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Effect of jet lag on brain white matter functional connectivity

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摘要 Background:A long-haul flight across more than five time zones may produce a circadian rhythm sleep disorder known as jet lag.Little is known about the effect of jet lag on white matter(WM)functional connectivity(FC).Objective:The present study is to investigate changes in WM FC in subjects due to recovery from jet lag after flying across six time zones.Methods:Here,resting-state functional magnetic resonance imaging was performed in 23 participants within 24 hours of flying and again 50 days later.Gray matter(GM)and WM networks were identified by k-means clustering.WM FC and functional covariance connectivity(FCC)were analyzed.Next,a sliding window method was used to establish dynamic WM FC.WM static and dynamic FC and FCC were compared between when participants had initially completed their journey and 50 days later.Emotion was assessed using the Positive and Negative Affect Schedule and the State Anxiety Inventory.Results:All participants were confirmed to have jet lag symptoms by the Columbian Jet Lag Scale.The static FC strengthes of cingulate network(WM7)-sensorimotor network and ventral frontal network-visual network were lower after the long-haul flight compared with recovery.Corresponding results were obtained for the dynamic FC analysis.The analysis of FCC revealed weakened connections between the WM7 and several other brain networks,especially the precentral/postcentral network.Moreover,a negative correlation was found between emotion scores and the FC between the WM7 and sensorimotor related regions.Conclusions:The results of this study provide further evidence for the existence of WM networks and show that jet lag is associated with alterations in static and dynamic WM FC and FCC,especially in sensorimotor networks.Jet lag is a complex problem that not only is related to sleep rhythm but also influences emotion.
出处 《Psychoradiology》 2021年第2期55-65,共11页 精神影像学(英文)
基金 supported by the National Natural Science Foundation(Grant Nos.81771812,81971595,81621003,81820108018,and 81901828) the Innovation Spark Project of Sichuan University(No.2019SCUH0003).
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