognitive systemcohortearly detection biomarkersexpectationexternalizing behaviorflexibilityinnovationinsightneuralneural circuitneurodevelopmentnoveltrait
项目摘要
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
调节适应性参与和应对变化能力的认知系统受损
刺激和目标是精神病理学的标志识别潜在的认知和神经因素,
驱动功能障碍的行为动力学是精神病学研究的主要目标。无论多么传统
方法不能揭示控制这些动态过程的潜在结构。新型计算
需要采取方法来揭示与精神病理学相关的潜在行为动力学和特征,
他们的神经回路基础,在研究领域标准(RDoC)框架内。大多数,如果不是全部,精神病
这些障碍具有神经发育起源,并与认知脑的非典型成熟有关
网络.认知是一个动态的过程,它依赖于灵活的抑制控制、目标导向的信念
这会影响每时每刻的期望,以及从先前的决定中学习和适应的能力。
发展认知的动态潜在行为模型在精神病理学的背景下是重要的,
由于抑制控制、表现监控和信念更新的缺陷涉及多个
精神疾病,包括多动症、自闭症和精神分裂症。我们的首要目标是发展和
验证分层潜在变量动态(HLVD),一种新的综合计算方法,
发现强大的潜在行为结构及其神经回路基础。拟议的研究将
利用纵向青少年行为和认知发展(ABCD)研究,
产生了前所未有的大量“大数据”(N> 5,000),用于绘制认知和大脑发育的图表,
儿童和青少年随着时间。至关重要的是,HLVD将用于识别和验证新的潜在结构
行为动力学,预计是显着的维度预测外化症状
和发展性精神病理学建议的研究将大大提高我们对
RDoC构建并提供了对潜在行为动力学和与之相关的特征的新见解。
大脑发育中的精神病理学我们的研究与NIMH倡议的使命高度相关
RFA-MH-19-242,旨在加速对神经发育和精神疾病风险轨迹的研究。
病我们的创新方法最终将有助于开发用于早期检测的生物标志物,
精神疾病的治疗。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:3.7
- 作者:Yuan Zhang;S. Ryali;Weidong Cai;Kaustubh Supekar;R. Pasumarthy;A. Padmanabhan;Beatriz Luna;V. Menon
- 通讯作者:Yuan Zhang;S. Ryali;Weidong Cai;Kaustubh Supekar;R. Pasumarthy;A. Padmanabhan;Beatriz Luna;V. Menon
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VINOD MENON其他文献
VINOD MENON的其他文献
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{{ truncateString('VINOD MENON', 18)}}的其他基金
Circuit Mechanisms Governing the Default Mode Network
管理默认模式网络的电路机制
- 批准号:
10380898 - 财政年份:2021
- 资助金额:
$ 78.31万 - 项目类别:
Circuit Mechanisms Governing the Default Mode Network
管理默认模式网络的电路机制
- 批准号:
10576946 - 财政年份:2021
- 资助金额:
$ 78.31万 - 项目类别:
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:
10200653 - 财政年份:2019
- 资助金额:
$ 78.31万 - 项目类别:
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 - 财政年份:2019
- 资助金额:
$ 78.31万 - 项目类别:
Longitudinal Neurocognitive Studies of Mathematical Disabilities: trajectories and outcomes
数学障碍的纵向神经认知研究:轨迹和结果
- 批准号:
10468844 - 财政年份:2018
- 资助金额:
$ 78.31万 - 项目类别:
Longitudinal Neurocognitive Studies of Mathematical Disabilities: trajectories and outcomes
数学障碍的纵向神经认知研究:轨迹和结果
- 批准号:
9769805 - 财政年份:2018
- 资助金额:
$ 78.31万 - 项目类别:
Longitudinal Neurocognitive Studies of Mathematical Disabilities: Outcomes and Trajectories
数学障碍的纵向神经认知研究:结果和轨迹
- 批准号:
10842461 - 财政年份:2018
- 资助金额:
$ 78.31万 - 项目类别:
Longitudinal Neurocognitive Studies of Mathematical Disabilities: trajectories and outcomes
数学障碍的纵向神经认知研究:轨迹和结果
- 批准号:
10259850 - 财政年份:2018
- 资助金额:
$ 78.31万 - 项目类别:
Novel Bayesian linear dynamical systems-based methods for discovering human brain circuit dynamics in health and disease
新颖的——贝叶斯——线性——动态——基于系统的——方法——用于发现——人类——大脑——电路——健康和疾病的动力学
- 批准号:
9170593 - 财政年份:2016
- 资助金额:
$ 78.31万 - 项目类别:
Computational modeling of dynamic causal brain circuits underlying cognitive dysfunction in Alzheimer's disease
阿尔茨海默病认知功能障碍的动态因果脑回路的计算模型
- 批准号:
10301331 - 财政年份:2014
- 资助金额:
$ 78.31万 - 项目类别:
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