Tract-Specific Analysis of Brain White Matter

描述（由申请人提供）：弥散加权磁共振成像（DW-MRI）是一种功能强大但仍相对较新的体内成像技术，可对大脑连接进行前所未有的深入了解。尽管有许多研究，但定量扩散成像分析领域尚未完全成熟。特别是，急性兴趣纤维束成像，其能力，以捕捉扩散图像中的连接信息的燃料，尚未完全实现在组研究，因为开放的问题，涉及归一化和定量比较的纤维束在个人。当今的大多数扩散成像研究要么采用基于体素的形态学（VBM）或最近的基于道的空间统计（TBSS）方法的启发技术。这两种方法都没有结合纤维束成像捕获的连接信息，也没有通过这些方法获得的与特定解剖结构相关的推断。然而，近年来，人们对结构特异性的统计脑映射技术越来越感兴趣。考虑到特定解剖结构的独特性质的分析可以合理地预期具有比在整个大脑上逐点执行的分析更大的统计特异性，甚至灵敏度。结构特异性分析的一个关键特征是它能够沿着解剖学上有意义的方向沿着组合联合收割机或平均数据，同时尊重结构之间的边界，而不是在整个大脑上均匀平滑。此外，将其注意力限制在感兴趣结构上的分析产生的推断可以更有效地传达和可视化，并通过底层解剖结构进行上下文化。该应用程序的总体目标是开发、验证和分发DW-MRI的统计分析框架，该框架具有结构特异性，并充分利用了扩散图像中编码的连接信息。我们的方法主要是基于最近发表的方法，该方法使用基于表面的表示来模拟片状白色物质束，允许特定于束的表示，平滑和统计推断。我们的方法立即获得了该领域领导者的热情，TBSS和DTIstudio各自创建者的支持信也证明了这一点。如果获得资助，该应用程序将导致一套经过仔细验证的工具，使临床研究人员能够轻松地在白色物质的研究中利用特定的跟踪分析。具体目标1。用于脑白色物质特定区域分析（TSA）的交钥匙框架该目标将提供最终用户软件应用程序，使临床研究人员能够执行特定区域分析。[121]中对当前TSA框架提出的增强包括（1）纳入额外的白色物质束;（2）使用纤维交叉分辨率以获得更准确的纤维束成像;（3）先进的束分割方法;（4）用于指定广泛统计设计的灵活界面;（5）使用软件工程最佳实践的实施;（6）与现有工具和术语的互操作性。具体目标2。肌萎缩侧索硬化症（ALS）的验证和临床应用本目标将在神经退行性疾病条件下白色物质完整性的重要神经影像学研究的真实世界临床背景下评估和完善所开发的方法。具体地，所提出的ALS中上运动神经元疾病的研究，具有特定的生物学假设，将有助于定义所提出的束特异性建模和归一化策略对DW-MRI数据的神经成像分析的影响。根据2009年的《美国复苏和再投资法案》，宾夕法尼亚大学医学院为当地经济做出了巨大贡献。2008年，宾夕法尼亚大学医学院创造了37，000个就业机会和54亿美元的区域经济活动，该地区训练有素的劳动力为840个开放的宾夕法尼亚大学研究人员职位提供了24，600多份申请。目前的提案将有助于在费城地区创造或保留6个高技能工作岗位。 公共卫生相关性：该项目将提高科学家利用医学成像定量评估健康发育、衰老和疾病对人脑不同区域之间连接的影响的能力。近年来，科学界对大脑连通性的兴趣一直在上升，但目前可用的计算工具缺乏研究构成大脑白色物质的各个神经束如何受到疾病影响所需的特异性。通过提供开源、用户友好、广泛互操作和广泛验证的工具来进行特定区域的大脑连接分析，该项目将使广泛领域的科学家能够更有效地利用白色物质成像来诊断疾病，监测干预措施对白色物质区域的影响，并回答有关大脑连接的基础科学问题。

DESCRIPTION (provided by applicant): Diffusion weighted magnetic resonance imaging (DW-MRI) is a powerful but still relatively new in vivo imaging technique that allows an unprecedented level of insight into brain connectivity. Despite numerous studies, the field of quantitative diffusion imaging analysis has not fully matured. In particular, the acute interest in fiber tractography, fueled by its ability to capture connectivity information in diffusion images, has not been fully realized in group studies because of open problems that involve normalization and quantitative comparison of fiber tracts across individuals. The majority of today's diffusion imaging studies either employ techniques inspired by Voxel-Based Morphometry (VBM) or the more recent Tract-Based Spatial Statistics (TBSS) approach. Neither of these approaches incorporates the connectivity information captured by fiber tractography, nor are the inferences gained by these approaches associated with specific anatomical structures. Yet, in recent years, there has been an increased interest in statistical brain mapping techniques that are structure-specific. Analysis that takes into account the unique properties of specific anatomical structures can be reasonably expected to have greater statistical specificity, and even sensitivity, than analysis performed pointwise over the whole brain. A key feature of structure-specific analysis is its ability to combine or average data along anatomically meaningful directions while respecting the boundaries between structures, as opposed to uniform smoothing over the whole brain. Furthermore, analysis that restricts its attention to structures of interest produces inferences that can be communicated and visualized more effectively, contextualized by the underlying anatomy. The overall