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.

对情绪对奖励学习和关注的影响的计算精神病学调查 情绪与奖励处理之间的关系是双向的。一方面，心情受到 有奖励和惩罚的经验，使得情绪比预期的结果更好，并且 结局后的恶化，情况比预期的要好。另一方面，情绪本身会通过其偏见奖励处理 对注意力和加强学习（RL）等认知过程的影响。因此，病理情绪状态 情绪障碍（例如重度抑郁症和躁郁症）可能是相互作用模式的结果 在情绪，奖励学习和关注之间。 最近，我们和其他人开始使用计算模型来揭示相互互动的复杂模式 在情绪，奖励学习和关注之间（例如，Eldar＆Niv，2015； Eldar等，2016）。但是，这些模型 关于情绪障碍的神经计算底物的关键预测尚未测试。 特别是，我们预测，双相情感障碍和重度抑郁症可以在两者中彼此区分 行为和神经水平，就情绪，RL和注意力之间异常相互作用的不同模式而言。 在这里，我们建议使用来自人类的计算精神病学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AIM 1中，我们将测试躁郁症和重度抑郁症是否具有不同的特征 情绪，RL和注意力之间的相互作用。我们将使用两个自定义设计任务的行为实验来 分别测量情绪-RL相互作用的强度和情绪注意相互作用。计算模型 在患有情绪障碍和匹配控件中的两个受试者中，这些任务的数据都将适合这些任务。在AIM 2中，我们将评估 情绪-RL和情绪注意相互作用的效用，是对情绪障碍脆弱性的标志 一般人口。我们将使用与AIM 1相同的两个任务的基于Web的数据收集来探索之间的链接 情绪-RL和情绪注意相互作用以及一般人群中情绪障碍的亚临床表达 样本。最后，在AIM 3中，我们将确定介导情绪对RL影响的神经回路。我们将获得 来自健康受试者以及双相情感障碍和重大抑郁症患者的情绪-RL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 情绪。在该模型的预测的指导下，我们将评估情绪，增强之间的相互作用模式 在三种不同情况下学习和注意力：精神病行为样本，大规模的在线样本 普通人群和具有fMRI数据的样本，以帮助我们评估情绪认知相互作用的神经底物。 综上 对情绪障碍在内的临床理解，包括躁郁症和严重抑郁症。