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; 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 该申请建议为自闭症谱系障碍的精确医学方法奠定基础 （ASD）通过鉴定可重现的临床相关性脑连通子类的亚型。该提案解决 ASD的临床和生物异质性通过着重于系统分析的中间水平 神经科学遵循ASD与大脑功能连接组中异常相关的线索。 因此，我们旨在鉴定神经蛋白术（NS），即具有同质异型特征的个体的亚组， 基于内在功能连接性（IFC）的度量。主要目的是：1）产生一个大型， 回顾性地协调数据资源，并对IFC和临床表型进行全面评估； 2） 识别基于IFC的神经材料，并与临床相关的表型建立关联； 3）测试 在独立样本中，神经材料及其与表型度量的关联的可复制性。到 这一目的，我们建议利用自闭症大脑的现有大规模ASD神经影像学数据收集 成像数据交换，自闭症研究国家数据库和健康的大脑网络。样本： 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 上面的神经影像资源包含n = 3528； asd n = 2136，nt n = 1392。方法：以下 系统和广泛的数据组织，严格的质量保证以及预处理，我们将继续进行 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，战斗将用于协调MRI数据。它是由Co-i Shinohara开发的 控制基于MRI的措施以及受试者引起的错误的扫描仪之间的差异 测量协方差的差异。项目响应理论的最新进展将用于协调 表型数据，由初步临床工作告知。为了进一步增强我们的临床数据协调工作， 神经影像学数据将通过Co-Is Lord和Bishop（ASD n = 1513）。连接疗法特征：为了范围范围aSD Connectopathy，将有多个功能 第一次同时评估。 Neurosubtypes：在我们与Co-i Yeo的可行性合作的基础上建立 均匀的神经ASD亚组将通过新型的贝叶斯潜在因子建模来识别。它允许 为了使受试者在不同程度上属于亚型，确定杂种，分类和维度， 神经材料。其他关键问题包括研究的MRI功能的相关性， Neurosubtypes和跨填充方法的有效性。鉴定的神经材料和方法 统一的统一以及为大型分析生成的所有数据将定期共享，从 第二年。调查结果将解决关键的知识差距，新颖的资源将为科学界提供 追求独立询问的机会，改变了生物学研究和ASD的知识。