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Publication Detail
Sensorimotor cortex beta oscillations reflect motor skill learning ability after stroke.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Espenhahn S, Rossiter HE, van Wijk BCM, Redman N, Rondina JM, Diedrichsen J, Ward NS
  • Publication date:
    07/10/2020
  • Pagination:
    fcaa161
  • Journal:
    Brain Commun
  • Volume:
    2
  • Issue:
    2
  • Status:
    Published
  • Country:
    England
  • PII:
    fcaa161
  • Language:
    eng
  • Keywords:
    EEG, beta oscillations, motor learning, plasticity, stroke
Abstract
Recovery of skilled movement after stroke is assumed to depend on motor learning. However, the capacity for motor learning and factors that influence motor learning after stroke have received little attention. In this study, we first compared motor skill acquisition and retention between well-recovered stroke patients and age- and performance-matched healthy controls. We then tested whether beta oscillations (15-30 Hz) from sensorimotor cortices contribute to predicting training-related motor performance. Eighteen well-recovered chronic stroke survivors (mean age 64 ± 8 years, range: 50-74 years) and 20 age- and sex-matched healthy controls were trained on a continuous tracking task and subsequently retested after initial training (45-60 min and 24 h later). Scalp electroencephalography was recorded during the performance of a simple motor task before each training and retest session. Stroke patients demonstrated capacity for motor skill learning, but it was diminished compared to age- and performance-matched healthy controls. Furthermore, although the properties of beta oscillations prior to training were comparable between stroke patients and healthy controls, stroke patients did show less change in beta measures with motor learning. Lastly, although beta oscillations did not help to predict motor performance immediately after training, contralateral (ipsilesional) sensorimotor cortex post-movement beta rebound measured after training helped predict future motor performance, 24 h after training. This finding suggests that neurophysiological measures such as beta oscillations can help predict response to motor training in chronic stroke patients and may offer novel targets for therapeutic interventions.
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Clinical and Movement Neurosciences
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