1/3-Recurrence Markers, Cognitive Burden and Neurobiological Homeostasis in Late-life Depression (Rembrandt)

晚年抑郁症的 1/3 复发标记、认知负担和神经生物学稳态（伦勃朗）

• 批准号: 10118837
• 负责人: Warren D Taylor
• 金额: $ 36.65万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-15 至 2020-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10118837
• 关键词: Administrative Supplement; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Alzheimer' s disease therapeutic; Alzheimer’s disease biomarker; Behavioral; Biological Markers; Brain; Cognitive; Data; Dementia; Depressed mood; Elderly; Exhibits; Failure; Functional disorder; Homeostasis; Impaired cognition; Individual; Lead; Longitudinal Studies; Major Depressive Disorder; Modeling; Neurobiology; Patients; Pattern; Performance; Phenotype; Pittsburgh Compound-B; Population; Positron-Emission Tomography; Process; Recurrence; Residual state; Risk; Risk stratification; Structure; Subgroup; System; Therapeutic Studies; abeta accumulation; aging brain; basal forebrain; cholinergic; cognitive load; cognitive performance; cognitive testing; cohort; comorbidity; depressive symptoms; experience; geriatric depression; high risk; imaging biomarker; individualized medicine; neurobehavioral; neuroimaging; parent grant; personalized medicine; pre-clinical; therapy development; β-amyloid burden

PROJECT SUMMARY Approximately half of the individuals affected by Alzheimer's disease (AD) will experience clinical depression that is difficult to treat. In turn, Late-life depression (LLD) is associated with an increased risk for cognitive decline and dementia. Despite this overlap, little is known about how pathophysiological processes in preclinical AD influence and interact with depressive symptoms leading to altered intrinsic network function and cognitive and neurobehavioral changes. A better understanding of these relationships may aid in identifying which patients with LLD may be at highest risk of progressing to AD or an AD-related dementia. Moreover, a better understanding of the underlying neurobiological relationships may inform individualized treatment development in this comorbid population. This application for an Alzheimer's-focused administrative supplement will take advantage of an ongoing longitudinal study to examine the relationship between LLD, cognitive decline and AD. This supplement will add PET imaging using Pittsburgh Compound B (PiB) to the ongoing neuroimaging, cognitive, and behavioral data obtained through the parent grant. This will allow us to assess Aβ burden and degeneration of basal forebrain cholinergic system in a cohort of depressed and never-depressed elders to examine the relationship between and interactive effect of AD-related biomarkers and depressive symptoms on cognitive performance in LLD. We hypothesize that individuals with higher Aβ burden, more cholinergic degeneration, and more severe depressive symptoms will exhibit the poorest performance and greatest trial-to-trial variability on cognitive testing. We further propose that AD biomarker-positive LLD represents a preclinical phenotype of AD that is characterized by a distinct multivariate neurobehavioral pattern. This hypothesis is supported by pilot data examining structural imaging markers of accelerated brain aging in LLD, finding that, with more severe depressive symptoms, greater brain aging is associated with cognitive impairment. The cognitive/behavioral differences will further be reflected by differences in underlying intrinsic network function. In the presence of residual depressive symptoms, preclinical AD biomarkers may exacerbate network connectivity alterations and lead to greater disruptions in network stability when compared to remitted LLD without AD pathophysiology or biomarker-positive, non-depressed elders. Using data-driven group iterative multiple model estimation algorithms, we will identify subgroups of LLD individuals who exhibit a unique network topology and are characterized by impaired cognitive performance and greater AD biomarker levels. This hypothesis is supported by our previous data on network disruptions in LLD, which may compromise the brain's capability to reorganize during Aβ accumulation, thus contributing to an accelerated network failure in biomarker- positive LLD. The results of this study will help elucidate why individuals with LLD have an elevated risk of AD and AD-related dementia and identify new personalized treatment targets for AD therapeutic studies in LLD. It may also inform risk stratification to identify depressed elders at higher risk of accelerated cognitive decline.

大约一半受阿尔茨海默氏病（AD）影响的个体的项目摘要将经历 临床抑郁很难治疗。反过来，晚期抑郁症（LLD）与风险增加有关 尽管有重叠，但对病理生理过程的了解知之甚少 在临床前广告影响并与抑郁症状相互作用，导致内在网络功能改变 以及认知和神经行为的变化。更好地了解这些关系可能有助于确定 哪些LLD患者可能有发展为AD或与AD相关的痴呆症的最高风险。而且， 更好地理解潜在的神经生物学关系可能会为个性化治疗提供信息 该合并人群的发展。此应用于阿尔茨海默氏症的行政补充 将利用正在进行的纵向研究来检查LLD，认知能力下降之间的关系 和广告。这种补充剂将使用匹兹堡化合物B（PIB）添加PET成像 通过父授予获得的神经影像，认知和行为数据。这将使我们能够评估Aβ 基本前脑胆碱能系统的负担和变性 长辈检查与广告相关生物标志物和抑郁症之间的关系和互动效应 LLD认知表现的症状。我们假设患有较高AβBurnen的个体，更多 胆碱能变性和更严重的抑郁症状将表现出最差的性能和 认知测试中最大的试验变异性。我们进一步建议AD生物标志物阳性LLD 代表AD的临床前表型，其特征在于独特的多元神经行为模式。 该假设得到了试验数据，研究了加速大脑衰老的结构成像标记 LLD，发现，由于更严重的抑郁症状，大脑衰老与认知有关 损害。认知/行为差异将进一步反映出固有的差异 网络功能。在存在残留抑郁症状的情况下，临床前AD生物标志物可能恶化 与拒绝相比 没有AD病理生理学或生物标志物阳性，不抑郁的长者的LLD。使用数据驱动组迭代 多个模型估计算法，我们将确定暴露了独特网络的LLD个人的子组 拓扑结构的特征是认知性能受损和更高的AD生物标志物水平。这 我们先前关于LLD网络中断的数据支持了假设，这可能会损害大脑的 在Aβ加速期间重组的能力，从而导致生物标志物的加速网络失败 积极的lld。这项研究的结果将有助于阐明为什么LLD患者的AD风险较高 和与广告相关的痴呆症，并确定了LLD中广告疗法研究的新个性化治疗靶标。 还可以告知风险分层，以识别出较高的认知能力下降风险的沮丧长者。