Harmonizing multi-site diffusion MRI acquisitions for neuroscientific analysis across ages and brain disorders

协调多部位扩散 MRI 采集，用于跨年龄和脑部疾病的神经科学分析

• 批准号: 10553703
• 负责人: Lauren Jean O'Donnell
• 金额: $ 78.19万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10553703
• 关键词: 3-Dimensional; Address; Adolescent; Adult; Age; Algorithms; Alzheimer' s Disease; Anatomy; Anisotropy; Architecture; Atlases; Brain; Brain Diseases; Brain Stem; Brain imaging; Categories; Cerebellum; Child; Cloud Computing; Communities; Complex; Computer software; Data; Data Analyses; Data Set; Databases; Development; Diagnostic; Dictionary; Diffusion Magnetic Resonance Imaging; Disease; Fascicle; Fiber; Gender; Grant; Head; Health; Human; Human Resources; Infrastructure; Joints; Knowledge; Literature; Longevity; MRI Scans; Major Depressive Disorder; Manuals; Maps; Mental disorders; Methods; Monkeys; National Institute of Mental Health; Neuroanatomy; Neurobiology; Online Systems; Ontology; Outcome; Phenotype; Process; Publications; Real-Time Systems; Research; Research Infrastructure; Research Personnel; Research Project Grants; Signal Transduction; Site; Subjects Selections; System; Technology; Testing; Time; Tissues; Vendor; Visual; Visualization; Work; age group; analysis pipeline; antenatal; autism spectrum disorder; automated algorithm; biobank; cloud based; cognitive development; cohort; computer infrastructure; computing resources; connectome; data archive; data exploration; data harmonization; gray matter; human subject; improved; interest; large datasets; large scale data; magnetic field; mathematical algorithm; neonate; neural; neurodevelopment; neuroimaging; novel; open source; reconstruction; repository; terabyte; three-dimensional visualization; tool; tractography; web platform; white matter; young adult

Abstract( ! Diffusion MRI (dMRI) is the only non-invasive method that can map the living human brain’s connections and is critical for understanding mental disorders. Several large studies such as the Human Connectome Project (HCP) and the Adolescent Brain Cognitive Development (ABCD) have collected or are poised to collect diffusion MRI data from over 30,000 subjects. However, an important challenge is that these datasets collected from different scanners cannot be pooled for joint analysis due to large inter-scanner (inter-site) differences, caused by differences in vendor specific software for data reconstruction, the sensitivity of head coils etc. These scanner differences are often larger than the effect sizes observed between groups in psychiatric disorders. A second challenge for large-scale data analysis is the lack of a single consistent ontology-based definition and automated extraction of white matter connections across the lifespan (including neonates and children). A third challenge is the sheer size of the combined dMRI datasets (several terabytes), limiting the ability of researchers to test hypotheses as this requires expertise and complex computational resources for processing, storing, and visualizing such large volumes of data. In this grant, we propose to address these challenges to enable large- scale data-intensive analysis of dMRI data. Specifically, in Aim 1, we propose to develop novel mathematical algorithms to remove scanner-specific differences from data acquired at multiple sites. We will harmonize 10,000 subjects from the ABCD study acquired at 21 different sites, another 10,000 subjects from the HCP initiative spanning the entire lifespan and numerous disease indications and 10,000 subjects from the Healthy Brain Network. All the harmonized datasets (30,000 subjects), will be shared with the community using the NIMH data archive (NDA). In Aim 2, we will develop a formal ontology-based system for defining 189 white matter fascicles using neuroanatomical landmarks known from human and monkey literature on brain connectivity. Our main focus will be to develop novel algorithms for automated and consistent clustering and extraction of these fiber bundles spanning the entire human lifespan including neonates. To enable widespread use without the need for demanding computational resources and technical knowledge, in Aim 3, we will develop a web-based system for real-time 3D viewing and querying of the harmonized data and fascicles (integrating with NIMH data archive infrastructure) for a user-defined selection of subjects from the entire cohort of subjects across different diagnostic categories. Overall, the potential impact of this framework is significant, as it will, for the first time, allow a large-scale data-intensive analysis of dMRI data to study neurodevelopment as well as mental disorders cutting across diagnostic boundaries. !

抽象的（ 呢 扩散MRI（DMRI）是唯一可以绘制人类大脑联系的非侵入性方法，并且是 了解精神障碍至关重要。几项大型研究，例如人类连接项目（HCP） 青少年的脑认知发展（ABCD）已收集或中毒以收集扩散MRI 来自30,000多位受试者的数据。但是，一个重要的挑战是这些数据集从不同的 由于较大的扫描仪（地点间）差异，扫描仪无法合并进行联合分析 供应商特定软件的数据重建，头圈的灵敏度等。这些扫描仪 差异通常大于精神疾病中群体之间观察到的效果大小。第二 大规模数据分析的挑战是缺乏基于本体的单一定义定义和自动化的挑战 在整个寿命（包括新生儿和儿童）中提取白质连接。第三个挑战是 组合DMRI数据集的纯粹尺寸（几个trabytes）限制了研究人员测试的能力 假设需要专业知识和复杂的计算资源来处理，存储和 可视化如此庞大的数据。在这笔赠款中，我们建议应对这些挑战，以使大型 DMRI数据的比例数据密集型分析。具体而言，在AIM 1中，我们建议开发新颖的数学 从多个站点获得的数据中删除扫描仪特定差异的算法。我们将协调10,000 来自ABCD研究的受试者在21个不同的地点获得了，另外10,000名来自HCP初始的受试者 跨越整个寿命和许多疾病适应症和健康大脑的10,000名受试者 网络。所有统一的数据集（30,000名受试者）将使用NIMH数据与社区共享 档案（NDA）。在AIM 2中，我们将开发一个基于本体的正式系统，用于定义189个白质筋膜 使用从人类和猴子文献中闻名的有关大脑连通性的神经解剖学地标。我们的主要 重点将是开发新型算法，用于自动化和一致的聚类和提取这些纤维 跨越包括新生儿在内的整个人类寿命的束。在不需要的情况下启用宽度的使用 苛刻的计算资源和技术知识，在AIM 3中，我们将开发一个基于网络的系统 用于实时3D查看和查询统一数据和筋膜（与NIMH数据存档集成 基础架构）用于从不同的主题中选择用户定义的主题的选择 诊断类别。总体而言，该框架的潜在影响很大，因为它首次将 允许对DMRI数据进行大规模的数据密集型分析，以研究神经发育和精神障碍 切开诊断边界。 呢