Computational Modeling-Informed Reward Subgroups in Adolescent ADHD

尽管有明显的证据表明大脑奖励系统中存在ADHD异常，但这些证据是 由于其对有限数量的奖励结构的狭隘关注以及其不一致，因此受到限制 不同的研究。我们认为，这种不一致是未被识别的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，在这种情况下， 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。 对于这种在神经认知和神经生物学上截然不同的ADHD亚组，需要明确、决定性的证据来证明 支持ADHD神经科学的新兴范式转变，使其不再假设每个ADHD 患者有类似的病理生理学，最终应该被证明是多病因模型的更大 翻译的有用性。在这里，我们将专注于更好地理解ADHD的奖赏功能障碍，这是 与该病中经常发现的其他神经认知异常相比，研究相对不足。我们 建议对患有ADHD和非ADHD的青少年(n=150)进行大样本(n=200)的检查 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 奖励行为领域(例如，不同类型的强化学习、评估、成本处理、努力 支出等)--这一方法在我们的ADHD初步研究中被证明是非常成功的。这块电池 还将利用Complex在过去十年中在奖励表型方面的进步 奖赏选择行为和反应时数据的计算模型。然后我们将绘制以下方面的地图 强化学习中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患者分类为不同的 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赏受损的ADHD 具有不同奖赏功能障碍的亚群，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对 第一次在奖励领域，然后使用这些发现来改进我们对个人进行生物分型的方法 具有严格的分类方法的案例。由此产生的生物类型将使用其他功能磁共振进行验证 奖励任务。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 到目前为止，我们将免费向科学界提供ADHD，以加快 发现号。重新集中关注ADHD患者的奖赏功能障碍不仅是及时的，而且可能会 为未来病因学和转化学研究的重要进展奠定基础。不同类型的ADHD 奖赏功能障碍可能代表着新的干预开发的未开发的新靶点，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。

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.