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*
  • *Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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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.

Original languageEnglish
Pages (from-to)301-315
Number of pages15
JournalClinical Neurophysiology Practice
Volume10
DOIs
Publication statusPublished - 2025

Keywords

  • Amyotrophic lateral sclerosis
  • Biomarkers
  • Electroencephalography
  • Magnetoencephalography
  • Motor neurone disease
  • Outcome measures

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