Computational dissociation of the causes of cognitive rigidity in depression
抑郁症认知僵化原因的计算分离
基本信息
- 批准号:10684272
- 负责人:
- 金额:$ 18.83万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressAlgorithmsAnhedoniaAnimal ModelBehaviorBehavioralClinicalCognitiveComputer ModelsDataData SetDecision MakingDepressed moodDissociationEarly DiagnosisEarly InterventionEnvironmentEquipment and supply inventoriesFaceFailureFeeling hopelessFutureGoalsHumanImpairmentIndividual DifferencesLeadLearningLinkMajor Depressive DisorderMental DepressionMindModelingMoodsMusOutcomePatientsPatternPerceptionPersonsPhysiologicalPopulationProcessPsychological reinforcementResearchRewardsSamplingSeveritiesShapesSuicideSuicide attemptSymptomsTestingTranslatingTreatment outcomeUnited StatesVariantWorkanxiousbasecognitive controlcognitive processcognitive rigiditycomputer frameworkdepressive symptomseducational atmosphereenvironmental changeexpectationexperienceflexibilityinnovationmodel designmodels and simulationnovelpre-clinicalprematureprognosticresponsesuccesssuicidal risktheories
项目摘要
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
项目成果
期刊论文数量(0)
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Alexander Herman其他文献
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{{ truncateString('Alexander Herman', 18)}}的其他基金
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 - 财政年份:2022
- 资助金额:
$ 18.83万 - 项目类别:
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