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

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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.