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

连接奖励学习、评估和反应性的情绪相关动量动态的神经计算研究

• 批准号: 10662215
• 负责人: Alexandre Y. Dombrovski
• 金额: $ 107.12万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10662215
• 关键词: Affective; Anterior; Anxiety Disorders; Behavior; Behavioral; Brain; Clinical; Color; Communities; Computer Models; Corpus striatum structure; Coupling; Data; Data Collection; Diagnostic; Dimensions; Eating Disorders; Ecological momentary assessment; Electroencephalography; Elements; Feedback; Functional Imaging; Functional Magnetic Resonance Imaging; Future; Goals; Heart Rate; Hour; Image; Individual; Insula of Reil; Laboratory Study; Learning; Life; Link; Mediating; Mediator; Mental Depression; Mental disorders; Methods; Modeling; Moods; Outcome; Personality; Personality Disorders; Persons; Physiological; Physiology; Population; Positive Valence; Prefrontal Cortex; Process; Protocols documentation; Psychological reinforcement; Psychopathology; Research Domain Criteria; Rewards; Role; Safety; Sampling; Scanning; Schedule; Shapes; Signal Transduction; Stimulus; Suicide attempt; System; Systems Theory; Telephone; Testing; Time; Variant; Work; cognitive neuroscience; college; dynamic system; expectation; experimental study; falls; improved; indexing; insight; mobile computing; motivational processes; negative mood; neural; neural circuit; neuroimaging; neuromechanism; neurophysiology; positive mood; recruit; response; sensor; suicidal behavior; theories; tool; wearable sensor technology

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).

RDOC正价系统（PVS）涵盖了正常奖励基础的动机过程 指导行为及其在许多精神障碍中的改变。但是，PVS之间的理论联系 奖励响应能力，学习和估值的结构仍然不明显。因此，我们的目标是 用动量动态在新的计算加强学习模型下统一它们 动量反映了最近的结果是否通常超过我们的期望，或者跌倒 信号提高或令人担忧的奖励率。动力与情绪紧密相关，我们的模型提供 对情绪和奖励学习的相互作用的新见解。那我们正在寻求提供机械 转诊情绪动态和情感不稳定性（AI）的描述，心理病理学的维度 在抑郁，焦虑，饮食和个人疾病以及自杀行为中。而生态瞬间 对AI的评估（EMA）研究表明，心理疾病中的情绪随着时间的流逝如何变化，迄今为止我们已经有 没有正式的模型可以解释为什么它会因此而改变。另一方面，基于实验室的实验研究具有 使用了认知神经科学的工具来探索情感不稳定的潜在神经力学。尽管 有希望的实验室研究太简短了，无法捕获心理病理学中AI的临时动态，通常 在数小时或几天内展开。在这里，我们通过带来EMA和实验室研究的局限性 在以增强学习和动态系统为基础的框架内，这两者的关键要素在一起 理论。为此，我们将情绪跟踪与在日常生活中进行的学习实验相结合。 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。我们已经表明，该平台捕获了积极和身体的行为和身体影响 负面的结果，并且体育学习信号可以预测受试者情绪的日常变化。我们 将使用此平台检查社区采样的个人中的PVS结构和AI（样本1， n = 300）和在临床样本中的临床样本（样本2，n = 150） 宾夕法尼亚州匹兹堡和州立大学的两项正在进行的研究。在我们较早的研究中，影响的情绪诱导 奖励价值还影响了纹状体奖励响应能力。在这里，我们将研究Cortico-Striatal 通过组合移动纵向，与现实生活情绪波动有关的动量动力学的底物 用基于模型的fMRI评估。在扫描过程中，受试者将在实验刺激之间进行选择 以前以不同的心情遇到。这将使我们能够检查情绪如何影响神经价值 以及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 情绪的计算建模及其与EMA，生理和成像（ELDAR），计算的集成 临床人群（Dombrovski，Hallquist）和 神经影像学方法（Hallquist）。