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.

抽象的 浅表白质（SWM）直接位于皮质下方，包含短相关纤维，或 U纤维，连接相邻的回合。 SWM包含的纤维连接大约是深处的两倍 白质（DWM）在大脑发育，衰老和各种脑部疾病中起着至关重要的作用。现存的 但是，基于扩散MRI（DMRI）的连接组成像研究，主要集中于 即使在人类连接中取得了巨大进步，DWM中的长纤维束 成像具有大大改善的空间和角度分辨率。在我们R01项目的拟议续约中（Nibib） R01EB022744），我们将进行新型计算工具的系统开发以填补主要的技术空白 在当前的SWM研究中。我们的项目将为许多当前的目前提供根本新颖的解决方案 SWM Connectome研究中的挑战通过开发用于纤维跟踪的基于表面的工具，Atlas Construction， 和个性化分析。我们还将开发新颖的个性化DMRI协调方法 特别关注可变皮质解剖结构。这些发展将首次提供 专用的工具，以大大提高鲁棒性和准确性来建模SWM Connectome。有三个 我们项目中的具体目的：1。开发新型的基于表面的纤维跟踪和过滤算法 浅表白质连接性的建模。 2。开发基于表面的U纤维地图集和 个性化的SWM连接分析。 3。开发个性化扩散MRI协调工具 皮质解剖结构的一致性提高。严格验证我们新型基于表面的U纤维跟踪和 建模方法将对验尸大脑的高分辨率MRI进行，体内颅内神经 癫痫患者的手术植入电极的记录，并将其应用于多个大型 比例Connectome成像数据集（n> 5000）。该项目中开发的所有软件工具和地图集将是 公开共享，这将使大脑成像研究人员可以通过U-来增强其当前的连接模型 SWM中的纤维和更完全绘制的人脑连接组，以检测其改变 各种脑部疾病。