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. 2025 Sep 25;19:709–717. doi: 10.1016/j.ibneur.2025.09.013

Napping restores working memory and brain activation impaired by sleep deprivation

Anping Ouyang a, Jun Jiang a,b,c, Lin Wu a, Xinxin Lin a, Lingling Wang a, Qiutao Yan a, Xuqian Diao a, Yan Li d, Yuanqiang Zhu e, Lingli Zeng f, Jiaxi Peng g, Qianqian Dong h,i,j,⁎, Wei He a,k,⁎⁎, Peng Fang a,l,⁎⁎⁎
PMCID: PMC12546891  PMID: 41140581

Abstract

Background

Sleep deprivation (SD) impairs working memory (WM), yet the restorative potential of brief daytime naps remains underexplored. This study examines how naps counteract SD-induced WM deficits through behavioral and neuroimaging mechanisms, focusing on task-positive networks and default mode network (DMN) dynamics.

Method

A within-subject fMRI study employed 2-back WM tasks in 50 participants under three conditions: baseline wakefulness, post-30h SD, and post-nap recovery. Behavioral metrics (reaction times, accuracy) and fMRI activation patterns were analyzed using repeated-measures ANOVA and mixed-effects models to assess SD and nap effects.

Result

Naps partially restored SD-induced WM declines, improving reaction times and accuracy. Post-SD, reduced activation in the cerebellum, insula, and thalamus (attention/executive regions) rebounded post-nap. SD weakened DMN suppression (middle frontal gyrus, precuneus, superior temporal gyrus), with maximal DMN suppression post-nap. Improved WM performance correlated with reactivated task-positive networks.

Conclusion

Daytime naps mitigate SD-related WM deficits by rebalancing task-positive network activation (cerebellum, thalamus) and enhancing DMN suppression. These findings elucidate neurophysiological mechanisms of sleep-cognition interactions, supporting naps as a practical intervention for SD-induced cognitive impairment

Keywords: sleep deprivation, daytime napping, working memory, brain region activation

1. Introduction

Sleep plays an important role in the development of an individual’s physical and mental health and the stability of society (Xu et al., 2024). For many people, SD has become an unavoidable and crucial issue of modern life, which not only affects personal health and work efficiency but also may threaten social security and stability (Blumberg et al., 2022, Khubchandani and Price, 2020). SD affects basic arousal in all aspects of higher cognitive functioning, such as attention, memory, and decision-making (Ma et al., 2020). SD is a state of missing sleep time due to various reasons, which can lead to physiological and psychological problems (Bandyopadhyay and Sigua, 2019). Depending on the urgency of SD, it can be categorized into acute SD and chronic SD, where acute SD is defined as SD occurring continuously for at least 24 h (Choshen-Hillel et al., 2021). ‘Chronic SD’ is used to describe a state of persistent lack of adequate sleep over a long period. SD impairs performance on a wide range of cognitive tasks, but the extent of this impairment varies depending on the cognitive domain involved (Santisteban et al., 2019). It should be particularly emphasized that human cognitive abilities are not achieved solely through functional brain activity during the waking state. In recent years, several authoritative studies have shown that sleep plays a crucial role in people’s cognitive functioning, especially in memory and information processing (Kozlov, 2024).

Working memory is a key cognitive ability that temporarily stores and processes information to guide current behaviors and decisions and involves the ability to store and process information over a limited time period (Cowan, 2022). Typical experimental paradigms include the n-back task, the Sternberg Working Memory Task, and the Delayed Match to Sample Task (Baddeley, 2012). Professor Chee was the first to explore the behavioral and neuroimaging effects of SD on working memory (Chee and Choo, 2004), showing that when performing a working memory task in the normal waking state, the central brain regions activated included the frontal, parietal, and thalamus bilaterally; whereas after SD, activity in the left dorsolateral prefrontal cortex and bilateral thalamus showed significantly reduced activity (Chee et al., 2006a). Previous studies have demonstrated that brain regions such as the hippocampus and prefrontal lobes play an important role in memory consolidation during sleep (Rasch et al., 2007, Albouy et al., 2013). MacDonald et al. further explored the effects of SD on working memory (MacDonald et al., 2018a), noting that there were no significant differences in the initial stages of task performance between sleep-deprived and awake states, while the main effects of SD appeared in the later stages of the task, which may be due to the presence of some kind of compensatory mechanism in the brain during the initial stages of the task. Therefore, regulating SD-induced cognitive decline is of urgent need and practical significance.

