Functional Connectivity and Baseline Networks of the White Matter Brain: Development and Dissemination of Algorithms and Tools

白质脑的功能连接和基线网络：算法和工具的开发和传播

• 批准号: 10391136
• 负责人: Bharat Bhusan Biswal
• 金额: $ 54.5万
• 依托单位: NEW JERSEY INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-10 至 2025-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10391136
• 关键词: Address; Adult; Affect; Affective; Age; Algorithmic Analysis; Algorithmic Software; Anesthesia procedures; Applications Grants; Area; Atlases; Brain; Brain Diseases; Brain region; Characteristics; Clinical; Cognitive; Communities; Comprehension; Corpus Callosum; Data; Data Set; Development; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Disease Progression; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Graph; Growth; Human; Individual; Institutes; Intelligence; Investigation; Knowledge; Light; Liquid substance; Magnetic Resonance Imaging; Measures; Mental disorders; Methods; Mining; Monitor; Motor; Neurologic; Noise; Phenotype; Population; Process; Property; Reporting; Reproducibility; Rest; Sensory; Series; Short-Term Memory; Signal Transduction; Sleep; Statistical Models; System; Testing; Time; United States National Institutes of Health; Validity and Reliability; Vocabulary; Work; advanced analytics; age group; analytical tool; base; blood oxygen level dependent; brain volume; cognitive function; cohort; connectome; experience; gender fluid; gray matter; imaging study; improved; machine learning method; neglect; neuroimaging; neuropsychiatric disorder; response; secondary analysis; tool; trait; transmission process; vector; white matter

PROJECT ABSTRACT The discovery of functional brain connectivity (FC) and functional networks (FNs) have propelled the neuroimaging field, particularly in functional magnetic resonance imaging (fMRI), which has experienced an exponential growth in recent years. FNs have allowed us to better understand extrinsic and intrinsic brain properties in various disease and healthy states, leading to better characterization of neuropsychiatric disorders. However, current fMRI analyses are constrained to the gray matter (GM) region of the brain and fMRI data from the white matter (WM) region are often discarded, which makes up approximately 50% of the brain by volume. Many brain disorders have been associated with WM deficiencies, since WM is critical for the transmission of information to the GM cortical areas. Despite findings of blood-oxygen-level-dependent (BOLD) signals in the WM, WM-FNs are yet to be fully characterized, and neither the mechanism by which WM-FNs may affect GM-FNs, nor how WM-FNs are associated with phenotypic traits are known. The long-term goal of this project is to better understand the effect of WM-FNs on normal cognitive functions of the human brain and apply fMRI data from various healthy and diseased populations for more reliable diagnostics and monitoring. The rationale for this study is based on our preliminary studies which investigated WM-FNs using the Human Connectome Project dataset. We found that WM-FNs are correlated with subregions of the corpus callosum, a critical WM region relaying information between the two cortical hemispheres. Furthermore, we determined an overlap between the WM-FNs and tracts from diffusion tensor imaging (DTI). In this study we will examine WM-FNs of the whole brain using resting fMRI data from two large independent cohorts. We hypothesize that the FN measures derived from WM will be similar to that of GM and the metrics can be used to reliably predict phenotypic traits. The hypothesis will be tested with the following specific aims: Aim1: To develop and evaluate the time-series, FC and FN characteristics of WM of the whole - brain; Aim 2: To investigate WM-phenotype associations and the predictability of phenotypes using WM-FNs; and Aim 3: To develop and disseminate a WM-FN toolbox. To the best of our knowledge, this study will be the first to examine the reliability and validity of WM-FNs in resting fMRI data, and its relation to brain function. The proposed work holds significant contribution since it will facilitate the use of WM-FN methods for the neuroimaging community, which currently lacks the necessary analytic tools to reliably characterize WM function. This study will provide a strong foundation f or future clinical use of both WM-FNs and GM-FNs, to understand brain function more comprehensively, in addition to facilitating the use of reliable and reproducible WM-FC methods.

项目摘要 功能性大脑连接（FC）和功能性网络（FN）的发现推动了 神经影像领域，特别是功能性磁共振成像（fMRI），经历了 近年来呈指数级增长。 FN 让我们能够更好地了解大脑的外在和内在 各种疾病和健康状态下的特性，从而更好地表征神经精神学 失调。然而，当前的功能磁共振成像分析仅限于大脑的灰质（GM）区域 来自白质 (WM) 区域的 fMRI 数据经常被丢弃，该区域约占全部数据的 50% 大脑的体积。许多脑部疾病都与 WM 缺陷有关，因为 WM 对于大脑发育至关重要。 信息传输到 GM 皮质区域。尽管发现血氧水平依赖性 WM、WM-FN 中的（粗体）信号尚未完全表征，而且其机制也尚未完全确定。 WM-FN 可能会影响 GM-FN，但 WM-FN 如何与表型性状相关尚不清楚。 该项目的长期目标是更好地了解 WM-FN 对正常认知的影响 人类大脑的功能，并应用来自各种健康和患病人群的功能磁共振成像数据来了解更多信息 可靠的诊断和监测。这项研究的基本原理是基于我们的初步研究 使用人类连接组项目数据集研究了 WM-FN。我们发现 WM-FN 是相关的 胼胝体的子区域是在两个皮质之间传递信息的关键 WM 区域 半球。此外，我们还确定了 WM-FN 和扩散张量束之间的重叠 成像（DTI）。在这项研究中，我们将使用来自两个大的静息功能磁共振成像数据来检查整个大脑的 WM-FN。 独立队列。我们假设源自 WM 的 FN 度量将与 GM 的相似，并且 这些指标可用于可靠地预测表型性状。该假设将通过以下内容进行检验 具体目标： 目标 1：开发和评估整个 WM 的时间序列、FC 和 FN 特征 - 脑;目标 2：研究 WM-表型关联以及使用 WM-FN 的表型预测能力； 目标 3：开发并传播 WM-FN 工具箱。据我们所知，这项研究将是 首先检查静息功能磁共振数据中 WM-FN 的可靠性和有效性，及其与大脑功能的关系。这 拟议的工作具有重大贡献，因为它将促进 WM-FN 方法在 神经影像学界，目前缺乏必要的分析工具来可靠地表征 WM 功能。这项研究将为 WM-FN 和 GM-FN 的未来临床使用提供坚实的基础，以 除了促进可靠和可重复的使用之外，更全面地了解大脑功能 WM-FC 方法。