Characterization of executive function dimensions across pediatric psychiatric disorders

项目摘要 该提案响应RFA-MH-16 - 510，重点关注"认知系统"领域， “认知控制”和“工作记忆”以及整合分析单元“脑回路”和“行为”。这些 结构被归入执行功能（EF），即自愿约束思想和行动的能力 为目标服务。在儿科精神病分类中，EF缺陷定义为注意缺陷多动障碍 注意力缺陷多动障碍（ADHD），并与各种疾病共病，包括自闭症谱系障碍，破坏性 行为障碍、情绪和焦虑障碍、图雷特氏症/抽搐和学习障碍。跨这些 EF缺陷限制了适应性功能和行为干预的成功。改善EF 然而，缺乏是一个挑战，因为目前的EF疾病分类学福尔斯未能捕获异质性 在各种疾病之间。因此，主要的挑战是确定EF的维度， 捕捉各种疾病中损伤的具体性质。大多数过去的方法利用维度- 减少对共享方差敏感但不包括唯一方差的方法。在这里，我们解决 这一挑战通过新的数据驱动的行为配置文件为基础的EF维度的产生，来自 图论社区检测（以下[1，2]），应用于常见的临床家长报告措施（ADHD 评定量表，注意力不集中，多动/冲动，8种执行功能行为评定量表 子领域，儿童行为检查表内化和外化）。社区检测应用于N = 322 (8-13年龄;智商> 70;没有"医学"诊断），在儿童国家医学中心神经心理学 临床确定了三种EF特征，以缺陷和相对优势区分：1）工作记忆差; 良好的灵活性和抑制性; 2）抑制性差;良好的工作记忆; 3）灵活性和情绪差 良好的工作记忆。我们将从这个不断增长的群体中招募人员，以检查：目标1-寻求 通过测试一个新的更大的队列，用训练过的支持向量机分类进行复制。 初步数据。目标2-按组描述区分3种配置文件的功能网络 比较和维度分析。基于任务的功能连接将检验关于特定 使用fMRI区分新EF维度的电路：1）N-back工作记忆; 2）响应 3）适应性社会情绪认知控制。无任务静息态功能磁共振成像将检验假设 关于EF维度之间的大规模网络交互差异。目标3-检验假设， 新的EF维度与适应性功能的特定领域相关， 具体的功能网络。结果将：1）提供神经生物学验证的EF尺寸，用于重新评估。 概念化儿科精神疾病分类学，2）确定治疗目标并提高治疗的精确性。 测量治疗效果-即，谁应该接受什么样的治疗，如何最好地衡量反应， 结果，这两个都是必不可少的个性化的方法，以临床实践的成功。

PROJECT SUMMARY The proposal responds to RFA-MH-16-510 by focusing on the domain of “Cognitive Systems” and constructs “cognitive control” and “working memory” and integrating units of analysis “brain circuit” and “behavior”. These constructs are subsumed under executive function (EF), the ability to voluntarily constrain thoughts and actions in the service of goals. Among pediatric psychiatric categories, EF deficits define Attention Deficit Hyperactivity Disorder (ADHD) and are comorbid with a variety disorders, including Autism Spectrum Disorders, disruptive behavior disorders, mood and anxiety disorders, Tourette's/tics, and learning disabilities. Across these disorders, EF deficits limit adaptive functioning and success of behavioral intervention. Ameliorating EF deficits is a challenge, however, because current EF nosology falls short of capturing heterogeneity within and across disorders. The primary challenge then is identifying the dimensions of EF that capture the specific nature of impairment across disorders. Most past approaches utilize dimension- reducing methods that are sensitive to shared variance, but exclude unique variance. Here, we address this challenge through novel data-driven generation of behavioral profile-based EF dimensions derived from graph theory community-detection (following [1, 2]), applied to common clinical parent-report measures (ADHD Rating scale, inattention, hyperactivity/impulsivity, 8 Behavior Rating Inventory of Executive Function subdomains, Child Behavior Checklist internalizing, and externalizing). Community-detection applied to N=322 (8-13 yrs; IQ>70; no “medical” diagnosis) presenting at Children's National Medical Center neuropsychology clinics identified three EF profiles distinguished by deficits and relative strengths: 1) poor working memory; good flexibility and inhibition; 2) poor inhibition; good working memory; 3) poor flexibility and emotion regulation; good working memory. We will recruit from this growing cohort to examine: Aim 1 – seek replication by testing a new larger cohort with support vector machine classification trained on preliminary data. Aim 2 - characterize functional networks distinguishing the 3 profiles, by group comparison and dimensional analysis. Task-based functional connectivity will test hypothesis about specific circuits distinguishing the novel EF dimensions using fMRI during: 1) N-back working memory; 2) Response inhibition; and 3) Adaptive socio-emotional cognitive control. Task-free resting-state fMRI will test hypothesis about large-scale network interaction differences between EF dimensions. Aim 3 - test the hypothesis that the novel EF dimensions are associated with specific domains of adaptive function, mediated by specific functional networks. Results will: 1) provide neurobiologically validated EF dimensions for re- conceptualizing pediatric psychiatric nosology, and 2) identify treatment targets and increase precision in measuring treatment effects – i.e., who should receive what treatment and how to best measure response and outcome, both of which are essential to the success of a personalized approach to clinical practice.