The Computational Psychiatry of Major Depressive Disorder

抑郁症是全世界致残的主要原因，影响着3亿多人。抑郁症的社会和经济成本是巨大的。不幸的是，目前的抗抑郁治疗对许多抑郁症患者没有帮助。这个项目提出了新的方法来评估抑郁症，并确定哪些治疗方法最有可能有用。现在人们普遍认为，抑郁症可能由多种不同的原因引起，就像咳嗽可能有许多不同的潜在原因一样。目前还没有可靠的方法让精神科医生知道哪种治疗方法可能对帮助特定的抑郁症患者最有效。此外，研究人员还没有弄清楚是什么决定了一个人的情绪是否会恶化，以及何时会恶化，以及情绪变化时大脑会发生什么。缺乏对情绪决定因素的理解也使得开发治疗抑郁症等情绪障碍的新方法变得困难。我们最近的研究表明，我们可以测量瞬间的主观状态，比如幸福感，我们可以准确地预测幸福感在全球18,000多名智能手机玩家玩的决策游戏中是如何随时间变化的。在这个项目中，我们将量化在多个决策环境中情绪是如何被决定的。然后，我们会问，通过让健康和抑郁的人参与我们的任务，以游戏的形式呈现，在实验室或家里用智能手机玩，这种对情绪的更好理解是否可以用来更好地理解抑郁症。该项目有三个主要目标：1)增加对决定健康和抑郁个体情绪的神经回路的了解。2)开发一种新工具，使用智能手机远程评估抑郁症患者，并允许收集来自各种任务的行为和情绪数据，从而帮助临床医生做出更好的治疗决策。3)确定不同的抗抑郁药物如何影响行为和情绪，结果将有助于了解每种药物在治疗抑郁症时最有效。该项目开发的三个“游戏”将提供与玩家大脑当前状态相关的测量方法。例如，游戏可能会发现，在几个月的时间里，一个人越来越有可能冒险，或者当这些风险没有回报时，他会越来越沮丧。从游戏中测量的数字提供了个人当前状态的快照，因为它们提供了个人如何做出决定和对决定结果做出反应的信息，而这些信息反过来又反映了抑郁症中受影响的神经回路的运作。通过检查游戏的结果是否与治疗效果有关，我们可能能够预测哪种治疗方法对帮助抑郁症患者最有效。当临床医生评估她的病人时，除了对智能手机应用程序提出的问题的回答外，她可能有一天会参考病人的游戏分数分析。两个不同的抑郁症患者可能都情绪低落，但原因却截然不同。原则上，这些分数可以用来表明某一疗程可能是最有效的。例如，抗抑郁药物和特定认知行为疗法的结合可能对具有一定分数的人有效，这些分数反映了可能受抑郁症影响的神经回路的运作。然后，临床医生可以利用这些信息，结合她的专家评估和她对病人情况的了解，做出更好的治疗决定。通过这种方式，如果成功，该项目将展示一种收集丰富定量和临床相关数据的新方法，可以补充现有的临床信息并改善抑郁症的治疗。

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.