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

行政补充项目摘要/摘要 阿尔茨海默病(AD)的特征是改变，包括淀粉样蛋白b(Ab)斑块的积累和 神经原纤维tau缠结，皮质变薄，代谢不足，以及大脑连接中断。然而， 这种病理的存在并不是同时发生的，而是在几十年前传播到整个大脑皮层。 痴呆症的症状是显而易见的。研究人员已经注意到，抗体、低代谢和tau显示出一致的 局灶性破坏始于外侧顶叶、颞叶和扣带回后部。我们假设 这种病理的区域性传播导致大脑网络之间的通信中断，从而导致 认知能力下降的症状。这一提议试图1)描述大脑的时空进程 网络退化和2)确定神经元萎缩、脑网络功能障碍、 和认知能力下降。可以使用静息状态功能磁共振来测量大脑网络 成像以指示脑区之间血氧水平依赖信号的时间相关性。我们会 将脑区组织成典型的功能连通性脑网络并应用网络层次分析 (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(以血清神经丝光为指标；NFL)和痴呆症状(以 全球认知综合得分)。NLA是一种分析全连接体的创新方法 利用跨学科生物统计学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，并在网络级别控制假阳性率。 本行政副刊将扩展最初裁决的目标，该裁决建议对NLA进行验证 使用Human Connectome Project数据，将应用程序包括在AD中。具体地说，这个管理 补充将利用完全不确定的预先存在的数据集，其中包含功能连接、NFL和 常染色体显性遗传性阿尔茨海默病(ADAD)患者的认知测量 阿尔茨海默病网络(DIAN)研究。对ADAD患者数据的分析尤其重要 到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 网络退化的同时减少了与年龄相关的贡献。对DIAN的建议分析 使用NLA的数据实现了美国国家老龄研究所的目标A，即更好地理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。这个研究小组有专业知识 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(戈登博士)和在DIAN队列中生成功能性连接的资源 二次数据分析(博士)。这一补充将促进计算科学家之间的合作 和临床医生，并为未来合作研究AD的生物标记物提供机会。

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.