Graduate School of Sport Sciences, Waseda University, Tokorozawa, Japan.
Department of Psychology, Brock University, St. Catharines, ON, Canada.
Psychophysiology. 2021 Jan;58(1):e13708. doi: 10.1111/psyp.13708. Epub 2020 Oct 27.
Alterations in our environment require us to learn or alter motor skills to remain efficient. Also, damage or injury may require the relearning of motor skills. Two types have been identified: movement adaptation and motor sequence learning. Doyonet al. (2003, Distinct contribution of the cortico-striatal and cortico-cerebellar systems to motor skill learning. Neuropsychologia, 41(3), 252-262) proposed a model to explain the neural mechanisms related to adaptation (cortico-cerebellar) and motor sequence learning (cortico-striatum) tasks. We hypothesized that medial frontal negativities (MFNs), event-related electrocortical responses including the error-related negativity (ERN) and correct-response-related negativity (CRN), would be trait biomarkers for skill in motor sequence learning due to their relationship with striatal neural generators in a network involving the anterior cingulate and possibly the supplementary motor area. We examined 36 participants' improvement in a motor adaptation and a motor sequence learning task and measured MFNs elicited in a separate Spatial Stroop (conflict) task. We found both ERN and CRN strongly predicted performance improvement in the sequential motor task but not in the adaptation task, supporting this aspect of the Doyon model. Interestingly, the CRN accounted for additional unique variance over the variance shared with the ERN suggesting an expansion of the model.
我们的环境变化要求我们学习或改变运动技能以保持效率。此外,损伤或伤害可能需要重新学习运动技能。已经确定了两种类型:运动适应和运动序列学习。Doyonet 等人。(2003 年,皮质纹状体和皮质小脑系统对运动技能学习的不同贡献。神经心理学,41(3),252-262)提出了一个模型来解释与适应(皮质小脑)和运动序列学习(皮质纹状体)任务相关的神经机制。我们假设,内侧额部负波(MFN),包括错误相关负波(ERN)和正确反应相关负波(CRN)在内的与事件相关的皮层电反应,将成为运动序列学习技能的特征生物标志物,因为它们与包括前扣带和可能辅助运动区在内的网络中的纹状体神经发生器有关。我们检查了 36 名参与者在运动适应和运动序列学习任务中的改善情况,并在单独的空间斯特鲁普(冲突)任务中测量了引发的 MFN。我们发现 ERN 和 CRN 都强烈预测了序列运动任务的表现改善,但在适应任务中没有,这支持了 Doyon 模型的这一方面。有趣的是,CRN 解释了与 ERN 共享的方差之外的额外独特方差,这表明模型的扩展。