Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm
Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm
复制标题
利用锁相值算法计算高水平射击运动员与非运动员大脑网络的特征差异
DOI：
10.1016/j.bspc.2019.02.009
复制
发表时间：
2019-05-01
影响因子：
5.1
通讯作者：
Fu, Yunfa
中科院分区：
文献类型：
--
作者：
Gong, Anmin;Liu, Jianping;Fu, Yunfa
长期的专业运动训练可能导致高水平运动员的脑功能网络与非运动员的脑功能网络存在显著差异。为了验证这一假设，收集了20名高水平射击运动员和20名年龄和性别匹配的非运动员在闭眼休息状态下的脑电图（EEG）。根据每个参与者的个体α频率，频谱被分为四个波段：δ，θ，α 1和α 2。计算了脑电信号在各个频段的锁相值，并利用图论分析了基于锁相值连接的脑电脑功能网络的拓扑结构。结果表明，与非运动员相比，高水平射击运动员的左颞区、左后颞区、左额区、左中央区和右顶叶区的连接性更高。运动员在theta和tagal波段的网络聚类系数和小世界特征显著大于非运动员。这些结果支持了高水平射击运动员脑功能耦合比非运动员更紧密的假设，高水平射击运动员的脑网络比非运动员的脑网络具有更强的小世界特征。(C)2019爱思唯尔有限公司版权所有。
Long-term professional sport training may cause the brain functional network of high-level athletes to differ significantly from that of non-athletes. To test this hypothesis, electroencephalograms (EEGs) from 20 high-level shooting athletes and 20 age- and gender-matched non-athletes are collected in an eyes-closed resting state. The frequency spectrum was divided into four bands according to the individual alpha frequency of each participant: delta, theta, alphal, and alpha2. The phase-locking values of the EEG in each frequency band are calculated and graph theory is used to analyze the topology of the EEG brain functional network based on the phase-locking-value connection. The results show that, compared with non-athletes, high-level shooters have higher connectivity in the left-temporal region, left-posterior temporal region, left-frontal region, left-central region, and right-parietal region. The network-clustering coefficients and small-world characteristics of athletes in the theta and alphal bands are significantly greater than that of non-athletes. These results support the hypothesis that brain function coupling in high-level shooting athletes is more connected than that in non-athletes, and the brain networks of high-level athlete have stronger small-world characteristics than those of non-athletes. (C) 2019 Elsevier Ltd. All rights reserved.