Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion
Tóm tắt
The data quality of simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) can be strongly affected by motion. Recent work has shown that the quality of fMRI data can be improved by using a Moiré-Phase-Tracker (MPT)-camera system for prospective motion correction. The use of the head position acquired by the MPT-camera-system has also been shown to correct motion-induced voltages, ballistocardiogram (BCG) and gradient artefact residuals separately. In this work we show the concept of an integrated framework based on the general linear model to provide a unified motion informed model of in-MRI artefacts. This model (retrospective EEG motion educated gradient artefact suppression, REEG-MEGAS) is capable of correcting voltage-induced, BCG and gradient artefact residuals of EEG data acquired simultaneously with prospective motion corrected fMRI. In our results, we have verified that applying REEG-MEGAS correction to EEG data acquired during subject motion improves the data quality in terms of motion induced voltages and also GA residuals in comparison to standard Artefact Averaging Subtraction and Retrospective EEG Motion Artefact Suppression. Besides that, we provide preliminary evidence that although adding more regressors to a model may slightly affect the power of physiological signals such as the alpha-rhythm, its application may increase the overall quality of a dataset, particularly when strongly affected by motion. This was verified by analysing the EEG traces, power spectra density and the topographic distribution from two healthy subjects. We also have verified that the correction by REEG-MEGAS improves higher frequency artefact correction by decreasing the power of Gradient Artefact harmonics. Our method showed promising results for decreasing the power of artefacts for frequencies up to 250 Hz. Additionally, REEG-MEGAS is a hybrid framework that can be implemented for real time prospective motion correction of EEG and fMRI data. Among other EEG-fMRI applications, the approach described here may benefit applications such as EEG-fMRI neurofeedback and brain computer interface, which strongly rely on the prospective acquisition and application of motion artefact removal.
Tài liệu tham khảo
Abreu R, Leal A, Figueiredo P (2018) EEG-informed fMRI: a review of data analysis methods. Front Hum Neurosci 12:29. https://doi.org/10.3389/fnhum.2018.00029
Allen PJ, Josephs O, Turner R (2000) A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12(2):230–239. https://doi.org/10.1006/nimg.2000.0599
Bianciardi M, Fukunaga M, van Gelderen P, Horovitz SG, de Zwart JA, Shmueli K, Duyn JH (2009) Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. Magn Reson Imaging 27(8):1019–1029. https://doi.org/10.1016/j.mri.2009.02.004
Bonmassar G, Purdon PL, Jääskeläinen IP, Chiappa K, Solo V, Brown EN, Belliveau JW (2002) Motion and ballistocardiogram artifact removal for interleaved recording of EEG and EPs during MRI. Neuroimage 16(4):1127–1141. https://doi.org/10.1006/nimg.2002.1125
Centeno M, Tierney TM, Perani S, Shamshiri EA, St Pier K, Wilkinson C et al (2017) Combined electroencephalography–functional magnetic resonance imaging and electrical source imaging improves localization of pediatric focal epilepsy. Ann Neurol 82(2):278–287. https://doi.org/10.1002/ana.25003
Chowdhury ME, Mullinger KJ, Glover P, Bowtell R (2014) Reference layer artefact subtraction (RLAS): a novel method of minimizing EEG artefacts during simultaneous fMRI. Neuroimage 84:307–319. https://doi.org/10.1016/j.neuroimage.2013.08.039
Daniel AJ, Smith JA, Spencer GS, Jorge J, Bowtell R, Mullinger KJ (2019) Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI. Hum Brain Mapp 40(2):578–596. https://doi.org/10.1002/hbm.24396
Freyer F, Becker R, Anami K, Curio G, Villringer A, Ritter P (2009) Ultrahigh-frequency EEG during fMRI: pushing the limits of imaging-artifact correction. Neuroimage 48(1):94–108. https://doi.org/10.1016/j.neuroimage.2009.06.022
