USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
Epilepsy Res. 2023 Sep;195:107201. doi: 10.1016/j.eplepsyres.2023.107201. Epub 2023 Aug 2.
Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.
临床前 MRI 研究已被用于发现预测创伤后癫痫 (PTE) 的生物标志物。然而，由于数据有限且单一，这些单站点研究往往缺乏统计学效力。因此，多站点研究，如抗癫痫发生治疗的癫痫生物信息学研究 (EpiBioS4Rx)，旨在创建大型、异质数据集，从而得出更具统计学意义的结果。EpiBioS4Rx 在国际上从多个站点收集临床前数据，包括美国、芬兰和澳大利亚。然而，在这样做的过程中，需要进行强大的标准化和协调处理，以获得具有统计学意义和可推广的结果。本工作描述了用于协调 EpiBioS4Rx 采集的多站点、多模态临床前成像数据的工具和程序。利用了四个主要的协调处理过程，包括文件格式协调、命名约定协调、图像坐标系协调和弥散张量成像 (DTI) 指标协调。通过使用 Python 工具和 bash 脚本，跨所有站点协调文件格式、文件名和图像坐标系。为了协调 DTI 指标，对图像中的每个体素估计值以生成表示整个图像的直方图。然后，利用定量成像工具包 (QIT) 模块将模式缩放为一个值，并描绘随后协调的直方图。文件格式、命名约定、坐标系和 DTI 指标的标准化进行了定性评估。为每个站点的个体啮齿动物生成了 DTI 指标的直方图。对于站点间分析，计算了个体扫描的平均值，以创建代表每个站点的直方图。为了确保可以在个体动物的水平上运行分析，分别对假手术和 TBI 队列进行了分析，这描绘了相同的协调因素。结果表明，这些过程定性地标准化了数据的文件格式、命名约定、坐标系和 DTI 指标。这有助于在研究中共享数据，并传播有助于其他研究人员增强研究统计效力并更紧密地分析数据的工具。
