Detecting visual evoked responses in simultaneous EEG-fMRI using Reference Layer Artefact Subtraction (RLAS)
Glyn S. Spencer1, Muhammad E. H. Chowdhury1,2, Karen J. Mullinger1,3, and Richard Bowtell1

1Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom, 2Electrical Engineering, Qatar University, Doha, Qatar, 3Birmingham University Imaging Centre, School of Psychology, University of Birmingham, Birmingham, United Kingdom

Synopsis

Simultaneous EEG-fMRI is limited by large artefacts in EEG recordings resulting from time-varying field gradients, cardiac-related motion and head movement within the magnetic field. Reference layer artefact subtraction (RLAS) reduces artefacts at source by subtraction of artefact voltages recorded from a reference layer on which the EEG leads and electrodes are replicated. Since RLAS does not require prior knowledge of the timing of artefact occurrences, it is an ideal method for correcting pulse and movement artefacts. Here, we apply RLAS in EEG-fMRI experiments for the first time, particularly focusing upon recovery of single-trial, low-frequency, visual-evoked responses from artefact-corrupted data.

Purpose of Work

To show that reference layer artefact subtraction (RLAS) has the potential to increase the utility of simultaneous EEG-fMRI methods for investigating brain function.

Background

Artefacts present in EEG data acquired concurrently with fMRI can make the recovery of neuronal signals in low-frequency bands (delta [0-4Hz] and theta [4-8Hz]) particularly problematic1. It is therefore important to improve artefact-removal techniques to extend the frequency range of EEG signals that are recoverable in EEG-fMRI studies. The RLAS method, which uses a reference layer carrying electrodes and leads that pick up similar artefacts to those appearing in scalp recordings (Figure 1), was previously shown to effectively attenuate all EEG artefacts; including the movement (MA) and pulse artefacts (PA) which dominate the low-frequency bands2. However, safety considerations prevented the application of RF during proof-of-concept studies. Subsequent tests showed that the RLAS set-up induces no adverse RF-heating effects. Here we explore the benefits of RLAS in a full simultaneous EEG-fMRI experiment, focusing upon detection of low-frequency, driven neural responses.

Methods

Custom made Ag/AgCl ring-electrode pairs were set up with one electrode attached to the subject’s scalp and the second connected to a conducting, agar reference layer (Fig. 1). The scalp and reference layer electrodes were clipped together, but separated by a 60µm-thick polyethylene insulating layer which is shaped to fit each subject’s head. Eight electrode pairs were positioned approximately at locations Fpz, T7/8, Tp7/8, O1/2 and Oz in the 10-20 system. A reference electrode pair was placed near Cz. A ground electrode was attached to the scalp near Pz and shorted to the reference layer using conductive gel. Electrode pairs were connected to star-quad cables and fed out around the edge of the reference layer (Fig. 1A).

Recordings were made on 3 healthy-subjects inside a 3T MR scanner. A BrainAmp MRplus system (5kHz sampling-rate, 0.016-250Hz passband) recorded EEG data during an fMRI acquisition (EPI: TR/TE=2000/40ms, 20 slices, 270 volumes, 3mm resolution, 80x80 matrix). Data were collected during nine 30s periods of stimulation with a 2Hz flashing (one complete reversal), full-field, radial checkerboard interleaved with 30s rest periods (fixation cross). VCG recordings were acquired to aid PA correction3.

Analysis

fMRI data were analysed using standard pre-processing methods. A first level general linear model using a boxcar stimulus model was used to identify areas of BOLD activation.

EEG data were high-pass filtered (cut-off frequency, 0.02Hz) to remove baseline drift. The reference layer voltages were re-referenced to the reference electrode connected to the reference layer before subtraction from the corresponding scalp voltages. Average artefact subtraction (AAS) was used to further attenuate the gradient artefact (GA)4 and PA3,5. AASGA and AAS­PA sliding-window average templates consisted of 61 and 21 intervals, respectively. AAS methods were also applied just to scalp voltages, akin to standard EEG, for comparison.

The root-mean-square (RMS) voltages over time, before and after correction, were calculated for each lead. Artefact attenuation achieved using different methods was then calculated using:

$$-20\log_{10}{\frac{\mathrm{RMS_{corrected}}}{\mathrm{RMS_{uncorrected}}}}$$

Corrected datasets were band-pass filtered (1-40Hz) and re-referenced to an average of all channels. Using the known timings of the checkerboard reversals, 1080 single-trial visually-evoked potentials were obtained and visualised in a stack plot. Magnitude spectra were averaged over stimulation blocks and the relative amplitude of the data recovered using different correction methods compared.

