Parsing Neurobiological Bases of Heterogeneity in ADHD

解析 ADHD 异质性的神经生物学基础

• 批准号: 10155553
• 负责人: JEFF N. EPSTEIN
• 金额: $ 39.97万
• 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/10155553
• 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; improved; inattention; indexing; individual variation; individualized medicine; motor control; multidimensional data; neural correlate; neurobiological mechanism; neuroimaging; non-Gaussian model; prospective; 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).

项目概要/摘要 注意力缺陷/多动障碍（ADHD）是一种高度异质性的疾病，与多因素有关 病因学危险因素、症状的不同表现、合并症和长期轨迹。一个 解析这种异质性的方法是超越症状评级，转向具有临床意义的 与神经生物学系统有良好理论关系的表型测量。这种方法的作用是 NIH 研究领域标准 (RDoC) 框架的基础。在拟议的研究中，我们将探索 试图了解多动症儿童的异质性。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： 比较多动症和非多动症儿童。然而，虽然 RTV 被认为与 ADHD 有很强的相关性，但它 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 在多动症中至关重要。青少年大脑认知发展（ABCD）研究提供了无与伦比的 正如 RTV 所表明的那样，有机会在招募的大量儿童样本中检查注意力紊乱情况。 9至10岁并纵向跟踪。 ABCD 措施包括注意力任务、诊断访谈和 广泛的神经影像学检查。在基线时，ABCD 的 1079 名儿童符合 ADHD 的诊断标准。我们建议 利用机器学习利用整个 ABCD 神经影像学探索 RTV 的神经生物学基础 样本（n=9,598）。我们还将通过识别个体群体来探索多动症的异质性 被诊断患有 ADHD 的人具有独特的 RTV 和神经影像学特征。建立 为了验证这些配置文件的有效性，我们将检查它们与功能的关联。机器学习重点关注 从多维数据集中学习统计函数以做出可概括的预测 个人；它允许在个人层面上进行推论，并且对微妙分布的差异很敏感。 因此，它是获得受试者水平生物标志物的理想方法。第一个目标是确定哪个 神经影像数据与源自高斯、前高斯和的每个反应时间变量相关联 漂移扩散模型。第二个目标是探索RTV和RTV相应的发展趋势。 神经影像数据。第三个目标是 a) 识别具有相似注意力特征的 ADHD 受试者群体 b) 使用我们在目标 1 中得出的数据探索这些注意力特征的神经生物学特征。 第四个目标是检查凭经验确定的注意力概况的临床相关性。可以想象， 识别 RTV 反映的注意力紊乱的机械生物标志物可以改进药理学、 认知和行为干预；这可能会导致定向治疗成功的可能性更高 针对特定 ADHD 亚组中的个体的特定机制。这部作品也可以是 与其他以高水平 RTV 为特征的疾病（例如自闭症）中的注意力障碍有关。