Harmonization for multisite Connectomics: parsing heterogeneity and creating markers in ASD

多站点连接组学的协调：解析 ASD 中的异质性并创建标记

• 批准号: 10092221
• 负责人: Ragini Verma
• 金额: $ 69.04万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-08 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10092221
• 关键词: Address; Affect; Age; Anisotropy; Artificial Intelligence; Attention deficit hyperactivity disorder; Behavioral Genetics; Biological; Biological Markers; Biology; Brain; Brain region; Clinical; Cognitive; Communication; Communities; Custom; Data; Data Set; Diffuse; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Effectiveness; Future; Goals; Heterogeneity; Image; Individual; Investigation; Learning; Link; Magnetic Resonance Imaging; Measurement; Measures; Mental Health; Methods; Modality; Mood Disorders; Morphologic artifacts; Motion; Patients; Phenotype; Protocols documentation; Psychoses; Quality Control; Research; Sample Size; Sampling; Scanning; Site; Source; Structure; Subgroup; Symptoms; System; Tissues; Training; autism community; autism spectrum disorder; base; data harmonization; data integration; deep learning; design; diagnostic biomarker; indexing; large datasets; neuroimaging; precision medicine; preservation; repetitive behavior; sex; social; success; tool; white matter

Diffusion MRI (dMRI) provides a superior characterization of white matter and connectivity compared to other MRI modalities, and is routinely included in studies of disorders with atypical brain connectivity like autism spectrum disorder (ASD). The field could benefit tremendously from combining studies, to have comprehensive representation of the underlying heterogeneity in connectivity-based disorders. This is rendered challenging by dMRI being very sensitive to acquisition parameters, needing sophisticated statistical harmonization tools due to the complicated effect of scanner related changes. This also calls for a robust automated quality control (QC) protocol prior to data harmonization. Thus, in this proposal, we will develop tools to facilitate integration of dMRI data across studies. In Aim 1, we will develop and validate a deep learning based tool for automating QC for dMRI data that will identify different data artifacts (caused by multiple sources like scanner, coil, scan parameters, motion etc), and the appropriate action that needs to be taken (like motion and eddy correction). In Aim 2, we will develop a suite of tools for harmonizing dMRI measures to remove acquisition differences. The effectiveness of our proposed tools will be demonstrated by harmonizing ~1500 datasets (ages 6-32 years) from 11 ASD studies. These large harmonized datasets create the need for a subject-wise characterization of the sample and for diagnostic markers that harness the imaging heterogeneity of the larger harmonized sample. To address this new need, we will develop additional connectomic analysis tools, that will be adapted to ASD to create the CHARM (Connectomic Heterogeneity in Autism Research through Multi-site dMRI harmonization) suite comprising of a generalizable biomarker of ASD, as well as a dimensional connectomic coordinate system. In Aim 3, we will characterize each subject using a connectivity phenotype, cluster the integrated ASD sample based on this connectivity-phenotype, define a classifier for each cluster; and create a connectivity-based ensemble biomarker of ASD, called the CHARM-marker, combining these cluster-specific classifier decisions. Finally, in Aim 4, we will create a subject-wise characterization of ASD by designing a multi-dimensional connectomic coordinate system using metric learning, to quantify the dissimilarity of each subject from the harmonized healthy controls. We will elucidate the link of these CHARM-coordinates to ASD constructs, by correlating core ASD symptoms with the CHARM coordinates in the harmonized/combined sample. This will enable the ASD community to associate informative connectomic dimensions with each subject, facilitating subject-wise longitudinal assessment, paving the way for precision medicine. Such a group- based and subject-wise characterization of ASD could not have been possible without data integration. Additionally, the neuroimaging community will have new dMRI harmonization and connectomic analysis tools enabling the integration of studies for a more comprehensive connectomic investigation of existing data. It will pave the way for such studies in other connectivity-related disorders that affect mental health.

弥散MRI（dMRI）提供了白色物质和连通性的上级表征，与其他MRI相比， 磁共振成像模式，并经常包括在研究与非典型的大脑连接的障碍，如自闭症 谱系障碍（ASD）。该领域可以从结合研究中受益匪浅， 在连接性障碍的基础异质性的代表性。这是具有挑战性的， dMRI对采集参数非常敏感，需要复杂的统计协调工具， 扫描仪相关变化的复杂影响。这也需要强大的自动化质量控制 (QC)数据协调之前的协议。因此，在本提案中，我们将开发工具，以促进 研究中的dMRI数据。在目标1中，我们将开发和验证基于深度学习的自动化QC工具 对于将识别不同数据伪影的dMRI数据（由扫描仪、线圈、扫描等多个源引起 参数、运动等），以及需要采取的适当动作（如运动和涡流校正）。在 目标2，我们将开发一套工具，用于协调dMRI测量，以消除采集差异。的 我们提出的工具的有效性将通过协调约1500个数据集（年龄6-32岁）来证明 11篇ASD研究这些大型协调数据集需要对以下内容进行主题表征： 样品和诊断标记物利用较大的协调的 sample.为了满足这一新的需求，我们将开发额外的连接组学分析工具， ASD创建CHARM（通过多位点dMRI进行自闭症研究的连接组异质性） 协调）套件，其包含ASD的可推广生物标志物，以及维度连接组学（dimensional connectomic）。 坐标系在目标3中，我们将使用连接表型来表征每个主题， 基于该连接性表型的集成ASD样本，为每个聚类定义分类器;并创建 ASD的基于连接性的整体生物标志物，称为CHARM标志物，结合这些簇特异性 分类器决策最后，在目标4中，我们将通过设计一个 使用度量学习的多维连接组坐标系统，以量化每个连接组坐标系统的相异性。 来自协调健康对照的受试者。我们将阐明这些CHARM坐标与ASD的联系 通过将核心ASD症状与协调/组合中的CHARM坐标相关联， sample.这将使ASD社区能够将信息连接组学维度与每个 这有助于对受试者进行纵向评估，为精准医疗铺平道路。这样一个群体- 如果没有数据集成，ASD的基于主题的表征是不可能的。 此外，神经影像学界将有新的dMRI协调和连接组学分析工具 使得能够整合研究以用于现有数据的更全面的连接组学调查。它将 为其他影响心理健康的与连接有关的疾病的研究铺平道路。