MEG Detection of High Frequency Oscillations and Intracranial-EEG Validation in Pediatric Epilepsy Surgery

Abstract

Objective: To assess the feasibility of automatically detecting high frequency oscillations (HFOs) in magnetoencephalography (MEG) recordings in a group of ten paediatric epilepsy surgery patients who had undergone intracranial electroencephalography (iEEG). Methods: A beamforming source-analysis method was used to construct virtual sensors and an automatic algorithm was applied to detect HFOs (80–250 Hz). We evaluated the concordance of MEG findings with the sources of iEEG HFOs, the clinically defined seizure onset zone (SOZ), the location of resected brain structures, and with post-operative outcome. Results: In 8/9 patients there was good concordance between the sources of MEG HFOs and iEEG HFOs and the SOZ. Significantly more HFOs were detected in iEEG relative to MEG t(71) = 2.85, p <.05. There was good concordance between sources of MEG HFOs and the resected area in patients with good and poor outcome, however HFOs were also detected outside of the resected area in patients with poor outcome. Conclusion: Our findings demonstrate the feasibility of automatically detecting HFOs non-invasively in MEG recordings in paediatric patients, and confirm compatibility of results with invasive recordings. Significance: This approach provides support for the non-invasive detection of HFOs to aid surgical planning and potentially reduce the need for invasive monitoring, which is pertinent to paediatric patients.

Publication DOI: https://doi.org/10.1016/j.clinph.2021.06.005
Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences
College of Health & Life Sciences > Clinical and Systems Neuroscience
Additional Information: © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/. Funding: This study has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant agreement No. 655016.
Uncontrolled Keywords: Automatic detection,Beamforming,Epilepsy,HFOs,Kurtosis,MEG,Paediatric age,iEEG,Sensory Systems,Neurology,Clinical Neurology,Physiology (medical)
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Related URLs: https://linking ... 388245721006155 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-09
Published Online Date: 2021-06-20
Accepted Date: 2021-06-15
Authors: Foley, Elaine
Quitadamo, Lucia R. (ORCID Profile 0000-0003-1877-4672)
Richard Walsh, A.
Bill, Peter
Hillebrand, Arjan
Seri, Stefano (ORCID Profile 0000-0002-9247-8102)

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 20 June 2022.

License: Creative Commons Attribution Non-commercial No Derivatives


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