Computational analysis of diffusion tensor images: application to schizophrenia

描述（由申请人提供）：由于扩散张量成像（DTI）提供的白色物质（WM）的上级表征，它越来越多地用于研究几种WM疾病。具体而言，DTI在非局灶性神经发育疾病（如精神分裂症）的研究中发挥着至关重要的作用，其中WM异常更为复杂和微妙，可能表现为分散在整个大脑中的髓鞘形成或连接中断的变化。这导致了对基于组的DTI数据分析的需求日益增长，期望更好地阐明这些微妙的异常。这就产生了对用于DTI处理和分析的复杂和全自动计算神经解剖学技术的需求，这是至关重要的，因为传统的放射学评价未能检测到实质性的白色物质差异。这种方法的发展是具有挑战性的DTI数据，因为它需要解决的几个数学和技术问题所产生的高维和复杂的底层结构的张量数据。虽然从张量计算的标量图像（例如扩散率和各向异性图）的分析通常用作张量数据分析的第一步，但是这些图像通常从张量提取有限的信息，因此不能捕获病理的全部效果。类似的和附加的限制也是光纤跟踪所固有的。该项目旨在通过开发直接应用于整个扩散张量数据的分析方法来缓解这些问题，而无需中间步骤集中于标量测量，从而将计算神经解剖学的成熟方法扩展到张量数据。该项目的关键在于开发一套全面的工具，用于DTI数据的形态测量分析，旨在促进各种神经成像研究。扩散张量场统计分析的综合框架将在目标2中开发，使用流形学习技术，确定DT措施的基础流形结构，然后对这些流形进行体素统计分析。这样一个基于组的分析将大大促进了基于WM的空间归一化框架的DTI数据在目标1，其中张量的特点是丰富的和独特的形态学签名获得定向过滤器的发展。最后，在目标3中，这些方法的效用将在精神分裂症患者，他们的亲属和健康对照的大型数据库中进行测试，通过研究三组之间结构连接的差异，并将这些与症状严重程度的临床评级和情绪和认知的神经心理学测量的表现相关联。我们希望在成功完成该项目后，我们将为大规模人群DTI研究开发一种通用、全面和计算效率高的处理和分析工具，这套DTI分析工具可用于测试涉及白色物质的其他疾病的临床假设。

DESCRIPTION (provided by applicant): Owing to the superior characterization of white matter (WM) provided by Diffusion tensor imaging (DTI), it is being increasingly used in the investigation of several WM diseases. Specifically, DTI has a crucial role to play in the study of non-focal neurodevelopmental diseases such as schizophrenia where the WM abnormalities are more complex and subtle and may manifest as changes in myelination or disruptions in connectivity, dispersed over the whole brain. This has led to a growing need for group-based analysis of DTI data that is expected to better elucidate these subtle anomalies. This has generated the need for sophisticated and fully automated computational neuroanatomy techniques for DTI processing and analysis, crucial because the conventional radiological evaluations have failed to detect substantial white matter differences. Development of such methods is challenging for DTI data as it requires the resolution of several mathematical and technical issues arising from the high dimensionality and complex underlying structure of the tensor data. Although analysis of scalar images, such as diffusivity and anisotropy maps, that are computed from tensors is often used as a first step in analysis of tensor data, these images generally extract limited information from the tensors and therefore do not capture the full effect of pathology. Similar and additional limitations are inherent to fiber tracking also. This project seeks to alleviate these issues by developing analysis methods that apply directly to the diffusion tensor data in its entirety, without having intermediate steps concentrate on scalar measures, thereby extending well-established methods of computational neuroanatomy to tensor data. The crux of the project lies in developing a comprehensive set of tools for the morphometric analysis of DTI data, aiming at facilitating a variety of neuro-imaging studies. An integrated framework for the statistical analysis of diffusion tensor fields will be developed in Aim 2, using manifold learning techniques that determine the underlying manifold structure of the DT measures followed by voxel-wise statistical analysis on these manifolds. Such a group-based analysis will be greatly facilitated by the development of a WM-based spatial normalization framework for DTI data in Aim 1, in which tensors are characterized by rich and distinctive morphological signatures obtained using oriented filters. Finally, in Aim 3, the utility of these methods will be tested on a well characterized large database of schizophrenia patients, their relatives and healthy controls, by studying differences in structural connectivity between the three groups and correlating these with clinical ratings of symptom severity and performance on neuropsychological measurements of emotion and cognition. We expect that on successful completion of the project we will have developed a general, comprehensive and computationally efficient processing and analysis tools for large population DTI studies, set of tools for DTI analysis that can be used to test clinical hypotheses in other disorders involving white matter.