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 大脑功能连接(FC)和功能网络(FN)的发现推动了 神经成像领域，特别是在功能磁共振成像(FMRI)方面，经历了 近几年呈指数级增长。FNS使我们能够更好地了解外在和内在的大脑 各种疾病和健康状态下的特性，导致更好地描述神经精神疾病 精神错乱。然而，目前的功能磁共振分析仅限于大脑的灰质(GM)区域和 来自白质(WM)区域的fMRI数据经常被丢弃，它约占 大脑的体积。许多大脑疾病都与WM缺乏有关，因为WM对 将信息传输到转基因大脑皮层。尽管发现血液中的氧气水平依赖 (BOLD)WM、WM-FN中的信号尚未完全表征，也没有 WM-FN可能会影响GM-FN，也不知道WM-FN如何与表型性状相关。 该项目的长期目标是更好地了解WM-FNS对正常认知的影响 人脑功能，并应用来自不同健康和疾病人群的fMRI数据 可靠的诊断和监控。这项研究的理论基础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 在两个皮质之间传递信息的关键WM区域 半个半球。此外，我们根据扩散张量确定了WM-FN和Tracts之间的重叠 成像(DTI)。在这项研究中，我们将使用来自两个大鼠的静息fMRI数据来检查整个大脑的WM-FN 独立的队列。我们假设由WM得出的FN度量将类似于GM和 这些指标可以用来可靠地预测表型性状。这一假设将通过以下几点进行检验 具体目标：目标1：开发和评估整体工作记忆的时间序列、Fc和Fn特征 目的2：利用WM-FNS研究WM与表型的相关性和表型的可预测性； 目标3：开发和传播WM-FN工具箱。据我们所知，这项研究将是 首先检验WM-FNS在静息fMRI数据中的信度和效度，及其与脑功能的关系。这个 拟议的工作具有重大贡献，因为它将促进将WM-FN方法用于 神经成像社区，目前缺乏必要的分析工具来可靠地描述WM 功能。本研究将为WM-FNS和GM-FNS未来的临床应用奠定坚实的基础 除了促进可靠和可重复性的使用外，更全面地了解大脑功能 WM-FC方法。