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).

项目摘要/摘要 注意力缺陷/多动障碍(ADHD)是一种高度异质性的疾病，具有多因素 病因危险因素、症状的不同表现、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分级，转向具有临床意义的 与神经生物学系统有良好理论联系的表型指标。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，我们将探讨 注意，试图了解ADHD儿童的异质性。反应时间变异性(RTV)， 注意力的一个指数，是认知相关性，通常在以下情况下表现出最大的效果大小 将ADHD儿童与非ADHD儿童进行比较。然而，尽管RTV被认为与ADHD有很强的相关性，但其 病因尚不清楚，ADHD患者本身在RTV指标上也有很大差异。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ADHD中是至关重要的。青少年大脑认知发展(ABCD)研究提供了一个无与伦比的 RTV指出，有机会在招募的大样本儿童中检查无序注意力 年龄从9岁到10岁，纵向跟随。Cha ku衡量标准包括注意力任务、诊断性访谈和 广泛的神经成像。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神经成像探索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ADHD内部的异质性 被诊断为ADHD的患者具有独特的RTV和神经成像特征。要建立 这些配置文件的有效性，我们将检查它们与功能的关联。机器学习侧重于 从多维数据集中学习统计函数以对以下各项做出概括性预测 个人；它允许在个人层面上进行推断，并对微妙分布的差异敏感。 因此，这是一种获得主题水平生物标志物的理想方法。第一个目标是确定哪一个 神经成像数据与每个反应时间变量相关联，这些变量来自高斯、前高斯和 漂移扩散模型。第二个目标是探索RTV和RTV的相应发展趋势 神经成像数据。第三个目标是a)确定注意力特征相似的ADHD受试者群体 以及，b)使用我们在目标1中获得的数据来探索这些注意特征的神经生物学特征。 第四个目标是检验经验决定的注意力特征的临床相关性。可想而知， 识别RTV反映的注意力紊乱的机械性生物标记物可以提炼药理学， 认知和行为干预；这可能导致定向治疗的更高成功率 针对特定ADHD亚组中的个人的特定机制。这项工作也可能是 与以RTV水平高为特征的其他障碍(如自闭症)的注意力紊乱有关。