A mega-analysis framework for delineating autism neurosubtypes

摘要 这项申请旨在为自闭症谱系障碍的精准医学方法奠定基础 (ASD)通过识别可重复的临床相关的基于脑连接体的亚型。该提案涉及 ASD的临床和生物学异质性，侧重于系统分析的中间水平 神经科学，以下线索，ASD是与异常的大脑功能连接体。 因此，我们的目标是识别神经亚型（NS），即，具有同质非典型特征的个体的亚组， 基于内在功能连接性（iFC）的测量。主要目标是：1）产生一个大的， 回顾性协调数据资源，全面评估iFC和临床表型; 2） 鉴定基于iFC的神经亚型，并建立其与临床相关表型的关联; 3）检测 神经亚型的可复制性及其与独立样本中表型测量的相关性。到 为此，我们建议利用现有的大规模ASD神经成像数据收集自闭症大脑 成像数据交换，国家自闭症研究数据库和健康大脑网络。样品： 年龄/性别：男孩和女孩，6-18岁。诊断：ASD和神经型（NT）个体。尺寸：迄今为止， 上述神经影像学资源共包含N=3528; ASD n=2136，NT n=1392。方法：以下 系统和广泛的数据组织，严格的质量保证和预处理，我们将继续下去 使用最先进的方法统一定量数据。CovBat，最先进的 贝叶斯框架ComBat将用于协调MRI数据。由Co-I Shinohara开发 控制基于MRI的测量中的扫描仪间差异，以及受试者引起的错误 测量协方差的差异。项目反应理论的最新进展将用于协调 表型数据，通过初步临床工作获得。为了进一步加强我们的临床数据协调工作， 神经影像学数据将与Co-Is Lord和Bishop（ASD）的仅表型数据集汇总 n=1513）。结缔组织病特征：为了涵盖ASD结缔组织病的整个范围，将 第一次同时评估。神经亚型：基于我们与Co-I Yeo的可行性研究， 同质神经ASD亚组将通过新的贝叶斯潜在因素建模来识别。它允许 对于不同程度属于亚型的受试者，识别混合型、分类型和维度型， 神经亚型其他关键问题包括所研究的MRI特征的相关性， 神经亚型和交叉亚型方法的有效性。所鉴定的神经亚型和用于 从2010年年底开始，将定期分享协调统一的数据，沿着分享为大型分析生成的所有数据。 第二年。调查结果将解决关键的知识差距，新的资源将为科学界提供 有机会进行独立调查，改变ASD的生物学研究和知识。

ABSTRACT This application proposes to lay the groundwork for precision medicine approaches to autism spectrum disorder (ASD) by identifying reproducible clinically relevant brain-connectome-based subtypes. The proposal addresses the clinical and biological heterogeneity of ASD by focusing on the intermediate level of analysis of systems neuroscience, following clues that ASD is associated with abnormalities in the brain functional connectome. Thus, we aim to identify neurosubtypes (NS), i.e., subgroups of individuals with homogeneous atypical features, based on measures of intrinsic functional connectivity (iFC). Primary aims are to: 1) generate a large, retrospectively harmonized data resource with comprehensive assessment of iFC and clinical phenotypes; 2) identify iFC-based neurosubtypes and establish their associations with clinically relevant phenotypes; 3) test the replicability of neurosubtypes and their associations with phenotypic measures in an independent sample . To this end, we propose to leverage existing large-scale ASD neuroimaging data collections from the Autism Brain Imaging Data Exchange, the National Database for Autism Research, and the Healthy Brain Network. Sample: Age/Sex: Boys and girls, 6-18 years old. Diagnosis: ASD and neurotypical (NT) individuals. Size: to date, the above neuroimaging resources contain a total N=3528; ASD n=2136, NT n=1392. Methods: Following systematic and extensive data organization, rigorous quality assurance, and preprocessing we will proceed with quantitative data harmonization using state-of-the-art methods. CovBat, the most advanced version of the Bayesian framework, ComBat, will be applied to harmonize MRI data. It has been developed by Co-I Shinohara to control for inter-scanner differences in MRI-based measures, as well as for errors arising from subject differences in measurement covariance. Recent advances in item response theory will be used to harmonize phenotypic data, informed by preliminary clinical work. To further enhance our clinical data harmonization efforts, the neuroimaging data will be aggregated with phenotypic-only collections from Co-Is Lord and Bishop (ASD n=1513). Connectopathy features: To scope the entire spectrum of ASD connectopathy, multiple features will be assessed simultaneously for the first time. Neurosubtypes: Building on our feasibility work with Co-I Yeo, homogeneous neural ASD subgroups will be identified through novel Bayesian latent factor modeling. It allows for subjects to belong to subtypes in varying degrees, identifying hybrid, categorical and dimensional, neurosubtypes. Other key questions include the relevance of MRI features studied, the diagnostic specificity of neurosubtypes, and cross-subtyping method validity. The neurosubtypes identified and methods for harmonization, along with all data generated for mega-analyses will be regularly shared, starting at the end of year two. Findings will address critical knowledge gaps and the novel resource will offer the scientific community opportunities to pursue independent inquiries transforming biological research and knowledge of ASD.