Feasibility of magnetoencephalography recording in an epilepsy patient with implanted responsive cortical stimulation device

Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices

Drug-resistant epilepsy (DRE) is often treated with surgery or neuromodulation. Specifically, responsive neurostimulation (RNS) is a widely used therapy that is programmed to detect abnormal brain activity and intervene with tailored stimulation. Despite the success of RNS, some patients require further interventions. However, having an RNS device in situ is a hindrance to the performance of neuroimaging techniques. Magnetoencephalography (MEG), a non-invasive neurophysiologic and functional imaging technique, aids epilepsy assessment and surgery planning. MEG performed post-RNS is complicated by signal distortions. This study proposes an independent component analysis (ICA)-based approach to enhance MEG signal quality, facilitating improved assessment for epilepsy patients with implanted RNS devices. Three epilepsy patients, two with RNS implants and one without, underwent MEG scans. Preprocessing included temporal signal space separation (tSSS) and an automated ICA-based approach with MNE-Python. Power spectral density (PSD) and signal-to-noise ratio (SNR) were analyzed, and MEG dipole analysis was conducted using single equivalent current dipole (SECD) modeling. The ICA-based noise removal preprocessing method substantially improved the signal-to-noise ratio (SNR) for MEG data from epilepsy patients with implanted RNS devices. Qualitative assessment confirmed enhanced signal readability and improved MEG dipole analysis. ICA-based processing markedly enhanced MEG data quality in RNS patients, emphasizing its clinical relevance.

Recognizing and Correcting MEG Artifacts

Noise sources in magnetoencephalography (MEG) include: (1) interference from outside the shielded room, (2) other people and devices inside the shielded room, (3) physiologic or nonphysiologic sources inside the patient, (4) activity from inside the head that is unrelated to the signal of interest, (5) intrinsic sensor and recording electronics noise, and (6) artifacts from other apparatus used during recording such as evoked response stimulators. There are other factors which corrupt MEG recording and interpretation and should also be considered "artifacts": (7) inadequate positioning of the patient, (8) changes in the head position during the recording, (9) incorrect co-registration, (10) spurious signals introduced during postprocessing, and (11) errors in fitting. The major means whereby magnetic interference can be reduced or eliminated are by recording inside a magnetically shielded room, using gradiometers that measure differential magnetic fields, real-time active compensation using reference sensors, and postprocessing with advanced spatio-temporal filters. Many of the artifacts that plague MEG are also seen in EEG, so an experienced electroencephalographer will have the advantage of being able to transfer his knowledge about artifacts to MEG. However, many of the procedures and software used during acquisition and analysis may themselves contribute artifact or distortion that must be recognized or prevented. In summary, MEG artifacts are not worse than EEG artifacts, but many are different, and-as with EEG-must be attended to.

Epileptic focus localization based on resting state interictal MEG recordings is feasible irrespective of the presence or absence of spikes

OBJECTIVE

To investigate whether epileptogenic focus localization is possible based on resting state connectivity analysis of magnetoencephalographic (MEG) data.

METHODS

A multivariate autoregressive (MVAR) model was constructed using the sensor space data and was projected to the source space using lead field and inverse matrix. The generalized partial directed coherence was estimated from the MVAR model in the source space. The dipole with the maximum information inflow was hypothesized to be within the epileptogenic focus.

RESULTS

Applying the focus localization algorithm (FLA) to the interictal MEG recordings from five patients with neocortical epilepsy, who underwent presurgical evaluation for the identification of epileptogenic focus, we were able to correctly localize the focus, on the basis of maximum interictal information inflow in the presence or absence of interictal epileptic spikes in the data, with three out of five patients undergoing resective surgery and being seizure free since.

CONCLUSION

Our preliminary results suggest that accurate localization of the epileptogenic focus may be accomplished using noninvasive spontaneous "resting-state" recordings of relatively brief duration and without the need to capture definite interictal and/or ictal abnormalities.

SIGNIFICANCE

Epileptogenic focus localization is possible through connectivity analysis of resting state MEG data irrespective of the presence/absence of spikes.