Computational mechanisms of memory disruption in depression

抑郁症记忆破坏的计算机制

• 批准号: 10051420
• 负责人: DANIEL G DILLON
• 金额: $ 41万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10051420
• 关键词: Address; Adult; Affect; Amygdaloid structure; Anhedonia; Anxiety; Brain region; Categories; Chronic stress; Computer Models; Corpus striatum structure; Data; Decision Making; Depressed mood; Development; Diffusion; Dimensions; Disease; Electroencephalogram; Emotional; Episodic memory; Event; Event-Related Potentials; Executive Dysfunction; Exposure to; Failure; Feeling hopeless; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Hippocampus (Brain); Impairment; Individual; Individual Differences; Investigation; Knowledge; Lead; Learning; Link; Literature; Major Depressive Disorder; Memory; Memory impairment; Mental Depression; Methodology; Modeling; Moods; Multimodal Imaging; Neurocognitive; Parietal; Parietal Lobe; Participant; Patients; Process; Public Health; Research; Retrieval; Rewards; Risk Factors; Severities; Signal Transduction; Source; Speed; Stimulus; Stress; Structure; Suicide; Symptoms; Testing; Time; Walkers; Work; acute stress; chronic depression; clinically relevant; cost; depressive symptoms; design; disability; emotion dysregulation; executive function; imaging study; improved; innovation; insight; intraparietal sulcus; memory encoding; memory recognition; memory retrieval; multimodality; neuroimaging; relating to nervous system; response; stem; success

Relative to healthy adults, depressed individuals typically show excellent memory for negative material but poor memory for positive material. Furthermore, depression impairs recollection—the ability to retrieve vivid, contextual details about an event. These abnormalities trouble patients and appear to prolong depressive episodes, but they are not well understood. Therefore, this proposal will use multi-modal neuroimaging and computational modeling to investigate the encoding and retrieval of emotional memories in depressed adults. To investigate categorical effects of depression, electroencephalogram (EEG)/event-related potential (ERP) data will be collected from 64 unmedicated adults with MDD and 64 healthy controls (n = 128). To investigate dimensional effects of depression, functional magnetic resonance imaging (fMRI) data will be acquired from adults selected for minimal, mild, moderate, or severe depressive symptoms (n = 36). On Day 1, the participants will study negative and positive words in the context of two encoding tasks. On Day 2, they will return for a recognition memory test in which the “old” encoded words will be presented with similar “new” words. When a participant recognizes an old word, source memory (recollection) will be tested by asking which task the word was encoded with. Day 2 will include exposure to acute stress, to potentiate emotional biases. This comprehensive design will support three aims. Aim 1 will use EEG/ERP to test the hypothesis that MDD blunts cortical responses to positive vs. negative stimuli at encoding and retrieval. We expect ERPs linked to memory formation and retrieval to be reduced for positive material, but not negative material, in adults with MDD vs. controls. Moreover, we expect such effects to be exaggerated after stress exposure. Importantly, the EEG/ERP methodology cannot detect activity in subcortical brain regions important for memory, such as the amygdala. Therefore, Aim 2 will use fMRI to test the hypothesis that depressive severity correlates with activation in subcortical structures that support retrieval. We expect that as depressive severity increases, activation of the amygdala, hippocampus, and parietal cortex to negative memory probes will increase. By contrast, activation of the striatum, hippocampus, and parietal cortex to positive memory probes should decrease. Finally, to gain insight into the underlying mechanisms that support memory, Aim 3 will use the HDDM to reveal the impact of depression on decision-making at retrieval. The Hierarchical Drift Diffusion Model (HDDM) is a computational model that can estimate the evidence accumulation process that enables us to choose between two options (e.g., old vs. new). We predict that the speed of evidence accumulation—drift rate—will be reduced for positive, but not negative, memory probes in depressed adults. Moreover, increased depression is expected to weaken relationships between drift rate and EEG/fMRI signals that support memory for positive material, but it should strengthen such relationships for negative material. This combination of computational modeling and multi-modal imaging will yield new insight into memory deficits in depression.

相对于健康的成年人，沮丧的个体通常会显示出负面材料的出色记忆，但 积极物质的记忆力不佳。此外，抑郁症会损害恢复 - 检索生动的能力， 关于事件的上下文详细信息。这些异常麻烦患者，似乎延长了抑郁症 情节，但他们不太了解。因此，该建议将使用多模式神经影像学和 计算建模，以研究抑郁症成年人情绪记忆的编码和检索。 为了研究抑郁症的分类影响，脑电图（EEG）/事件相关电位 （ERP）数据将从64名具有MDD的未经药物的成年人和64个健康对照组（n = 128）收集。到 研究抑郁症，功能磁共振成像（fMRI）数据的尺寸效应将是 从被选为最小，轻度，中度或严重抑郁症状的成年人获得（n = 36）。在第1天， 参与者将在两个编码任务的背景下研究否定词和积极词。在第2天，他们将 返回进行识别记忆测试，其中“旧”编码的单词将以类似的“新”形式呈现 字。当参与者识别一个旧单词时，将通过询问哪个来测试源内存（回忆） 任务该单词是编码的。第2天将包括暴露于急性压力，潜在的情绪偏见。 这种全面的设计将支持三个目标。 AIM 1将使用EEG/ERP来检验以下假设。 MDD在编码和检索时对阳性刺激的皮质反应钝化。我们期望ERP 与成人的积极材料（而不是负面材料）相关的记忆形成和检索将减少 使用MDD与控件。此外，我们期望在压力暴露后夸大这种影响。重要的是， EEG/ERP方法论无法检测到对记忆很重要的皮质下脑区域的活动，例如 杏仁核。因此，AIM 2将使用fMRI检验抑郁严重程度与 支持检索的皮层结构的激活。我们期望随着抑郁严重程度的增加， 杏仁核，海马和顶叶皮层的激活将增加。经过 对比，纹状体，海马和顶叶皮层的激活应与正记忆问题 减少。最后，为了深入了解支持内存的基本机制，AIM 3将使用 HDDM揭示抑郁症对检索决策的影响。分层漂移扩散 模型（HDDM）是一个计算模型，可以估算证据积累过程，使我们能够 在两个选项之间进行选择（例如，旧与新）。我们预测证据的速度积累 - 速率 - 对于抑郁的成年人的正面而不是负面的记忆问题将减少。而且，增加了 预计抑郁症会弱化的漂移率和支持记忆的脑电图/fMRI信号之间的关系 对于积极的材料，但应加强负面材料的这种关系。这种组合 计算建模和多模式成像将为抑郁症的记忆定义提供新的见解。