A research and clinical tool for connectivity-based analysis of brainstem anatomy

描述（由申请人提供）：神经退行性综合征包括广泛的疾病，具有显著的发病率，但治疗不足。更好地了解选择性神经元易感性、区域结构-功能关系和系统连通性可以使我们加深对发病机制的理解，确定潜在治疗途径，并制定疾病指数。特别是，开发和验证一种测量疾病标志物的工具不仅有助于了解基本的发病机制，而且有助于诊断（特别是在早期和潜在的可治疗阶段），以及分期、预后和评估治疗干预反应的临床必要性。为了满足未来临床研究的可行性标准，我们建议开发、验证和准备验证基于“s图像”连接的参考框架，作为DT-MRI分析的新型临床和研究工具。s图像是多维数据的有组织的直线表示，这样每个光纤束（光纤束的单体素细分）的数据就可以根据它们与感兴趣区域（ROI）的曲线距离来表示。s-image框架将允许我们：1)解析个体脑干束，2)计算准确和具体的白质完整性测量，3)比较不同时间、不同个体和不同诊断的值。首先，我们将开发s图像软件框架，并使用来自小脑梗的体内和死后数据进行解剖验证。然后，我们将收集初步数据，以设计未来的研究，使用神经和神经认知功能作为结构-功能相关性的探针。定量神经认知测试与DT-MRI测量的相关性也将使我们能够阐明1)额叶受累，2)小脑受累，或3)额小脑断开可能对“额叶”执行功能（导致神经退行性疾病致残的关键因素）的贡献。我们提出：1)通过验证s-图像允许我们检测白质病理，2)通过验证s-图像检测复杂脑干区域临床显著的病理性小脑蒂变化的能力，我们有理由期望s-图像也能够检测其他复杂区域的病理变化。一个重要的子假设是，认知域的解剖定位将使我们能够使用行为测量来预测神经变性中白质疾病的解剖模式。第二个子假设是，白质异常可能是退行性变的早期标志，因此可能先于行为或体积异常。虽然这是一个雄心勃勃的项目，但我们的团队具有独特的优势，因为我们的多学科专业知识和长期成功的合作将小脑/脑干成像和脊髓小脑共济失调的病理生理联系起来。我们相信，这一发展和验证用于脑干和小脑病理分析的s-图像框架的建议是一个非常有前途的研究途径，将引发进一步的基础研究，为改进小脑变性的诊断、分期和管理奠定基础，并提供一种适用于所有中枢神经系统疾病的方法。神经退行性疾病，如阿尔茨海默氏症、帕金森氏症和小脑性共济失调，对许多人的生活有着毁灭性的影响。我们建议开发一种疾病标志物，不仅可以帮助了解疾病中的大脑行为，还可以用作诊断、评估疾病阶段、预测病程和监测治疗反应的工具。具体来说，我们将开发“s图像”，这是一种新的分析框架，将从扩散张量磁共振成像中获取数据，这是一种观察水沿着活体大脑白质束定向运动的方法。这不仅对神经退行性疾病，而且对所有影响大脑的疾病，包括中风、感染和肿瘤，都具有重要意义。

DESCRIPTION (provided by applicant): The neurodegenerative syndromes comprise a wide spectrum of disease with significant morbidity yet inadequate therapy. A better understanding of the selective neuronal vulnerability, regional structure-function relationships, and systems connectivity could allow us to hone our understanding of pathogenesis, identify avenues of potential therapy, and develop an index of disease. In particular, development and validation of a tool to measure a disease marker could help not only to understand the basic pathogenesis but also with diagnosis (particularly in the early and potentially treatable stages) and the clinical imperatives of staging, prognosis, and evaluation of response to therapeutic interventions. In order to meet feasibility criteria for a future clinical study, we propose to develop, verify, and prepare to validate the "s-image" connectivity-based reference framework as a novel clinical and research tool for DT-MRI analysis. An s-image is an organized rectilinear representation of multidimensional data, such that data for each fiber bundlet (a single-voxel subdivision of a fiber bundle) are represented according to their curvilinear distance from a region of interest (ROI). The s-image framework will allow us: 1) to resolve individual brainstem tracts, 2) to calculate accurate and specific measures of white matter integrity, and 3) to compare values over time, across individuals, and across diagnoses. First we will develop the s-image software framework and perform anatomic validation using in vivo and post-mortem data from the cerebellar peduncles. Then, we will collect preliminary data to design a future study using neurologic and neurocognitive function as probes for structure-function correlations. Correlation of quantitative neurocognitive testing with DT-MRI measures will also allow us to elucidate what contribution 1) frontal involvement, 2) cerebellar involvement, or 3) frontocerebellar disconnection may make to "frontal" executive function, a key factor leading to disability from neurodegenerative disease. We propose that: 1) by verifying that s-images allow us to detect white matter pathology, and 2) by validating the ability of s-images to detect clinically significant pathological cerebellar peduncle changes in complex brainstem regions, it is reasonable to expect that s-images would also be able to detect pathological changes in other complex regions. An important subhypothesis is that the anatomic localization of cognitive domains will allow us to use behavioral measures to predict anatomic patterns of white matter disease in neurodegeneration. A second subhypothesis is that white matter abnormalities may be an early marker of degeneration and thus may precede behavioral or volumetric abnormalities. Although this is an ambitious project, our team is uniquely positioned to succeed because of our multidisciplinary expertise and long history of successful collaboration correlating cerebellar/brainstem imaging and pathophysiology of spinocerebellar ataxias. We believe that this proposal for development and validation of the s-image framework for analysis of brainstem and cerebellar pathology is a highly promising avenue of research that will beget further basic research, establish a basis for improved diagnosis, staging, and management of cerebellar degeneration, and provide an approach applicable to all central nervous system disease. PUBLIC HEALTH RELEVANCE: Project narrative Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and cerebellar ataxia, have a devastating impact on many lives. We propose to develop a disease marker that not only could help to understand the behavior of the brain in disease, but also could be used as a tool for diagnosis, evaluation of the stage of disease, prediction of disease course, and monitoring response to therapy. Specifically, we will develop the "s-image," a new framework for analysis that will take data from diffusion tensor magnetic resonance imaging - a way to observe the directed motion of water along the white matter tracts in the living brain. This has important implications not only for neurodegenerative disease but for all conditions affecting the brain, including strokes, infections, and tumors.