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

RDoC正价系统（PVS）包括正常奖励背后的动机过程- 在许多精神疾病中的引导行为及其改变。然而，PVS和 奖励响应性、学习和评估的结构仍然不明确。因此，我们的目标是 将它们统一在具有动量动力学的计算强化学习的新模型下， 势头反映了最近的成果是否普遍超过或低于我们的预期， 表示奖励率的提高或降低。动量与情绪密切相关，我们的模型提供了 对情绪和奖励学习相互作用的新见解。因此，我们正在寻求提供一种机制， 跨诊断情绪动力学和情感不稳定（AI）的解释， 抑郁症、焦虑症、饮食和人格障碍以及自杀行为。虽然生态瞬间 人工智能评估（EMA）研究已经表明，情绪如何随着时间的推移在精神疾病中变化，到目前为止，我们已经 没有一个正式的模型可以解释它为什么会这样变化。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感不稳定的潜在神经机制。虽然 有希望的是，实验室研究过于简短，无法捕捉人工智能在精神病理学中的时间动态， 在几小时或几天内展开。在这里，我们克服了EMA和实验室研究的局限性， 在强化学习和动态系统的基础上， 理论为此，我们将联合收割机的情绪跟踪与学习实验在日常生活中进行了超过4 周，通过可穿戴心率和脑电图同时记录神经生理信号 传感器.我们已经证明，这个平台捕捉了积极和消极的行为和生理影响。 负面结果，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AI（样本1， n = 300）和边缘型人格个体的临床样本（样本2，n = 150）， 在匹兹堡和宾夕法尼亚州立大学进行的两项研究。在我们早期的研究中，情绪诱导影响了 奖赏评价也影响纹状体的奖赏反应。在这里，我们将研究皮质-纹状体 通过结合移动的纵向运动， 基于模型的fMRI评估。在扫描过程中，受试者将在实验刺激之间进行选择， 以前遇到过不同的情绪。这将使我们能够研究情绪如何影响神经评估 以及学习信号如何塑造未来的情绪。我们的跨学科团队拥有以下专业知识： 情绪的计算建模及其与EMA、生理学和成像（Eldar）的集成，计算 临床人群中的模型增强功能成像和EMA（Dombrovski，Hallquist），以及 神经影像学方法（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).