Goldman RI, Stern JM, Engel J Jr, Cohen MS (2000) Acquiring simultaneous EEG and functional MRI. Clin Neurophysiol 111(11):1974–1980. https://doi.org/10.1016/s1388-2457(00)00456-9
Jorge J, Grouiller F, Gruetter R, van der Zwaag W, Figueiredo P (2015) Towards high-quality simultaneous EEG-fMRI at 7 T: detection and reduction of EEG artifacts due to head motion. Neuroimage 120:143–153. https://doi.org/10.1016/j.neuroimage.2015.07.020
LeVan P, Maclaren J, Herbst M, Sostheim R, Zaitsev M, Hennig J (2013) Ballistocardiographic artifact removal from simultaneous EEG-fMRI using an optical motion-tracking system. Neuroimage 75:1–11. https://doi.org/10.1016/j.neuroimage.2013.02.039
LeVan P, Zhang S, Knowles B, Zaitsev M, Hennig J (2016) EEG-fMRI gradient artifact correction by multiple motion-related templates. IEEE Trans Biomed Eng 63(12):2647–2653. https://doi.org/10.1109/tbme.2016.2593726
Luo Q, Huang X, Glover GH (2014) Ballistocardiogram artifact removal with a reference layer and standard EEG cap. J Neurosci Methods 233:137–149. https://doi.org/10.1016/j.jneumeth.2014.06.021
Maclaren J, Armstrong BS, Barrows RT, Danishad KA, Ernst T, Foster CL et al (2012) Measurement and correction of microscopic head motion during magnetic resonance imaging of the brain. PLoS ONE 7(11):e48088. https://doi.org/10.1371/journal.pone.0048088
Maclaren J, Herbst M, Speck O, Zaitsev M (2013) Prospective motion correction in brain imaging: a review. Magn Reson Med 69(3):621–636. https://doi.org/10.1002/mrm.24314
Mandelkow H, Halder P, Boesiger P, Brandeis D (2006) Synchronization facilitates removal of MRI artefacts from concurrent EEG recordings and increases usable bandwidth. Neuroimage 32(3):1120–1126. https://doi.org/10.1016/j.neuroimage.2006.04.231
Masterton RA, Abbott DF, Fleming SW, Jackson GD (2007) Measurement and reduction of motion and ballistocardiogram artefacts from simultaneous EEG and fMRI recordings. Neuroimage 37(1):202–211. https://doi.org/10.1016/j.neuroimage.2007.02.060
Maziero D, Velasco TR, Hunt N, Payne E, Lemieux L, Salmon CEG, Carmichael DW (2016) Towards motion insensitive EEG-fMRI: correcting motion-induced voltages and gradient artefact instability in EEG using an fMRI prospective motion correction (PMC) system. Neuroimage 138:13–27. https://doi.org/10.1016/j.neuroimage.2016.05.003
Maziero D, Rondinoni C, Marins T, Stenger VA, Ernst T (2020) Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion. Neuroimage 212:116594. https://doi.org/10.1016/j.neuroimage.2020.116594
Mullinger KJ, Havenhand J, Bowtell R (2013) Identifying the sources of the pulse artefact in EEG recordings made inside an MR scanner. Neuroimage 71:75–83. https://doi.org/10.1016/j.neuroimage.2012.12.070
Niazy RK, Beckmann CF, Iannetti GD, Brady JM, Smith SM (2005) Removal of FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage 28(3):720–737. https://doi.org/10.1016/j.neuroimage.2005.06.067
Singh A, Zahneisen B, Keating B, Herbst M, Chang L, Zaitsev M, Ernst T (2015) Optical tracking with two markers for robust prospective motion correction for brain imaging. Magn Reson Mater Phys Biol Med 28(6):523–534. https://doi.org/10.1007/s10334-015-0493-4
Todd N, Josephs O, Callaghan MF, Lutti A, Weiskopf N (2015) Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking. Neuroimage 113:1–12. https://doi.org/10.1016/j.neuroimage.2015.03.013
Yan WX, Mullinger KJ, Brookes MJ, Bowtell R (2009) Understanding gradient artefacts in simultaneous EEG/fMRI. Neuroimage 46(2):459–471. https://doi.org/10.1016/j.neuroimage.2009.01.029
Yan WX, Mullinger KJ, Geirsdottir GB, Bowtell R (2010) Physical modeling of pulse artefact sources in simultaneous EEG/fMRI. Hum Brain Mapp 31(4):604–620. https://doi.org/10.1002/hbm.20891
Zaitsev M, Akin B, LeVan P, Knowles BR (2017) Prospective motion correction in functional MRI. Neuroimage 154:33–42. https://doi.org/10.1016/j.neuroimage.2016.11.014