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: 10335117
• 负责人: Ragini Verma
• 金额: $ 66.91万
• 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/10335117
• 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; 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.

弥散磁共振成像(DMRI)对脑白质和连接性的定性优于其他成像技术 核磁共振成像，并经常被纳入自闭症等具有非典型脑连接障碍的研究中 谱系障碍(ASD)。该领域可以从综合研究中受益匪浅， 在基于连接性的障碍中表现潜在的异质性。这使得这一点具有挑战性 DMRI对采集参数非常敏感，需要复杂的统计协调工具 对扫描仪相关变化的复杂影响。这也需要强有力的自动化质量控制 (QC)数据统一之前的协议。因此，在这项提案中，我们将开发工具来促进 不同研究的dMRI数据。在目标1中，我们将开发并验证基于深度学习的QC自动化工具 对于将识别不同数据伪像的dMRI数据(由扫描仪、线圈、扫描等多个来源引起 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……里面 目标2，我们将开发一套工具来协调dMRI措施，以消除获取差异。这个 我们建议的工具的有效性将通过协调~1500个数据集(年龄为6-32岁)进行演示 来自11项自闭症研究。这些大型协调数据集产生了对主题的描述的需要 样本和诊断标记，以利用较大的协调图像的异质性 样本。为了满足这一新需求，我们将开发额外的连接分析工具，并对其进行调整 到ASD创造魅力(通过多点dMRI研究孤独症的连接性异质性) 协调)套件，包括ASD的可概括生物标记物以及维度连接 坐标系。在目标3中，我们将使用连接表型来描述每个受试者，将 基于这种连接性表型的集成ASD样本，为每个集群定义一个分类器；并创建 基于连接性的ASD集成生物标记，称为魅力标记，结合了这些特定于集群的标记 分类器决定。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通坐标系，以量化每个坐标系的差异性 受试者来自协调的健康对照。我们将阐明这些魅力坐标与ASD的联系 通过将核心ASD症状与协调/组合中的魅力坐标相关联来构建 样本。这将使ASD社区能够将信息性连接维度与每个 学科，促进学科纵向评估，为精准医学铺平道路。这样的一群人- 如果没有数据整合，就不可能对ASD进行以对象为基础的表征。 此外，神经成像社区将拥有新的dMRI协调和连接分析工具 能够整合研究，以便对现有数据进行更全面的连接调查。会的 为其他影响心理健康的连通性相关障碍的研究铺平道路。