Removal of pulse artefact from EEG data recorded in MR environment at 3T. Setting of ICA parameters for marking artefactual components: application to resting-state data.
Adult; Artifacts; Brain/physiology; Electroencephalography; Female; Humans; Magnetic Resonance Imaging; Male; Reproducibility of Results; Rest; Signal Processing, Computer-Assisted; Time Factors
Abstract :
[en] Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content.
Disciplines :
Human health sciences: Multidisciplinary, general & others
Removal of pulse artefact from EEG data recorded in MR environment at 3T. Setting of ICA parameters for marking artefactual components: application to resting-state data.
Publication date :
2014
Journal title :
PLoS ONE
eISSN :
1932-6203
Publisher :
Public Library of Science, United States - California
Grech R, Cassar T, Muscat J, Camilleri K, Fabri S, et al. (2008) Review on solving the inverse problem in EEG source analysis. Journal of Neuroengineering and Rehabilitation 5: 25.
Pascual-Marqui R, Esslen M, Kochi K, Lehmann D (2002) Functional imaging with low-resolution brain electromagnetic tomography (LORETA): A review. Methods and Findings in Experimental and Clinical Pharmacology 24: 91-95.
Logothetis NK (2008). What we can do and what we cannot do with fMRI. Nature 453: 869-878.
Zijlmans M, Huiskamp G, Hersevoort M, Seppenwoolde J, van Huffelen AC, et al. (2007) EEG-fMRI in the preoperative work-up for epilepsy surgery. Brain 130: 2343-2353.
Laufs H, Holt JL, Elfont R, Krams M, Paul JS, et al. (2006) Where the BOLD signal goes when alpha EEG leaves. Neuroimage 31: 1408-1418.
Laufs H (2008) Endogenous brain oscillations and related networks detected by surface EEG-combined fMRI. Human Brain Mapping 29: 762-769.
Rosa MJ, Kilner J, Blankenburg F, Josephs O, Penny W (2010) Estimating the transfer function from neuronal activity to BOLD using simultaneous EEGf-MRI. Neuroimage 49: 1496-1509.
Babiloni F, Cincotti F, Babiloni C, Carducci F, Mattia D, et al. (2005) Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function. Neuroimage 24: 118-131.
Lei X, Ostwald D, Hu J, Qiu C, Porcaro C, et al. (2011) Multimodal functional network connectivity: An EEG-fMRI fusion in network space. PloS One 6: e24642.
Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M (2007) Electrophysiological signatures of resting-state networks in the human brain. Proceedings of the National Academy of Sciences 104: 13170-13175.
Varela F, Lachaux J, Rodriguez E, Martinerie J (2001) The brainweb: Phase synchronization and large-scale integration. Nature Reviews Neuroscience 2: 229-239.
Engel AK, Fries P, Singer W (2001) Dynamic predictions: Oscillations and synchrony in top-down processing. Nature Reviews Neuroscience 2: 704-716.
Raichle ME (2006) The brain's dark energy. Science 314: 1249-1250.
Greicius MD, Krasnow B, Reiss AL, Menon V (2003) Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences 100: 253-258.
Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine 34: 537-541.
Hampson M, Peterson BS, Skudlarski P, Gatenby JC, Gore JC (2002) Detection of functional connectivity using temporal correlations in MR images. Human Brain Mapping 15: 247-262.
Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME (2006) Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences 103: 10046-10051.
Mazoyer B, Zago L, Mellet E, Bricogne S, Etard O, et al. (2001) Cortical networks for working memory and executive functions sustain the conscious resting-state in man. Brain Research Bulletin 54: 287-298.
Allen PJ, Josephs O, Turner R (2000) A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12: 230-239.
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.
Neuner I, Arrubla J, Felder J, Shah NJ (2013) Simultaneous EEG-fMRI acquisition at low, high and ultra-high magnetic fields up to 9.4 T: Perspectives and challenges. Neuroimage, In press.
Neuner I, Warbrick T, Arrubla J, Felder J, Celik A, et al. (2013) EEG acquisition in ultra-high static magnetic fields up to 9.4 T. Neuroimage 68: 214-220.
Niazy R, Beckmann C, Iannetti G, Brady J, Smith S (2005) Removal of FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage 28: 720-737.
Comon P. (1992) Independent component analysis. Higher-Order Statistics: 29-38.
Jung T, Makeig S, Humphries C, Lee T, Mckeown MJ, et al. (2000) Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37: 163-178.
Srivastava G, Crottaz-Herbette S, Lau K, Glover G, Menon V (2005) ICA-based procedures for removing ballistocardiogram artifacts from EEG data acquired in the MRI scanner. Neuroimage 24: 50-60.
Briselli E, Garreffa G, Bianchi L, Bianciardi M, Macaluso E, et al. (2006). An independent component analysis-based approach on ballistocardiogram artifact removing. Magnetic Resonance Imaging 24: 393-400.
Grouiller F, Vercueil L, Krainik A, Segebarth C, Kahane P, et al. (2007). A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI. Neuroimage 38: 124-137.
Debener S, Ullsperger M, Siegel M, Fiehler K, Von Cramon DY, et al. (2005) Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring. The Journal of Neuroscience 25: 11730-11737.
Debener S, Strobel A, Sorger B, Peters J, Kranczioch C, et al. (2007) Improved quality of auditory event-related potentials recorded simultaneously with 3-T fMRI: Removal of the ballistocardiogram artefact. Neuroimage 34: 587-597.
Vanderperren K, De Vos M, Ramautar JR, Novitskiy N, Mennes M, et al. (2010) Removal of BCG artifacts from EEG recordings inside the MR scanner: A comparison of methodological and validation-related aspects. Neuroimage 50: 920-934.
Britz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 52: 1162-1170.
Nakamura W, Anami K, Mori T, Saitoh O, Cichocki A, et al. (2006) Removal of ballistocardiogram artifacts from simultaneously recorded EEG and fMRI data using independent component analysis. Biomedical Engineering, IEEE Transactions on 53: 1294-1308.
Huiskamp G (2006) Reduction of the ballistocardiogram artifact in simultaneous EEG-fMRI using ICA. Conference Proceedings of IEEE Engineering in Medicine and Biology Society 4: 3691-3694.
Debener S, Mullinger KJ, Niazy RK, Bowtell RW (2008) Properties of the ballistocardiogram artefact as revealed by EEG recordings at 1.5, 3 and 7 T static magnetic field strength. International Journal of Psychophysiology 67: 189-199.
Warbrick T, Arrubla J, Boers F, Neuner I, Shah NJ (2014) Attention to detail: Why considering task demands is essential for single-trial analysis of BOLD correlates of the visual P1 and N1. Journal of cognitive neuroscience 26: 529-542.
Warbrick T, Reske M, Shah NJ (2013) Do EEG paradigms work in fMRI? Varying task demands in the visual oddball paradigm: Implications for task design and results interpretation. Neuroimage 77: 177-185.
Delorme A, Makeig S (2004) EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 134: 9-21.
Lee T, Girolami M, Sejnowski TJ (1999) Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation 11: 417-441.
Vanderperren K, Ramautar J, Novitski N, De Vos M, Mennes M, et al. (2007) Ballistocardiogram artifacts in simultaneous EEG-fMRI acquisitions. International Journal of Bioelectromagnetism, Special Issue on Methods for the Estimation of Brain Activity 9: 146-150.