权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

行政管理项目概要/摘要阿尔茨海默氏病（AD）的特征在于包括淀粉样蛋白-b（Ab）斑块的积累和神经元tau蛋白缠结、皮质变薄、代谢减退和脑连接中断。但这种病理学的存在并不是同时发生的，而是在几十年前在整个皮层中传播。痴呆症的症状很明显。研究人员指出，Ab、代谢低下和tau表现出一致性从外侧顶叶、颞叶和后扣带回开始出现局灶性破坏。我们假设病理的这种区域性传播导致大脑网络之间的通信中断，认知能力下降的症状该建议旨在1）表征大脑的时空进展网络变性和2）确定神经元萎缩，脑网络功能障碍，和认知能力下降大脑网络可以使用静息状态功能磁共振测量成像以指示脑区域之间的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性大脑网络，并应用网络水平分析 (NLA)作为K99 EB 029343的一部分开发的分析软件，用于确定大脑网络与神经元萎缩（如用血清神经丝光（NfL）指数化）和痴呆症状（如用整体认知综合得分）。NLA是一种新颖的全连接组分析方法，协会利用跨学科的生物统计方法和本体框架，允许用于推导基于网络的脑-行为关系和在网络级控制假阳性率。这一行政补充将扩大原裁决的目的，原裁决建议对NLA进行验证。使用人类连接组项目数据，以包括AD中的应用。具体而言，这一行政补充将利用完全去识别的预先存在的数据集，该数据集包含功能性连接体、NfL和从显性遗传性阿尔茨海默病（ADAD）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患者数据的分析特别重要，因为已知的时间范围和认知症状的早期发作，这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乱的贡献。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水平分析（Wheelock博士），算法开发（Eggebrecht博士），阿尔茨海默病病理生理学（Gordon博士）和在DIAN队列中生成功能性连接体的资源，二次数据分析（Ances博士）。这一补充将促进计算科学家之间的合作和临床医生，并为未来的合作提供机会，以研究AD中的生物标志物。