# Effects of Sleep Deprivation on the Brain Electrical Activity in Mice
## 3 Results and Discussion
On the one hand, we can take longer datasets (e.g., several hours), estimate the corresponding values of \(\alpha\) and \(\beta\), and then compare these quantities for the two states under consideration. However, this way is accompanied by time-varying dynamics and several types of nonstationary behavior that can change the expected values of the scaling exponents. On the other hand, we can select fairly homogeneous (more stationary) segments, analyze them with EDFA, and then average the results for each animal and each condition. Our preliminary analysis of the simulated datasets [27] showed that this way gives more stable and reliable estimations, and we use it here for EEG processing. The established distinctions caused by SD for the entire group of animals are given in Figure 5, where different symbols indicate distinct responses, and in Table 1. For six out of 10 mice, a pronounced effect of SD is observed, characterized by a decrease in the \(\alpha\) and \(\beta\) exponents, i.e., positive values of \(\Delta\alpha\) and \(\Delta\beta\) (Figure 5, circles). Several animals (three out of 10 mice) demonstrated relatively subtle signs of SD-induced changes (Figure 5, triangles), although these changes are significant according to the Mann-Whitney test (\(p<0.05\)). In this study, only one day of sleep deprivation was used. Longer SD periods are expected to elicit stronger responses; however, our goal was to examine the effects of short-term SD when sleep deprivation is not associated with neurodegenerative disorders. According to Figure 5, one mouse showed a different reaction (square), but this behavior can be treated atypical compared to other animals. Our results indicate that short-term effects of SD can be detected in EEG recordings, although the strength of the response is subject-dependent. Moreover, accounting for the \(\beta\) exponent of the proposed EDFA can surpass the \(\alpha\) exponent of the standard DFA--the \(\Delta\beta\) range is about twice as large as the \(\Delta\alpha\) range (\(0.12\pm 0.04\) versus \(0.05\pm 0.02\)). Consequently, the changes in the features of nonstationarity caused by SD are more pronounced than the changes in the properties of long-range correlations associated with them. Thus, in this study we show that EDFA sensitively reflects the changes induced by SD. The sleep is a natural factor of activation of the lymphatic clearing and drainage functions of the brain [1; 13]. The SD causes significant suppression of the clearance of toxins from the brain [1]. There are animal data suggesting that sleep deficit leads to sterile inflammation [7; 8; 9], an increase in the BBB permeability [8; 10], and long SD is accompanied by hallucination and various cognitive deficiencies [12]. We hypothesized that sleep is a biomarker of the BBB permeability and EEG is an important informative platform for the analysis of BBB leakage and the cerebral lymphatic functions [20]. We show that EDFA may be applied to study changes in the electrical brain activity after SD. Figure 5: Individual responses of mice from the entire group to one-day SD quantified with the differences between the scaling exponents (7) before and after SD (\(\Delta\alpha\) and \(\Delta\beta\)). Maroon circles show pronounced effects of SD, blue triangles indicate relatively subtle signs of SD-induced changes, and an orange square marks atypical response.