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CRCNS US-Israel Research Proposal: Computational Phenotyping of Decision Making in Adolescent Psychopathology

CRCNS 美国-以色列研究提案：青少年精神病理学决策的计算表型

基本信息

批准号：
10663070
负责人：
Yael Niv
金额：
$ 22.72万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10663070
关键词：
Adolescence Adolescent Adolescent Development Adolescent and Young Adult Adult Age Anxiety Attention deficit hyperactivity disorder Back Behavior Behavioral Brain Characteristics Child Childhood Clinical Cognitive Computer Models Data Decision Making Development Diagnosis Diagnostic Diffusion Magnetic Resonance Imaging Dimensions Disease Early Diagnosis Early Intervention Early treatment Functional Magnetic Resonance Imaging Goals Individual Israel Joints Learning Link Maps Measures Mental Depression Mental disorders Modeling Motivation Neurocognitive Outcome Pathology Patient Self-Report Phenotype Population Prefrontal Cortex Prevalence Process Prognosis Property Psychological reinforcement Psychopathology Research Research Activity Research Proposals Running Sampling Scanning Structure Symptoms Testing age related clinical diagnosis clinical diagnostics clinical predictors cognitive process comorbidity computer framework cost follow-up generalized anxiety improved indexing multimodality multitask neural neural circuit neural network neuroimaging neuromechanism novel peer prevent response support network symptomatology web based interface

项目摘要

Adolescence is characterized by changes in decision-making, accompanied by the progressive development of the prefrontal cortex and reconfiguration of brain networks that support goal-directed decision-making. Adolescence is also the typical age of clinical onset and peak prevalence for many forms of mental illness. Recent advances in computational modeling of cognitive processes have enabled the quantification of parameters that govern learning and decision and characterization of how they differ in mental illnesses. There are several differentiating properties of learning and decision making processes in the brain: learning can be model-free (based on past trial and error) vs. model-based (learning the structure of a task and computing a best course of action given that structure), Pavlovian (with innate sensitivities to different motivationally relevant outcomes) vs. instrumental (arbitrarily adaptive), and learning occurs from positive and negative consequences. Furthermore, responses can be biased toward action or inaction, and can be more or less exploratory (variable). We will use three reinforcement-learning tasks that, together with computational models, index these multiple differentiable features of learning and decision making, in order to jointly define an individual “computational phenotype” of learning and decision processes. In Aim 1 this computational phenotype will be defined in a large online sample age 10-25 in order to map changes in symptom dimensions across adolescent development. In Aim 2 we will use neuroimaging to characterize the relationship between decision-making phenotypes and neural connectivity in children, adolescents, and young adults. In Aim 3 we will characterize the relation between decision-making phenotypes and clinical symptomatology in a diagnostically heterogeneous sample of adolescents with generalized anxiety, depression, ADHD or OCD. Throughout, computational modeling of task behavior and self-reported symptom dimensions will build on state-of-the-art hierarchical modeling of multimodal and multi-task data. The research activities described in this proposal hold the potential to improve our understanding of the cognitive and neural mechanisms that underpin adolescent psychopathology, a question of broad societal impact given the prevalence and cost of mental illness, and the super-additive benefits of early detection and treatment.

青春期的特点是决策的变化，伴随着前额皮质的逐渐发育和支持目标导向决策的大脑网络的重新配置。青春期也是许多精神疾病临床发病和流行高峰的典型年龄。在认知过程的计算建模方面的最新进展，已经能够量化控制学习和决策的参数，并表征它们在精神疾病中的差异。大脑中的学习和决策过程有几个不同的特性：学习可以是无模型的（基于过去的尝试和错误）和基于模型的（学习任务的结构并计算给定该结构的最佳行动方案），巴甫洛夫的（对不同动机相关结果具有天生的敏感性）和工具的（任意适应），学习发生在积极和消极的结果中。此外，反应可能偏向于行动或不行动，可能或多或少具有探索性（变量）。我们将使用三个强化学习任务，连同计算模型，索引这些学习和决策的多个可微分特征，以便共同定义学习和决策过程的单个“计算表型”。在目标1中，这种计算表型将在10-25岁的大型在线样本中定义，以便绘制青春期发育过程中症状维度的变化。