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 情绪和奖赏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，使情绪往往改善后，比预期的结果， 在结果比预期的更糟之后恶化。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， 情绪障碍如重度抑郁症和双相情感障碍可能是异常的互动模式的结果 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 关于情绪障碍的神经计算基质的关键预测尚未得到检验。 特别是，我们预测双相情感障碍和重性抑郁症可以在两个方面相互区分。 行为和神经水平，在情绪，RL和注意力之间的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的收敛方法来测试这一预测，包括人类 患者研究、大规模在线数据收集和功能性磁共振成像。 在目标1中，我们将测试双相情感障碍和重性抑郁症是否以不同的模式为特征， 情绪，RL和注意力之间的相互作用。我们将使用两个定制设计任务的行为实验， 分别测量情绪-RL交互和情绪-注意力交互的强度。计算模型 将适合这些任务的数据，在这两个主题与情绪障碍和匹配的控制。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情绪障碍易感性标志的效用 一般人口。我们将使用基于网络的数据收集与目标1中相同的两个任务，以探索 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现 sample.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 来自健康受试者、双相情感障碍和重性抑郁症患者的情绪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。 这个项目将使用计算精神病学的最先进的工具来测试和完善一个神经计算模型， 心情在这个模型的预测的指导下，我们将评估情绪、强化和情感之间的相互作用模式。 学习和注意力在三个不同的背景下：精神病行为样本，大规模的在线样本， 一般人群，和一个样本与功能磁共振成像数据，以帮助我们评估情绪认知相互作用的神经基板。 总之，这些目标将使我们能够评估一个有能力改变情绪的神经计算模型。 对情感障碍包括双相情感障碍和重性抑郁症的临床理解。

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.