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Askari P, Cardoso da Fonseca N, Pruitt T, Maldjian JA, Alick-Lindstrom S, Davenport EM. Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices. Brain Sci 2024; 14:173. [PMID: 38391747 PMCID: PMC10887328 DOI: 10.3390/brainsci14020173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
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.
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Affiliation(s)
- Pegah Askari
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, The University of Texas at Arlington, Arlington, TX 76010, USA
| | - Natascha Cardoso da Fonseca
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tyrell Pruitt
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Joseph A Maldjian
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Sasha Alick-Lindstrom
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Neurology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Elizabeth M Davenport
- Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Biomedical Engineering Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Abstract
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.
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Abstract
This scientific commentary refers to ‘Magnetoencephalography for epileptic focus localization in a series of 1000 cases’, by Rampp et al. (doi:10.1093/brain/awz231).
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Affiliation(s)
- Richard C Burgess
- Director, Magnetoencephalography Laboratory, Cleveland Clinic Epilepsy Center, Cleveland, Ohio, USA
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Migliorelli C, Alonso JF, Romero S, Mañanas MA, Nowak R, Russi A. Influence of metallic artifact filtering on MEG signals for source localization during interictal epileptiform activity. J Neural Eng 2016; 13:026029. [DOI: 10.1088/1741-2560/13/2/026029] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Migliorelli C, Alonso JF, Romero S, Mañanas MA, Nowak R, Russi A. Automatic BSS-based filtering of metallic interference in MEG recordings: definition and validation using simulated signals. J Neural Eng 2015; 12:046001. [DOI: 10.1088/1741-2560/12/4/046001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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