Institute of Sports Medicine, Paderborn University, Paderborn, Germany.
Department of Mathematics, Paderborn University, Paderborn, Germany.
Neuroscience. 2018 Sep 15;388:203-213. doi: 10.1016/j.neuroscience.2018.07.025. Epub 2018 Jul 24.
Age-related deterioration of force control is evident on behavioral and neural levels. Extensive and deliberate practice can decrease these changes. This study focused on detecting electrophysiological correlates of age- and expertise-related differences in force control. We examined young (20-27 years) and late middle-aged (57-67 years) novices as well as late middle-aged experts in the field of fine motor control. Therefore, EEG data were recorded while participants performed a force maintenance task. Variability and complexity of force data were analyzed. To detect electrophysiological correlates, dynamic mode decomposition (DMD) was applied to EEG data. DMD allows assessing brain network dynamics by extracting electrode interrelations and their dynamics. Defining clusters of electrodes, we focused on sensorimotor and attentional networks. We confirmed that force control in late middle-aged novices was more variable and less complex than in other groups. Analysis of task-related overall network characteristics, showed a decrease within the α band and increase within low β, high β, and θ band. Compared to the other groups young novices presented a decreased α magnitude. High β magnitude was lower in late middle-aged novices than for other groups. Comparing left and right hands' performance, young novices showed higher low β magnitude for the left hand. Late middle-aged novices showed high values for both hands while late middle-aged experts showed higher values for the right than for their left hand. Activation of attentional networks was lower in late middle-aged experts compared to novices. These results may relate to different control strategies of the three groups.
年龄相关的力量控制恶化在行为和神经水平上都很明显。广泛而刻意的练习可以减少这些变化。本研究专注于检测与年龄和专业知识相关的力量控制差异的电生理相关性。我们研究了年轻(20-27 岁)和中老年(57-67 岁)新手以及精细运动控制领域的中老年专家。因此,在参与者执行力量维持任务时记录了 EEG 数据。分析了力量数据的可变性和复杂性。为了检测电生理相关性,对 EEG 数据应用了动态模式分解(DMD)。DMD 通过提取电极相互关系及其动力学来评估大脑网络动力学。通过定义电极集群,我们专注于感觉运动和注意力网络。我们证实,中老年新手的力量控制比其他组更具可变性且更不复杂。对与任务相关的整体网络特征的分析表明,α 波段内的减少和低β、高β和θ波段内的增加。与其他组相比,年轻新手的α 幅度降低。与其他组相比,中老年新手的高β幅度较低。比较左手和右手的表现,年轻新手的左手低β幅度较高。中老年新手的双手高β幅度较高,而中老年专家的右手高β幅度高于左手。与新手相比,中老年专家的注意力网络激活程度较低。这些结果可能与三组不同的控制策略有关。