EEG acquisition in ultra-high static magnetic fields up to 9.4T

The simultaneous acquisition of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data has gained momentum in recent years due to the synergistic effects of the two modalities with regard to temporal and spatial resolution. Currently, only EEG-data recorded in fields of up to 7 T have been reported. We investigated the feasibility of recording EEG inside a 9.4 T static magnetic field, specifically to determine whether meaningful EEG information could be recovered from the data after removal of the cardiac-related artefact. EEG-data were recorded reliably and reproducibly at 9.4 T and the cardiac-related artefact increased in amplitude with increasing B0, as expected. Furthermore, we were able to correct for the cardiac-related artefact and identify auditory event related responses at 9.4 T in 75% of subjects using independent component analysis (ICA). Also by means of ICA we detected event related spectral perturbations (ERSP) in subjects at 9.4 T in response to opening/closing the eyes comparable with the response at 0 T. Overall our results suggest that it is possible to record meaningful EEG data at ultra-high magnetic fields. The simultaneous EEG-fMRI approach at ultra-high-fields opens up the horizon for investigating brain dynamics at a superb spatial resolution and a temporal resolution in the millisecond domain.

[1]  Dirk Ostwald,et al.  An information theoretic approach to EEG–fMRI integration of visually evoked responses , 2010, NeuroImage.

[2]  M. Czisch,et al.  Development of the brain's default mode network from wakefulness to slow wave sleep. , 2011, Cerebral cortex.

[3]  Maximilian Reiser,et al.  Age effects on the P300 potential and the corresponding fMRI BOLD-signal , 2012, NeuroImage.

[4]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[5]  M. Walker,et al.  Epileptic Networks in Focal Cortical Dysplasia Revealed Using Electroencephalography–Functional Magnetic Resonance Imaging , 2011, Annals of neurology.

[6]  Rainer Goebel,et al.  Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI. , 2010, Magnetic resonance imaging.

[7]  S. Debener,et al.  Properties of the ballistocardiogram artefact as revealed by EEG recordings at 1.5, 3 and 7 T static magnetic field strength. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[8]  Essa Yacoub,et al.  High-field fMRI unveils orientation columns in humans , 2008, Proceedings of the National Academy of Sciences.

[9]  T F Budinger,et al.  Cardiovascular alterations in Macaca monkeys exposed to stationary magnetic fields: experimental observations and theoretical analysis. , 1983, Bioelectromagnetics.

[10]  Hoon-Chul Kang,et al.  Neuroimaging in Identifying Focal Cortical Dysplasia and Prognostic Factors in Pediatric and Adolescent Epilepsy Surgery. Imaging Focal Cortical Dysplasia in Refractory Epilepsy , 2022 .

[11]  Nikos K. Logothetis,et al.  Intracortical recordings and fMRI: An attempt to study operational modules and networks simultaneously , 2012, NeuroImage.

[12]  Stefan Debener,et al.  Integration of EEG and fMRI. Editorial. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[13]  M. Fox,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[14]  Louis Lemieux,et al.  Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction , 1998, NeuroImage.

[15]  N. Logothetis What we can do and what we cannot do with fMRI , 2008, Nature.

[16]  Christoph Mulert,et al.  Integration of EEG and fMRI , 2009 .

[17]  Robert Turner,et al.  A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI , 2000, NeuroImage.

[18]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[19]  Satoru Miyauchi,et al.  Connectivity pattern changes in default-mode network with deep non-REM and REM sleep , 2011, Neuroscience Research.

[20]  Jeff H. Duyn,et al.  EEG-fMRI Methods for the Study of Brain Networks during Sleep , 2012, Front. Neur..

[21]  Robert Oostenveld,et al.  The five percent electrode system for high-resolution EEG and ERP measurements , 2001, Clinical Neurophysiology.

[22]  Sabine Van Huffel,et al.  Removal of BCG artifacts from EEG recordings inside the MR scanner: A comparison of methodological and validation-related aspects , 2010, NeuroImage.

[23]  Maarten De Vos,et al.  The benefits of simultaneous EEG–fMRI for EEG analysis , 2011, Clinical Neurophysiology.

[24]  S Makeig,et al.  Blind separation of auditory event-related brain responses into independent components. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[25]  M. Greicius,et al.  Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity , 2009, Brain Structure and Function.

[26]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[27]  Simon B Eickhoff,et al.  Electrophysiology meets fMRI: Neural correlates of the startle reflex assessed by simultaneous EMG–fMRI data acquisition , 2010, Human brain mapping.

[28]  A. Engel,et al.  Single-trial EEG–fMRI reveals the dynamics of cognitive function , 2006, Trends in Cognitive Sciences.

[29]  Sabine Van Huffel,et al.  Multimodal Imaging of Human Brain Activity: Rational, Biophysical Aspects and Modes of Integration , 2009, Comput. Intell. Neurosci..

[30]  S. Makeig,et al.  Mining event-related brain dynamics , 2004, Trends in Cognitive Sciences.

[31]  Richard Bowtell,et al.  Exploring the feasibility of simultaneous electroencephalography/functional magnetic resonance imaging at 7 T. , 2008, Magnetic resonance imaging.

[32]  Rami K. Niazy,et al.  Removal of FMRI environment artifacts from EEG data using optimal basis sets , 2005, NeuroImage.