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：
10239260
负责人：
Yael Niv
金额：
$ 27.7万
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/10239260
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 adaptive learning age related base clinical Diagnosis clinical predictors cognitive process computer framework cost follow-up generalized anxiety improved indexing multimodality multitask neural circuit neural network neuroimaging neuromechanism novel peer prevent relating to nervous system 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.

青少年的特征是决策的变化，这是通过前额叶皮层的逐步发展和支持目标指导决策的大脑网络的重新配置而实现的。青少年也是许多形式的精神疾病的临床发作和峰值患病率的典型年龄。认知过程的计算模型的最新进展已使能够量化管理学习和决策的参数，并表征他们在心理方面的差异，大脑中学习和决策过程的学习和决策过程的差异很大：学习可以是模型的（基于过去的试验和错误）基于模型与模型的相关性（与该结构的最佳动作相关的效果（peveriantiant the Interration the Interration the Inderations the Inderations the Indrate the Indrate），而不同的是自在）与工具（任意自适应），学习是从积极和负面后果发生的。此外，回答我们将使用三个加强学习任务，这些任务与计算模型一起索引了学习和决策的这些多个可区分的特征，以共同定义学习和决策过程的单个“计算表型”。在AIM 1中，该计算表型将在大型的在线样本年龄10-25中定义，以绘制青少年发育中症状维度的变化。在AIM 2中，我们将使用神经影像来表征儿童，青少年和年轻人的决策表型与神经元连通性之间的关系。在AIM 3中，我们将表征决策表型与临床症状学之间的关系，这些诊断为具有广义动画，抑郁症，ADHD或OCD的青少年样本。在整个过程中，任务行为和自我报告的症状维度的计算建模将基于多模式和多任务数据的最新分层建模。该提案中描述的研究活动有可能提高我们对基于青少年心理病理学的认知和神经元机制的理解，鉴于精神疾病的患病率和成本以及早期发现和治疗的超级增长益处，这是一个广泛的社会影响问题。