Novel computational methods for higher order diffusion MRI in autism

自闭症高阶扩散 MRI 的新计算方法

• 批准号: 8023344
• 负责人: Ragini Verma
• 金额: $ 70.43万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-28 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8023344
• 关键词: Novel; computational; methods; higher; order

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.

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育历史。 随着扩散加权磁共振成像（DW-MRI）的高级形式的发展，预计成像将阐明白质（WM）结构的病理诱导的和神经发展的变化，并提供诊断和预测性的解剖生物标志物。 我们旨在开发用于处理和分析高阶扩散模型（HOMS）的高角度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对复杂WM区域的基于病理的连通性变化提供了更丰富的了解，以及对WM异常程度的量化。 当与症状严重程度的临床指标相关时，这些成像措施将提供对病理及其进展的更多见解，从而使该项目具有临床意义。 有望了解这种复杂的WM区域有助于研究ASD，其中可能与WM异常相关的缺陷，并通过纤维区域的结构连通性破坏。 获取可以安装HOMS的数据的进步又需要使用新颖的自动化工具来分析此类数据，因为为HOMS开发的现有方法不适用。 我们建议通过以下特定目的实现这一目标：在AIM 1中，我们将定义HOMS的本地和全球措施，并使用它们来获取基于功能的算法，以形成HOM图像的可变形注册，以准备它们以进行后续分析。 在AIM 2中，我们将使用基于体素的，基于歧管的和基于区域的分析的组合来开发和验证HOMS人群统计的综合框架。 在AIM 3中，我们将使用HOM特征设计高维多元模式分类器，以获得脑异常的空间模式并为每个大脑分配异常。 在AIM 4中，我们将在AIM 1-3中开发的方法应用于ASD患者的大数据库，并在人口统计学上平衡通常会发展志愿者，并确定患者控制差异，并与患者症状严重程度的临床评级相关。 群体差异和连通性中断模式的量化有望洞悉自闭症中观察到的缺陷，例如社交互动受损，语言和沟通受损以及刻板印象，受限和重复的行为。 在文献中从未尝试过研究ASD的HOMS的使用，其中大多数工作仅限于对DTI数据计算出的各向异性和扩散性测量的分析。 我们预计，在项目成功完成后，我们已经开发了一种一般且全面的，数学上一致的，计算有效的处理和分析范式，用于使用HOMS的大型人群研究，这将有助于识别和量化病理学引起的连通性变化的复杂模式。 公共卫生相关性：该项目旨在开发用于分析具有较高模型的扩散MRI数据的计算方法，这些模型唯一地表征了自闭症谱系障碍（ASD）的复杂白质区域。 这些经过良好验证的方法将应用于对ASD种群的分析，以产生大脑连通性和白质完整性异常的量化。 与临床诊断措施的相关性将提供基于图像的联系，与自闭症中观察到的缺陷，例如社交互动受损，语言和沟通以及限制和重复行为，从而有助于预后和研究疾病进展。