The Microstructural Basis of Abnormal Connectivity in Autism

自闭症异常连接的微观结构基础

• 批准号: 7795043
• 负责人: JANET Elizabeth LAINHART
• 金额: $ 33.64万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-01 至 2014-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7795043
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DESCRIPTION (provided by applicant): Autism spectrum disorders are now among the most prevalent medical conditions of childhood. Only a small fraction of the 486,000 individuals under 20 years of age with an autism-spectrum disorder (ASD) in the U.S are young enough to benefit from intensive early intervention. Overall prognosis for the older children with autism is not good. Despite improvements in treatment and education over the past 30 years, adult outcome even for non-mentally retarded individuals with autism has not significantly improved. The majority continue to need high levels of parental and community support throughout their lives. One reason for this huge public health problem is that the brain-basis of fundamental deficits in older children and adults with autism is not well understood. We propose collaboration between a longitudinal neuroimaging, clinical, and neuropsychological study of late neurodevelopment in autism and the National Alliance for Medical Imaging Computing (NA- MIC), one of the NIH Roadmap National Centers for Biomedical Computing. The collaboration will bring state-of-the-art brain imaging analysis tools developed by NA-MIC into autism clinical research and form a new highly interactive multidisciplinary research team. Working together, the computer scientists and clinical researchers will use critical biological questions in autism to "drive" the development of NA-MIC "tools". The critical biological questions are 1) what is the microstructural basis of abnormal brain connectivity during late neurodevelopment in autism, and 2) how is brain microstructure related to deficits, developmental trajectory, and outcome. We will use Time 1 and Time 2 high-resolution MRI and diffusion tensor imaging data that have already been collected on a single 3Tesla scanner on a cohort of 100 males with high-functioning autism and 72 typically developing males. Time 3 and 4 data are being collected over the next 5 years (MH080826). We will use novel, non-tractography-based, diffusion tensor image analysis methods developed by NA-MIC to compare, at the level of both individuals and groups, microstructural features along entire white matter tracts in language, social, and repetitive behavior neural networks. We will integrate the white matter analysis with structural image analysis of cortical and subcortical gray matter. We will determine how microstructural white matter features, gray matter morphometric features, and clinical deficits are related to each other and change over time in autism. Autism is now one of the most prevalent medical disorders of childhood. Adult outcome for even high-functioning older children and adolescents with autism is still usually poor, with a high need for lifelong clinical and community support. Understanding the brain- basis of deficits, disease course, and outcome in these individuals is critical for development of autism-specific treatments as well as secondary and tertiary preventive interventions.

描述（由申请人提供）：自闭症谱系障碍现在是儿童最普遍的医疗条件之一。在美国，486，000名20岁以下的自闭症谱系障碍（ASD）患者中只有一小部分人足够年轻，可以从强化的早期干预中受益。大一点的自闭症儿童总体预后不好。尽管在过去的30年里，治疗和教育有所改善，但即使是非智力迟钝的自闭症患者的成年结局也没有显着改善。大多数儿童在其一生中仍然需要父母和社区的大力支持。这个巨大的公共卫生问题的一个原因是，对患有自闭症的大龄儿童和成人的基本缺陷的大脑基础还没有很好的理解。我们建议在自闭症晚期神经发育的纵向神经影像学，临床和神经心理学研究与国家医学成像计算联盟（NA-MIC），NIH国家生物医学计算路线图中心之一之间进行合作。此次合作将把NA-MIC开发的最先进的脑成像分析工具引入自闭症临床研究，并形成一个新的高度互动的多学科研究团队。计算机科学家和临床研究人员将共同努力，利用自闭症中的关键生物学问题来“推动”NA-MIC“工具”的开发。关键的生物学问题是：1）自闭症晚期神经发育过程中异常大脑连接的微观结构基础是什么？2）大脑微观结构如何与缺陷、发育轨迹和结果相关。我们将使用时间1和时间2的高分辨率MRI和扩散张量成像数据，这些数据已经在一台3 Tesla扫描仪上收集，用于100名高功能自闭症男性和72名正常发育男性的队列。在未来5年内收集时间3和时间4数据（MH 080826）。我们将使用NA-MIC开发的新型非纤维束成像弥散张量图像分析方法，在个体和群体水平上比较语言、社交和重复行为神经网络中整个白色束沿着微结构特征。我们将整合白色物质分析与皮质和皮质下灰质的结构图像分析。我们将确定自闭症患者的微结构白色物质特征、灰质形态特征和临床缺陷是如何相互关联并随时间变化的。孤独症现在是儿童期最普遍的医学疾病之一。即使是高功能的大龄自闭症儿童和青少年的成人结局通常仍然很差，需要终身的临床和社区支持。了解这些个体的缺陷、病程和结果的大脑基础对于开发自闭症特异性治疗以及二级和三级预防干预至关重要。