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: 8308691
• 负责人: Ragini Verma
• 金额: $ 72.55万
• 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 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8308691
• 关键词: Address; Advanced Development; Affect; Algorithms; Amygdaloid structure; Anisotropy; Architecture; Autistic Disorder; Behavior; Behavioral; Biological Markers; Brain; Brain region; Child; Classification; Clinical; Clinical Trials; Communication; Companions; Complex; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Development; Diagnosis; Diagnostic; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Disease Progression; Equilibrium; Face Processing; Fiber; Funding; Fusiform gyrus; Grant; Image; Individual; Language; Language Disorders; Link; Literature; Magnetic Resonance Imaging; Maps; Measures; Medial; Methods; Metric; Mind; Modeling; Participant; Pathology; Patients; Pattern; Population; Population Statistics; Population Study; Prefrontal Cortex; Process; Recording of previous events; Resolution; Severities; Social Interaction; Structure of superior temporal sulcus; Symptoms; System; Testing; Time; Tissue Model; Weight; Work; autism spectrum disorder; base; clinically significant; data acquisition; design; disorder control; gray matter; indexing; insight; interest; novel; outcome forecast; public health relevance; skills; social; social cognition; social communication; theories; tool; volunteer; white matter

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史。 随着扩散加权磁共振成像（DW-MRI）的发展，预计成像将阐明白色物质（WM）结构中的病理诱导和神经发育变化，并提供诊断和预测解剖学生物标志物。 我们的目标是发展计算方法，用于处理和分析高角分辨率扩散成像数据，已与高阶扩散模型（HOM）。 与扩散张量成像（DTI）中的张量模型相比，HOM提供了对复杂WM区域中基于病理的连接性变化的更丰富的理解，以及WM异常程度的量化。 这些成像测量与症状严重程度的临床测量相关时，将提供对病理及其进展的额外见解，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异常和通过纤维束的结构连接中断有关。 在获取可以与HOM相匹配的数据方面的进展反过来又要求用于分析这些数据的新的自动化工具，因为为张量开发的现有方法不适用于HOM。 我们建议通过以下具体目标来实现这一目标：在目标1中，我们将定义HOM的局部和全局措施，并使用这些来获得基于特征的算法，用于HOM图像的变形配准，为后续分析做准备。 在目标2中，我们将使用基于体素、基于流形和基于域的分析相结合的方法，开发和验证HOM人口统计的综合框架。 在目标3中，我们将使用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中开发的方法应用于ASD患者和人口统计学平衡的典型发育志愿者的大型数据库，并确定患者对照差异，并与患者症状严重程度的临床评级相关。 对群体差异和连接中断模式的量化有望为自闭症中观察到的缺陷提供深入了解，如社交互动受损，语言和沟通受损以及刻板，限制和重复行为。 在文献中从未尝试过使用HOM来研究ASD，大部分工作仅限于分析从DTI数据计算的各向异性和扩散率测量。 我们希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、计算上高效的处理和分析范式，用于使用HOM进行大规模人群研究，这将有助于识别和量化病理学引起的连接变化的复杂模式。 公共卫生相关性：该项目旨在开发用于分析扩散MRI数据的计算方法，该数据与高阶模型拟合，该模型独特地表征自闭症谱系障碍（ASD）中受影响的复杂白色物质区域。 这些经过充分验证的方法将应用于ASD人群的分析，以产生大脑连接和白色物质完整性异常的量化。 与临床诊断措施的相关性将提供与自闭症中观察到的缺陷的基于图像的联系，例如受损的社会互动，语言和沟通以及限制和重复行为，因此有助于预后和研究疾病进展。