scholarly journals Motor Skill Learning on the Ipsi-Lateral Upper Extremity to the Damaged Hemisphere in Stroke Patients

2019 ◽  
Vol 31 (4) ◽  
pp. 212-215
Author(s):  
Sung Min Son ◽  
Yoon Tae Hwang ◽  
Seok Hyun Nam ◽  
Yonghyun Kwon
2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Svenja Espenhahn ◽  
Holly E Rossiter ◽  
Bernadette C M van Wijk ◽  
Nell Redman ◽  
Jane M Rondina ◽  
...  

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.


Author(s):  
Stéphanie Lefebvre ◽  
Laurence Dricot ◽  
Patrice Laloux ◽  
Wojciech Gradkowski ◽  
Philippe Desfontaines ◽  
...  

2020 ◽  
Author(s):  
Svenja Espenhahn ◽  
Holly E Rossiter ◽  
Bernadette CM van Wijk ◽  
Nell Redman ◽  
Jane M Rondina ◽  
...  

AbstractRecovery 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 firstly compared motor skill acquisition and retention between well-recovered stroke patients and age- and performance-matched healthy controls. We then tested whether beta oscillations (15–30Hz) 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 twenty age- and sex-matched healthy controls were trained on a continuous tracking task and subsequently retested after initial training (45–60 min and 24 hours later). Scalp EEG 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. Further, 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 (PMBR) measured after training helped predict future motor performance, 24 hours 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.


2013 ◽  
Vol 333 ◽  
pp. e571
Author(s):  
S. Lefebvre ◽  
L. Dricot ◽  
W. Gradkowski ◽  
P. Laloux ◽  
P. Desfontaines ◽  
...  

2020 ◽  
Vol 120 (2) ◽  
pp. 365-374
Author(s):  
Marius Baguma ◽  
Maral Yeganeh Doost ◽  
Audrey Riga ◽  
Patrice Laloux ◽  
Benoît Bihin ◽  
...  

2008 ◽  
Author(s):  
Michelle V. Thompson ◽  
Janet L. Utschig ◽  
Mikaela K. Vaughan ◽  
Marc V. Richard ◽  
Benjamin A. Clegg

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