Modeling the Neurobehavioral Basis of Extrinsic and Volitional Motivation in ADHD

项目总结/摘要 注意力缺陷/多动障碍（ADHD）是一种常见的神经发育障碍，其特征在于以下缺陷： 影响数百万儿童和成人的行政控制。动机受损也是一种常见的抱怨; 事实上，多巴胺系统的改变，动机的主要神经生物学基质，长期以来一直是 与多动症有关尽管如此，动机缺陷影响认知过程的方式，如 注意力缺陷多动症患者的学习和决策能力尚未完全了解。这一知识空白 这是一个重大障碍：需要新的方法来开发免费的治疗干预措施。 注意力缺陷多动障碍现有疗法的副作用。在这里，我们提出了几项研究，以推进 使用计算建模理解ADHD的动机过程;我们进一步测试 一种新的干预受损的动机，认知神经刺激（CN），在ADHD的学习。CN是一个非 一种侵入性神经行为训练技术，已被证明可以可靠和可持续地刺激 多巴胺能回路通过自我产生的动机图像。在目标1中，我们将使用眼动追踪， 注意力漂移扩散模型（aDDM），以表征注意力-动机的相互作用，在多动症使用 基于模型与无模型强化学习（RL）范式，先前已被证明具有 在精神疾病中的解释和预测能力。鉴于无模型策略 与多巴胺神经传递中断有关，我们预测注意力缺陷的严重程度将是 反映在噪声较大的决策过程和对无模型的相对依赖（即，相对于模型， 基于（即，目标导向的学习。在目标2中，我们将测试CN对目标1中描述的系统的影响， 测试动机状态的意志增强如何影响注意力-动机互动（如 由DDM模型参数表征）。我们预测，CN将通过刺激 多巴胺系统，改善RL和增加注意力增益。最后，在目标3中，我们将使用探索性的 方法来测试是否aDDM模型参数可以预测个人对CN的反应。我们预测， 由于其独特的能力，以描述注意力-动机的相互作用，这些参数将，相比， 其他行为指标，更好地反映了ADHD的潜在神经生物学过程， 由CN调制。总之，这些发现将促进我们对神经和行为的理解。 机制塑造了ADHD中被破坏的动机，同时也为ADHD的有效性提供了新的见解。 对这一人群行为改变的潜在干预。这项研究还将展示如何 一个跨学科的、以神经科学为基础的方法可能会为个性化行为的干预设计提供信息 改变，这一过程对整个心理健康领域的最佳福祉都有影响。

PROJECT SUMMARY/ABSTRACT Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder marked by deficits in executive control affecting millions of children and adults. Impaired motivation is also a frequent complaint; indeed, alterations in the dopamine system, the primary neurobiological substrate of motivation, have long been implicated in ADHD. Nonetheless, the way in which motivational deficits impact cognitive processes, such as learning and decision making, in individuals with ADHD is not yet fully understood. This knowledge gap represents a significant hurdle in translation: new approaches are required to develop curative interventions free of the adverse side effects of existing therapies for ADHD. Here, we propose several studies to advance understanding of motivational processes in ADHD using computational modeling; we further test the efficacy of a novel intervention for impaired motivation, cognitive neurostimulation (CN), on learning in ADHD. CN is a non- invasive neurobehavioral training technique that has been shown to reliably and sustainably stimulate dopaminergic circuitry through self-generated motivational imagery. In Aim 1, we will use eye tracking and attentional drift diffusion modeling (aDDM) to characterize attention-motivation interactions in ADHD using a model-based versus model-free reinforcement learning (RL) paradigm that has previously been shown to have substantial explanatory and predictive power in in psychiatric disorders. Given that model-free strategies have been associated with disrupted dopamine neurotransmission, we predict that attention deficit severity will be reflected in noisier decision processes and a relative reliance on model-free (i.e., trial-by-trial) relative to model- based (i.e., goal-oriented) learning. In Aim 2, we will test the effect of CN on the system characterized in Aim 1, testing how volitional enhancement of motivational state impacts attention-motivation interactions (as characterized by aDDM model parameters). We predict that CN will enhance motivation through stimulation of the dopamine system, improving RL and increasing attentional gain. Finally, in Aim 3, we will use an exploratory approach to test whether aDDM model parameters can predict individual response to CN. We predict that, because of its unique ability to describe attention-motivation interactions, these parameters will, compared to other behavioral metrics, better reflect underlying neurobiological processes in ADHD and their ability to be modulated by CN. Together, these findings will advance our understanding of the neural and behavioral mechanisms shaping disrupted motivation in ADHD while also providing new insight into the efficacy of a potential intervention for behavior change in this population. The proposed research will also demonstrate how an interdisciplinary, neuroscience-based approach might inform intervention design for individualized behavior change, a process that has implications for optimal wellbeing across the spectrum of mental health.