Parsing Neurobiological Bases of Heterogeneity in ADHD

解析 ADHD 异质性的神经生物学基础

• 批准号: 10609948
• 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/10609948
• 关键词: Adolescent; Age; Attention; Attention deficit hyperactivity disorder; Behavior Therapy; Behavioral; Behavioral Symptoms; Biological Markers; Brain; Child; Childhood; Clinical; Code; Cognitive; Complex; Data; Detection; Development; Diagnosis; Diagnostic; Diffusion; Disease; Disparate; Etiology; Functional disorder; Future; Goals; Heterogeneity; Impairment; Individual; Interview; Machine Learning; Measures; Mental disorders; Modeling; National Institute of Mental Health; Neurobiology; Pattern; 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; 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; pharmacologic; prospective; psychiatric comorbidity; psychologic; recruit; social; statistical learning; success; sustained attention; targeted treatment; tau Proteins; theories; trend; Adolescent; Age; Attention; Attention deficit hyperactivity disorder; Behavior Therapy; Behavioral; Behavioral Symptoms; Biological Markers; Brain; Child; Childhood; Clinical; Code; Cognitive; Complex; Data; Detection; Development; Diagnosis; Diagnostic; Diffusion; Disease; Disparate; Etiology; Functional disorder; Future; Goals; Heterogeneity; Impairment; Individual; Interview; Machine Learning; Measures; Mental disorders; Modeling; National Institute of Mental Health; Neurobiology; Pattern; 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; 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; pharmacologic; prospective; psychiatric comorbidity; psychologic; recruit; social; statistical learning; success; sustained attention; targeted treatment; tau Proteins; theories; 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 注意缺陷/多动症（ADHD）是一种高度异质性疾病，具有多因素 病因的风险因素，症状的各种表达，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等级向临床有意义 与神经生物学系统具有良好关系的表型措施。这种方法是 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 注意，试图了解多动症儿童的异质性。反应时间变异性（RTV）， 注意索引是认知相关性，通常证明效果大小最大时 将多动症与非ADHD儿童进行比较。但是，尽管RTV被认为是ADHD的牢固关联 病因尚不清楚，患有多动症的人本身在RTV的指标上有很大不同。因此，首先 建立RTV的神经生物学基础，然后探索是否可以用于理解异质性 在多动症中至关重要。青少年脑认知发展（ABCD）研究提供了无与伦比的 RTV表明，在大量招募的儿童中，检查了注意力不h注意的机会 9至10岁，纵向跟随。 ABCD措施包括注意任务，诊断访谈和 广泛的神经影像学。基线时，ABCD中有1079名儿童符合多动症的诊断标准。我们建议 利用机器学习来探索RTV的神经生物学基础，使用整个ABCD神经影像学 样品（n = 9,598）。我们还将通过识别个人组来探索多动症中的异质性 被诊断为具有独特的RTV和神经影像特征的ADHD。建立 这些概况的有效性，我们将检查它们与功能的关联。机器学习专注于 从多维数据集中学习统计功能，以做出有关可概括的预测 个人它允许在个体的水平上进行推断，并且对巧妙的分布差异敏感。 因此，这是推导主题级生物标志物的理想方法。第一个目的是确定哪个 神经影像数据与从高斯，前高斯和 漂移扩散模型。第二个目的是探索RTV和 神经影像数据。第三个目的是a）识别具有相似注意力概况的ADHD受试者组 并且，b）使用我们在AIM 1中得出的数据探索这些注意力谱的神经生物学特征。 第四个目的是检查经验确定的注意力特征的临床相关性。可以想象， 识别RTV反映的无序注意力的机械生物标志物可以完善药理， 认知和行为干预；这可能会导致指导治疗的成功可能性更高 迈向特定的ADHD亚组中个体的特定机制。这项工作也可能是 与以高水平RTV（例如自闭症）为特征的其他疾病中的注意力不齐有关。