Various evidences show that prolonged sleep recovery after SD is considered the best way to alleviate the adverse effects caused by SD (Belenky et al., 2003). However, for most people, the demands of work and socializing often prevent the opportunity to get enough restorative sleep. In addition to predominantly nocturnal sleep, daytime napping is a typical sleep behavior (Souissi et al., 2020). By definition, the duration of a nap should be at least 50 % shorter than the individual’s average nighttime sleep duration. It is important to note that napping is not the same as shortening or compressing nighttime sleep. Rapid eye movement sleep may occur in naps lasting approximately 1 h but is unlikely to occur in naps lasting 30 min or less (Kharas et al., 2024a). Several studies have demonstrated the benefits of daytime naps, particularly in reducing fatigue and restoring wakefulness (Kharas et al., 2024b). From the perspective of the brain control network, napping after SD can partially restore brain function (Wu et al., 2024). In addition, napping can help overcome some of the adverse emotional, physical, and cognitive effects of SD (Wang et al., 2019). Despite the usual restorative effects of naps, sleep inertia can occur briefly after naps (approximately 20–30 min), during which cognitive function and performance may decline, thus temporarily counteracting the restorative effects of naps (Norbury et al., 2023).

There are still few studies in the existing literature that systematically explore the effects of SD and daytime naps on working memory from multiple perspectives. Specifically, how working memory and brain function are affected after SD and whether daytime naps are effective in restoring working memory capacity are understudied issues that deserve further exploration.

The present study further explored the relationship between sleep and cognitive functioning, particularly the key cognitive component of working memory, using task-state magnetic resonance imaging in an n-back experimental paradigm to investigate the effects of working memory by SD. In this study, 50 college students were recruited to perform an n-back task in an MRI scanner during normal wakefulness, after 30 h of SD, and after a subsequent 30-minute nap, and to analyze the dynamics of the brain’s responses in the three states.

2. Subjects and methods

2.1. Subjects

Fifty right-handed cadets from the Air Force Medical University, aged between 18 and 25 years, were recruited for this study. All subjects completed a health questionnaire before the experiment, which contained the subjects’ basic health status and medical history. The questionnaire was used to find out whether the subjects met the screening criteria for the experiment, and they were excluded if they met any of the following criteria: (1) history of psychiatric or neurological disorders; (2) presence of sleep disorders; (3) extreme early-morning-type or extreme-night-type of work and rest (as assessed by a scale) (Horne and Ostberg, 1976); (4) history of alcohol or drug abuse; and (5) the need to work in shifts. Subjects were asked to use a sleep diary one week prior to the start of the formal experiment, which was used to record their time of falling asleep, time of awakening, length of sleep, and quality of sleep. Only subjects with a routine (sleeping >6.5 h per night, going to sleep no later than 1:00 a.m., and waking up no later than 9:00 a.m.) were screened, and 50 subjects were finally included(all males) with a mean age of 20 ± 1.86 years and a mean BMI of 23.48 ± 2.42. Participants completed the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality. Inclusion in the study required a PSQI score of ≤ 5, indicating normal sleep quality. This criterion was implemented to exclude individuals with significant sleep disturbances or disorders that could potentially affect the study outcomes. All subjects provided written informed consent, and the Ethics Committee of Xijing Hospital approved the study.

2.2. N-back task

In the n-back task, subjects are required to compare the currently presented stimulus with the preceding nth stimulus. By controlling the number of intervals between the current stimulus and the target stimulus, the experiment can effectively manipulate the cognitive load of the task. In the experimental design of this study, a 2-back task model was used, and a letter-matching task was specifically chosen for the study. In this 2-back letter matching task, subjects were required to pay sustained attention to a series of letter-by-letter stimuli presented one by one. For each newly presented letter, subjects were asked to compare it with the letters in its first two positions (i.e., the ‘2-back’) to determine whether it was the same type of letter. It should be emphasized that this task focused only on matching letter types, ignoring their presentation position. The specific process is shown in Fig. 1A.

