INTERGRATED METHODS FOR MEASURING NEUROANATOMY IN AUTISM

自闭症是一种早期发病的复杂障碍，包括奇怪和重复的动作，严重的社交障碍，社会认知缺陷和语言障碍。虽然自闭症的多种体征和症状表明，几种不同的大脑系统可能参与了其病理生物学，但事实仍然是，大多数旨在分析与自闭症相关的神经解剖结构的努力（主要是磁共振（MR）图像）都局限于相当粗略的特征的测量，例如大脑的总体大小和横截面积，或测量胼胝体和小脑蚓部，使用相当小的样本。这些限制在很大程度上是因为，到目前为止，人工和计算机辅助的半自动化神经解剖学分割/测量是一个繁琐、劳动密集型和昂贵的过程，受制于人类的可变性。本文提出的研究旨在进一步发展一种图像分析策略，该策略将准确，可重复，稳健和有效地从3D高分辨率MR图像中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.)使用耦合微分方程分割皮层结构，同时定位灰色/白色和灰色/CSF表面；Ii .)在结合边界发现和区域生长的方法之前，通过增加形状和结构间空间关系来分割皮层下结构；iii.)对区域神经解剖结构进行非线性注册，生成地图集，并将其与分割的信息进行匹配，用于标记皮层脑回，指导皮层下的分割过程。该方法的一个关键特点是，最终的标记和测量是通过仔细地关注大脑的单个区域来完成的，一次一个。单个算法组件对成像参数、场不均匀性和噪声的准确性和鲁棒性将通过验证分割、配准、标记和测量算法结果来证明，这些算法结果来自使用MR图像模拟器对金标准源图像创建的合成数据。通过在30名正常对照和30名患有自闭症和/或相关疾病的受试者中运行该算法，从一个大型的、特征明确的、单独由nih资助的受试者数据库中取样，来评估图像分析策略在各种与自闭症相关的大脑皮层和皮层下区域中获得稳健、准确测量结果的效用。

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.