Novel computational methods for higher order diffusion MRI in autism

自闭症高阶扩散 MRI 的新计算方法

基本信息

批准号：
8150423
负责人：
Ragini Verma
金额：
$ 66.56万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
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/8150423
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

项目摘要

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）的诊断目前基于儿童的行为和发育史。随着先进形式的扩散加权磁共振成像 (DW-MRI) 的发展，预计成像将阐明白质 (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。我们的目标是开发计算方法来处理和分析已安装高阶扩散模型（HOM）的高角分辨率扩散成像数据。与扩散张量成像 (DTI) 中的张量模型相比，HOM 可以更丰富地理解复杂 WM 区域中基于病理的连接变化，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时，将为病理学及其进展提供额外的见解，从而使该项目非常具有临床意义。了解如此复杂的 WM 区域有望有助于 ASD 的研究，自闭症谱系障碍的缺陷可能与 WM 异常和纤维束结构连接中断有关。适合 HOM 的数据采集方面的进步反过来又需要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 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 的局部和全局测量，并使用这些测量来获得基于特征的 HOM 图像可变形配准算法，为后续分析做好准备。在目标 2 中，我们将结合基于体素、基于流形和基于区域的分析，开发并验证 HOM 种群统计的综合框架。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，我们将在目标 1 - 3 中开发的方法应用于 ASD 患者和人口统计平衡的典型发展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联。对群体差异和连通性中断模式的量化预计将有助于深入了解自闭症患者所观察到的缺陷，例如社交互动受损、语言和沟通受损以及刻板、受限和重复行为。使用 HOM 来研究 ASD 是以前文献中从未尝试过的，大部分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。我们预计，在该项目成功完成后，我们将为使用 HOM 的大规模人群研究开发出一种通用且全面、数学上一致且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。公共健康相关性：该项目旨在开发计算方法，用于分析配备高阶模型的扩散 MRI 数据，这些模型独特地表征了受自闭症谱系障碍 (ASD) 影响的复杂白质区域。这些经过充分验证的方法将应用于 ASD 人群的分析，以量化大脑连接和白质完整性的异常情况。与临床诊断措施的相关性将为自闭症中观察到的缺陷提供基于图像的联系，例如社交互动、语言和沟通受损以及受限和重复行为，从而有助于预后和研究疾病进展。