Neurocomputational studies of mood-related momentum dynamics linking reward learning, valuation and responsivity

RDoC正价系统(PVS)包含了正常奖励背后的激励过程- 引导性行为及其在许多精神障碍中的变化。然而，PVS之间的理论联系 奖励响应性、学习和估值的结构仍然没有具体说明。因此，我们的目标是 在动量动力学计算强化学习的新模型下将它们统一起来 势头反映了最近的结果是否普遍超出或低于我们的预期， 发出奖励率提高或恶化的信号。势头与情绪密切相关，我们的模型提供了 对情绪和奖励学习的相互作用的新见解。因此，我们正在寻求提供一种机械性的 跨诊断情绪动力学和情感不稳定(AI)的描述，这是精神病理学的一个维度 在抑郁、焦虑、进食和人格障碍以及自杀行为方面。而生态瞬间 到目前为止，对人工智能的评估(EMA)研究表明，精神疾病患者的情绪是如何随着时间的推移而变化的 没有正式的模型可以解释为什么它会发生这样的变化。另一方面，基于实验室的实验研究 使用认知神经科学的工具来探索情绪不稳定的潜在神经机制。尽管 有希望的是，实验室研究太简短，无法捕捉精神病理学中人工智能的时间动态，这通常是 在几个小时或几天内展开。在这里，我们克服了EMA和实验室研究的局限性，通过将 在以强化学习和动态系统为基础的框架内将两者的关键要素结合在一起 理论。为此，我们将结合情绪跟踪和日常生活中进行的4个以上的学习实验 几周，通过可穿戴式心率和脑电图仪同时记录神经生理信号 传感器。我们已经证明，这个平台捕捉到了积极的行为和生理影响 负面结果，以及生理学习信号预测受试者情绪的日常变化。我们 将使用这个平台来检查从社区抽样的个体中的PVS结构和人工智能(样本1， N=300)和从以下来源招募的边缘人格个体的临床样本(样本2，n=150 匹兹堡和宾夕法尼亚州州立大学正在进行的两项研究。在我们早期的研究中，情绪诱导会影响 奖赏价值也影响纹状体奖赏反应。在这里，我们将研究皮质纹状体 结合纵向移动的动量动力学与现实生活中情绪波动的基础 使用基于模型的功能磁共振成像进行评估。在扫描过程中，受试者将在他们的实验刺激中进行选择 以前在不同的情绪下遇到过。这将使我们能够研究情绪如何影响神经评估。 学习信号以及学习信号如何塑造未来的情绪。我们的跨学科团队在 心境的计算模型及其与EMA、生理学和成像的集成(Eldar)，计算 临床人群中的模型增强功能成像和EMA(Dombrovski，Hallquist)，以及 神经成像方法(Hallquist)。

The RDoC Positive Valence Systems (PVS) encompass motivational processes underlying normal reward- guided behavior and its alterations in many mental disorders. Yet, the theoretical links between the PVS constructs of Reward Responsiveness, Learning, and Valuation remain under-specified. Hence, our goal is to unify them under a new model of computational reinforcement learning with momentum dynamics wherein momentum reflects whether recent outcomes have generally exceeded or fallen short of our expectations, signaling an improving or worsening reward rate. Momentum is closely linked with mood and our model offers new insights into the interplay of mood and reward learning. Thus, we are seeking to provide a mechanistic account of transdiagnostic mood dynamics and affective instability (AI), a dimension of psychopathology seen in depression, anxiety, eating and personality disorders, and suicidal behavior. While ecological momentary assessment (EMA) studies of AI have shown how mood changes over time in mental illness, to date we have no formal model that can explain why it changes thus. On the other hand, lab-based experimental studies have used tools from cognitive neuroscience to explore potential neural mechanisms of affective instability. Though promising, lab studies are too brief to capture the temporal dynamics of AI in psychopathology, which typically unfold over hours or days. Here, we overcome the limitations of EMA and laboratory studies to date by bringing together key elements of both within a framework grounded in reinforcement learning and dynamical systems theory. To this end, we will combine mood tracking with learning experiments carried out in daily life over 4 weeks, concurrently recording neurophysiological signals via wearable heart rate and electroencephalography sensors. We have shown that this platform captures the behavioral and physiological effects of positive and negative outcomes, and that physiological learning signals predict day-to-day changes in subjects’ mood. We will use this platform to examine PVS constructs and AI in individuals sampled from the community (Sample 1, n = 300) and in a clinical sample of individuals with borderline personality (Sample 2, n = 150) recruited from two ongoing studies in Pittsburgh and State College, PA. In our earlier study, mood induction that impacted reward valuation also impacted striatal reward responsiveness. Here, we will investigate the cortico-striatal substrates of momentum dynamics in relation to real-life mood fluctuations by combining mobile longitudinal assessment with model-based fMRI. During the scan, subjects will choose between experimental stimuli they previously encountered in different moods. This will allow us to examine how mood impacts neural valuation and learning signals and how learning signals shape future mood. Our interdisciplinary team has expertise in computational modeling of mood and its integration with EMA, physiology and imaging (Eldar), computational model-augmented functional imaging and EMA in clinical populations (Dombrovski, Hallquist), and neuroimaging methods (Hallquist).