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: 10188649
• 负责人: Russell Takeshi Shinohara
• 金额: $ 60.39万
• 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/10188649
• 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.

项目概要 在过去的十年中，由于神经影像学研究的不断增长，大型多中心神经影像研究的数量猛增。 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，这些数据已被 这些努力都集中在协议上，取得了不同程度的技术协调。 与成像模型的协调和比较，在减少内部 不幸的是，MRI 显示了仪器间的差异。 即使在最严格控制的研究中也存在偏见。 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。 为了解决这个问题，该领域一直在快速开发用于协调成像数据的工具 我们已经提出了几种这样的工具，我们的工作通常集中在适应。 我们最近的此类工作涉及 ComBat 基因组研究中用于批量效应校正的方法。 方法，该方法使用经验贝叶斯估计来校正均值和方差的位点效应 迄今为止，这些工具已成功应用于皮质研究。 然而，不幸的是，还有一些问题。 ComBat 成像研究方法的主要局限性源于其最初的基因概念 表达研究。 ComBat 旨在研究横截面采集数据中扫描仪间的差异。 虽然横断面研究引起了人们极大的兴趣并且非常普遍，但更多的关注点是 在这种情况下，健康的大脑发育和衰老已经转向测量纵向轨迹。 ComBat 的天真应用是有味道的，方法学研究对于适当的协调是必要的 此外，需要开发更复杂的嵌套研究设计，其中使用多个扫描仪。 每个机构或为了协调目的而在多个扫描仪上对受试者的子集进行成像， 现代神经影像学研究中另一个日益常见的关键领域是关注区域间的研究。 结构或功能连接，并使用多元模式分析（MVPA）来改进我们的 不幸的是，目前对表型关联的理解以及个性化预测。 最先进的图像协调忽略了测量之间的相关结构，因此 扫描仪差异通常持续存在。 在这个项目中，我们提出了适用于复杂研究的新一代技术 为涉及 MVPA 应用的研究进行适当的设计和协调。 提案中，我们将在以下背景下应用为更复杂的研究设计和 MVPA 开发的方法 NIH 资助的整个生命周期中最大的两个多中心联盟。