Novel computational methods for higher order diffusion MRI in autism

描述(由申请人提供)：目前自闭症谱系障碍(ASD)的诊断是基于儿童的行为和发育史。随着扩散加权磁共振成像(DW-MRI)技术的发展，有望阐明白质(WM)结构的病理和神经发育改变，并提供诊断和预测的解剖学生物标志物。我们的目标是开发用于处理和分析高角分辨率扩散成像数据的计算方法，这些数据已经用高阶扩散模型(HOMS)进行了拟合。与扩散张量成像(DTI)中的张量模型相比，HOMS提供了更丰富的对复杂WM区域基于病理的连通性变化的理解，以及对WM异常程度的量化。当这些成像测量与临床症状严重程度测量相关联时，将提供对病理及其进展的额外洞察，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异常和通过纤维束的结构连接中断有关。在获取可与HOMS拟合的数据方面的进步反过来要求使用新的自动化工具来分析此类数据，因为为张量开发的现有方法不适用于HOMS。我们建议通过以下具体目标来实现这一点：在目标1中，我们将定义HOM图像的局部和全局度量，并使用这些度量来获得基于特征的HOM图像可变形配准算法，为后续分析做准备。在目标2中，我们将利用基于体素、基于流形和基于区域的分析相结合的方法，开发和验证霍姆斯人口统计的综合框架。在目标3中，我们将利用Hom特征设计高维多变量模式分类器，以获得大脑异常的空间模式，并为每个大脑分配一个异常。在目标4中，我们将把在目标1-3中开发的方法应用于ASD患者的大型数据库和人口统计学平衡的典型发展中的志愿者，并识别患者对照差异并与患者的症状严重程度的临床分级相关联。通过对群体差异和连接中断模式的量化，有望深入了解自闭症患者中观察到的缺陷，如社交互动障碍、语言和沟通障碍以及刻板印象、限制性和重复性行为。使用文献中从未尝试过的HOMS来研究ASD，大多数工作仅限于分析根据DTI数据计算的各向异性和扩散率测量。我们期望在该项目成功完成后，我们将利用HOMS为大规模人口研究开发一种通用和全面的、数学上一致的和计算高效的处理和分析范式，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.