Computational Tools for Modeling Human and Mouse Connectome with Multi-Shell Diffusion Imaging

利用多壳扩散成像对人类和小鼠连接组进行建模的计算工具

• 批准号: 9768460
• 负责人: Yonggang Shi
• 金额: $ 39.08万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-22 至 2023-05-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9768460
• 关键词: Algorithmic Analysis; Algorithms; Anatomy; Atlases; Axon; Biological; Brain; Brain Mapping; Communities; Computer software; Data; Data Analyses; Data Set; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Early Diagnosis; Early treatment; Environment; Fiber; Grain; Guidelines; Human; Image; Impairment; Individual; Injections; Intuition; Investigation; Joints; Knowledge; Location; Maps; Methods; Modeling; Mus; Neurologic; Obsessive-Compulsive Disorder; Performance; Public Health; Research; Resolution; Resources; Retinal; Signal Transduction; Site; Software Tools; Structure; System; Techniques; Testing; Tracer; Validation; Vision Disorders; Visual Pathways; Visual impairment; Visual system structure; Work; base; computer framework; computerized tools; connectome; data acquisition; denoising; design; digital; early detection biomarkers; extrastriate visual cortex; human data; human model; imaging biomarker; improved; in vivo; in vivo Model; mouse model; novel; preservation; reconstruction; software development; success; tool; tractography; web site

ABSTRACT For the in vivo investigation of brain connectome, diffusion MRI (dMRI) is an important tool as it provides highly sensitive imaging markers and allows the examination of connection paths via tractography. With the success of the Human Connectome Project (HCP), high resolution, multi-shell diffusion imaging is emerging as the standard approach for dMRI data acquisition in connectome studies. To fully unleash the potential of multi-shell dMRI, in this project we will develop a suite of novel computational tools that jointly estimate fiber orientation distributions (FOD) and compartmental parameters. With FOD-based tractography, we can reliably resolve crossing fibers and reconstruct fiber bundles that faithfully follow known anatomy such as the retinotopy of visual pathways. Compartmental parameters provide sensitive imaging markers for studying local cellular environment surrounding the axons. Our tools are generally applicable for both human and mouse connectome research. One main challenge in diffusion tractography is the lack of rigorous validations with biologically meaningful ground truth. With large-scale tracer injection data of mouse brains from the Mouse Connectome Project (MCP) at USC and the Allen Mouse Brain Connectivity Atlas, we will perform a systematic validation and optimization of our FOD-based techniques from the denoising of imaging signals to the configuration of compartment models to the selection of tractography parameters. This will create a well-validated system for studying mouse connectome with multi-shell imaging, and provide intuitive guidelines for the design of human studies with FOD-based connectome. There are three specific aims in our project: 1. To develop a general computational framework for the joint estimation of fiber orientation and compartment models from multi-shell diffusion imaging. 2. To validate and optimize the computational tools using multi-shell dMRI data of mouse brains and axonal projection maps from tracer injections. 3. To develop a comprehensive toolkit for fiber bundle reconstruction from multi-shell dMRI data of human brains. In this project we will apply our software tools to analyze data collected in two disease studies. In the first study, we will focus on the cortico-striato- thalamo-cortical (CSTC) network and examine its connectivity changes in the BTBD3 mouse model of obsessive-compulsive disorder (OCD). In the second study, we will apply our tools to study the relation of retinal impairment and visual pathway integrity via the retinotopy-preserving connectivity between visual areas. All software tools developed in this project will be distributed freely to the research community.

抽象的 对于脑连接组的体内研究，扩散 MRI (dMRI) 是一个重要的工具，因为它提供了高度 敏感的成像标记，并允许通过纤维束成像检查连接路径。随着成功 人类连接组计划 (HCP) 提出，高分辨率、多壳扩散成像正在成为 连接组研究中 dMRI 数据采集的标准方法。充分释放多壳潜力 dMRI，在这个项目中我们将开发一套新颖的计算工具来共同估计纤维取向 分布（FOD）和分区参数。通过基于 FOD 的纤维束成像，我们可以可靠地解决 交叉纤维并重建纤维束，忠实地遵循已知的解剖结构，例如视网膜的视网膜结构 视觉通路。区室参数为研究局部细胞提供敏感的成像标记 轴突周围的环境。我们的工具通常适用于人类和小鼠连接组 研究。扩散纤维束成像的一个主要挑战是缺乏严格的生物学验证 有意义的基本事实。具有来自小鼠连接组的小鼠大脑的大规模示踪剂注射数据 南加州大学的项目（MCP）和艾伦小鼠大脑连接图谱，我们将进行系统验证 以及我们基于 FOD 的技术的优化，从成像信号的去噪到配置 室模型来选择纤维束成像参数。这将创建一个经过充分验证的系统 通过多壳成像研究小鼠连接组，并为人类的设计提供直观的指导 基于 FOD 的连接组研究。我们的项目有三个具体目标： 1. 制定一个通用的 用于联合估计多壳纤维取向和隔室模型的计算框架 扩散成像。 2. 使用小鼠多壳 dMRI 数据验证和优化计算工具 示踪剂注射的大脑和轴突投影图。 3. 开发纤维综合工具包 根据人脑的多壳 dMRI 数据进行束重建。在这个项目中我们将应用我们的软件 用于分析两项疾病研究中收集的数据的工具。在第一项研究中，我们将重点关注皮质纹状体 丘脑皮质（CSTC）网络并检查其在 BTBD3 小鼠模型中的连接变化 强迫症（OCD）。在第二项研究中，我们将应用我们的工具来研究 通过保留视觉区域之间的视网膜功能连接来维持视网膜损伤和视觉通路完整性。 该项目中开发的所有软件工具将免费分发给研究界。