A mega-analysis framework for delineating autism neurosubtypes

描述自闭症神经亚型的大型分析框架

• 批准号: 10681965
• 负责人: Adriana Di Martino
• 金额: $ 78.96万
• 依托单位: CHILD MIND INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10681965
• 关键词: 18 year old; Address; Age; Algorithms; Attention; Bayesian Method; Bayesian Modeling; Biological; Brain; Brain imaging; Caring; Characteristics; Child; Childhood; Clinical; Clinical Data; Clinical Protocols; Collection; Communities; Data; Data Aggregation; Data Collection; Databases; Diagnosis; Diagnostic Specificity; Dimensions; Functional Magnetic Resonance Imaging; Goals; Grain; Heterogeneity; Hybrids; Image; Individual; Knowledge; Magnetic Resonance Imaging; Measurement; Measures; Methodology; Methods; Modeling; Nature; Neurosciences; Pattern; Phenotype; Reproducibility; Research; Resources; Rest; Sample Size; Sampling; Site; Source; Structure; Subgroup; Symptoms; Syndrome; System; Systems Analysis; Testing; Time; Variant; Work; autism spectrum disorder; biological heterogeneity; biological research; boys; brain abnormalities; brain behavior; clinical heterogeneity; clinical phenotype; clinically relevant; combat; connectome; data exchange; data harmonization; data resource; girls; improved; indexing; individuals with autism spectrum disorder; large datasets; mosaic; neural; neuroimaging; neurophysiology; novel; phenotypic data; precision medicine; quality assurance; repetitive behavior; response; sex; social communication; social structure; theories

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.

抽象的 该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组的亚型。该提案涉及 通过关注系统分析的中间水平来研究 ASD 的临床和生物学异质性 神经科学，遵循自闭症谱系障碍与大脑功能连接组异常相关的线索。 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亚组， 基于内在功能连接（iFC）的测量。主要目标是：1）产生一个大的、 通过对 iFC 和临床表型的综合评估，回顾性地协调数据资源； 2） 识别基于 iFC 的神经亚型并建立它们与临床相关表型的关联； 3）测试 神经亚型的可复制性及其与独立样本中表型测量的关联。到 为此，我们建议利用来自自闭症大脑的现有大规模 ASD 神经影像数据收集 影像数据交换、国家自闭症研究数据库和健康大脑网络。样本： 年龄/性别：男女生，6-18岁。诊断：自闭症谱系障碍 (ASD) 和神经典型 (NT) 个体。尺寸：迄今为止， 上述神经影像资源共包含N=3528； ASD n=2136，NT n=1392。方法：以下 系统而广泛的数据组织、严格的质量保证以及我们将进行的预处理 使用最先进的方法进行定量数据协调。 CovBat，最先进的版本 贝叶斯框架 ComBat 将用于协调 MRI 数据。它是由 Co-I Shinohara 开发的 控制基于 MRI 的测量中扫描仪间的差异以及受试者产生的错误 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，由初步临床工作提供。为了进一步加强我们的临床数据协调工作， 神经影像数据将与 Co-Is Lord 和 Bishop (ASD n=1513）。连接病特征：为了了解自闭症谱系障碍（ASD）连接病的整个范围，将有多个特征 首次同时评估。神经亚型：以我们与 Co-I Yeo 的可行性工作为基础， 将通过新颖的贝叶斯潜在因素模型来识别同质神经 ASD 亚群。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、诊断特异性 神经亚型和交叉亚型方法的有效性。已识别的神经亚型及其方法 从年底开始，将定期共享协调以及为大型分析生成的所有数据 第二年。研究结果将解决关键的知识差距，新颖的资源将为科学界提供帮助 进行独立调查的机会，改变 ASD 的生物学研究和知识。