Effect of jet lag on brain white matter functional connectivity

Abstract

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.

Increased functional connectivity of white-matter in myotonic dystrophy type 1

“…According to the spatial location, we named them WM1 (superior longitudinal fasciculus network), WM2 (inferior longitudinal fasciculus network), WM3 (anterior corpus callosum network), WM4 (corpus callosum network), WM5 (occipital network), WM6 (inferior temporal), WM7 (prefrontal network), WM8 (superior corona radiata network), WM9 (post-central network), WM10 (cerebellar network), WM11 (posterior callosum network), WM12 (inferior frontoparietal network), and WM13 (deep network). The present networks WM3, WM4, WM5, WM7, WM8, WM9, and WM13 are similar to the results of the previous researches ( Jiang et al, 2019a ; Li et al, 2022 ), while the other WM networks can also be obtained in some studies ( Peer et al, 2017 ; Zhang et al, 2021b ). Consistent with previous studies, these WM networks can be divided into three layers (superficial, middle, and deep) ( Jiang et al, 2019a ; Fan et al, 2020 ) (shown in Figure 3A ).…”

“…For instance, Jiang et al found that patients with schizophrenia showed increased FC in the perception-motor white-matter network ( Jiang et al, 2019a ). More recently, the abnormalities of FC in the white-matter functional network were also revealed in other diseases, including epilepsy, attention deficit hyperactivity disorder, bipolar disorder, schizophrenia, Parkinson’s, and depression ( Jiang et al, 2019a , b ; Bu et al, 2020 ; Fan et al, 2020 ; Li et al, 2020 ; Lu et al, 2021 ) and even in participants with orthodontic pain or jet lag ( Zhang et al, 2021a , b ). Taken together, these studies provided strong evidence of the existence of functional brain activity in the white-matter and suggested that the functional information from white-matter can be detected by fMRI.…”

“…The spatial visualization and the detailed information of these networks are presented in Figure 2 and Table 2 separately. According to the spatial location, we named them WM1 (superior longitudinal fasciculus network), WM2 (inferior longitudinal fasciculus network), WM3 (anterior corpus callosum network), WM4 (corpus callosum network), WM5 (occipital network), WM6 (inferior temporal), WM7 (prefrontal (Jiang et al, 2019a;Li et al, 2022), while the other WM networks can also be obtained in some studies (Peer et al, 2017;Zhang et al, 2021b). Consistent with previous studies, these WM networks can be divided into three layers (superficial, middle, and deep) (Jiang et al, 2019a;Fan et al, 2020) (shown in Figure 3A).…”

Evidence of White Matter Integrity Changes in the Anterior Cingulum Among Shift Workers: A Cross-Sectional Study

“…Functional changes in the cingulum were associated with recovery from jetlag, which can be interpreted as re-alignment after circadian disruption. 9 However, it remains unclear why the increased FA correlated with poor sleep quality in shift workers but not in non-shift workers. A future study featuring long-term longitudinal imaging is required.…”

“…Sleep quality has been associated with structural white matter changes. [8][9][10][11] Previous studies reported that white matter alterations were related to the sleep quality of college students 10 and older adults. 11 A relationship between white matter integrity and sleep quality has also been observed in patients with insomnia.…”