The comparative performance of DBS artefact rejection methods for MEG recordings

Ahmet Levent Kandemir,Vladimir Litvak,Esther Florin

doi:10.1016/j.neuroimage.2020.117057

Abstract

Deep brain stimulation (DBS) can be a very efficient treatment option for movement disorders and psychiatric diseases. To better understand DBS mechanisms, brain activity can be recorded using magnetoencephalography (MEG) with the stimulator turned on. However, DBS produces large artefacts compromising MEG data quality due to both the applied current and the movement of wires connecting the stimulator with the electrode. To filter out these artefacts, several methods to suppress the DBS artefact have been proposed in the literature. A comparative study evaluating each method’s effectiveness, however, is missing so far.In this study, we evaluate the performance of four artefact rejection methods on MEG data from phantom recordings with DBS acquired with an Elekta Neuromag and a CTF system: (i) Hampel-filter, (ii) spectral signal space projection (S3P), (iii) independent component analysis with mutual information (ICA-MI), and (iv) temporal signal space separation (tSSS). In the sensor space, the largest increase in signal-to-noise (SNR) ratio was achieved by ICA-MI, while the best correspondence in terms of source activations was obtained by tSSS. LCMV beamforming alone was not sufficient to suppress the DBS-induced artefacts.

Highlights

Deep Brain Stimulation (DBS) is an invasive treatment option for neurological and psychiatric disorders (Wichmann and DeLong, 2006), which can improve the patient’s quality of life substantially
We evaluate the performance of four artefact rejection methods on MEG data from phantom recordings with DBS acquired with an Elekta Neuromag and a CTF system: (i) Hampel-filter, (ii) spectral signal space projection (S3P), (iii) independent component analysis with mutual information (ICA-MI), and (iv) temporal signal space separation
We performed a MEG phantom study under realistic DBS stimulation conditions to identify artefacts introduced by DBS and to evaluate the performance of different DBS artefact rejection methods previously employed in the literature

Summary

Introduction

Deep Brain Stimulation (DBS) is an invasive treatment option for neurological and psychiatric disorders (Wichmann and DeLong, 2006), which can improve the patient’s quality of life substantially. Employing MEG in combination with DBS requires suitable cleaning algorithms to remove DBS artefacts. There are two types of artefact with different characteristics that need to be removed when recording patients with a DBS system implanted in the MEG. The first type of artefact produced by DBS systems is directly due to the stimulation with electric pulses. Electrical stimulation used for clinically-effective DBS consists of narrow pulses (60 μs - 200 μs) delivered at frequencies larger than 70 Hz to targeted brain areas (Lio et al, 2018). Actual MEG systems sample at a few kHz at most, which - in combination with the aforementioned DBS characteristics - results in missed or under-sampled DBS pulses (Lio et al, 2018). Sampled DBS pulses in turn give rise to peaks all over the frequency spectrum (Jech et al, 2006; Sun et al, 2014), which can not be taken care of by the hardware low-pass filter

Methods

Results

Conclusion