Outcome prediction in acute monohemispheric stroke via magnetoencephalography

Franca Tecchio, Patrizio Pasqualetti, Filippo Zappasodi, Mario Tombini, Domenico Lupoi, Fabrizio Vernieri, Paolo Maria Rossini

Research output: Contribution to journalArticlepeer-review


Background: Following an ischemic stroke a highly variable clinical outcome is commonly evident despite similar onset symptoms as well as lesion characteristics. The aim of this study was to identify indexes providing early prediction of functional recovery, in addition to clinical severity and lesion dimension at onset of stroke. Methods: In 32 patients, magnetoencephalographic (MEG) parameters collected in the acute phase (<10 days from symptoms onset, T0) from affected (AH) and unaffected (UH) hemispheres at rest and evoked by sensory stimuli were evaluated in association with the clinical outcome in a stabilized phase (T1, median 7.8 months) classified with three levels: worsening, partial and full recovery. Results: Multiple multinomial logistic regression indicated AH gamma and UH delta band powers able to prognosticate clinical outcome at T1. After inclusion in this analysis, lesion volume had the strongest predictive ability, and UH delta band power remained as a predictive factor with a measurable cut-off, maximizing both sensitivity and specificity of the prediction: a patient with UH delta below cut-off would recover to some extent; a patient with UH delta above cut-off would have a probability of about 70% to worsen. Conclusions: MEG UH delta and AH gamma band powers were found to provide useful information about long-term outcome prognosis. Only the increase of delta band activity in the unaffected hemisphere contains information about the outcome in addition to the lesion volume.

Original languageEnglish
Pages (from-to)296-305
Number of pages10
JournalJournal of Neurology
Issue number3
Publication statusPublished - Mar 2007


  • Acute stroke
  • Clinical outcome
  • MEG
  • Middle cerebral artery (MCA)
  • Prognostic indications

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology


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