Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, 33 Queen Square, WC1N 3BG, London, UK; Department of Radiology, Cumming School of Medicine, University of Calgary, 2500, University Drive NW, Calgary, AB T2N 4N1, Canada.
Integrative Model-based Cognitive Neuroscience Research Unit, Department of Psychology, University of Amsterdam, Postbus 15926, 1001, NK, Amsterdam, the Netherlands; Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, WC1N 3BG, London, UK.
Neuroimage. 2019 Jul 15;195:340-353. doi: 10.1016/j.neuroimage.2019.03.079. Epub 2019 Apr 4.
People vary in their capacity to learn and retain new motor skills. Although the relationship between neuronal oscillations in the beta frequency range (15-30 Hz) and motor behaviour is well established, the electrophysiological mechanisms underlying individual differences in motor learning are incompletely understood. Here, we investigated the degree to which measures of resting and movement-related beta power from sensorimotor cortex account for inter-individual differences in motor learning behaviour in the young and elderly. Twenty young (18-30 years) and twenty elderly (62-77 years) healthy adults were trained on a novel wrist flexion/extension tracking task and subsequently retested at two different time points (45-60 min and 24 h after initial training). Scalp EEG was recorded during a separate simple motor task before each training and retest session. Although short-term motor learning was comparable between young and elderly individuals, there was considerable variability within groups with subsequent analysis aiming to find the predictors of this variability. As expected, performance during the training phase was the best predictor of performance at later time points. However, regression analysis revealed that movement-related beta activity significantly explained additional variance in individual performance levels 45-60 min, but not 24 h after initial training. In the context of disease, these findings suggest that measurements of beta-band activity may offer novel targets for therapeutic interventions designed to promote rehabilitative outcomes.
个体在学习和保持新运动技能的能力上存在差异。虽然β频带（15-30Hz）神经元振荡与运动行为之间的关系已得到充分证实，但运动学习个体差异的电生理机制仍不完全清楚。在这里，我们研究了感觉运动皮层的静息和运动相关β功率测量值在多大程度上可以解释年轻人和老年人运动学习行为的个体差异。20 名年轻（18-30 岁）和 20 名老年人（62-77 岁）健康成年人接受了一项新的腕部屈伸跟踪任务的训练，随后在两个不同的时间点（初始训练后 45-60 分钟和 24 小时）进行了重新测试。在每次训练和重新测试之前，分别在单独的简单运动任务期间记录头皮 EEG。尽管短期运动学习在年轻人和老年人之间相当，但组内存在相当大的可变性，随后的分析旨在找到这种可变性的预测因素。正如预期的那样，训练阶段的表现是以后时间点表现的最佳预测因素。然而，回归分析显示，运动相关β活动显著解释了初始训练后 45-60 分钟个体表现水平的额外差异，但不能解释 24 小时后的差异。在疾病背景下，这些发现表明β波段活动的测量可能为旨在促进康复结果的治疗干预提供新的目标。
