Evidence map›Paper›PMID 35053857›Full record

ArticleBrain sciences2022

Validating EEG, MEG and Combined MEG and EEG Beamforming for an Estimation of the Epileptogenic Zone in Focal Cortical Dysplasia.

Frank Neugebauer, Marios Antonakakis, Kanjana Unnwongse, Yaroslav Parpaley, Jörg Wellmer, Stefan Rampp, Carsten H Wolters

Open access · goldAbstract read
In one paragraph

Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Review
  3. Electromagnetic source imaging predicts surgical outcome in children with focal cortical dysplasia.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2023
    Article
  4. Article
  5. CutFEM forward modeling for EEG source analysis.Frontiers in human neuroscience · 2023
    Article
  6. A Novel Epilepsy Detection Method Based on Feature Extraction by Deep Autoencoder on EEG Signal.International journal of environmental research and public health · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 3 countries.

Frank NeugebauerInstitute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany.
Marios AntonakakisInstitute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany.
Kanjana UnnwongseRuhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University, 44892 Bochum, Germany.
Yaroslav ParpaleyDepartment of Neurosurgery, University Hospital Knappschaftskrankenhaus, Ruhr-University, 44892 Bochum, Germany.
Jörg WellmerRuhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University, 44892 Bochum, Germany.
Stefan RamppDepartment of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany.ORCID 0000-0002-4826-1520
Carsten H WoltersInstitute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany.ORCID 0000-0001-6233-424X
Universitätsklinikum Knappschaftskrankenhaus Bochum · DEUniversity of Münster · DETampere University · FIUniversitätsklinikum Erlangen · DE

Funding

Deutsche Forschungsgemeinschaft RA2062/1-1Deutsche Forschungsgemeinschaft WO1425/7-1Federal Ministry of Health ZMI1-2521FSB006
6 · The paper itself

Abstract

MEG and EEG source analysis is frequently used for the presurgical evaluation of pharmacoresistant epilepsy patients. The source localization of the epileptogenic zone depends, among other aspects, on the selected inverse and forward approaches and their respective parameter choices. In this validation study, we compare the standard dipole scanning method with two beamformer approaches for the inverse problem, and we investigate the influence of the covariance estimation method and the strength of regularization on the localization performance for EEG, MEG, and combined EEG and MEG. For forward modelling, we investigate the difference between calibrated six-compartment and standard three-compartment head modelling. In a retrospective study, two patients with focal epilepsy due to focal cortical dysplasia type IIb and seizure freedom following lesionectomy or radiofrequency-guided thermocoagulation (RFTC) used the distance of the localization of interictal epileptic spikes to the resection cavity resp. RFTC lesion as reference for good localization. We found that beamformer localization can be sensitive to the choice of the regularization parameter, which has to be individually optimized. Estimation of the covariance matrix with averaged spike data yielded more robust results across the modalities. MEG was the dominant modality and provided a good localization in one case, while it was EEG for the other. When combining the modalities, the good results of the dominant modality were mostly not spoiled by the weaker modality. For appropriate regularization parameter choices, the beamformer localized better than the standard dipole scan. Compared to the importance of an appropriate regularization, the sensitivity of the localization to the head modelling was smaller, due to similar skull conductivity modelling and the fixed source space without orientation constraint.

Indexed as

beamformerEEGepilepsyMEGregularizationsource analysisvalidation

Identifiers

PMID35053857
PMCPMC8796031
OpenAlexW4224919166

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.