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: 10684272
• 负责人: Alexander Herman
• 金额: $ 18.83万
• 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/10684272
• 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; 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; educational atmosphere; 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.

抑郁症扭曲了对过去和未来的看法，表现为绝望和绝望等症状。 快感缺失，沿着越来越僵化的决策模式。然而， 抑郁症和抑郁性强直的重要预后症状仍然知之甚少。在很大程度上， 这是因为我们缺乏有效的计算模型来解释如何评估过去， 对未来的看法可以机械地促成认知的刚性或灵活性。在这里，我们构建 并测试一个机械模型，使我们能够量化抑郁症状对认知功能的影响， 灵活性，并为早期诊断和干预开辟了新的途径。 之前使用强化学习（RL）对抑郁症决策进行建模的工作已经揭示了这一点 关于快感缺乏等抑郁症状如何改变基于奖励的判断。然而，标准RL缺乏 需要有效性来解释抑郁症的僵化。在这里，我们从另一个强大的 计算框架：觅食理论。我们设计这个模型时考虑到了抑郁症状， 明确地将从过去学习和预测未来与认知灵活性联系起来。 核心假设是，从过去的奖励中学习和估计未来的奖励是不可分离的 控制认知灵活性的机制。本提案的具体目标是：（1）确定如何 对过去和未来的评价塑造了认知灵活性;（2）检查 环境限制认知灵活性。为了实现这些目标，我们将描述如何判断 关于过去和未来的影响决策刚性和应对环境变化， 模型仿真与分析我们将确定这些认知过程中的个体差异 从环境中学习，如果他们通过管理灵活的决策任务来预测刚性， 临床抑郁症和大量在线样本。收集抑郁症状清单沿着任务数据 将使我们能够探究像快感缺乏这样的抑郁症状导致僵化的机制。到 为了测试临床前工作的跨物种有效性，我们将此模型应用于先前收集的小鼠行为 数据集。 创新：我们的新模型将决定如何看待过去和未来，以及他们对未来的反应。 环境，有助于认知僵化，以及他们如何受到抑郁症状的影响， sample.研究结果将指导未来的假设驱动的研究，进入抑郁僵化的算法基础 在病人身上。具体来说，未来的R 01应用程序将测试该模型在预测抑郁症亚型方面的实用性 和临床人群的治疗结果。这个模型也将使我们能够研究 这些计算过程在动物模型和人类进行侵入性和非侵入性 神经生理学研究。