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A Computational Approach to Understanding Maladaptive Cognition in Depression

理解抑郁症适应不良认知的计算方法

基本信息

批准号：
ES/S015922/1
负责人：
Nura Sidarus
金额：
$ 30.14万
依托单位：
Royal Holloway University of London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FS015922%2F1
关键词：
Computational Approach Understanding Maladaptive Cognition

项目摘要

CONTEXTDepression is the single leading cause of disability worldwide and a major public health problem. Even with the best treatments, around 30% of patients remain unwell, demonstrating the importance of improving our understanding of depression. Decades of research in clinical psychology suggests that vulnerability to depression is associated with negative cognitive styles, such as attributing negative events to stable and global causes, often blaming oneself, and maladaptive metacognitive beliefs (about one's own cognitive processes), such as low self-confidence. These biases are a focus of psychological therapies such as cognitive behavioural therapy (CBT), but the assessment of maladaptive depressive cognition is limited by imprecise measurement, relying on introspection and self-report. AIMS AND OBJECTIVESThis project aims to improve our understanding of the maladaptive cognitions driving depressive symptoms. To gain a more mechanistic understanding of the neurocognitive bases of (mal)adaptive cognition, we will leverage computational models of behaviour. This overarching goal will be achieved by conceptualising maladaptive depressive cognition as maladaptive attributions. To test this we will measure: (a) biases in the attribution of positive and negative events to the self vs. external causes; (b) biases in the metacognitive evaluations of decision confidence and their potential misattribution to action-outcome learning.Using cutting-edge analysis methods, across online, clinical, and neuroimaging studies, this project will achieve the following objectives: 1. Clarify the neurocognitive mechanisms underlying adaptive attribution (of external events and of metacognitive signals), in healthy participants. 2. Identify behavioural markers of maladaptive attribution related to depressive symptoms in a non-clinical sample. 3. Test the specificity of markers of maladaptive attribution to depressive symptoms, relative to other common mental health problems. 4. Test the clinical relevance of markers of maladaptive attribution.POTENTIAL APPLICATIONS AND BENEFITSImproving our understanding of the mechanisms that drive maladaptive cognition in depression, and underpin attributional processes in healthy participants, will constitute an important scientific contribution to the fields of clinical psychology and cognitive and computational neuroscience. Given the high societal cost of depression, this research is of high societal and clinical relevance. Disseminating our findings to the wider society will demonstrate how a better understanding of basic cognitive processes may translate to understanding everyday behaviour. Presenting our project and findings to people with mental health problems, including service users, will allow receiving their feedback on our experimental designs and findings, and help broaden the perspective for future research. The work will also be regularly disseminated to academic audiences, through publications and conferences, across the fields of psychology, neuroscience, and mental health. Engaging with clinical experts, by organising an interdisciplinary workshop, will help increase our clinical impact, establish novel collaborations, and receive expert feedback. Identifying behavioural and neural markers related to maladaptive cognition in depression offers a unique opportunity to develop novel tools that may subsequently help to refine differential diagnosis and improve treatment selection, as well as provide a foundation for the development of novel psychological interventions.

抑郁症是世界范围内导致残疾的唯一主要原因，也是一个主要的公共卫生问题。即使采用最好的治疗方法，仍有大约30%的患者身体不适，这表明提高我们对抑郁症的理解的重要性。数十年的临床心理学研究表明，易患抑郁症与消极的认知方式有关，比如将消极事件归因于稳定的和全球性的原因，经常责备自己，以及适应不良的元认知信念（关于自己的认知过程），比如缺乏自信。这些偏差是认知行为疗法（CBT）等心理治疗的焦点，但对适应不良抑郁症认知的评估受到不精确测量的限制，依赖于内省和自我报告。目的与目的本项目旨在提高我们对导致抑郁症状的不适应认知的理解。为了获得对（不良）适应性认知的神经认知基础的更机械的理解，我们将利用行为的计算模型。这一总体目标将通过将适应不良抑郁认知概念化为适应不良归因来实现。为了验证这一点，我们将测量：(a)将积极和消极事件归因于自我与外部原因的偏差；(b)决策自信元认知评价中的偏差及其对行动-结果学习的潜在错误归因。利用前沿的分析方法，跨越在线、临床和神经影像学研究，本项目将实现以下目标：阐明健康参与者适应性归因（外部事件和元认知信号）的神经认知机制。2. 在非临床样本中识别与抑郁症状相关的适应不良归因的行为标记。3. 相对于其他常见的心理健康问题，测试适应不良归因于抑郁症状的标记的特异性。4. 检验适应不良归因标记的临床相关性。潜在的应用和益处提高我们对抑郁症中导致适应不良认知的机制的理解，并支持健康参与者的归因过程，将对临床心理学、认知和计算神经科学领域做出重要的科学贡献。鉴于抑郁症的高社会成本，本研究具有很高的社会和临床相关性。将我们的发现传播到更广泛的社会将证明对基本认知过程的更好理解如何转化为对日常行为的理解。向有心理健康问题的人（包括服务使用者）展示我们的项目和研究结果，可以让他们对我们的实验设计和研究结果获得反馈，并有助于拓宽未来研究的视角。这项工作还将通过出版物和会议定期向心理学、神经科学和心理健康领域的学术受众传播。通过组织跨学科研讨会，与临床专家合作，将有助于提高我们的临床影响，建立新的合作关系，并获得专家反馈。识别与抑郁症中适应性认知不良相关的行为和神经标记为开发新的工具提供了一个独特的机会，这些工具可能随后有助于改进鉴别诊断和改善治疗选择，并为开发新的心理干预措施提供基础。