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 /摘要 该提案旨在对公共获取fMRI的大型档案进行小说，次要分析 研究以量化白质（WM）的功能特征及其在期间的变化 正常衰老和阿尔茨海默氏病（AD）的进展。血液氧合水平依赖 （BOLD）效果已用于检测灰质（GM）的神经活动多年，但大胆 传统上，WM中的信号被忽略了，因此在先前的分析中尚未考虑它们。 但是，WM Bold信号已在相对较少的健康大脑中进行了评估，这些信号已 研究表明，WM表明响应于刺激，WM的鲁棒，特异性的粗体变化 在静止状态下展示区域间相关性，类似于用于推断功能连接的静止状态 静止状态下WM道中的皮层和信号波动显示与特定GM的密切相关性 皮质体积在功能网络中合作。因此，我们建议调整工具 开发用于分析GM连接性和WM的扩散成像以分析功能变化 在WM中，年龄> 7,900个成像研究公开可用。在AIM 1中，我们将检测和特征 通过分析巴尔的摩纵向的受试者，WM功能网络的变化与正常老化的变化 衰老研究（BLSA），开放式成像研究（OASIS-3）和阿尔茨海默氏病 神经影像倡议（ADNI）。 AD的神经影像学研究表明，WM异常存在于A 疾病的临床前阶段，因此检测和量化WM功能可能是重要的 该疾病功能变化的指标。在AIM 2中，我们将测量WM功能的变化 在ADNI，BLSA和OASIS-3数据库中注册的受试者的网络。在两个目标中，我们也将 测量WM连接性与行为，临床和遗传评估的共同变化以建立 WM功能指标如何随着认知障碍的增加来反映行为和认知和变化。 在AIM 3中，我们将从当前的扩散MRI研究中扩展地图集和机器学习的创建 通过创建WM功能性MRI特性的年龄调整的地图集来量化WM功能MRI 启用正常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 全脑受损。白质功能完整性的失败显然与 衰老和神经变性。该建议将对功能变化的新理解产生新的理解 WM在整个寿命中，通过AD确定WM功能的病理变化，并创建新的数据驱动 WM fMRI解释的工具。