Results and Discussion

Figure 2 shows undistorted BOLD activation due to the visual stimulus. This data suggests that the presence of the additional electrodes and cabling has not had a significant effect upon the MRI data quality (also confirmed in B1- and B0-mapping experiments).

Figure 3 shows EEG artefact attenuations averaged over all non-saturated channels. These reflect the improvement in quality achieved by RLAS-AASGA-AASPA compared with AASGA-AASPA. Saturation occurred on some channels due to the large wire-loop sizes. After minimising the wire-loop sizes, similar benefits were realised as shown in Fig. 4. RLAS provides attenuation of MA due to gross movements (Fig. 4, time=3-5s) as well as small artefacts probably generated by cable vibrations or small involuntary head movements6 undetected in fMRI realignment (Fig. 4, inset). Consequently RLAS-AASGA-AASPA affords a more accurate identification of amplitude and latency of single-trial evoked potentials, notably the P100, than AAS alone (Fig. 5A&B). Moreover, in the frequency domain (Fig. 5E) RLAS-AASGA-AASPA is effective at recovering low-frequency neuronal activity typically obscured by MA and PA when only applying AAS.

This work shows the potential of RLAS to expand the utility of EEG-fMRI by reducing the likelihood of spurious correlations between EEG and fMRI data1, particularly in the low-frequency domain. Work is now required to increase the number of electrodes and the robustness of the system.

Acknowledgements

We thank EasyCap for help in making the electrodes. This work was funded by EPSRC Project Grant (EP/J006823/1), EPSRC Impact Acceleration Award, University of Nottingham Hermes Fellowship (GSS), University of Nottingham Anne McLaren Fellowship (KJM).

References

1. Jansen, M., et al., Motion-related artefacts in EEG predict neuronally plausible patterns of activation in fMRI data. Neuroimage, 2012. 59(1): p. 261-270.

2. Chowdhury, M.E., et al., Reference layer artefact subtraction (RLAS): a novel method of minimizing EEG artefacts during simultaneous fMRI. NeuroImage, 2013. 84: p. 307-19.

3. Mullinger, K.J., P.S. Morgan, and R.W. Bowtell, Improved artifact correction for combined electroencephalography/functional MRI by means of synchronization and use of vectorcardiogram recordings. J Magn Reson Imaging, 2008. 27(3): p. 607-16.

4. Allen, P.J., O. Josephs, and R. Turner, A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI. NeuroImage, 2000. 12(2): p. 230-239.

5. Allen, P.J., et al., Identification of EEG events in the MR scanner: The problem of pulse artifact and a method for its subtraction. Neuroimage, 1998. 8(3): p. 229-239.

6. Maclaren, J., et al., Measurement and correction of microscopic head motion during magnetic resonance imaging of the brain. PLoS One, 2012. 7(11): p. e48088.

Figures

Schematic representation of the reference layer experimental setup showing connection of electrode pairs with star-quad cable (A); stacking of electrode pairs connected to scalp and reference layers (B); and EEG traces recorded during oscillatory movement from the scalp (C) and reference layer (D). From Chowdhury et al.2

BOLD activation map (P<0.05 corrected) of an individual representative subject, showing a clear response in the visual cortex to the stimulus. Cross hairs at peak of activity.

Table of attenuation measures for the various combinations of correction methods. Mean and standard deviation of attenuation calculated across a total of 14 channels from the three subjects.10 channels were excluded due to issues with cable configurations and amplifier saturation.

Time traces comparing the results of applying AASGA-AASPA and RLAS-AASGA-AASPA for a 5s segment of EEG data. Head translation in the z-direction 0.4mm (from fMRI realignment parameters) began at t=3s. The inset shows a zoomed version of the time-courses when no gross movement was present.

Single-trial EEG data, segmented according to checkerboard presentation and averaged across three occipital channels, corrected using AASGA-AASPA (A) and RLAS-AASGA-AASPA (B). Averages over trials shown in C&D. Spectra averaged across stimulation periods and occipital channels (E) show better recovery of harmonics of 4Hz driven signal (arrows) when RLAS is applied.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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