Movement-activated myoclonus in genetically defined progressive myoclonic epilepsies: EEG-EMG relationship estimated using autoregressive models

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Abstract

Objective: To study electroencephalography-electromyography (EEG-EMG) relationships in patients with different forms of progressive myoclonic epilepsies (PME). Methods: EEG-EMG auto-spectra, coherence and phase functions were estimated by means of bivariate and time varying autoregressive (AR) models in 15 patients: 8 with Unverricht-Lundborg, 4 with Lafora body disease, and 3 with sialidosis. Results: The coherence spectra of the EMG epochs including action myoclonus and contralateral frontocentral EEG derivations showed a main beta peak (average coherence: 0.60-0.79) in all patients, regardless of the type of PME. The time lag from cortex to muscle was 13.0-21.3ms. Significantly, coherent gamma activity was consistently found only in the 3 patients with sialidosis; the most heterogeneous results were obtained in the patients with Lafora disease, who showed a more complex coherence profile. Periods of normal muscle contractions, which could be recorded in patients with Unverricht-Lundborg PME, were characterised by the presence of an EEG-EMG beta coherence peak on the same frequency as in the case of action myoclonus, but with a lower coherence value. Conclusions: AR models were capable of describing EEG-EMG relationships in patients with PME, and indicated that coherent cortical and EMG beta oscillations are crucially involved in the generation of myoclonus. Moreover, they could detect the uneven spectral profiles characterising the different forms of PME.

Original languageEnglish
Pages (from-to)1041-1052
Number of pages12
JournalClinical Neurophysiology
Volume114
Issue number6
DOIs
Publication statusPublished - Jun 1 2003

Keywords

  • Action-myoclonus
  • Autoregressive analysis
  • Coherent beta activity
  • Gamma activity
  • Progressive myoclonus epilepsies

ASJC Scopus subject areas

  • Clinical Neurology
  • Physiology (medical)
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Sensory Systems

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