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: 9356511
• 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 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9356511
• 关键词: Algorithmic Analysis; Algorithms; Anatomy; Atlases; Axon; Biological; Brain; Brain Mapping; Cereals; Communities; Computer software; Data; Data Analyses; Data Set; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Early Diagnosis; Early treatment; Environment; Fiber; Guidelines; Human; Image; Impairment; Individual; Injection of therapeutic agent; 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; design; digital; early detection biomarkers; extrastriate visual cortex; human data; imaging biomarker; improved; in vivo; in vivo Model; mouse model; novel; 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.

摘要 对于脑连接体的活体研究，扩散MRI（dMRI）是重要的工具，因为它提供了高度的 敏感的成像标记，并允许通过纤维束成像检查连接路径。的成功 在人类连接组计划（HCP）中，高分辨率、多壳层扩散成像作为 连接体研究中dMRI数据采集的标准方法。为了充分释放多壳的潜力 dMRI，在这个项目中，我们将开发一套新的计算工具，共同估计纤维取向 分布（FOD）和房室参数。通过基于FOD的纤维束成像，我们可以可靠地解决 交叉纤维和重建纤维束，忠实地遵循已知的解剖学，如视网膜病变的 视觉路径房室参数为研究局部细胞提供了敏感的成像标记物 轴突周围的环境。我们的工具普遍适用于人类和小鼠的连接体 research.弥散纤维束成像的一个主要挑战是缺乏严格的生物学验证， 有意义的地面真相利用来自小鼠连接组的小鼠大脑的大规模示踪剂注射数据， 项目（MCP）在南加州大学和艾伦小鼠脑连接图谱，我们将进行系统的验证 和优化我们的基于FOD的技术，从成像信号的去噪到 房室模型对纤维束成像参数选择的影响。这将创建一个经过验证的系统， 利用多壳层成像技术研究小鼠连接体，为人体神经元的设计提供直观的指导 基于FOD的连接体研究。我们的项目有三个具体目标：1.培养一个将军 多壳纤维取向和隔室模型联合估计的计算框架 扩散成像2.使用小鼠多壳层dMRI数据验证和优化计算工具 大脑和轴突投影图。3.开发一个全面的光纤工具包 从人脑的多壳层dMRI数据进行束重建。在这个项目中，我们将应用我们的软件 工具来分析两项疾病研究中收集的数据。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BTBD 3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 视网膜损伤和视觉通路的完整性，通过视区之间的视网膜色素变性保持连通性。 在这个项目中开发的所有软件工具将免费分发给研究社区。