Parsing Neurobiological Bases of Heterogeneity in ADHD

解析 ADHD 异质性的神经生物学基础

• 批准号: 10379072
• 负责人: JEFF N. EPSTEIN
• 金额: $ 39.39万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10379072
• 关键词: Adolescent; Age; Attention; Attention deficit hyperactivity disorder; Behavioral; Behavioral Symptoms; Biological Markers; Brain; Categories; Child; Childhood; Clinical; Code; Cognitive; Cognitive Therapy; Complex; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Diffusion; Disease; Etiology; Functional disorder; Future; Gaussian model; Goals; Heterogeneity; Impairment; Individual; Interview; Lead; Machine Learning; Measures; Mental disorders; Modeling; National Institute of Mental Health; Neurobiology; Pattern; Pharmacology; Phenotype; Population; Probability; Process; Psychopathology; Reaction Time; Research Domain Criteria; Risk Factors; Sampling; Severities; Source; Structure; Subgroup; Symptoms; System; Thick; United States National Institutes of Health; Validation; Variant; Work; age related; aged; associated symptom; autism spectrum disorder; base; biobehavior; biological systems; clinical heterogeneity; cognitive development; comorbidity; diagnostic criteria; improved; inattention; indexing; individual variation; individualized medicine; motor control; multidimensional data; neural correlate; neurobiological mechanism; neuroimaging; non-Gaussian model; prospective; psychiatric comorbidity; psychologic; recruit; social; statistical learning; success; sustained attention; targeted treatment; tau Proteins; trend

PROJECT SUMMARY/ABSTRACT Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly heterogeneous disorder, with multifactorial etiological risk factors, diverse expressions of symptoms, comorbidities, and long-term trajectories. An approach to parsing such heterogeneity is to move beyond symptom ratings toward clinically meaningful phenotypic measures that have well-theorized relations with neurobiological systems. This approach serves as the basis of the NIH Research Domain Criteria (RDoC) framework. In the proposed study, we will explore attention in an attempt to understand heterogeneity within children with ADHD. Reaction time variability (RTV), an index of attention, is the cognitive correlate that typically demonstrates the largest effect size when comparing ADHD to non-ADHD children. However, while RTV is considered a robust correlate of ADHD, its etiology is unclear and individuals with ADHD themselves vary considerably on indices of RTV. Thus, first establishing the neurobiological basis for RTV and then exploring if it can be used to understand heterogeneity in ADHD is critical. The Adolescent Brain Cognitive Development (ABCD) study provides an unparalleled opportunity to examine disordered attention, as indicated by RTV, in a large sample of children recruited at ages 9 to 10 and followed longitudinally. ABCD measures include attentional tasks, diagnostic interviews, and extensive neuroimaging. At baseline, 1079 children in ABCD met diagnostic criteria for ADHD. We propose to utilize machine learning to explore the neurobiological basis of RTV using the entire ABCD neuroimaging sample (n=9,598). We will also explore heterogeneity within ADHD by identifying groups of individuals diagnosed with ADHD who are characterized by unique RTV and neuroimaging profiles. To establish the validity of these profiles, we will examine their association with functioning. Machine learning focuses on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals; it allows for inferences at the level of the individual and is sensitive to subtly distributed differences. Thus, it is an ideal approach for deriving subject-level biomarkers. The first aim is to determine which neuroimaging data are associated with each reaction time variable derived from Gaussian, ex-Gaussian, and drift diffusion models. The second aim is to explore corresponding developmental trends in RTV and neuroimaging data. The third aim is to a) identify groups of ADHD subjects with similar attentional profiles and, b) explore the neurobiological signature of these attentional profiles using the data we derived in aim 1. The fourth aim is to examine the clinical correlates of empirically-determined attentional profiles. Conceivably, identifying mechanistic biomarkers of disordered attention reflected by RTV could refine pharmacological, cognitive, and behavioral interventions; this could lead to a higher probability of success for treatments directed toward that particular mechanism for individuals within specific ADHD subgroups. This work could also be relevant for disordered attention in other disorders characterized by high levels of RTV (e.g., Autism).

项目总结/摘要 注意力缺陷多动障碍（ADHD）是一种高度异质性的疾病， 病因风险因素、症状的多样性表达、合并症和长期轨迹。一个 分析这种异质性的方法是超越症状评级， 与神经生物学系统具有良好理论关系的表型测量。这种方法作为 NIH研究领域标准（RDoC）框架。在拟议的研究中，我们将探讨 注意力，试图了解ADHD儿童的异质性。反应时间变异性（RTV）， 注意力指数是认知相关性，通常在以下情况下表现出最大的效应量： ADHD儿童与非ADHD儿童的比较。然而，虽然RTV被认为是ADHD的一个强有力的相关因素， 病因尚不清楚，ADHD患者本身的RTV指数差异很大。因此，首先， 建立RTV的神经生物学基础，然后探索它是否可以用来理解异质性 注意力缺陷多动障碍的症状青少年大脑认知发展（ABCD）研究提供了一个无与伦比的 有机会检查注意力紊乱，如RTV所示，在一个大样本的儿童招募， 年龄在9到10岁之间，纵向跟踪。ABCD测量包括注意力任务，诊断性访谈， 广泛的神经成像在基线时，ABCD中有1079名儿童符合ADHD的诊断标准。我们建议 利用机器学习探索RTV的神经生物学基础，使用整个ABCD神经成像 样本（n= 9，598）。我们还将通过识别个体群体来探索ADHD的异质性 被诊断患有ADHD的儿童，其特征在于独特的RTV和神经影像学特征。建立 这些配置文件的有效性，我们将检查它们与功能的关联。机器学习专注于 从多维数据集学习统计函数，以做出可推广的预测 个体;它允许在个体层面进行推断，并且对微妙分布的差异敏感。 因此，这是一种用于获得受试者水平生物标志物的理想方法。第一个目标是确定 神经成像数据与从高斯、非高斯和 漂移扩散模型二是探讨RTV的发展趋势， 神经成像数据。第三个目标是a）识别具有相似注意力特征的ADHD受试者组 以及，B）使用我们在目标1中获得的数据探索这些注意特征的神经生物学特征。 第四个目的是检查临床相关的临床确定的注意力配置文件。可以想象， 识别由RTV反映的注意力障碍的机制生物标志物可以改进药理学， 认知和行为干预;这可能会导致更高的成功率，治疗直接 针对特定ADHD亚组中的个体的特定机制。这项工作也可以 与以高水平RTV为特征的其他障碍中的注意力障碍相关（例如，自闭症）。