Computational Modeling-Informed Reward Subgroups in Adolescent ADHD

青少年多动症的计算模型知情奖励亚组

基本信息

批准号：
10322181
负责人：
Michael C Stevens
金额：
$ 68.1万
依托单位：
HARTFORD HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-15 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10322181
关键词：
Adolescence Adolescent Age Anterior Architecture Attention deficit hyperactivity disorder Behavior Behavioral Brain Choice Behavior Classification Communities Complex Computer Models Corpus striatum structure Costs and Benefits DSM-V Data Databases Decision Making Development Diagnosis Diagnostic Disease Dorsal Etiology Expenditure Functional Magnetic Resonance Imaging Functional disorder Future Goals Heterogeneity Impairment Individual Individual Differences Learning Link Maps Measures Methodology Methods Modeling Neurobiology Neurocognitive Neurocognitive Deficit Neurosciences Normal Statistical Distribution Pathway interactions Patients Phenotype Prefrontal Cortex Prevalence Psychological reinforcement Reaction Time Research Domain Criteria Research Project Grants Resources Rest Rewards Sampling Signal Transduction Subgroup Symptoms System Testing Time Translational Research Ventral Striatum base behavior influence behavior test brain behavior causal model cost data fusion design discounting falls interest learning network network architecture neurocognitive disorder neurodevelopment novel predictive modeling relating to nervous system sex therapy development

项目摘要

Although there is notable evidence for ADHD abnormalities in the brain's reward system, that evidence is limited due to both its narrow focus on a limited number of reward constructs, as well as its inconsistencies across studies. We believe the inconsistencies are the result of unrecognized ADHD neurobiological heterogeneity. This project was designed to test the prediction that ADHD is a disorder where several, wholly different types of neurobiological dysfunction are capable of producing the same ADHD diagnostic phenotype. Clear, decisive evidence for such neurocognitively and neurobiologically distinct ADHD subgroups is needed to support an emerging paradigm shift in ADHD neuroscience away from the assumption that every ADHD patient has similar pathophysiology, to a multi-etiology model that ultimately should prove to have greater translational usefulness. Here, we will focus on better understanding reward dysfunction in ADHD, which is relatively under-studied compared to the other neurocognitive abnormalities often found in the disorder. We propose to examine a large (n=200) sample of ADHD-diagnosed and non-ADHD (n=150) adolescents with an ambitious reward-based phenotyping approach using a diverse reward test battery to assess numerous domains of reward behavior (e.g., different types of reinforcement learning, valuation, cost processing, effort expenditure, etc.) – an approach that proved highly successful in our preliminary ADHD study. This battery also will leverage the advances in reward phenotyping made over the past decade by sophisticated computational modeling of reward choice behavior and reaction time data. We then will map aspects of individual differences in these abilities to frontostriatal network connectivity during reinforcement learning `prediction errors.' Our ultimate goal is to use this information to classify ADHD patients into different biotypes. Although our preliminary data suggest there likely are at least two different reward-impaired ADHD subgroups with disparate profiles of reward dysfunction, our approach will be more rigorous than a simple replication attempt. We will test the fit of broad conceptual models of ADHD neurocognitive abnormality for the first time in the reward domain, then use those findings to refine our approach to biotyping individual cases with rigorous classification methodology. The resulting biotypes will be validated using other fMRI reward tasks. This project will create the most detailed and extensive database describing reward dysfunction in ADHD to date, which we will make freely available to the scientific community to accelerate the pace of discovery. A renewed, concentrated focus on reward dysfunction in ADHD is not just timely, but also likely to set the stage for important advances in future etiological and translational research. Different types of ADHD reward dysfunction could represent untapped new targets for novel intervention development, where treatments are matched to the type of neurobiological dysfunction instead of the broad DSM ADHD diagnosis.

尽管有显着的证据表明多动症的大脑奖励系统异常，但该证据是由于其对有限数量的奖励结构的狭隘关注以及其不一致而受到限制我们认为这些不一致是由于多动症神经生物学未被认识到的结果。该项目旨在检验 ADHD 是一种疾病的预测，其中多种、全部。不同类型的神经生物学功能障碍能够产生相同的多动症诊断表型。需要明确、决定性的证据来证明这种在神经认知和神经生物学上不同的 ADHD 亚组支持 ADHD 神经科学中正在出现的范式转变，远离每个 ADHD 的假设患者具有与多病因模型相似的病理生理学，最终应证明具有更大的在这里，我们将重点更好地理解多动症中的奖励功能障碍，即与该疾病中常见的其他神经认知异常相比，对该疾病的研究相对较少。建议检查大量（n = 200）被诊断为 ADHD 和非 ADHD 青少年（n = 150）的样本，雄心勃勃的基于奖励的表型方法，使用多样化的奖励测试电池来评估众多奖励行为的领域（例如，不同类型的强化学习、估值、成本处理、努力支出等）——这一方法在我们的 ADHD 初步研究中被证明非常成功。还将利用过去十年来复杂的奖励表型分析方面取得的进步然后我们将绘制奖励选择行为和反应时间数据的计算模型。强化学习期间额纹状体网络连接能力的个体差异 “预测错误。”我们的最终目标是利用这些信息将多动症患者分类为不同的类型尽管我们的初步数据表明可能存在至少两种不同的奖赏受损多动症。对于具有不同奖励功能障碍的亚组，我们的方法将比简单的方法更严格我们将测试 ADHD 神经认知异常的广泛概念模型的适用性。第一次在奖励领域，然后利用这些发现来完善我们对个体进行生物分型的方法所得到的生物型将使用其他功能磁共振成像进行验证。该项目将创建描述奖励功能障碍的最详细和最广泛的数据库。迄今为止，我们将免费向科学界提供这些信息，以加快研究 ADHD 的步伐对多动症奖励功能障碍的重新集中关注不仅是及时的，而且也可能是有益的。为未来不同类型多动症的病因学和转化研究的重要进展奠定了基础。奖励功能障碍可能代表新颖干预开发的未开发新目标，其中治疗方法与神经生物学功能障碍的类型相匹配，而不是与广泛的 DSM ADHD 诊断相匹配。