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

摘要( 好了！ 弥散磁共振成像(DMRI)是唯一一种非侵入性的方法，可以绘制活体人脑的连接图，并 对理解精神障碍至关重要。几项大型研究，如人类连接组计划(HCP) 和青少年大脑认知发育(ABCD)已经收集或准备收集弥散磁共振 数据来自30,000多名受试者。然而，一个重要的挑战是，这些数据集收集自不同的 由于扫描仪间(站点间)差异较大，无法汇集扫描仪进行联合分析，原因如下 用于数据重建的供应商特定软件、磁头线圈的灵敏度等方面的差异。 在精神障碍患者中，差异往往大于组间观察到的效果大小。一秒钟 大规模数据分析面临的挑战是缺乏单一一致的基于本体的定义和自动化 提取整个生命周期(包括新生儿和儿童)的脑白质连接。第三个挑战是 组合的dMRI数据集的绝对大小(几TB)，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 可视化如此大量的数据。在这笔赠款中，我们建议应对这些挑战，使大型- 对dmri数据进行大规模数据密集型分析。具体地说，在目标1中，我们建议开发新的数学 从多个站点采集的数据中消除扫描仪特定差异的算法。我们将调和10,000 来自ABCD研究的受试者在21个不同的地点获得，另外10,000名受试者来自HCP倡议 跨越整个生命周期和众多疾病适应症，以及来自健康大脑的10,000名受试者 网络。所有协调一致的数据集(30,000名受试者)将使用NIMH数据与社区共享 存档(NDA)。在目标2中，我们将开发一个正式的基于本体的系统来定义189个白质束 使用人类和猴子文献中关于大脑连通性的神经解剖学里程碑。我们的Main 重点将是开发用于自动和一致地聚集和提取这些纤维的新算法 横跨整个人类寿命的束，包括新生儿。实现广泛使用，而不需要 需要计算资源和技术知识，在目标3中，我们将开发一个基于Web的系统 用于协调数据和分页的实时3D查看和查询(与NIMH数据归档集成 基础设施)，用于从不同主题的整个队列中选择用户定义的主题 诊断类别。总体而言，这一框架的潜在影响是重大的，因为它将第一次， 允许对dMRI数据进行大规模数据密集型分析，以研究神经发育和精神障碍 跨越诊断界限。 好了！

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. !