The Computational Psychiatry of Major Depressive Disorder

重度抑郁症的计算精神病学

基本信息

  • 批准号:
    MR/N02401X/1
  • 负责人:
  • 金额:
    $ 135.36万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Fellowship
  • 财政年份:
    2016
  • 资助国家:
    英国
  • 起止时间:
    2016 至 无数据
  • 项目状态:
    已结题

项目摘要

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.
抑郁症是全球残疾的主要原因,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,目前的抗抑郁治疗并不能帮助许多抑郁症患者。该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是有益的。现在人们认为抑郁症可以由各种不同的来源引起,就像咳嗽可以有许多不同的潜在原因一样。目前,精神科医生没有可靠的方法来知道哪种治疗方法对帮助特定的抑郁症患者可能是最有效的。此外,研究人员还没有设法提供一个清晰的画面,是什么决定了一个人的情绪是否会恶化,以及何时会恶化,以及当情绪变化时大脑会发生什么。对情绪决定因素的缺乏了解也使得开发抑郁症等情绪障碍的新疗法变得困难。我们最近的研究表明,可以测量像幸福感这样的瞬间主观状态,并且我们可以精确地预测全球18,000多名玩家在智能手机上玩决策游戏时幸福感会如何变化。在这个项目中,我们将量化在多个决策环境中情绪是如何决定的。然后,我们会问,这种对情绪的更好理解是否可以通过让健康和抑郁的个体参与我们的任务来更好地理解抑郁症,这些任务以游戏的形式呈现,并在实验室或家中在智能手机上玩。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)开发一种新的工具,使用智能手机远程评估抑郁个体,并允许从各种任务中收集行为和情绪数据,以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何时可能对治疗抑郁症最有效。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措施。例如,游戏可能会发现,在几个月内,一个人越来越有可能冒险,或者当这些风险没有得到回报时,他会越来越沮丧。从游戏中测得的数字提供了一个人当前状态的快照,因为它们提供了有关个人如何做出决策和对决策结果做出反应的信息,这些信息反过来反映了受抑郁症影响的神经回路的工作情况。通过检查游戏结果是否与治疗效果有关,我们或许能够预测哪种治疗方法对抑郁症患者最有效。当临床医生评估患者时,她可能会在某一天参考患者对智能手机应用程序所提问题的回答以及对游戏得分的分析。两个不同的抑郁症患者可能都有情绪低落,但原因却截然不同。原则上,这些分数可以用来表明某个疗程可能是最有效的。例如,抗抑郁药物和特定的认知行为疗法的组合通常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工作。然后,临床医生可以使用这些信息,结合她的专家评估和她对患者情况的了解,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的定量和临床相关数据的新方法,这些数据可以补充现有的临床信息并改善抑郁症的治疗。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Risk taking for potential losses but not gains increases with time of day.
  • DOI:
    10.1038/s41598-023-31738-x
  • 发表时间:
    2023-04-04
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Bedder, Rachel L.;Vaghi, Matilde M.;Dolan, Raymond J.;Rutledge, Robb B.
  • 通讯作者:
    Rutledge, Robb B.
Smartphones and the Neuroscience of Mental Health.
  • DOI:
    10.1146/annurev-neuro-101220-014053
  • 发表时间:
    2021-07-08
  • 期刊:
  • 影响因子:
    13.9
  • 作者:
  • 通讯作者:
A Neurocomputational Model for Intrinsic Reward.
Momentary subjective well-being depends on learning and not reward.
  • DOI:
    10.7554/elife.57977
  • 发表时间:
    2020-11-17
  • 期刊:
  • 影响因子:
    7.7
  • 作者:
    Blain B;Rutledge RB
  • 通讯作者:
    Rutledge RB
Age-dependent Pavlovian biases influence motor decision-making.
  • DOI:
    10.1371/journal.pcbi.1006304
  • 发表时间:
    2018-07
  • 期刊:
  • 影响因子:
    4.3
  • 作者:
    Chen X;Rutledge RB;Brown HR;Dolan RJ;Bestmann S;Galea JM
  • 通讯作者:
    Galea JM
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Robb Rutledge其他文献

Title: Imbalanced basal ganglia connectivity is associated with motor deficits and apathy in Huntington’s disease
标题:基底神经节连接失衡与亨廷顿病的运动缺陷和冷漠有关
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. Nair;Adeel Razi;S. Gregory;Robb Rutledge;G. Rees;S. Tabrizi
  • 通讯作者:
    S. Tabrizi
70. Strong Pessimistic Priors in Highly Antagonistic Individuals Disrupts Adaptive Social Learning
  • DOI:
    10.1016/j.biopsych.2023.02.310
  • 发表时间:
    2023-05-01
  • 期刊:
  • 影响因子:
  • 作者:
    Yeon Soon Shin;Lauren Wilkins;Jenifer Siegel;Christoph Mathys;Robb Rutledge;Kate Saunders;M.J. Crockett
  • 通讯作者:
    M.J. Crockett

Robb Rutledge的其他文献

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