Sleep deprivation changes frequency-specific functional organization of the resting human brain

• Published in: Brain research bulletin 2024-05, Vol.210, p.110925-110925, Article 110925
• Main Authors: Luo, Zhiguo, Yin, Erwei, Yan, Ye, Zhao, Shaokai, Xie, Liang, Shen, Hui, Zeng, Ling-Li, Wang, Lubin, Hu, Dewen
• Format: Article
• Language: English
• Subjects: Cerebellum; Frequency specificity; Robust feature selection; Sleep deprivation; Spectral connection; Topology
• ISSN: 0361-9230; 1873-2747
• DOI: 10.1016/j.brainresbull.2024.110925

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01–0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain. •We proposed an RSF+SVM framework which achieved satisfactory RW-SD classification performance.•We revealed the frequency-specific topology changes caused by SD using rs-fMRI data.•Hubs of spectral networks are more likely to be discriminative nodes.•The cerebellum was a hub region after SD from the frequency perspective instead of in the time domain.