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.

项目摘要大约一半阿尔茨海默病(AD)患者将经历 难以治疗的临床抑郁症。反过来，老年抑郁症(LLD)与风险增加有关 治疗认知功能减退和痴呆症。尽管有这种重叠，但人们对病理生理过程知之甚少 在临床前AD中，抑郁症状影响并相互作用，导致固有网络功能改变 认知和神经行为的改变。更好地理解这些关系可能有助于识别 哪些LLD患者可能是进展为AD或AD相关痴呆的最高风险。此外，一个 更好地理解潜在的神经生物学关系可能有助于个体化治疗 在这个并存的人口中的发展。这份针对阿尔茨海默氏症的行政补充申请 将利用一项正在进行的纵向研究来检查LLD、认知衰退之间的关系 和AD。本附录将使用匹兹堡化合物B(PIB)进行PET成像 通过父母资助获得的神经成像、认知和行为数据。这将使我们能够评估β 抑郁症和非抑郁者队列中基底前脑胆碱能系统的负荷和变性 老年人研究AD相关生物标志物与抑郁的关系及交互作用 LLD患者的认知表现症状。我们假设Aβ负荷较高的个体，更多 胆碱能变性和更严重的抑郁症状将表现出最差的表现和 认知测试中试验与试验之间的最大变异性。我们进一步提出AD生物标记物阳性的LLD 代表阿尔茨海默病的临床前表型，其特征是明显的多变量神经行为模式。 这一假设得到了检测大脑加速老化的结构成像标志的试点数据的支持 LLD的研究发现，抑郁症状越严重，大脑老化程度越大，认知能力越强 减损。认知/行为的差异将进一步反映在潜在的内在差异上 网络功能。在存在残余抑郁症状的情况下，临床前AD生物标志物可能会加剧 与汇款相比，网络连接更改并导致更大的网络稳定性中断 无AD病理生理或生物标记物阳性、无抑郁的LLD老年人。使用数据驱动的分组迭代 多种模型估计算法，我们将识别表现出独特网络的LLD个体的子组 阿尔茨海默病的特点是认知能力受损和AD生物标志物水平升高。这 我们之前关于LLD网络中断的数据支持了这一假说，这可能会损害大脑的 在β积累期间进行重组的能力，从而加速了生物标志物中的网络故障- 阳性LLD。这项研究的结果将有助于阐明为什么患有LLD的人患AD的风险增加 和AD相关痴呆症，并为LLD的AD治疗研究确定新的个性化治疗目标。它 也可以为风险分层提供信息，以识别认知能力加速下降风险较高的抑郁老年人。