Fig. 1.

Fig. 1

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Experimental flow chart. Note: A is the flow diagram of the working memory task; B is the flow chart of an experiment with 30 h sleep deprivation followed by a 30 min nap to perform a working memory task.

2.3. Study protocol

In this within-subjects design, participants completed all three conditions: Resting Wakefulness (RW), Sleep Deprivation (SD), and Post-Nap (NAP). Comparing brain activation patterns and cognitive performance within participants across these conditions minimized between-subject variability. All subjects were required to visit the laboratory three times to participate in the experiment. During the first visit, subjects were briefly introduced to the protocol, and all subjects signed an informed consent form. In addition, subjects began wearing a watch (Mini-Mitter Actiwatch; Philips Respironics) to record their activity and sleep patterns until the end of the experiment. During the second visit, subjects underwent a baseline n-back task test after normal sleep (pre-SD or after recovery from regular sleep) and underwent a Magnetic Resonance Imaging (MRI) scan. During the third visit, participants were required to experience 30 h of SD, after which they underwent a second n-back task test and MRI scan, followed by a 30-minute nap, which was immediately followed by a third n-back task test and MRI scan. The order of the second and third visits was randomized to minimize the effect of scanning order on the experimental results. To avoid persistent effects of SD on the waking state, the interval between the latter two visits was at least 1 week. The 30 h SD started at 8:00 a.m. and ended at 2:00 p.m. the next day, during which subjects were allowed to read, watch videos, or surf the Internet for a limited period of time (10:00–2:00 p.m. the next day) to prevent the participants from becoming overexcited. No strenuous activity or consumption of caffeinated or tea-containing foods was allowed during the experiment. The room temperature was approximately 24℃, and standard light conditions (340 lux) were used. Two investigators accompanied subjects during SD and scanning to prevent subjects from falling asleep, and subjects were asked to report their status at random moments during the experiment. For the RW condition, the MRI scans of all subjects were performed at 8:00–9:00 a.m., whereas the post-SD MRI scans were scheduled to be performed at 14:00–15:00 a.m. After completion of the scans in the SD group, the subjects were allowed to sleep for 30 min and then woke up to undergo a final MRI scan. Prior to the scan, participants were provided with a 20-minute rest period to allow subjects to recover. To minimize the possible effects of nap sleep inertia. During the naps, their sleep time was recorded using a watch to ensure that each participant slept for at least 20 min, and subjects were asked if they were all asleep, and all were reported as asleep. The specific process is shown in Fig. 1B.

2.4. MRI acquisition

All subjects underwent a series of scans using a 3 T scanner (Discovery MR 750; GE Healthcare) in the Department of Radiology, Xijing Hospital, Fourth Military Medical University. A standard 8-channel head coil and constrained foam pads were used to minimize head movement and scanner noise. Task-state MRI was performed using a gradient-echo-planar imaging sequence. For each subject, a total of 210 images were acquired (repetition time [TR]/echo time [TE]: 2000ms/30ms, field of view: 240 ×240 mm², matrix size: 64 ×64, flip angle: 90°, in-plane resolution: 3.75 ×3.75 mm², layer thickness 3.5 mm, no gaps, 45 axial layers.) At the first time point, before SD, high-resolution T1-weighted images were acquired using a volumetric 3D scrambled phase gradient echo sequence (TR/TE: 8.2ms/3.18ms, field of view: 256 × 256 mm², matrix size: 512 × 512, flip angle = 9°, in-plane resolution: 0.5 × 0.5 mm², layer thickness = 1 mm, 196 sagittal (layers).

