La Parker L, Bell Tiffany K, Craig William, Doan Quynh, Beauchamp Miriam H, Zemek Roger, Yeates Keith Owen, Harris Ashley D
Department of Radiology, University of Calgary, Calgary, AB, Canada.
Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
Front Psychol. 2023 Apr 20;14:1130188. doi: 10.3389/fpsyg.2023.1130188. eCollection 2023.
The effects caused by differences in data acquisition can be substantial and may impact data interpretation in multi-site/scanner studies using magnetic resonance spectroscopy (MRS). Given the increasing use of multi-site studies, a better understanding of how to account for different scanners is needed. Using data from a concussion population, we compare ComBat harmonization with different statistical methods in controlling for site, vendor, and scanner as covariates to determine how to best control for multi-site data.
The data for the current study included 545 MRS datasets to measure tNAA, tCr, tCho, Glx, and mI to study the pediatric concussion acquired across five sites, six scanners, and two different MRI vendors. For each metabolite, the site and vendor were accounted for in seven different models of general linear models (GLM) or mixed-effects models while testing for group differences between the concussion and orthopedic injury. Models 1 and 2 controlled for vendor and site. Models 3 and 4 controlled for scanner. Models 5 and 6 controlled for site applied to data harmonized by vendor using ComBat. Model 7 controlled for scanner applied to data harmonized by scanner using ComBat. All the models controlled for age and sex as covariates.
Models 1 and 2, controlling for site and vendor, showed no significant group effect in any metabolites, but the vendor and site were significant factors in the GLM. Model 3, which included a scanner, showed a significant group effect for tNAA and tCho, and the scanner was a significant factor. Model 4, controlling for the scanner, did not show a group effect in the mixed model. The data harmonized by the vendor using ComBat (Models 5 and 6) had no significant group effect in both the GLM and mixed models. Lastly, the data harmonized by the scanner using ComBat (Model 7) showed no significant group effect. The individual site data suggest there were no group differences.
Using data from a large clinical concussion population, different analysis techniques to control for site, vendor, and scanner in MRS data yielded different results. The findings support the use of ComBat harmonization for clinical MRS data, as it removes the site and vendor effects.
数据采集差异所造成的影响可能很大,并且可能会影响使用磁共振波谱(MRS)的多中心/多台扫描仪研究中的数据解读。鉴于多中心研究的使用日益增加,需要更好地理解如何处理不同的扫描仪。利用来自脑震荡人群的数据,我们将ComBat归一化方法与不同的统计方法进行比较,以将站点、供应商和扫描仪作为协变量进行控制,从而确定如何最好地控制多中心数据。
本研究的数据包括545个MRS数据集,用于测量N-乙酰天门冬氨酸(tNAA)、肌酸(tCr)、胆碱(tCho)、谷氨酰胺和谷氨酸复合物(Glx)以及肌醇(mI),以研究在五个站点、六台扫描仪和两个不同MRI供应商处获得的小儿脑震荡情况。对于每种代谢物,在一般线性模型(GLM)或混合效应模型的七种不同模型中考虑了站点和供应商,同时测试脑震荡组与骨科损伤组之间的差异。模型1和模型2控制供应商和站点。模型3和模型4控制扫描仪。模型5和模型6控制应用于通过ComBat按供应商进行归一化处理的数据的站点。模型7控制应用于通过ComBat按扫描仪进行归一化处理的数据的扫描仪。所有模型均将年龄和性别作为协变量进行控制。
控制站点和供应商的模型1和模型2在任何代谢物中均未显示出显著的组效应,但供应商和站点在GLM中是显著因素。包含扫描仪的模型3在tNAA和tCho方面显示出显著的组效应,并且扫描仪是一个显著因素。在混合模型中,控制扫描仪的模型4未显示出组效应。通过供应商使用ComBat进行归一化处理的数据(模型5和模型6)在GLM和混合模型中均未显示出显著的组效应。最后,通过扫描仪使用ComBat进行归一化处理的数据(模型七)未显示出显著的组效应。各个站点的数据表明不存在组间差异。
利用来自大量临床脑震荡人群的数据,在MRS数据中控制站点、供应商和扫描仪的不同分析技术产生了不同结果.研究结果支持将ComBat归一化法用于临床MRS数据,因为它消除了站点和供应商效应。