School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China.
Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland.
Eur J Neurosci. 2023 Sep;58(6):3466-3487. doi: 10.1111/ejn.16120. Epub 2023 Aug 30.
Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.
将多站点研究的磁共振成像 (MRI) 数据结合起来是构建更大数据集的一种常用方法，这极大地提高了神经科学研究的可靠性和可重复性。然而，扫描仪/站点的变异性是一个显著的混杂因素，使得结果的解释变得复杂，因此需要有效地、完全地去除扫描仪/站点的变异性，才能充分发挥多站点数据集的优势。基于独立成分分析 (ICA) 和广义线性模型 (GLM) 的调和方法是消除扫描仪/站点效应的两种主要方法。不幸的是，当感兴趣的信号和与扫描仪/站点效应相关的变量相关时，基于 ICA 和基于 GLM 的调和方法都存在挑战，无法完全去除站点效应，这种情况可能会出现在神经科学研究中。在这项研究中，我们提出了一种有效的、强大的调和策略，该策略基于 ICA 实现双投影 (DP) 理论，以更完全地去除扫描仪/站点效应。这种方法可以将与站点变量相关的信号效应与已识别的站点效应分离出来进行去除，而不会丢失感兴趣的信号。我们使用模拟数据和活体结构 MRI 数据集（包括来自 Autism Brain Imaging Data Exchange II 的数据集和来自脑科学战略研究计划的旅行受试者数据集）来测试基于 DP 的 ICA 调和方法的性能。结果表明，与基于 GLM 和传统 ICA 的调和方法相比，基于 DP 的 ICA 调和方法在去除站点效应和提高检测感兴趣信号的灵敏度方面具有更好的性能。
