INTERGRATED METHODS FOR MEASURING NEUROANATOMY IN AUTISM

自闭症神经解剖学测量的综合方法

• 批准号: 6187298
• 负责人: JAMES S DUNCAN
• 金额: $ 25.35万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-06-01 至 2003-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6187298
• 关键词: autism; bioimaging /biomedical imaging; brain imaging /visualization /scanning; clinical research; computational neuroscience; computer program /software; human data; human subject; image processing; magnetic resonance imaging; mathematical model; method development; neuroanatomy; syndrome

Autism is a complex disorder of early onset, involving odd and repetitive movements, severe social disability, deficits in social cognition, and disruption of language. While the multiple signs and symptoms in autism suggest several different brain systems are likely involved in its pathobiology, it remains the fact that most efforts aimed at the analysis of neuroanatomical structure related to autism from (primarily Magnetic Resonance (MR) images have been limited to the measurement of rather gross features, such as overall brain size and cross sectional area, or measurements of the corpus callosum and cerebellar vermis, using fairly small samples. These limitations are in large part because, to date, manual and computer-assisted, semi-automated segmentation/measurement of neuroanatomy is a tedious, labor-intensive, and costly process, subject to human variability. The research proposed here is aimed at the further development of an image analysis strategy that will accurately, reproducibly, robustly and efficiently analyze neuroanatomical structure relevant to autism from 3D high resolution MR images. At the core of this effort are unique mathematical approaches to: i.) segment cortical structure using coupled differential equations to simultaneously locate the gray/white and gray/CSF surfaces; ii.) segment subcortical structure by adding shape and inter-structure spatial relationship priors to an approach that integrates boundary finding and region growing; and iii.) nonlinearly register regional neuroanatomical structure to create atlases and match them to segmented information for the purpose of labeling cortical gyri and guiding the subcortical segmentation process. A key feature of the approach is that the final labeling and measurement that is performed is done by carefully focusing on individual regions of the brain, one at a time. The accuracy and robustness of the individual algorithm components to imaging parameters, field inhomogeneities and noise will be demonstrated by validating segmentation, registration, labeling and measurement algorithm results from synthetic data created using an MR image simulator against gold standard source images. The utility of the image analysis strategy for deriving robust, accurate measures in a variety of cortical and subcortical brain regions relevant to autism will be evaluated by running the algorithm on a cohort of 30 normal control and 30 subjects having autism and/or related conditions, sampled from a large, well characterized and separately NIH-funded subject database.

自闭症是一种复杂的早发性障碍，涉及奇怪和重复的运动，严重的社会残疾，社会认知障碍和语言障碍。虽然自闭症的多个体征和症状提示其病理生物学可能涉及几个不同的大脑系统，但仍然存在的一个事实是，大多数旨在从(主要是磁共振)图像中分析与自闭症相关的神经解剖结构的努力一直局限于测量相当粗略的特征，如整体大脑大小和横截面积，或使用相当小的样本测量胼胝体和小脑蛔虫。这些局限性在很大程度上是因为，到目前为止，神经解剖学的人工和计算机辅助的半自动分割/测量是一个繁琐、劳动密集型和昂贵的过程，受人类变异性的影响。本文提出的研究旨在进一步开发一种图像分析策略，从3D高分辨率MR图像中准确、可重复性、健壮和高效地分析与自闭症相关的神经解剖结构。这项工作的核心是独特的数学方法：i.)利用耦合微分方程组对大脑皮质结构进行分割，同时定位灰色/白色和灰色/脑脊液表面；2)通过在结合边界查找和区域生长的方法之前添加形状和结构间空间关系来分割皮质下结构；以及以非线性方式记录区域神经解剖结构以创建图谱，并将其与分割的信息进行匹配，以标记皮质回并指导皮质下分割过程。该方法的一个关键特点是，最后进行的标记和测量是通过仔细地关注大脑的各个区域来完成的，一次一个。通过验证分割、配准、标记和测量算法结果，以黄金标准源图像为对照，验证使用MR图像模拟器创建的合成数据的分割、配准、标记和测量算法结果，从而证明各个算法组件对成像参数、场不均匀和噪声的准确性和稳健性。图像分析策略在与自闭症相关的各种皮质和皮质下大脑区域中得出健壮、准确的测量方法的有效性将通过在30名正常对照组和30名患有自闭症和/或相关疾病的受试者的队列上运行该算法进行评估，这些受试者来自一个大型的、特征良好的、单独由NIH资助的主题数据库。