Computational dissociation of the causes of cognitive rigidity in depression

抑郁症认知僵化原因的计算分离

• 批准号: 10517168
• 负责人: Alexander Herman
• 金额: $ 23.47万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10517168
• 关键词: Address; Algorithms; Anhedonia; Animal Model; Behavior; Behavioral; Clinical; Cognitive; Computer Models; Data; Data Set; Decision Making; Depressed mood; Dissociation; Early Diagnosis; Early Intervention; Environment; Equipment and supply inventories; Face; Failure; Feeling hopeless; Future; Goals; Human; Impairment; Individual Differences; Lead; Learning; Light; Link; Major Depressive Disorder; Mental Depression; Mind; Modeling; Moods; Mus; Outcome; Patients; Pattern; Perception; Persons; Physiological; Population; Process; Psychological reinforcement; Research; Rewards; Sampling; Severities; Shapes; Suicide; Suicide attempt; Symptoms; Testing; Translating; Treatment outcome; United States; Variant; Work; anxious; base; cognitive control; cognitive process; cognitive rigidity; computer framework; depressive symptoms; environmental change; expectation; experience; flexibility; innovation; model design; models and simulation; novel; pre-clinical; premature; prognostic; response; success; suicidal risk; theories

Depression distorts perceptions of the past and future, manifesting in symptoms such as hopelessness and anhedonia, along with increasingly rigid patterns of decision-making. However, the mechanistic link between important prognostic symptoms in depression and depressive rigidity remains poorly understood. In large part, this is because we lack validated computational models that explain how appraisals of the past and views of the future can mechanistically contribute to cognitive rigidity or flexibility. Here, we construct and test a mechanistic model that will allow us to quantify the impact of depressive symptoms on cognitive flexibility and open new avenues for early diagnosis and intervention. Previous work modeling decision-making in depression with reinforcement learning (RL) has shed light on how depressive symptoms like anhedonia alter reward-based judgements. However, standard RL lacks the validity needed to explain depressive rigidity. Here, we develop a novel model from another powerful computational framework: foraging theory. We designed this model with depressive symptoms in mind to explicitly link learning from the past and estimating the future to cognitive flexibility. The central hypothesis is that learning from past rewards and estimating future rewards are dissociable mechanisms that control cognitive flexibility. The specific aims of this proposal are to (1) Determine how appraisals of the past and future shape cognitive flexibility and (2) Examine how variations in the environment constrain cognitive flexibility. To accomplish these aims, we will characterize how judgements about the past and future influence decision-making rigidity and respond to environmental changes through model simulation and analysis. We will determine how individual differences in these cognitive processes are learned from the environment and if they predict rigidity by administering a flexible decision-making task to clinical depression and large online samples. Collecting depressive symptom inventories along with task data will allow us to interrogate the mechanisms by which depressive symptoms like anhedonia lead to rigidity. To test cross-species validity for preclinical work, we apply this model to a previously collected mouse behavioral dataset. Innovation: Our novel model will determine how views of the past and future, and their responses to the environment, contribute to cognitive rigidity, and how they are impacted by depressive symptoms in an online sample. The results will guide future hypothesis-driven research into the algorithmic basis of depressive rigidity in patients. Specifically, a future R01 application will test the model’s utility for predicting depression subtypes and treatment outcomes in a clinical population. This model will also enable us to study the physiological bases of these computational processes in animal models and humans undergoing invasive and non-invasive neurophysiolgoical studies.

抑郁扭曲了对过去和未来的看法，表现在绝望和 Anhedonia，以及越来越严格的决策模式。但是， 抑郁症和抑郁型僵化中的重要预后症状仍然知之甚少。在很大程度上， 这是因为我们缺乏验证的计算模型，这些模型解释了过去和过去的评估 对未来的看法可以机械地有助于认知僵化或灵活性。在这里，我们构建 并测试一种机械模型，该模型将使我们能够量化抑郁症状对认知的影响 灵活性和开放新途径，用于早期诊断和干预。 以前的工作建模抑郁症的决策通过加强学习（RL）浮出水面 关于Anhedonia等抑郁症状如何改变基于奖励的判断。但是，标准RL缺乏 有效性需要解释抑郁型僵硬。在这里，我们从另一个强大的 计算框架：觅食理论。我们设计了这个模型，考虑到抑郁症状 明确将学习与过去的学习联系起来，并将未来估算到认知灵活性。 中心假设是从过去的奖励中学习并估算未来的奖励是可分开的 控制认知灵活性的机制。该提案的具体目的是（1）确定如何 对过去和未来的认知灵活性的评估，以及（2）检查如何变化 环境约束认知灵活性。为了实现这些目标，我们将表征如何判断 关于过去和未来的影响决策僵化，并通过 模型模拟和分析。我们将确定这些认知过程中的个体差异如何 从环境中学到的学习以及他们是否通过管理灵活的决策任务来预测刚性 临床抑郁症和大型在线样本。收集抑郁症状清单以及任务数据 将使我们能够审问抑郁症状等机制，例如抗逆转录病毒症，导致僵化。到 临床前工作的测试跨物种有效性，我们将此模型应用于先前收集的小鼠行为 数据集。 创新：我们的新颖模型将决定过去和未来的观点，以及它们对 环境，有助于认知僵化，以及在线中如何受到抑郁症状的影响 样本。结果将指导未来以假设为驱动的假设驱动的研究，以抑制刚度的算法基础 在患者中。具体而言，未来的R01应用程序将测试该模型的预测抑郁症子类型的实用性 和临床人群的治疗结果。该模型还将使我们能够研究物理基础 在动物模型和人类中经历侵入性和非侵入性的这些计算过程中 神经生理学研究。