A Computational Psychiatry Investigation of the effects of Mood on Reward Learning and Attention

情绪对奖励学习和注意力影响的计算精神病学研究

• 批准号: 10449368
• 负责人: Yael Niv
• 金额: $ 44.82万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-26 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10449368
• 关键词: Affect; Amygdaloid structure; Attention; Behavioral; Biological Markers; Bipolar Disorder; Brain; Clinical; Cognition; Cognitive; Cognitive Science; Complex; Computer Analysis; Computer Models; Computing Methodologies; Corpus striatum structure; Cues; Custom; Data; Data Collection; Depressed mood; Diagnosis; Dimensions; Disease; Dissociation; Event; Feedback; Functional Magnetic Resonance Imaging; Functional disorder; General Population; Health; Human; Investigation; Learning; Link; Major Depressive Disorder; Measurable; Measures; Mediating; Mental Depression; Modeling; Mood Disorders; Moods; Online Systems; Outcome; Participant; Pathologic; Pathology; Patient Self-Report; Patients; Pattern; Population; Prefrontal Cortex; Psychiatry; Psychological reinforcement; Punishment; Questionnaires; Regulation; Rewards; Sampling; Stimulus; Testing; Unipolar Depression; Ventral Striatum; Weight; base; bipolar patients; cognitive process; computerized tools; depressed patient; depressive symptoms; design; experience; experimental study; hypomania; improved; insight; instrument; negative mood; neural circuit; neural correlate; novel; positive mood; predictive modeling; relating to nervous system; reward processing; sample fixation; simulation; success; symptomatology; theories; trend; visual tracking

A Computational Psychiatry Investigation of the effects of Mood on Reward Learning and Attention The relationship between mood and reward processing is bidirectional. On the one hand, mood is affected by the experience of rewards and punishments, such that mood tends to improve after better-than-expected outcomes and deteriorate after outcomes that are worse than expected. On the other hand, mood itself biases reward processing via its effects on cognitive processes such as attention and reinforcement learning (RL). As such, pathological mood states in mood disorders such as major depressive disorder and bipolar disorder may be the result of aberrant patterns of interaction between mood, reward learning, and attention. Recently, we and others have begun to use computational models to unravel the complex patterns of reciprocal interaction between mood, reward learning, and attention (e.g., Eldar & Niv, 2015; Eldar et al., 2016). However, these models' critical predictions regarding the neurocomputational substrates of mood disorders have not yet been tested. In particular, we predict that bipolar disorder and major depression can be distinguished from one another at both a behavioral and a neural level, in terms of different patterns of abnormal interaction between mood, RL, and attention. Here, we propose to test this prediction using convergent methodologies from computational psychiatry including human patient studies, large-scale online data collection and functional magnetic resonance imaging. In Aim 1, we will test whether bipolar disorder and major depression are characterized by distinct patterns of interaction between mood, RL, and attention. We will use behavioral experiments with two custom-designed tasks to measure the strength of the mood-RL interaction and the mood-attention interaction, respectively. Computational models will be fit to data from these tasks in both subjects with mood disorders and in matched controls. In Aim 2, we will assess the utility of mood-RL and mood-attention interactions as markers of vulnerability to mood disorders in the general population. We will use web-based data collection with the same two tasks as in Aim 1 to explore links between mood-RL and mood-attention interactions and the subclinical expression of mood disorders in a general population sample. Finally, in Aim 3 we will identify the neural circuits mediating the effect of mood on RL. We will acquire fMRI data on the mood-RL task from healthy subjects and from patients with bipolar disorder and major depressive and will use these data to describe the neurocomputational interactions of mood and reward in health and disease. This project will use state-of-the-art tools from computational psychiatry to test and refine a neurocomputational model of mood. Guided by the predictions of this model, we will assess patterns of interaction between mood, reinforcement learning, and attention in three different contexts: a psychiatric behavioral sample, a large-scale online sample of the general population, and a sample with fMRI data to help us assess the neural substrates of mood-cognition interactions. Taken together, these aims will allow us to assess a neurocomputational model of mood that has the capacity to transform the clinical understanding of mood disorders including bipolar disorder and major depression.

情绪对奖励学习和注意力影响的计算精神病学研究 情绪和奖励处理之间的关系是双向的。一方面，情绪会受到影响。 奖励和惩罚的体验，这样在结果好于预期之后情绪往往会改善， 结果比预期更糟糕后恶化。另一方面，情绪本身通过其奖励处理过程产生偏差。 对注意力和强化学习（RL）等认知过程的影响。因此，病态情绪状态 重度抑郁症和双相情感障碍等情绪障碍可能是异常互动模式的结果 情绪、奖励学习和注意力之间的关系。 最近，我们和其他人开始使用计算模型来揭示相互交互的复杂模式 情绪、奖励学习和注意力之间的关系（例如，Eldar & Niv，2015；Eldar 等人，2016）。然而，这些模型的 关于情绪障碍的神经计算基础的关键预测尚未经过测试。 特别是，我们预测双相情感障碍和重度抑郁症可以在两个方面进行区分： 行为和神经层面，即情绪、强化学习和注意力之间异常相互作用的不同模式。 在这里，我们建议使用计算精神病学（包括人类）的收敛方法来测试这一预测 患者研究、大规模在线数据收集和功能磁共振成像。 在目标 1 中，我们将测试双相情感障碍和重度抑郁症是否具有不同的模式特征。 情绪、RL 和注意力之间的相互作用。我们将使用行为实验和两个定制设计的任务来 分别测量情绪-强化学习相互作用和情绪-注意力相互作用的强度。计算模型 将适合患有情绪障碍的受试者和匹配的对照者中这些任务的数据。在目标 2 中，我们将评估 情绪-RL 和情绪-注意力相互作用作为易受情绪障碍影响的标记的效用 一般人群。我们将使用基于网络的数据收集来完成与目标 1 中相同的两个任务，以探索之间的联系 情绪-RL 和情绪-注意力相互作用以及一般人群情绪障碍的亚临床表达 样本。最后，在目标 3 中，我们将确定调节情绪对 RL 影响的神经回路。我们将收购 来自健康受试者以及双相情感障碍和重度抑郁症患者的情绪强化学习任务的 fMRI 数据 将使用这些数据来描述健康和疾病中情绪和奖励的神经计算相互作用。 该项目将使用计算精神病学的最先进工具来测试和完善神经计算模型 情绪。在该模型的预测指导下，我们将评估情绪、强化之间的相互作用模式 三种不同背景下的学习和注意力：精神病行为样本、大规模在线样本 一般人群，以及带有功能磁共振成像数据的样本，可帮助我们评估情绪认知相互作用的神经基础。 总而言之，这些目标将使我们能够评估一种能够改变情绪的神经计算模型 对情绪障碍（包括双相情感障碍和重度抑郁症）的临床理解。