The Computational Psychiatry of Major Depressive Disorder

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Depression is the leading cause of disability worldwide, affecting more than 300 million people. The social and economic costs of depression are enormous. Unfortunately, current antidepressant treatments do not help many of those who suffer from depression. This project proposes new approaches to assessing depression and identifying which treatments are most likely to be helpful.It is now accepted that depression can result from a variety of different sources, much like a cough can have many different underlying causes. There is at present no reliable way for a psychiatrist to know which treatment is likely to be most effective for helping a particular depressed individual. Furthermore, researchers have not yet managed to provide a clear picture of what determines if, and when, an individual's mood will worsen and what happens in the brain when mood changes. This lack of understanding of the determinants of mood also makes it difficult to develop new treatments for mood disorders like depression. Our recent research has shown that it possible to measure momentary subjective states like happiness and that we can predict precisely how happiness will change from moment to moment during a decision-making game played on smartphones by over 18,000 players worldwide.In this project, we will quantify how mood is determined in multiple decision-making environments. We will then ask whether this improved understanding of mood can be used to better understand depression by having healthy and depressed individuals engage in our tasks, presented in the form of games and played either in the lab or at home on smartphones. The project has three major goals:1) To increase knowledge of the neural circuits that determine mood in both healthy and depressed individuals.2) To develop a new tool that uses smartphones to remotely assess depressed individuals and allows behaviour and mood data from a variety of tasks to be collected that could help clinicians make better treatment decisions.3) To determine how different antidepressant drugs affect behaviour and mood, results that will help to understand when each drug might be most effective in treating depression.The three 'games' developed in the project will provide measures that relate to the current state of a player's brain. For example, the games might detect that over several months an individual is becoming more and more likely to take risks, or is increasingly upset when those risks do not pay off. The numbers measured from the games provide a snapshot of the individual's current state, since they provide information about how the individual makes decisions and responds to decision outcomes that in turn reflect the workings of neural circuits affected in depression. By examining whether results of the games relate to treatment efficacy, we might be able to predict which treatment will be most effective for helping a depressed individual.When a clinician is evaluating her patient, she might someday refer to an analysis of the patient's game scores in addition to responses to questions asked by the smartphone app. Two different depressed individuals may both have low mood but for very different reasons. The scores can in principle be used to suggest that a certain course of treatment is likely to be most effective. For example, a combination of an antidepressant medication and a specific cognitive behavioural therapy may often be effective in people with a certain set of scores that reflect the workings of neural circuits that can be affected in depression. The clinician could then use that information, in combination with her expert evaluation and her knowledge of the patient's circumstances, to make a better treatment decision. In this way, the project will, if successful, demonstrate a new way to gather rich quantitative and clinically relevant data that can complement existing clinical information and improve the treatment of depression.