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Network level analysis of progressive brain degeneration in autosomal dominant Alzheimer disease

常染色体显性阿尔茨海默病进行性脑退化的网络水平分析

基本信息

批准号：
10288428
负责人：
Muriah D Wheelock
金额：
$ 23.14万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10288428
关键词：
Administrative Supplement Alzheimer&apos s Disease Amyloid beta-Protein Atrophic Award Biological Markers Biology of Aging Biometry Brain Brain region Cognition Cognitive Collaborations Communication Computer software Data Data Analyses Data Set Dementia Derivation procedure Disease Fostering Functional Magnetic Resonance Imaging Functional disorder Future Goals Human Impaired cognition Individual Inherited Lateral Light Measures National Institute on Aging Network-based Neurobehavioral Manifestations Neurons Ontology Parietal Participant Pathology Pre-Clinical Model Prevention Prognosis Research Research Personnel Resources Rest Scientist Senile Plaques Serum Signal Transduction Symptoms Thinness Validation age related algorithm development analysis pipeline autosomal dominant Alzheimer&apos s disease blood oxygen level dependent blood-based biomarker brain behavior cingulate gyrus cohort connectome disability early onset indexing innovation network architecture network dysfunction neurofilament recruit spatiotemporal tau Proteins tau aggregation

项目摘要

ADMINISTRATIVE SUPPLEMENT PROJECT SUMMARY/ABSTRACT Alzheimer’s disease (AD) is characterized by changes including the accrual of amyloid-b (Ab) plaques and neurofibrillary tau tangles, cortical thinning, hypometabolism, and disruptions in brain connectivity. However, the presence of this pathology does not occur simultaneously, but propagates throughout the cortex decades before symptoms of dementia are apparent. Researchers have noted that Ab, hypometabolism, and tau show consistent focal disruption beginning in lateral parietal, temporal, and the posterior cingulate gyrus. We hypothesize that this regional spread of pathology results in disrupted communication among brain networks resulting in symptoms of cognitive decline. This proposal seeks to 1) characterize the spatiotemporal progression of brain network degeneration and 2) determine the relationship between neuronal atrophy, brain network dysfunction, and cognitive decline. Brain networks can be measured using resting state functional magnetic resonance imaging to index temporal correlations in blood oxygen level dependent signal between brain regions. We will organize brain regions into canonical functional connectivity brain networks and apply the Network Level Analysis (NLA) analysis software, developed as part of K99 EB029343, to determine brain network associations with neuronal atrophy (as indexed with serum neurofilament light; NfL) and symptoms of dementia (as indexed with a global cognition composite score). NLA is an innovative approach to the analysis of connectome-wide associations that leverages cross disciplinary biostatistical approaches and an ontological framework, allowing for derivation of network-based brain-behavior relationships and control of false positive rate at the network level. This administrative supplement will extend the aims of the original award, which proposed validation of NLA using Human Connectome Project data, to include applications in AD. Specifically, this administrative supplement will leverage a fully de-identified pre-existing dataset containing functional connectomes, NfL, and cognitive measures in participants with autosomal dominant AD (ADAD) recruited from the Dominantly Inherited Alzheimer Network (DIAN) study. The analysis of data from individuals with ADAD is particularly significant due to the known timeframe and early onset of cognitive symptoms which allows for modeling of preclinical brain network degeneration while reducing the contribution of age-related confounds. The proposed analyses of DIAN data using NLA fulfills the National Institute of Aging Goal A to “Better understand the biology of aging and its impact on the prevention, progression, and prognosis of disease and disability.” The research team has expertise in Network Level Analysis (Dr. Wheelock), algorithm development (Dr. Eggebrecht), Alzheimer disease pathophysiology (Dr. Gordon) and the resources to generate functional connectomes in the DIAN cohort for secondary data analysis (Dr. Ances). This supplement will foster collaboration between computational scientists and clinicians and afford opportunities for future collaboration to investigate biomarkers in AD.

行政补充项目摘要/摘要阿尔茨海默氏病 (AD) 的特征是淀粉样蛋白 -b (Ab) 斑块的累积和神经原纤维 tau 蛋白缠结、皮质变薄、代谢减退和大脑连接中断。然而，这种病理的存在不会同时发生，而是在几十年前就在整个皮层中传播痴呆症的症状很明显。研究人员注意到 Ab、代谢减退和 tau 蛋白表现出一致局灶性破坏始于侧顶叶、颞叶和后扣带回。我们假设这种病理学的区域传播导致大脑网络之间的通讯中断，从而导致认知能力下降的症状。该提案旨在 1）表征大脑的时空进展网络退化和2)确定神经元萎缩、脑网络功能障碍之间的关系，和认知能力下降。可以使用静息态功能磁共振来测量大脑网络成像以索引大脑区域之间血氧水平依赖性信号的时间相关性。我们将将大脑区域组织成规范的功能连接大脑网络并应用网络水平分析 (NLA) 分析软件，作为 K99 EB029343 的一部分开发，用于确定大脑网络与神经元萎缩（以血清神经丝光为索引；NfL）和痴呆症状（以血清神经丝光为索引）全局认知综合得分）。 NLA 是一种分析全连接组的创新方法利用跨学科生物统计方法和本体论框架的协会，允许用于推导基于网络的大脑行为关系并控制网络级别的误报率。该行政补充将扩展原裁决的目标，其中提议验证 NLA 使用人类连接组项目数据，包括 AD 中的应用程序。具体来说，本次行政补充将利用完全去识别化的现有数据集，其中包含功能连接组、NfL 和从显性遗传中招募的常染色体显性 AD (ADAD) 参与者的认知测量阿尔茨海默病网络（DIAN）研究。对 ADAD 患者数据的分析尤为重要，因为已知的时间范围和认知症状的早期发作，允许对临床前大脑进行建模网络退化，同时减少与年龄相关的混杂因素的贡献。 DIAN 的拟议分析使用 NLA 的数据实现了国家老龄化研究所的目标 A，即“更好地了解衰老的生物学及其影响” 对疾病和残疾的预防、进展和预后的影响。”研究团队拥有专业知识网络级分析（Wheelock 博士）、算法开发（Eggebrecht 博士）、阿尔茨海默病病理生理学（戈登博士）以及在 DIAN 队列中生成功能连接体的资源二次数据分析（Ances 博士）。该增刊将促进计算科学家之间的合作和临床医生，并为未来合作研究 AD 生物标志物提供机会。