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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

Title
Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices
Author
Askari, Pegah 1   VIAFID ORCID Logo  ; Natascha Cardoso da Fonseca 2   VIAFID ORCID Logo  ; Pruitt, Tyrell 2   VIAFID ORCID Logo  ; Maldjian, Joseph A 3   VIAFID ORCID Logo  ; Alick-Lindstrom, Sasha 4   VIAFID ORCID Logo  ; Davenport, Elizabeth M 3   VIAFID ORCID Logo 

 Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; [email protected] (P.A.); [email protected] (N.C.d.F.); [email protected] (T.P.); [email protected] (J.A.M.); [email protected] (S.A.-L.); 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 
 Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; [email protected] (P.A.); [email protected] (N.C.d.F.); [email protected] (T.P.); [email protected] (J.A.M.); [email protected] (S.A.-L.); MEG Center of Excellence, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA 
 Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; [email protected] (P.A.); [email protected] (N.C.d.F.); [email protected] (T.P.); [email protected] (J.A.M.); [email protected] (S.A.-L.); 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 
 Radiology Department, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; [email protected] (P.A.); [email protected] (N.C.d.F.); [email protected] (T.P.); [email protected] (J.A.M.); [email protected] (S.A.-L.); 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 
First page
173
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
20763425
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2930535733
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.