Bridging Diffusion MRI and Chemical Tracing for Validation and Inference of Fiber Architectures

连接扩散 MRI 和化学示踪以验证和推断纤维结构

• 批准号: 10530636
• 负责人: Anastasia Yendiki
• 金额: $ 64.78万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-15 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10530636
• 关键词: Acceleration; Address; Algorithmic Analysis; Algorithms; Anatomy; Animals; Architecture; Area; Axon; Brain; Brain Diseases; Brain imaging; Brain region; Brain scan; Chemicals; Collaborations; Communities; Data; Data Analyses; Data Set; Databases; Development; Diameter; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Engineering; Error Sources; Exhibits; Fiber; Goals; Histologic; Histology; Human; Imaging Techniques; Injections; Knowledge; Label; Link; MRI Scans; Macaca; Magnetic Resonance Imaging; Maps; Methodology; Methods; Modeling; Monkeys; Myelin; Outcome; Pathway interactions; Pattern; Performance; Prefrontal Cortex; Process; Property; Protocols documentation; Research; Research Personnel; Resolution; Resources; Role; Sampling; Signal Transduction; Site; Source; Structural Models; Structure; Techniques; Therapeutic Intervention; Tracer; Training; Universities; Validation; Visualization; Work; anatomical tracer; automated algorithm; brain pathway; cingulate cortex; convolutional neural network; data acquisition; deep learning; design; histological stains; improved; in vivo; insight; multimodality; neuroimaging; next generation; nonhuman primate; novel; novel strategies; reconstruction; sample fixation; synergism; targeted treatment; tool; tractography

Project summary: This project will collect a unique, multi-modal, multi-contrast dataset with tracer injections and diffusion MRI in the same macaque brains. We will use this dataset to develop novel algorithms for inferring local fiber architectures from diffusion MRI. The goal is to overcome the limitations of current methods for diffusion orientation reconstruction, which are designed to resolve fiber crossings but not to distinguish between crossings and other configurations, such as branching, turning, fanning, etc. More broadly, the proposed dataset will allow us to investigate organizational principles of brain pathways and to provide a testbed for the neuroimaging community to evaluate the accuracy of diffusion tractography and microstructural modeling techniques. The project is a collaboration between groups with extensive expertise in diffusion MRI methodological development (MGH Martinos Center) and anatomical tracer studies (University of Rochester). We have previously collected high-resolution ex vivo diffusion MRI data on a set of macaque brains that had also received tracer injections. We have recently used these data in an open tractography challenge, with the participation of research teams from around the world. This was the first challenge of its kind to provide diffusion MRI data suitable for all state-of-the-art diffusion reconstruction methods (e.g., multi-shell or Cartesian grid sampling), in addition to providing the tracer injections in the same brains as the MRI scans. Our own preliminary studies and the challenge itself offer several insights into the performance of state-of-the-art tractography methods. For example, our results indicate that, while most tractography methods would require their parameters to be tuned differently to achieve optimal accuracy for different cortical seed regions, there are approaches that are robust across cortical areas. Furthermore, our results suggest that errors occur frequently in areas where the fiber architecture is not well modeled by a crossing. Thus there is a need for novel tractography approaches that go beyond the crossing-fiber paradigm. Here we propose to develop such an approach. Our prior work included injection sites in the frontal, prefrontal, and cingulate cortices only. Here we propose to investigate the extent to which our prior findings generalize across the brain, by performing tracer injections that sample a wider range of cortical areas. Furthermore, we will extend our acquisition protocol to acquire data appropriate not only for tractography, but also for microstructural and myelin mapping. These data will allow us to answer a broader range of questions about tractography, microstructure, and their intersection. Beyond the methodological development proposed in this project, the data will also be an invaluable resource to the neuroimaging community, providing researchers with a framework for the objective assessment of current diffusion MRI analysis methods and identifying areas for improvement to guide the development of next-generation techniques.

项目摘要：该项目将通过示踪剂注射收集独特的、多模式、多对比度的数据集 和弥散磁共振成像在同一只猕猴的大脑中。我们将使用此数据集来开发新的算法 从弥散磁共振推断局部纤维结构。其目标是克服当前方法的局限性。 用于扩散方向重建，旨在解决纤维交叉问题，但不能区分 在交叉路口和其他配置之间，例如分支、转弯、扇形等。更广泛地说， 提议的数据集将使我们能够研究大脑通路的组织原则，并提供 用于神经成像社区评估弥散射束成像和显微结构准确性的试验床 建模技术。该项目是在扩散磁共振方面拥有广泛专业知识的小组之间的合作 方法学发展(MGH Martinos中心)和解剖示踪研究(罗切斯特大学)。 我们之前收集了一组猕猴大脑的高分辨率体外扩散磁共振数据 也接受了示踪剂注射。我们最近在一项公开的轨迹摄影挑战中使用了这些数据， 来自世界各地的研究团队的参与。这是此类挑战中的第一次 适用于所有最先进的扩散重建方法(例如，多壳或笛卡尔重建方法)的扩散MRI数据 除了在与核磁共振扫描相同的大脑中提供示踪剂注射外，还可以进行网格采样)。我们自己的 初步研究和挑战本身为最先进的表演提供了几个见解 光迹照相法。例如，我们的结果表明，尽管大多数光刻方法需要 它们的参数要进行不同的调整，以针对不同的皮质种子区域实现最佳精度 是在大脑皮层区域都很健壮的方法。此外，我们的结果表明，错误会发生 通常在光纤架构不能通过交叉很好地建模的区域。因此，有必要 超越交叉纤维范例的新的纤维束成像方法。在这里，我们建议开发这样的 一种方法。我们之前的工作只包括额叶、前额叶和扣带回皮质的注射部位。这里 我们建议调查我们之前的发现在多大程度上概括了整个大脑，通过执行 示踪剂注射对更广泛的皮质区域进行采样。此外，我们将延长收购期限 协议，以获取不仅适用于光谱分析，而且也适用于显微结构和髓鞘成像的数据。 这些数据将使我们能够回答更广泛的问题，关于光谱分析，微结构，以及它们的 交叉口。除了这个项目中提出的方法学发展，数据也将是一种 为神经成像社区提供了宝贵的资源，为研究人员提供了目标框架 评估目前的磁共振扩散分析方法，并找出需要改进的地方，以指导 下一代技术的发展。