Characteristics of Resting-State Electroencephalogram Network in α-Band of Table Tennis Athletes

运动员 静息状态功能磁共振成像 脑功能偏侧化 脑电图 心理学 认知 表(数据库) 物理医学与康复 物理疗法 计算机科学 神经科学 医学 数据挖掘
作者
Jilong Shi,Fatima A. Nasrallah,Xuechen Mao,Qin Huang,Jun Pan,An-Min Li
出处
期刊:Brain Sciences [MDPI AG]
卷期号:14 (3): 222-222 被引量:1
标识
DOI:10.3390/brainsci14030222
摘要

Background: Table tennis athletes have been extensively studied for their cognitive processing advantages and brain plasticity. However, limited research has focused on the resting-state function of their brains. This study aims to investigate the network characteristics of the resting-state electroencephalogram in table tennis athletes and identify specific brain network biomarkers. Methods: A total of 48 healthy right-handed college students participated in this study, including 24 table tennis athletes and 24 controls with no exercise experience. Electroencephalogram data were collected using a 64-conductive active electrode system during eyes-closed resting conditions. The analysis involved examining the average power spectral density and constructing brain functional networks using the weighted phase-lag index. Network topological characteristics were then calculated. Results: The results revealed that table tennis athletes exhibited significantly higher average power spectral density in the α band compared to the control group. Moreover, athletes not only demonstrated stronger functional connections, but they also exhibited enhanced transmission efficiency in the brain network, particularly at the local level. Additionally, a lateralization effect was observed, with more potent interconnected hubs identified in the left hemisphere of the athletes’ brain. Conclusions: Our findings imply that the α band may be uniquely associated with table tennis athletes and their motor skills. The brain network characteristics of athletes during the resting state are worth further attention to gain a better understanding of adaptability of and changes in their brains during training and competition.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
聪明德天发布了新的文献求助10
1秒前
Ziyi_Xu发布了新的文献求助10
1秒前
谷明洋完成签到,获得积分10
2秒前
在水一方应助清秀的月亮采纳,获得10
3秒前
wwwdddyyy完成签到,获得积分10
3秒前
科目三应助nini采纳,获得10
3秒前
Violets关注了科研通微信公众号
3秒前
热心的荣轩完成签到 ,获得积分10
3秒前
小二郎应助BLUE采纳,获得10
3秒前
4秒前
JJ完成签到 ,获得积分10
4秒前
4秒前
5秒前
6秒前
6秒前
7秒前
小夏完成签到,获得积分10
7秒前
在水一方应助王皮皮采纳,获得10
7秒前
桐桐应助Lignin采纳,获得10
7秒前
8899发布了新的文献求助10
7秒前
大力的灵雁应助maguodrgon采纳,获得30
7秒前
7秒前
科研川应助maguodrgon采纳,获得30
7秒前
7秒前
俏皮雁凡完成签到,获得积分10
7秒前
abcd1234完成签到,获得积分20
8秒前
8秒前
8秒前
崔峰完成签到,获得积分10
8秒前
洁净艳一完成签到,获得积分10
9秒前
蓝莓橘子酱应助biubiubiu采纳,获得20
9秒前
wangkai030709完成签到,获得积分20
9秒前
9秒前
GPTea应助nian采纳,获得40
9秒前
abcd1234发布了新的文献求助10
10秒前
研友_nV21Vn发布了新的文献求助10
10秒前
11秒前
沙拉酱发布了新的文献求助10
11秒前
彭于晏应助华年采纳,获得100
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Digital Twins of Advanced Materials Processing 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6041186
求助须知:如何正确求助?哪些是违规求助? 7779820
关于积分的说明 16233436
捐赠科研通 5187140
什么是DOI,文献DOI怎么找? 2775723
邀请新用户注册赠送积分活动 1758816
关于科研通互助平台的介绍 1642296