Connectomic Biomarkers of Preclinical Alzheimer's Disease within Multi-Synaptic Pathways

项目总结/摘要 拟议研究的总体目标是定义特定的大脑网络是脆弱的， 抗衰老和阿尔茨海默病（AD），并随后获得新的准确，精确和强大的 用于（特别是临床前）AD的连接组成像生物标志物，其可以改善诊断，疾病分期， 预测、进展评估和治疗功效。信息在人脑中的流动是通过 复杂的结构和功能网络。大脑区域之间的完整连接图，即 连接体，可以帮助更好地了解大脑结构和功能的脆弱性和弹性 衰老效应和衰弱性神经退行性疾病，如AD，并发现诊断和 治疗上重要的生物标志物。关注大脑区域，而不是区域间的连接，可能会 阻碍了理解和治疗以“分离综合征”为特征的疾病的进展。 弥散加权磁共振成像（dMRI）和静息态功能磁共振成像（rs-fMRI）用于非侵入性定量 结构和功能的大脑网络。基于网络的大脑分析被证明是有前途的 在揭示轻度认知障碍（MCI）和AD认知功能障碍的基础上， 与健康衰老的人不同。开发预防或延迟AD发作的治疗方法将是 通过非侵入性、敏感性和特异性的诊断生物标志物， 正常人和MCI患者将从健康衰老的人发展为AD。 两个大脑区域之间的结构连接性通常基于dMRI纤维束成像导出的脑电图来定义。 它们之间的流线型。连接两个大脑区域的直接纤维束预计是主要信号 然而，多突触神经通路（通过其他区域介导的通路）也 提供连通性。研究人员在该项目中提出，开发和验证新的数学和 大脑连接的算法模型，同时考虑多突触神经通路，以识别 在老化和/或临床前AD中脆弱或有弹性的连接（目标1）。此外，他们建议 包括一组全面的大脑区域（目标2），因为一些重要的大脑结构， AD，如蓝斑、基底前脑和下丘脑，不容易被包括在共同的 神经成像工具箱。这项研究的完成将提高我们对大脑网络如何 这将有助于获得更准确的AD生物标志物。在这个连接组学分析中， 健康老年人、MCI和AD人群的现有异构dMRI/rs-fMRI数据库，总计 将合并约6000例受试者，预计将改善分层、预测和 预后研究人员将通过疾病分期和相关性来验证他们的网络衍生生物标志物 与横截面数据集的临床和遗传数据，并通过预后和转化的预测， 健康或MCI受试者在纵向数据集上的AD。

Project Summary / Abstract The overall goal of the proposed research is to define the specific brain networks that are vulnerable or resilient in aging and Alzheimer’s disease (AD), and subsequently derive new accurate, precise, and robust connectomic imaging biomarkers for (especially preclinical) AD, which could improve diagnosis, disease staging, prediction, assessment of progression, and therapeutic efficacy. Information flows in the human brain through a complex set of structural and functional networks. The complete connectivity map among brain areas, i.e. the connectome, can help to better understand the vulnerability and resilience of the brain architecture and function to aging effects and debilitating neurodegenerative diseases, such as AD, and to discover diagnostically and therapeutically important biomarkers. Focusing on brain regions, but not interregional connectivity, may have hindered progress in understanding and treating disorders characterized as “disconnection syndromes”. Diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI) are used to noninvasively quantify structural and functional brain networks, respectively. Network-based analysis of the brain has proved promising in revealing the basis of cognitive dysfunction in mild cognitive impairment (MCI) and AD, demonstrating changes distinct from those with healthy aging. Development of treatments to prevent or delay the onset of AD would be greatly facilitated by a noninvasive, sensitive, and specific diagnostic biomarker able to discriminate cognitively normal people and MCI patients who will progress to AD from those who will age healthily. Structural connectivity between two brain regions is often defined based on the dMRI tractography-derived streamlines between them. The direct fiber bundle connecting two brain areas is expected to be the major signal carrier between them; however, multi-synaptic neural pathways (those mediated through other regions) also provide connectivity. The investigators propose in this project to develop and validate novel mathematical and algorithmic models for brain connectivity, while accounting for multi-synaptic neural pathways, to identify connections that are vulnerable or resilience in aging and/or preclinical AD (Aim 1). Furthermore, they propose to include a comprehensive set of brain regions (Aim 2), given that some brain structures that are important in AD, such as locus coeruleus, basal forebrain, and hypothalamus, are not readily included in common neuroimaging toolboxes. The completion of this study will improve our understanding of how brain networks are affected in aging and AD and will help to derive more accurate AD biomarkers. In this connectomic analysis, ten existing heterogeneous dMRI/rs-fMRI databases of healthy elderly, MCI, and AD populations, totaling approximately 6000 subjects, will be combined, which is expected to improve stratification, prediction, and prognosis. The investigators will validate their network-derived biomarkers via disease staging and correlation with clinical and genetic data on cross-sectional datasets, and via prognosis and prediction of conversion of healthy or MCI subjects to AD on longitudinal datasets.