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.

虽然有明显的证据表明ADHD患者大脑的奖励系统异常，但这些证据是 有限的，由于它的狭隘的重点是有限数量的奖励结构，以及它的不一致性 跨研究。我们认为这些不一致是未被认识到的ADHD神经生物学的结果。 异质性这个项目旨在测试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。 需要明确的，决定性的证据来证明这些神经认知和神经生物学上不同的ADHD亚组， 支持ADHD神经科学的一个新的范式转变，不再假设每个ADHD患者 患者具有相似的病理生理学，多病因模型最终应证明具有更大的 翻译有用性在这里，我们将专注于更好地理解ADHD中的奖励功能障碍， 相对于在这种疾病中经常发现的其他神经认知异常而言，研究相对不足。我们 建议检查一个大样本（n=200）的ADHD诊断和非ADHD（n=150）的青少年， 一种雄心勃勃的基于奖励的表型分析方法，使用不同的奖励测试组合来评估许多 奖励行为的域（例如，不同类型的强化学习，估值，成本处理，努力 支出等）- 这个方法在我们的ADHD初步研究中非常成功。这个电池 还将利用过去十年中由复杂的基因工程师在奖励表型方面取得的进展， 奖励选择行为和反应时间数据的计算建模。然后，我们将绘制 在强化学习过程中，这些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ADHD患者分为不同的类型， 生物型虽然我们的初步数据表明，至少有两种不同的奖励受损多动症 对于具有不同奖励功能障碍特征的亚组，我们的方法将比简单的 复制尝试我们将测试ADHD神经认知异常的广泛概念模型是否适合 第一次在奖励领域，然后使用这些发现来完善我们对个体进行生物分型的方法 采用严格的分类方法。所产生的生物型将使用其他功能磁共振成像进行验证 奖励任务。这个项目将创建最详细和广泛的数据库描述奖励功能障碍 到目前为止，我们将免费提供给科学界，以加快 的发现对ADHD中奖励功能障碍的重新关注不仅是及时的，而且可能会 为未来病因学和转化研究的重要进展奠定了基础。不同类型的ADHD 奖励功能障碍可能是新干预开发的未开发的新目标， 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。