INTERGRATED METHODS FOR MEASURING NEUROANATOMY IN AUTISM

自闭症是一种复杂的早期发病障碍，涉及奇怪和重复的动作，严重的社会残疾，社会认知缺陷和语言中断。 虽然自闭症的多种体征和症状表明几个不同的大脑系统可能参与其病理生物学，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）图像已经被限制于相当粗略的特征的测量，例如整个大脑尺寸和横截面积，或胼胝体和小脑蚓部的测量，使用相当小的样本。 这些限制在很大程度上是因为，迄今为止，人工和计算机辅助的半自动分割/测量神经解剖是一个繁琐的，劳动密集型的，昂贵的过程，受人为的变化。 本文提出的研究旨在进一步开发一种图像分析策略，该策略将准确，可重复，鲁棒性和有效地分析与自闭症相关的神经解剖结构，从3D高分辨率MR图像。在这项工作的核心是独特的数学方法：i。使用耦合的微分方程对皮质结构进行分段以同时定位灰色/白色和灰色/CSF表面; ii.）通过将形状和结构间空间关系先验添加到集成边界寻找和区域生长的方法来分割皮质下结构;以及iii.）非线性地配准区域神经解剖结构以创建图谱并将它们与分割信息匹配，以便标记皮质脑回并指导皮质下分割过程。 该方法的一个关键特征是，最终的标记和测量是通过仔细关注大脑的各个区域来完成的，一次一个。 将通过验证分割、配准、标记和测量算法结果（使用MR图像模拟器创建的合成数据与金标准源图像），证明各个算法组件对成像参数、场不均匀性和噪声的准确性和稳健性。 将通过在30名正常对照和30名患有自闭症和/或相关病症的受试者的队列上运行算法来评估图像分析策略用于在与自闭症相关的各种皮层和皮层下脑区域中获得稳健、准确测量的效用，所述受试者从大型、良好表征且单独由NIH资助的受试者数据库中取样。

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.