2.5. Data processing

Images from the first 10 time points were first discarded to ensure the stability of the MRI data, and then images from the remaining 200-time points were layer-time corrected and realigned with the first image, during which time the mean frame-wise displacement (FD) was calculated (no difference between the three groups). Head displacement during scanning was assessed using translation and rotation parameters, with exclusion criteria being the presence of > 2.5 mm displacement and/or > 2.5° rotation at each time point and orientation. Since computationally assessed brain activity is susceptible to head displacement, the Friston-24 parameter was used to regress its effect. In addition, to further reduce the effect of confounding factors, the signals from the cerebrospinal fluid and white matter were also regressed, but the global signal was not removed. The data were then normalized to MNI spatial images using the DARTEL toolbox (Kaida et al., 2007), and finally, the resulting images were smoothed using a 6 mm FWHM Gaussian kernel. The 2-back task employed Block Design as the experimental strategy. The present study also employed a mixed-effects model for an exhaustive analysis of the task-state functional MRI data, which was divided into two main phases. In the first phase, for each participant, a convolution with the hemodynamic response function was performed at each presented letter stimulus. Next, the data were processed using a general linear model to determine which brain regions showed a significant increase or decrease in activity when presented with the memory task compared to the resting state. In order to minimize data bias due to head movements, the six head movement parameters obtained during pre-processing were also integrated into this general linear model. In the second stage, the data analysis followed several steps: first, a one-sample t-test was applied to assess the overall brain activation pattern of the 2-back task under each specific experimental condition (i.e., regular wakefulness (RW), 30 h of SD, and 30 min of napping). Subsequently, whether there were significant differences in brain activation patterns triggered by the 2-back task across experimental conditions was further examined by one-way repeated measures analyses. To increase the precision of the analyses, post hoc tests were also conducted, specifically comparing each pair of experimental conditions two by two. Ultimately, for the brain regions that showed significant differences across the three different conditions, the present study extracted their corresponding brain activation values and plotted detailed graphs accordingly.

2.6. Statistical analysis

For 2-back behavioral results (mean at response and mean accuracy), this study used a one-way repeated measures ANOVA in which the significance threshold was set at p < 0.01 with FDR standard correction. In post hoc analyses, multiple comparisons were corrected by Bonferroni, with the significance threshold correction set at 0.05. For 2-back imaging, the FDR criterion was also used for multiple comparison correction. For 2-back activation in different conditions, for F comparisons in different conditions, and two-by-two comparisons in post hoc analyses, the statistical threshold was set to PFDR < 0.05. Additionally, correlation analyses were conducted to examine the relationship between changes in brain activation and improvements in reaction time, specifically between the sleep-deprived (SD) and post-nap (Nap) conditions. Pearson's correlation coefficient was used to calculate the strength and direction of these associations. To control for multiple comparisons, all correlation results were corrected using the FDR method at a threshold of p < 0.05.

3. Results

3.1. 2-back behavioral analysis

2-back descriptive statistical analysis of behavioral performance and tests of variance are shown in Table 1.

Table 1.

2-back behavioural analysis(X ± SD).

2-back RW SD NAP F value P value
Average response time (ms) 710.73 ± 145.72 860.32 ± 157.48 822.17 ± 160.78 70.33 < 0.001
Average accuracy (0−1) 0.89 ± 0.09 0.83 ± 0.15 0.86 ± 0.11 7.36 < 0.001
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One-way repeated measures ANOVA showed that the average response time (F(2,98) = 70.33, p < 0.001) and accuracy (F(2,98) = 7.36, p < 0.001) had significant time differences.

Note: Pairwise differences were found between correct rates and reaction times of the 2-back task in the RW, SD, and Nap conditions, with RT of Nap being significantly lower than SD but still higher than RW and correct rates of Nap being significantly higher than SD but still lower than RW.

Normality tests indicated that all data under each condition followed a normal distribution.

T value obtained by using the paired t-test.

3.2. 2-back imaging analysis

One-way repeated measures ANOVA was used to investigate the dynamics of 2-back activation in the three conditions. Fig. 2 shows statistically significant changes in brain responses in bilateral middle frontal gyrus, bilateral supplementary motor area, bilateral inferior frontal gyrus, left cingulate gyrus, right insula, and bilateral inferior parietal gyrus.

Fig. 2.

Fig. 2

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Plot of one-sample t-test activation patterns under the three experimental conditions. Note: Fig. 2 shows the dynamics of 2-back activation under the three conditions RW, SD, and Nap. The changes of brain responses in bilateral middle frontal gyrus, bilateral supplementary motor area, bilateral inferior frontal gyrus, left cingulate gyrus, right insular lobe and bilateral inferior parietal gyrus were statistically significant at baseline, after sleep deprivation and after nap, *p < 0.05.

Fig. 3 and Table 2 show the brain regions that differed significantly between the three conditions, mainly including the cerebellum, right insula, left thalamus, precuneus, superior temporal gyrus, and middle temporal gyrus.

