Computational mechanisms of memory disruption in depression

相对于健康的成年人，抑郁的人通常对消极的东西表现出出色的记忆力， 对正面材料记忆力差。此外，抑郁症会损害记忆力--回忆生动， 关于事件的上下文细节。这些异常困扰患者，并似乎延长抑郁症 情节，但他们没有很好地理解。因此，该提案将使用多模态神经成像， 计算模型来研究抑郁症成年人情绪记忆的编码和提取。 探讨抑郁症的分类效应，脑电图（EEG）/事件相关电位 (ERP)将从64名未用药的MDD成人和64名健康对照（n = 128）中收集数据。到 研究抑郁症的维度效应，功能性磁共振成像（fMRI）数据将被 从选择为轻微、轻度、中度或重度抑郁症状的成年人获得（n = 36）。在第一天， 参与者将在两个编码任务的背景下学习否定词和肯定词。第二天，他们将 返回进行识别记忆测试，在该测试中，“旧”编码的单词将与类似的“新”编码的单词一起呈现。 话当参与者识别出一个旧单词时，将通过询问哪个单词来测试源记忆（回忆）。 这个词被编码的任务。第二天将包括暴露于急性压力，以加强情绪偏见。 这一全面设计将支持三个目标。目的1将使用EEG/ERP来检验假设， 在编码和提取时，MDD使皮质对正刺激和负刺激的反应变钝。我们期待ERP 与记忆形成和提取有关，在成年人中， MDD与对照组。此外，我们预计这种影响在压力暴露后会被夸大。重要的是， EEG/ERP方法不能检测对记忆重要皮层下脑区域的活动，例如 杏仁核因此，目标2将使用功能磁共振成像来检验抑郁严重程度与 皮层下结构的激活来支持检索。我们预计随着抑郁症严重程度的增加， 杏仁核、海马和顶叶皮层对负性记忆探针的激活将增加。通过 相反，纹状体、海马和顶叶皮层对阳性记忆探针的激活， 减少。最后，为了深入了解支持记忆的潜在机制，Aim 3将使用 HDDM揭示抑郁对检索决策的影响。分层漂移扩散 模型（HDDM）是一个计算模型，可以估计证据积累过程，使我们能够 在两个选项之间进行选择（例如，旧vs.新）。我们预测证据积累-漂移的速度 率-将减少积极的，但不是消极的，记忆探针在抑郁症的成年人。此外，增加 抑郁症会削弱漂移率和支持记忆的EEG/fMRI信号之间的关系 对于积极的材料，它应该加强这种关系的消极材料。的这种组合 计算建模和多模态成像将产生新的洞察抑郁症的记忆缺陷。

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.