Evidence map›Paper›PMID 40726565›Full record

ReviewClinical neurophysiology practice2025

Novel approaches to EEG and MEG in motor neurone disease: IFCN Handbook Chapter.

Stefan Dukic, Rosanne Govaarts, Arjan Hillebrand, Marianne de Visser, Margitta Seeck, Roisin McMackin

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In one paragraph

Review in Clinical neurophysiology practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Stefan DukicDepartment of Neurology, University Medical Centre Utrecht Brain Centre, Utrecht University, Utrecht, the Netherlands.
Rosanne GovaartsC.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
Arjan HillebrandAmsterdam UMC Location Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Neurology, De Boelelaan 1117, Amsterdam, the Netherlands.
Marianne de VisserDepartment of Neurology, Amsterdam University Medical Center, AMC, Neuroscience Institute, University of Amsterdam, Amsterdam, the Netherlands.
Margitta SeeckDepartment of Clinical Neurosciences, University Hospital of Geneva, Faculty of Medicine, Switzerland.
Roisin McMackinDiscipline of Physiology, School of Medicine, Trinity College, The University of Dublin, Dublin, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motor neurone diseases (MNDs) are increasingly being acknowledged as network disorders, with cortical dysfunction and degeneration extending beyond the motor cortex. Measures of this broader cortical pathophysiology are providing promising candidates in the search for diagnostic and prognostic biomarkers of the MNDs. Electroencephalography (EEG) and magnetoencephalography (MEG) offer a direct view of neural network activity by detecting changes in electromagnetic fields of the brain. Measurements based on EEG/MEG have often been overlooked in the search for MND biomarkers, largely due to their limited spatial resolution and the perceived challenges associated with noise in these signals. However, with recent developments in sensor technology and source reconstruction algorithms, alongside substantial improvement in pipelines that address noise, EEG/MEG-based measures can now be readily employed for spatiotemporally-precise, economical and non-invasive characterisation of cortical network pathophysiology in MNDs. Here, we provide an overview of how EEG/MEG signals have been employed to quantify neural network function in MND. We outline the advantages and limitations of these measurements, discuss the most clinically promising EEG/MEG studies of MNDs to date, and highlight future directions warranted to harness the full potential of these technologies for understanding and assessing MNDs.

Indexed as

Amyotrophic lateral sclerosisBiomarkersElectroencephalographyMagnetoencephalographyMotor neurone diseaseOutcome measures

Identifiers

PMID40726565
PMCPMC12302732

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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.