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• Clinical Article •
Prediction of sleepiness level based on cerebral fMRI of pilots and analysis of critical characteristics of network connection
LIU Yaohan  MA Ziyang  YU Ying  CHEN Haiyang  LI Junqiang  HU Bo  CUI Guangbin 

DOI:10.12015/issn.1674-8034.2025.11.012.


[Abstract] Objective To study the degree of daytime sleepiness, insomnia, and the main causes of insomnia in pilots using sleep-related scales, and to establish an imaging prediction model for the degree of daytime sleepiness in pilots using resting-state functional magnetic resonance imaging (fMRI) and connectome-based predictive modeling (CPM).Materials and Methods From May 2023 to October 2024, 96 pilots were recruited from Lintong Sanatorium. The Epworth Sleeping Scale (ESS) was used to assess the daytime mental status of pilots, and pilots were divided into a sleepiness group and a normal group based on their scores. The Insomnia Severity Index (ISI) was used to assess the severity of insomnia, the Pittsburgh Sleep Quality Index (PSQI) was used to assess the quality of sleep, the Self-rating Anxiety Scale (SAS) was used to assess the level of anxiety, and the Self-rating Depression Scale (SDS) was used to assess the level of depression. The fMRI data of pilots was collected and functional connectivity matrices were constructed. The CPM was used to construct a prediction model for the degree of daytime sleepiness in pilots.Results The proportion of pilots experiencing daytime sleepiness was approximately 15.6%. The ISI score of pilots in the sleepiness group was higher than that of pilots in the normal group (FDR-corrected P = 0.042), while the PSQI score of sleep quality was lower than that of pilots in the normal group (FDR-corrected P = 0.047). There was no significant difference in anxiety and depression levels between the two groups. There was a weak correlation between the ISI score and anxiety level (r = 0.236, P = 0.020), as well as the depression level (r = 0.212, P = 0.040). There was also a weak correlation between the flight hours of pilots and the sleep disorder (r = 0.216, P = 0.035), as well as the total PSQI score (r = 0.202, P = 0.048). There was a correlation between the predicted ESS score and its true value (r = 0.296), and permutation testing indicated that this correlation was significant (P = 0.004). The functional connectivity that contributed most to the predictive efficacy of the model primarily existed between the limbic system and the default mode network, ventral attention network, and frontal and parietal networks.Conclusions The proportion of pilots experiencing daytime sleepiness is approximately 15.6%, and it is associated with insomnia and poor sleep quality. CPM effectively predicted the degree of daytime sleepiness in pilots, and the functional connectivity that significantly contributed to this prediction were between the limbic system and the default mode network, ventral attention network, and frontal and parietal networks.
[Keywords] pilot；sleep；insomnia；magnetic resonance imaging；machine learning；functional connectivity

LIU Yaohan1, 2   MA Ziyang2   YU Ying2   CHEN Haiyang3   LI Junqiang3   HU Bo2*   CUI Guangbin1, 2*  

1 Department of Air Force Health Service Training Base of PLA, Fourth Military Medical University, Xi'an 710032, China

2 Department of Radiology,Tangdu Hospital,Fourth Military Medical University,Xi'an 710038, China

3 Lintong Rehabilitation and Convalescent Centre of PLA Joint Logistics Support Force, Xi'an 710600, China

Corresponding author: HU B, E-mail: rayhb@foxmail.com CUI G B, E-mail: cuigbtd@fmmu.edu.cn

Conflicts of interest   None.

Received  2025-07-15
Accepted  2025-10-09
DOI: 10.12015/issn.1674-8034.2025.11.012
DOI:10.12015/issn.1674-8034.2025.11.012.

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