Novel computational methods for higher order diffusion MRI in autism

描述（由申请人提供）：自闭症谱系障碍（ASD）的诊断目前是基于儿童的行为和发展史。随着扩散加权磁共振成像（DW-MRI）先进技术的发展，人们期望成像技术能够阐明白质（WM）结构的病理诱导和神经发育变化，并提供诊断和预测解剖生物标志物。我们的目标是开发处理和分析高阶扩散模型（HOMs）的高角分辨率扩散成像数据的计算方法。与扩散张量成像（DTI）中的张量模型相比，HOMs提供了对复杂WM区域基于病理的连通性变化的更丰富的理解，以及对WM异常程度的量化。当这些影像学测量与症状严重程度的临床测量相关联时，将为病理及其进展提供额外的见解，从而使该项目具有非常重要的临床意义。了解这种复杂的WM区域有望有助于ASD的研究，ASD的缺陷可能与WM异常和纤维束结构连接的中断有关。由于现有的张量分析方法不适用于HOMs，数据采集的进步反过来又要求新的自动化工具来分析这些数据。我们建议通过以下具体目标来实现这一目标：在Aim 1中，我们将定义来自HOMs的局部和全局度量，并使用这些度量来获得基于特征的算法，用于HOMs图像的可变形配准，为后续分析做准备。在目标2中，我们将使用基于体素的、基于流形的和基于通道的分析相结合的方法，开发和验证HOMs人口统计的综合框架。在Aim 3中，我们将使用HOM特征设计高维多元模式分类器，以获得大脑异常的空间模式，并将异常分配给每个大脑。在目标4中，我们将把目标1 - 3中开发的方法应用到ASD患者和人口统计学平衡的典型发展志愿者的大型数据库中，并确定患者-对照差异以及与患者症状严重程度临床评分的相关性。群体差异和连通性中断模式的量化有望提供对自闭症中观察到的缺陷的见解，如社交互动受损，语言和沟通受损以及刻板印象，限制和重复行为。在文献中从未尝试过使用HOMs来研究ASD，大多数工作仅限于分析从DTI数据计算的各向异性和扩散系数。我们期望，在成功完成该项目后，我们已经开发出一种通用的、全面的、数学上一致的、计算效率高的处理和分析范式，用于使用HOMs进行大规模人口研究，这将有助于识别和量化由病理引起的连接变化的复杂模式。

DESCRIPTION (provided by applicant): The diagnosis of autism spectrum disorder (ASD) is currently based on behavior and developmental history of the child. With the development of advanced forms of diffusion-weighted magnetic resonance imaging (DW-MRI), it is expected that imaging will elucidate pathology-induced and neuro-developmental changes in white matter (WM) architecture, and provide diagnostic and predictive anatomical biomarkers. We aim at developing computational methods for processing and analysis of high angular resolution diffusion imaging data that has been fitted with higher order diffusion models (HOMs). Compared to the tensor model in diffusion tensor imaging (DTI), HOMs provide a much richer understanding of pathology-based connectivity changes in complex WM regions, as well as a quantification of the degree of abnormality of WM. These imaging measures when correlated with clinical measures of symptom severity will provide additional insight into the pathology and its progression, thus making this project very clinically significant. Understanding such complex WM regions is expected to aid in the study of ASD, deficits in which can be linked with WM abnormalities and disruptions in structural connectivity via fiber tracts. The advances in acquisition of data that can be fitted with HOMs in turn calls for novel automated tools for analyzing such data, as existing methods developed for tensors are inapplicable to HOMs. We propose to achieve this by the following specific aims: In Aim 1, we will define local and global measures from HOMs and use these to obtain a feature-based algorithm for deformable registration of HOM images preparing them for subsequent analysis. In Aim 2, we will develop and validate an integrated framework for population statistics of HOMs using a combination of voxel-based, manifold-based and tract-based analysis. In Aim 3, we will design high- dimensional multivariate pattern classifiers using HOM features, to obtain spatial patterns of brain abnormality and assign an abnormality to each brain. In Aim 4, we will apply the methods developed in Aims 1 - 3 to a large database of ASD patients and demographically balanced typically developing volunteers and identify patient-control differences and correlate with clinical ratings of symptom severity in patients. The quantification of patterns of group differences and connectivity disruptions are expected to provide insight into the deficits observed in autism such as impaired social interactions, impaired language and communication and stereotypical, restricted and repetitive behaviors. The use of HOMs that has never been attempted before in literature to study ASD, with most of the work limited to the analysis of anisotropy and diffusivity measures computed from DTI data. We expect that upon successful completion of the project, we have developed a general and comprehensive, mathematically consistent and computationally efficient processing and analysis paradigm for large population studies using HOMs that will help identify and quantify complex patterns of connectivity changes induced by pathology. PUBLIC HEALTH RELEVANCE: This project aims at developing computational methods for analyzing diffusion MRI data fitted with higher order models that uniquely characterize complex white matter regions, affected in Autism Spectrum Disorder (ASD). These well validated methods will be applied to the analysis of an ASD population to produce a quantification of abnormalities in brain connectivity and white matter integrity. Correlation with clinical diagnostic measures will provide an image-based link to deficits observed in autism such as impaired social interactions, language and communication and restricted and repetitive behaviors, and hence aid in prognosis and in studying disease progression.