Exploring Neural Mechanisms Underlying Sleep Disorders in Male Athletes:A Complex Network Model Approach
Objective:This study employs a complex network model approach,incorporating non-targeted unweighted and targeted weighted techniques,to investigate the intricate interac-tions between pre-sleep neural functioning and sleep quality in male athletes with sleep disor-ders.The objective is to uncover specific neural functional characteristics associated with sleep disorders in athletes.Methods:Fourteen highly trained athletes with sleep disorders were moni-tored over 4 nights using polysomnography.Before sleep,their central nervous system status,neurotransmitter levels in the brain,autonomic nervous system status and mode state were as-sessed using separate methods,including portable electroencephalograph,supra-slow encephalo-fluctuogram technology,Polar H10 heart rate monitors and the profile of mood state question-naire.Based on test data,a complex network model with non-targeted unweighted characteris-tics,as well as a weighted complex network model targeting sleep quality,has been established.Results:In the non-targeted unweighted network model,the betweenness centrality results indi-cate that emotions such as panic,energy and anger,α%inhibitory rate,norepinephrine(NE),do-pamine,the proportion of NN50 divided by the total number of NN(R-R)intervals(PNN50),mean R-R interval,mean heart rate(MHR)and the total time of sleep are key influencing nodes of this network.According to the results of eigenvector centrality analysis,in addition to PNN50,NE and MHR in the above indicators,depression,tension,fatigue and other indicators remain important connection nodes within the network.In the weighted complex network mod-els targeting sleep quality,the results of the targeted betweenness centrality analysis find that the mood state indicator has the highest frequency in the shortest path to reach the sleep quality indicator(48.57%).In turn,neurotransmitters and sleep indicators,each with the same frequen-cy(14.29%),are found.The central nervous system status and autonomic nervous system status exhibit the lowest frequency(11.43%).In the results of targeted eigenvector centrality,mood state emerges as the most important influencing factor on sleep quality,with a frequency of 82.86%.Following this is the autonomic nervous system status,with a frequency of 8.57%.Subsequently,the central nervous system status,neurotransmitters and sleep indicators each share the same frequency(2.86%).Conclusions:The primary manifestations of sleep disorders in athletes include a shortened total sleep duration and prolonged wake time during sleep,result-ing in low sleep efficiency.Utilizing complex network modeling methods,the research has re-vealed that emotions,particularly negative emotions,serve as the primary influential nodes and key factors in the occurrence of poor sleep quality and sleep disorders.Among them,feelings of anger and anxiety are associated with brain neurotransmitters(5-HT and DA)and the level of central fatigue,while energy levels are associated with central fatigue and autonomic nervous system coordination.
sleep qualitymood statenervous systemcomplex network model
NETL
NSTL
万方数据
维普
