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

情绪对奖赏学习和注意影响的计算精神病学研究 情绪和奖励加工之间的关系是双向的。一方面，情绪受到 奖励和惩罚的经验，这样的情绪往往会在好于预期的结果和 在结果比预期更糟糕之后，情况会恶化。另一方面，情绪本身通过其 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 情绪障碍，如重度抑郁障碍和双相情感障碍，可能是异常互动模式的结果 在情绪、奖励、学习和注意力之间。 最近，我们和其他人已经开始使用计算模型来解开相互作用的复杂模式 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底物的关键预测还没有得到测试。 特别是，我们预测双相情感障碍和重度抑郁症可以在两种情况下相互区分。 行为层面和神经层面，根据情绪、RL和注意力之间异常交互的不同模式。 在这里，我们建议使用包括人类在内的计算精神病学的融合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 情绪、RL和注意力之间的相互作用。我们将使用两个定制任务的行为实验来 测量情绪-RL交互作用和情绪-注意交互作用的强度。计算模型 将与情绪障碍受试者和匹配对照组的这些任务的数据相吻合。在目标2中，我们将评估 心境-RL和心境-注意交互作用作为易患心境障碍的标志的效用 普通人口。我们将使用基于Web的数据收集，完成与目标1相同的两项任务，以探索 情绪-RL和情绪-注意交互作用与一般人群中情绪障碍的亚临床表现 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。我们将获得 健康受试者和双相情感障碍和抑郁症患者的心境-RL任务的fMRI数据 将使用这些数据来描述情绪和奖励在健康和疾病中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 心情。在这个模型的预测的指导下，我们将评估情绪之间的互动模式，强化 三种不同情境下的学习和注意力：一个精神行为样本，一个大规模的在线样本 和一个样本的fMRI数据，以帮助我们评估情绪-认知相互作用的神经基础。 综上所述，这些目标将使我们能够评估具有转换能力的情绪的神经计算模型 情绪障碍包括双相情感障碍和抑郁症的临床认识。

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.