Harmonization of Multi-Site Neuroimaging Data from Complex Study Designs

协调复杂研究设计中的多部位神经影像数据

• 批准号: 10028642
• 负责人: Russell Takeshi Shinohara
• 金额: $ 60.2万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-10 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10028642
• 关键词: Address; Adolescent; Adopted; Adoption; Advocate; Aging; Alzheimer' s Disease; Area; Atrophic; Benchmarking; Biological; Brain; Complex; Computer software; Conceptions; Controlled Study; Cross-Sectional Studies; Data; Databases; Development; Dwarfism; Federal Government; Functional Magnetic Resonance Imaging; Funding; Gene Expression; Generations; Genomics; Gold; Image; Imaging Phantoms; Institution; Intelligence; Investments; Liquid substance; Location; Longevity; Machine Learning; Magnetic Resonance Imaging; Manufacturer Name; Measurement; Measures; Methodology; Methods; Modeling; Modernization; Multicenter Studies; Multivariate Analysis; Pathology; Pattern; Performance; Phenotype; Privatization; Protocols documentation; Reproducibility; Research Design; Research Methodology; Sex Differences; Site; Statistical Methods; Structure; Techniques; Thick; Travel; United States National Institutes of Health; Work; cognitive development; combat; complex data; data harmonization; design; gray matter; healthy aging; image reconstruction; imaging modality; imaging study; improved; instrument; interest; mild cognitive impairment; neuroimaging; next generation; novel; personalized predictions; predictive modeling; stem; tool; white matter

PROJECT SUMMARY Over the past decade, the number of large multi-center neuroimaging studies has skyrocketed due to growing investments by federal governments and private entities interested in brain development, aging, and pathology. This has led to the accumulation of vast amounts of magnetic resonance imaging (MRI) data which have been acquired with varying amounts of technical harmonization. Such efforts, which have focused on protocol harmonization and comparisons with imaging phantoms, have shown great strides toward reducing inter- scanner differences in imaging features extracted for further study. Unfortunately, MRI show inter-instrument biases even in the most carefully controlled studies. Our group, among many others, has shown that these differences often dwarf biological differences of interest measured using both structural and functional MRI. To address this, the field has rapidly been developing tools for the harmonization of imaging data after acquisition. We have proposed several such tools, and our work has often focused on the adaptation of methods used in genomic studies for batch effect correction. Our most recent such work involved the ComBat method, which uses empirical Bayesian estimation to correct for site effects in both means and variances of imaging features under study. To date, these tools have been successfully applied in studies of cortical thickness, white matter microstructure, and functional connectivity. However, there are unfortunately several key limitations to the ComBat method for imaging studies that stem from its original conception for gene expression studies. ComBat was designed for the study of inter-scanner differences in cross-sectionally acquired data. While cross-sectional studies are of great interest and exceedingly common, much focus in the context of healthy brain development and aging has shifted to measuring longitudinal trajectories. In such cases, the naïve application of ComBat is flawed and methodological research is necessary for appropriate harmonization tools to be developed. Furthermore, more complex nested study design in which multiple scanners are used per institution, or a subset of subjects are imaged on multiple scanners for harmonization purposes, are increasingly common. Another key area of interest in modern neuroimaging studies is to focus on inter-region structural or functional connectivity and uses multivariate pattern analysis (MVPA) to improve our understanding of phenotypic associations as well as for personalized predictions. Unfortunately, the current state-of-the-art in image harmonization ignores correlation structure between measurements, and thus inter- scanner differences often persist. In this project, we propose a new generation of techniques that are applicable under complex study designs and harmonize appropriately for studies involving applications of MVPA. In our final aim of this proposal, we will apply the methods developed for more complex study designs and MVPA in the context of two of the largest NIH-funded multi-center consortia across the lifespan.

项目摘要 在过去的十年中，大型多中心神经影像学研究的数量激增， 联邦政府和对大脑发育、衰老和病理学感兴趣的私人实体的投资。 这导致了大量磁共振成像（MRI）数据的积累， 获得了不同数量的技术协调。这些努力侧重于礼仪， 与成像体模的协调和比较，已经显示出在减少相互干扰方面的巨大进步。 扫描仪差异提取的成像特征可供进一步研究。不幸的是，核磁共振显示仪器间 即使在最严格控制的研究中也存在偏差。我们的团队，以及其他许多人，已经表明，这些 差异常常使使用结构和功能MRI测量的感兴趣的生物学差异相形见绌。 为了解决这一问题，该领域已经迅速开发了用于协调成像数据的工具， 采集我们已经提出了几个这样的工具，我们的工作往往集中在适应 用于基因组研究的批量效应校正方法。我们最近的此类工作涉及ComBat 方法，该方法使用经验贝叶斯估计来校正场地效应的均值和方差， 研究中的影像学特征。到目前为止，这些工具已成功地应用于大脑皮层的研究， 厚度、白色物质微观结构和功能连接性。然而，不幸的是， ComBat方法用于成像研究的主要局限性源于其最初的基因概念 表达研究。 ComBat被设计用于研究横截面采集数据中的扫描仪间差异。 虽然横截面研究是非常感兴趣和非常普遍的，但在 健康的大脑发育和衰老已经转向测量纵向轨迹。在这种情况下， 单纯地应用ComBat是有缺陷的，需要进行方法学研究，以便进行适当的协调 工具有待开发。此外，使用多台扫描仪的更复杂的嵌套研究设计 每个机构或受试者子集在多台扫描仪上成像，以实现协调， 越来越常见。现代神经影像学研究的另一个关键领域是关注区域间 结构或功能的连接性，并使用多变量模式分析（MVPA），以提高我们的 对表型关联的理解以及个性化预测。可惜现在的 图像协调中的现有技术忽略了测量之间的相关性结构， 扫描仪的差异往往持续存在。 在这个项目中，我们提出了新一代的技术，适用于复杂的研究 设计并适当协调涉及MVPA应用的研究。我们的最终目标是 建议中，我们将应用为更复杂的研究设计和MVPA开发的方法， NIH资助的两个最大的多中心联盟。