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

多突触通路内临床前阿尔茨海默病的连接组生物标志物

• 批准号: 10213243
• 负责人: Iman Aganj
• 金额: $ 182万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10213243
• 关键词: Accounting; Affect; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer’s disease biomarker; Architecture; Area; Biological Markers; Brain; Brain Stem; Brain region; Cerebrum; Clinical; Clinical Data; Cognitive; Complex; Data; Data Pooling; Data Set; Databases; Development; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Disease; Distant; Early treatment; Elderly; Exclusion; Fiber; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Graph; Human; Hypothalamic structure; Imaging Techniques; Impaired cognition; Individual; Label; Light; Magnetic Resonance Imaging; Manuals; Maps; Measures; Mediating; Medical Genetics; Methods; Modeling; Network-based; Neural Pathways; Neurodegenerative Disorders; Neurologic; Neurons; Outcome; Pathway interactions; Patients; Pattern; Population; Prognosis; Publishing; Research; Research Personnel; Rest; Signal Transduction; Speed; Staging; Stratification; Structure; Synapses; Syndrome; Therapeutic; Training; Treatment Efficacy; age effect; automated segmentation; basal forebrain; base; clinical outcome measures; connectome; diagnostic biomarker; drug discovery; experimental study; healthy aging; heterogenous data; imaging biomarker; improved; locus ceruleus structure; longitudinal dataset; mathematical algorithm; mild cognitive impairment; neuroimaging; normal aging; novel; pre-clinical; prevent; prototype; resilience; therapy development; tractography

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.

项目概要/摘要 拟议研究的总体目标是定义易受攻击或脆弱的特定大脑网络 抵御衰老和阿尔茨海默氏病 (AD)，并随后衍生出新的准确、精确和稳健的产品 用于（尤其是临床前）AD 的连接组成像生物标志物，可以改善诊断、疾病分期、 预测、进展评估和治疗效果信息通过人脑流动。 大脑区域之间的完整连接图，即复杂的结构和功能网络。 连接组，可以帮助更好地了解大脑结构和功能的脆弱性和恢复力 衰老效应和使人衰弱的神经退行性疾病（例如 AD），并进行诊断和发现 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。 阻碍了对“脱节综合症”的理解和治疗疾病的进展。 弥散加权 MRI (dMRI) 和静息态功能 MRI (rs-fMRI) 用于无创量化 分别对大脑结构和功能进行的大脑网络分析已被证明是有前景的。 揭示轻度认知障碍 (MCI) 和 AD 中认知功能障碍的基础，展示变化 与那些健康老龄化的人不同，开发预防或延缓 AD 发作的治疗方法将是不同的。 能够进行认知区分的非侵入性、敏感且特异性的诊断生物标志物极大地促进了这一过程 正常人和 MCI 患者将从健康老龄化的人群发展为 AD。 两个大脑区域之间的结构连接通常根据 dMRI 纤维束成像来定义 连接两个大脑区域的直接纤维束预计将成为主要信号。 它们之间的载体；然而，多突触神经通路（通过其他区域介导的）也 研究人员在该项目中建议开发和验证新颖的数学和 大脑连接的算法模型，同时考虑多突触神经通路，以识别 此外，他们还提出了在衰老和/或临床前 AD 中脆弱或有弹性的连接（目标 1）。 考虑到一些重要的大脑结构，包括一套全面的大脑区域（目标 2） AD，例如蓝斑、基底前脑和下丘脑，不容易包含在常见病中 这项研究的完成将提高我们对大脑网络的理解。 衰老和 AD 的影响，将有助于得出更准确的 AD 生物标志物。在这项连接组分析中，有 10 个。 健康老年人、MCI 和 AD 人群的现有异质 dMRI/rs-fMRI 数据库总计 大约 6000 个受试者将被合并，预计将改善分层、预测和 研究人员将通过疾病分期和相关性来验证其网络衍生的生物标志物。 利用横截面数据集的临床和遗传数据，并通过预后和预测的转换 健康或 MCI 受试者在纵向数据集上患有 AD。

项目成果

Lymph Node Graph Neural Networks for Cancer Metastasis Prediction

用于癌症转移预测的淋巴结图神经网络

• DOI: 10.1038/s41598-022-07034-5
• 发表时间: 2022-02-24
• 期刊: ArXiv
• 作者: Le WT;Vorontsov E;Romero FP;Seddik L;Elsharief MM;Nguyen-Tan PF;Roberge D;Bahig H;Kadoury S
• 通讯作者: Kadoury S