Characterization of executive function dimensions across pediatric psychiatric disorders

项目总结 该提案对RFA-MH-16-510做出了回应，将重点放在“认知系统”和构造域上 “认知控制”和“工作记忆”，以及整合分析单位“大脑回路”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 为球门服务。在儿科精神病学类别中，EF缺陷定义为注意力缺陷多动 精神障碍(ADHD)，并与多种障碍并存，包括自闭症谱系障碍、破坏性 行为障碍、情绪和焦虑障碍、抽动/抽动，以及学习障碍。穿过这些 精神障碍、EF缺陷限制了适应性功能和行为干预的成功。改善EF 然而，赤字是一个挑战，因为当前的EF病因学未能捕捉到异质性 在内部和跨障碍。因此，主要的挑战是确定EF的维度， 捕捉跨障碍的损害的具体性质。过去的大多数方法都利用维度- 对共同方差敏感，但排除唯一方差的减少方法。在这里，我们解决了 这一挑战是通过新颖的数据驱动生成基于行为配置文件的EF维度来实现的，该维度源自 图论社区检测(遵循[1，2])，应用于常见的临床父母报告测量(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 子域、儿童行为检查表、内化和外化)。社区检测应用于N=322 (8-13岁；IQ&GT；70；无“内科”诊断)在国家儿童医学中心神经心理学演讲 根据缺陷和相对强弱，诊所确定了三种EF特征：1)工作记忆差； 良好的灵活性和抑制力；2)抑制能力差；良好的工作记忆；3)灵活性和情绪性差 规则；良好的工作记忆力。我们将从这个不断增长的队列中招募人员进行检查：目标1-Seek 通过测试训练了支持向量机分类的新的更大的队列来进行复制 初步数据。目标2-按组描述区分3个配置文件的功能网络 比较和量纲分析。基于任务的功能连接将测试有关特定 使用fMRI识别新EF维度的电路在：1)N-back工作记忆；2)反应 抑制；3)适应性社会情绪认知控制。无任务静息状态功能磁共振成像将检验假设 关于大范围网络相互作用在EF维度之间的差异。目标3-检验以下假设 新的EF维度与自适应功能的特定领域相关，通过 特定的功能网络。结果将：1)提供神经生物学验证的EF维度 将儿科精神病学病因学概念化，以及2)确定治疗目标并提高 衡量治疗效果--即，谁应该接受什么治疗，如何最好地衡量反应和 结果，这两个因素对于临床实践个性化方法的成功至关重要。

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.