Secondary analysis of functional MRI and resting state connectivity in white matter

白质功能 MRI 和静息态连接的二次分析

• 批准号: 10190338
• 负责人: John C Gore
• 金额: $ 132.09万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 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/10190338
• 关键词: Adult; Age; Aging; Alzheimer' s Disease; Archives; Area; Atlases; BRAIN initiative; Baltimore; Behavior; Behavioral; Biological Markers; Brain; Characteristics; Clinical assessments; Cognition; Complex; Data; Data Analyses; Data Set; Databases; Degenerative Disorder; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Enrollment; Exhibits; Failure; Functional Magnetic Resonance Imaging; Genotype; Goals; Human; Image; Impaired cognition; Impairment; Individual; Longevity; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Measures; Medical Genetics; Methods; Modeling; Nerve Degeneration; Pathologic; Pathway Analysis; Population; Property; Reporting; Research; Rest; Role; Series; Signal Transduction; Stimulus; Structure; System; Validation; Variant; Vision; analytical tool; blood oxygen level dependent; cognitive change; deep learning; gray matter; imaging study; magnetic resonance imaging biomarker; neurobehavioral; neuroimaging; normal aging; novel; novel marker; pre-clinical; relating to nervous system; response; secondary analysis; tool; white matter; white matter change

Abstract / Summary This proposal aims to perform novel, secondary analyses on large archives of publicly-available fMRI studies in order to quantify the functional characteristics of white matter (WM) and their changes during normal aging and in the progression to Alzheimer’s Disease (AD). Blood oxygenation level dependent (BOLD) effects have been used to detect neural activity in grey matter (GM) for many years, but BOLD signals in WM have traditionally been ignored so they have not been considered in previous analyses. However, WM BOLD signals have been evaluated in relatively small numbers of healthy brains, and these studies have shown that WM shows robust, tract-specific BOLD changes in response to stimuli, WM exhibits inter-regional correlations in a resting state similar to those used to infer functional connectivity in cortex, and signal fluctuations within WM tracts in a resting state show strong correlations to specific GM cortical volumes engaged together in functional networks. We therefore propose to adapt the tools developed for analyzing GM connectivity and for diffusion imaging of WM to analyze the functional changes in WM with age in >7,900 imaging studies available publicly. In Aim 1, we will detect and characterize changes in WM functional networks with normal aging by analyzing subjects from the Baltimore Longitudinal Study of Aging (BLSA), the Open Access Series of Imaging Studies (OASIS-3) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neuroimaging studies of AD suggest that WM abnormalities exist at a preclinical stage of the disease, so detecting and quantifying altered WM function may be an important metric of functional changes in this disorder. In Aim 2 therefore we will measure alterations in WM functional networks in subjects enrolled in the ADNI, BLSA, and OASIS-3 databases. In both Aims we will also measure co-variations of WM connectivities with behavioral, clinical and genetic assessments to establish how WM functional metrics reflect behavior and cognition and change with increasing cognitive impairment. In Aim 3 we will extend the creation of atlases and machine learning from current studies of diffusion MRI to quantifying WM functional MRI by creating age-adjusted atlases of WM functional MRI properties to enable normative comparisons and provide canonical templates for both functional and structural connectivity network analyses. We will also apply data-driven deep learning to identify individual signatures of impairment on a whole-brain basis. A failure of white matter functional integrity is clearly implicated in aging and neurodegeneration. This proposal will develop new understandings of the functional changes in WM across the lifespan, identify pathological changes in WM function with AD, and create new data-driven tools for interpretation of WM fMRI.

摘要/总结 该提案旨在对公开可用的功能磁共振成像的大型档案进行新颖的二次分析 研究旨在量化白质（WM）的功能特征及其在过程中的变化 正常衰老和进展为阿尔茨海默病 (AD)。血氧水平依赖 （BOLD）效应多年来一直被用来检测灰质（GM）的神经活动，但是 BOLD WM 中的信号传统上被忽略，因此在之前的分析中没有考虑它们。 然而，WM BOLD 信号已在相对少数的健康大脑中进行了评估，并且这些 研究表明，WM 在对刺激的反应中表现出强烈的、特定区域的大胆变化，WM 在静息状态下表现出区域间相关性，类似于用于推断功能连接的区域间相关性 静息状态下 WM 束内的皮层和信号波动与特定 GM 具有很强的相关性 皮质卷一起参与功能网络。因此，我们建议调整这些工具 开发用于分析 GM 连接性和 WM 扩散成像以分析功能变化 公开提供的超过 7,900 项影像学研究中的 WM 年龄。在目标 1 中，我们将检测并表征 通过分析巴尔的摩纵向研究对象，了解正常衰老过程中 WM 功能网络的变化 衰老研究 (BLSA)、影像研究开放获取系列 (OASIS-3) 和阿尔茨海默病 神经影像倡议（ADNI）。 AD 的神经影像学研究表明 WM 异常存在于 疾病的临床前阶段，因此检测和量化 WM 功能的改变可能是重要的 这种疾病的功能变化的度量。因此，在目标 2 中，我们将测量 WM 功能的变化 ADNI、BLSA 和 OASIS-3 数据库中登记的受试者网络。在这两个目标中，我们还将 测量 WM 连接性与行为、临床和遗传评估的共同变化，以确定 WM 功能指标如何反映行为和认知以及随着认知障碍的增加而变化。 在目标 3 中，我们将根据扩散 MRI 的当前研究扩展图谱的创建和机器学习 通过创建年龄调整的 WM 功能 MRI 特性图集来量化 WM 功能 MRI 实现规范比较并为功能和结构提供规范模板 连接网络分析。我们还将应用数据驱动的深度学习来识别个人签名 以全脑为基础的损伤。白质功能完整性的失败显然与 衰老和神经退行性疾病。该提案将对功能变化产生新的理解 WM 跨生命周期，识别 WM 功能与 AD 的病理变化，并创建新的数据驱动 WM fMRI 解释工具。