Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data.
Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data.
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DOI：
10.1111/ejn.16120
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发表时间：
2023-09
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中科院分区：
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结合多点研究的磁共振成像(MRI)数据是构建更大数据集的一种流行方法，以极大地提高神经科学研究的可靠性和重复性。然而，扫描仪/站点的变异性是一个重大的混淆，使结果的解释复杂化，因此有效且完全消除扫描仪/站点的变异性是实现汇集多站点数据集的全部优势所必需的。独立分量分析(ICA)和基于一般线性模型(GLM)的调和方法是用于消除扫描仪/站点影响的两种主要方法。不幸的是，当感兴趣的信号和扫描仪/部位效应相关的变量相关时，基于ICA和基于GLM的协调方法都面临着完全消除部位效应的挑战，这可能会发生在神经科学研究中。在这项研究中，我们提出了一种有效和强大的协调策略，该策略实现了基于独立分量分析的对偶投影(DP)理论，以更彻底地消除扫描器/站点的影响。这种方法可以在不丢失感兴趣的信号的情况下，将与位置变量相关的信号效果从识别的位置效果中分离出来以进行去除。模拟和活体结构MRI数据集，包括来自自闭症脑成像数据交换II的数据集和来自脑科学战略研究计划的旅行对象数据集，被用来测试基于DP的ICA协调方法的性能。结果表明，与基于GLM的ICA协调方法和传统的ICA协调方法相比，基于DP的ICA协调方法在消除场地效应和提高对感兴趣信号的检测灵敏度方面具有更好的性能。
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.
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