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

• 批准号: 9897171
• 负责人: Michael C Stevens
• 金额: $ 75万
• 依托单位: HARTFORD HOSPITAL
• 依托单位国家: 美国
• 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/9897171
• 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

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.

尽管有明显的证据表明大脑奖励系统中的多动症异常，但证据是 由于其狭窄地关注有限数量的奖励结构以及其不一致之处，因此限制了 跨研究。我们认为，这些不一致是无法识别的ADHD神经生物学的结果 异质性。该项目旨在测试ADHD是一种疾病的预测 不同类型的神经生物学功能障碍能够产生相同的ADHD诊断表型。 对于这种神经认知和神经生物学上不同的ADHD亚组的明确，果断的证据是需要的 支持ADHD神经科学的新兴范式转变，从每个ADHD的假设 患者具有类似的病理生理学，与多属病因模型最终应该证明具有更大的状态 翻译实用性。在这里，我们将专注于更好地理解ADHD中的奖励功能障碍 与在该疾病中经常发现的其他神经认知异常相比，相关研究不足。我们 提议检查具有ADHD诊断和非ADHD（n = 150）青少年的大型（n = 200）样本 雄心勃勃的基于奖励的表型方法使用潜水员奖励测试电池评估许多 奖励行为的领域（例如，不同类型的强化学习，价值，成本处理，努力 支出等） - 一种在我们的初步多动症研究中证明非常成功的方法。这个电池 还将利用过去十年来奖励表型的进步 奖励选择行为和反应时间数据的计算建模。然后，我们将映射 在增强学习过程中，这些能力与额叶网络连接的个体差异 “预测错误”。我们的最终目标是使用此信息将ADHD患者分类为不同 生物型。尽管我们的初步数据表明可能至少有两种不同的奖励受损的多动症 奖励功能障碍不同的亚组，我们的方法比一个简单的方法更为严格 复制尝试。我们将测试ADHD神经认知异常的广泛概念模型的拟合 第一次在奖励领域中，然后使用这些发现来完善我们的生物分类方法 具有严格分类方法的案例。最终的生物型将使用其他fMRI验证 奖励任务。该项目将创建描述奖励功能障碍的最详细，最广泛的数据库 在迄今为止的多动症中，我们将为科学界免费提供，以加速 发现。重新集中于多动症中奖励功能障碍的重点不仅及时，而且很可能 在未来的病因和翻译研究中为重要进步奠定了基础。不同类型的多动症 奖励功能障碍可以代表未开发的新目标，以进行新的干预开发，其中 治疗与神经生物学功能障碍的类型相匹配，而不是广泛的DSM ADHD诊断。