aim of this application is to develop, validate and distribute a statistical analysis framework for DW- MRI that is structure-specific and fully leverages the connectivity information encoded in diffusion imagery. Our approach is based primarily on a recently published method that uses surface-based representation to model sheet-like white matter tracts, allowing tract-specific representation, smoothing and statistical inference. Our approach gained immediate enthusiasm from the leaders in the field, and the letters of support from the respective creators of TBSS and DTIstudio speak to that fact. If funded, this application would lead to a set of carefully validated tools that would allow clinical investigators to easily leverage tract-specific analysis in studies of white matter. Specific Aim 1. A Turnkey Framework for Tract-Specific Analysis (TSA) of Brain White Matter This aim will deliver an end-user software application that enables clinical investigators to perform tract-specific analysis. Proposed enhancements to the current TSA framework in [121] include (1) incorporation of additional white matter tracts; (2) use of fiber-crossing resolution for more accurate tractography; (3) advanced tract segmentation methodology; (4) a flexible interface for specifying a wide range of statistical designs; (5) implementation using software engineering best practices; (6) interoperability with existing tools and nomenclatures. Specific Aim 2. Validation and Clinical Applications in Amyotrophic Lateral Sclerosis (ALS) This aim will evaluate and refine the developed methodology within the real-world clinical context of a significant neuroimaging study of white matter integrity under neurodegenerative conditions. Specifically, the proposed study of upper motor neuron disease in ALS, with specific biological hypotheses, will help define the effects of the proposed tract-specific modeling and normalization strategy on neuroimaging analysis of DW- MRI data. Relevant to the American Recovery and Reinvestment Act of 2009, Penn Medicine contributes substantially to the local economy. In 2008, Penn Medicine created 37,000 jobs and $5.4 billion in regional economic activity, with the area's highly trained workforce producing more than 24,600 applications for just 840 open Penn staff research positions. The current proposal will help create or retain 6 highly skilled jobs in the Philadelphia region. PUBLIC HEALTH RELEVANCE: This project will improve scientists' ability to use medical imaging to quantitatively assess the effects of healthy development, aging and disease on the connectivity between different regions of the human brain. Scientific interest in brain connectivity has been rising in recent years, but the computational tools that are currently available lack the specificity needed to study how the individual tracts composing the brain white matter are affected by disease. By providing open-source, user-friendly, widely interoperable, and extensively validated tools for tract-specific brain connectivity analysis, the project will enable a wide field of scientists to leverage white matter imaging more effectively in diagnosing disease, monitoring the effects of interventions on white matter tracts, and answering basic science questions about brain connectivity.