Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi‐site MRI data
Hao, Y., Xu, H., Xia, M., Yan, C., Zhang, Y., Zhou, D., Kärkkäinen, T., Nickerson, L. D., Li, H., & Cong, F. (2023). Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi‐site MRI data. European Journal of Neuroscience, 58(6), 3466-3487. https://doi.org/10.1111/ejn.16120
Julkaistu sarjassa
European Journal of NeuroscienceTekijät
Päivämäärä
2023Oppiaine
TietotekniikkaSecure Communications Engineering and Signal ProcessingHuman and Machine based Intelligence in LearningTekniikkaMathematical Information TechnologySecure Communications Engineering and Signal ProcessingHuman and Machine based Intelligence in LearningEngineeringTekijänoikeudet
© 2023 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
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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Julkaisija
Wiley-BlackwellISSN Hae Julkaisufoorumista
0953-816XAsiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/184650007
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This work was supported by STI 2030—Major Projects 2022ZD0211500, Science and Technology Planning Project of Liaoning Province (numbers 2022JH2/10700002 and 2021JH1/10400049), Beijing United Imaging Research Institute of Intelligent Imaging Foundation [CRIBJZD202102], National Natural Science Foundation of China [grant number 81601484], National Foundation in China [grant number JCKY 2019110B009] and National Institutes of Health [NIA RF1 AG078304].![showless](/themes/JYX2//images/showless.png)
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