Surface-Based Fiber Tracking and Modeling Techniques for Mapping the Superficial White Matter Connectome with Diffusion MRI

基于表面的纤维跟踪和建模技术，用于利用扩散 MRI 绘制浅表白质连接组图

• 批准号: 10588001
• 负责人: Yonggang Shi
• 金额: $ 56.28万
• 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 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10588001
• 关键词: Accounting; Age; Aging; Algorithms; Anatomy; Area; Atlases; Attention; Autopsy; Brain; Brain Diseases; Brain imaging; Collaborations; Communities; Data; Data Pooling; Data Set; Dedications; Detection; Development; Diffusion Magnetic Resonance Imaging; Epilepsy; Fiber; Funding; Geometry; Goals; Health; Human; Image; Imaging Techniques; Implanted Electrodes; Informatics; Injections; Longevity; Magnetic Resonance Imaging; Manuals; Maps; Masks; Methods; Modeling; Mus; National Institute of Biomedical Imaging and Bioengineering; Normalcy; Operative Surgical Procedures; Pathway interactions; Patients; Play; Public Health; Research; Research Personnel; Resolution; Role; Series; Site; Software Tools; Surface; Techniques; Time; Tracer; Validation; Variant; Work; aging brain; computerized tools; connectome; data harmonization; data tools; deep learning algorithm; experience; experimental study; health disparity; image registration; improved; in vivo; large scale data; longitudinal dataset; multimodality; neural; neuroimaging; novel; reconstruction; recruit; shape analysis; tool; tractography; vector; white matter

Abstract The superficial white matter (SWM) lies directly beneath the cortex and contains the short association fibers, or U-fibers, connecting neighboring gyri. The SWM contains around twice as many fiber connections as the deep white matter (DWM) and plays a crucial role in brain development, aging, and various brain disorders. Existing connectome imaging research based on diffusion MRI (dMRI), however, mostly focuses on the connections of long fiber bundles in the DWM even though tremendous advances have been made in human connectome imaging with much improved spatial and angular resolution. In this proposed renewal of our R01 project (NIBIB R01EB022744), we will conduct systematic development of novel computational tools to fill major technical gaps in current SWM research. Our project will provide fundamentally novel solutions to many of the current challenges in SWM connectome research by developing surface-based tools for fiber tracking, atlas construction, and personalized analysis. We will also develop novel personalized dMRI harmonization methods with a particular focus on accounting for the variable cortical anatomy. These developments will for the first time provide dedicated tools for modeling SWM connectome with greatly improved robustness and accuracy. There are three specific aims in our project: 1. Development of novel surface-based fiber tracking and filtering algorithms for the modeling of superficial white matter connectivity. 2. Development of surface-based U-fiber atlases and personalized SWM connectivity analysis. 3. Development of personalized diffusion MRI harmonization tools with improved consistency in cortical anatomy. Rigorous validations of our novel surface-based U-fiber tracking and modeling methods will be performed on high-resolution MRI of post-mortem brains, in vivo intracranial neural recordings from surgically implanted electrodes in patients with epilepsy, and their application in multiple large- scale connectome imaging datasets (n>5000). All software tools and atlases developed in this project will be publicly shared, which will allow brain imaging researchers to augment their current connectome models with U- fibers in SWM and more completely map human brain connectomes for the detection of their alterations in various brain disorders.

摘要 浅层白质(SWM)位于皮质正下方，含有短的联合纤维，或 U形纤维，连接相邻的脑回。SWM包含的光纤连接数量大约是深度的两倍 白质(DWM)，在大脑发育、衰老和各种大脑疾病中起着至关重要的作用。现有 然而，基于弥散磁共振成像(DMRI)的连接体成像研究主要集中在 DWM中的长纤维束，尽管在人类连接组方面取得了巨大的进展 空间和角度分辨率大大提高的成像。在我们的R01项目(NIBIB)的这次拟议续签中 R01EB022744)，我们将进行新型计算工具的系统开发，以填补主要技术空白 在当前的SWM研究中。我们的项目将为当前的许多问题提供全新的解决方案 通过开发基于表面的工具用于光纤跟踪、图谱构建、 和个性化分析。我们还将开发新的个性化dMRI协调方法， 特别注重解释可变的皮质解剖结构。这些发展将首次提供 专用工具用于对SWM连接体进行建模，大大提高了健壮性和准确性。一共有三个 我们项目的具体目标是：1.开发基于表面的新型光纤跟踪和滤波算法 浅层白质连通性的建模。2.基于表面的U-纤维图谱的研制和 个性化SWM连接性分析。3.开发个性化扩散磁共振成像协调工具 改进了皮质解剖的一致性。我们的新的基于表面的U纤维跟踪和 建模方法将在高分辨率的死后脑MRI上进行，活体内的颅内神经 癫痫患者手术植入电极的记录及其在多发性大面积癫痫中的应用 缩放连接体成像数据集(n&gt；5000)。在这个项目中开发的所有软件工具和地图集将是 公开分享，这将允许大脑成像研究人员使用U-U来增强他们目前的连接体模型 SWM中的纤维和更完整地映射人脑连接以检测它们在 各种脑部疾病。