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The Default Mode Network may be the Key Substrate of Depressive Symptom-Related Cognitive Changes

机译:默认模式网络可以是抑郁症状相关认知变化的关键基板

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摘要

Depressive symptoms are associated with an increased risk of Alzheimer’s Disease (AD) but the mechanism(s) involved has not been well established. In a convenience sample of participants in the University of Kansas’ Brain Aging Project, we use structural equation modeling (SEM) to explicitly distinguish depressive symptom-related variance in cognitive task performance (i.e., “DEPCOG”) from that which is unrelated to a depressive symptoms. DEPCOG is strongly associated with the cognitive correlates of functional status (δ), which we previously associated with elements of the Default Mode Network (DMN). Both δ and DEPCOG map to a Posterior Cingulate seeded network that has recently been associated with β-amyloid deposition and includes elements of the DMN. Both contribute significantly to clinical dementia status and dementia severity, as measured by the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB). These findings suggest that the cognitive correlates of depressive symptoms, even in the absence of a major depressive episode, may contribute to dementia in their own right, and could be responsible for some cases of incident clinical “AD”. This conclusion suggests new opportunities for the latter’s diagnosis, prevention and treatment.
机译:抑郁症状与阿尔茨海默氏病(AD)的风险增加有关,但所涉及的机制尚未完全确定。在堪萨斯大学“大脑衰老项目”参与者的便利样本中,我们使用结构方程模型(SEM)明确区分了抑郁症状相关的认知任务绩效差异(即“ DEPCOG”)与与抑郁无关的差异。抑郁症状。 DEPCOG与功能状态(δ)的认知相关性密切相关,我们先前与默认模式网络(DMN)的元素相关联。 δ和DEPCOG都映射到后扣带状种子网络,该网络最近与β-淀粉样蛋白沉积有关,并包含DMN的元素。两者均通过临床痴呆症等级量表总和(CDR-SB)衡量,对临床痴呆症状态和痴呆症严重程度有重大贡献。这些发现表明,即使没有严重的抑郁发作,抑郁症状的认知相关性本身也可能导致痴呆,并且可能导致某些临床“ AD”事件。这一结论为后者的诊断,预防和治疗提供了新的机会。

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