Fig. 3.

Fig. 3

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Activation of differential brain regions between three experimental conditions. Note: Fig. 3 shows the brain regions with significant differences in the three conditions of RW, SD, and Nap; significant changes were observed in multiple brain regions, including the cerebellum, right insula, left thalamus, precuneus, STG, and MTG. STG, superior temporal gyrus, and MTG, middle temporal gyrus, *p < 0.05.

Table 2.

Brain regions with significant differences between the three conditions.

Region-(ANOVA) voxel size peak point coordinates (MNI)
 F-value
x y z
cerebellum
left 100 0 −51 −18 6.95
insula
right 110 39 −15 9 6.64
thalamus
left 103 −33 −24 6 6.97
precuneus
right 579 15 −54 15 9.41
Superior temporal gyrus
right 53 48 −48 6 5.65
Middle temporal gyrus
left 136 −15 −33 24 7.64
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Fig. 4 shows that the observed two-by-two differences were similar, i.e., there were significant differences between the RW and SD conditions and between the SD and nap conditions. Activity in the cerebellum, insula, and thalamus was significantly reduced after SD, and activity in these three regions largely returned to normal waking levels after the daytime nap.

Fig. 4.

Fig. 4

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Dynamics of activation strength of differential brain regions under different experimental conditions (Model 1). Note: Fig. 4 shows the degree of activation in the cerebellum, insula, and thalamus under the three conditions RW, SD, and Nap.

There was also another pattern, as shown in Fig. 5, where the middle frontal gyrus, precuneus, and superior temporal gyrus belonged to the negative activation pattern during normal wakefulness, but the degree of negative activation in these three regions became weaker after SD, and the negative activation in these three brain regions reached a maximum after the daytime nap.

Fig. 5.

Fig. 5

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Dynamic changes in activation intensity of differential brain regions under different experimental conditions (mode 2). Note: Fig. 5 shows the degree of activation in the middle frontal gyrus, precuneus, and superior temporal gyrus in the RW, SD, and Nap conditions.

3.3. Related analyses

Finally, the present study found a significant positive correlation between improvement in working memory reaction time after daytime naps and increased activation strength in the right insula (correlation coefficient r = 0.38, p < 0.005, Fig. 6).

Fig. 6.

Fig. 6

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Increased right insula activation positively correlates with improvement in response time after naps. Note: Fig. 6 shows that there is a significant positive correlation between the improvement in working memory after a nap and the degree of activation in the right insula.

4. Discussion

This study utilized MRI to investigate behavioral manifestations and neuroimaging mechanisms underlying working memory alterations induced by sleep deprivation (SD) and subsequent daytime napping. Our findings provide initial validation that brief daytime naps effectively improve working memory performance following SD. Specifically, SD significantly prolonged reaction times and reduced accuracy during working memory tasks, whereas post-nap recovery substantially restored both processing speed and response precision. Neuroimaging analyses revealed diminished amplitude of low-frequency fluctuations (ALFF) in the inferior frontal gyrus, precuneus, and parietal regions post-SD, with pronounced ALFF normalization observed after napping. These neural and behavioral recovery patterns align with mechanisms reported in studies using repetitive transcranial magnetic stimulation (rTMS) to ameliorate SD-induced working memory deficits (Guo et al., 2019).

There is increasing evidence that SD can cause adverse effects on declarative memory, emotional memory, spatial memory, etc., and also increase false memory formation (Cousins and Fernández, 2019, Lo et al., 2016). In the present study, we found prolonged reaction times and decreased accuracy in working memory tasks after SD, which is consistent with previous findings (Gerhardsson et al., 2019). A partial recovery in working memory performance was seen after a nap, and previous studies have similarly concluded that a short nap can restore nearly 50 % of cognitive decline (Lo et al., 2016, Cross et al., 2021).

The results of the one-sample t-test revealed that the working memory task mainly activated the frontoparietal network (including the right and left inferior frontal gyrus, inferior parietal gyrus, and supplementary motor areas), the salience network (including the right insula and left cingulate gyrus), and the default network (including the right and left middle frontal gyrus). By analyzing the dynamics of brain responses triggered by the 2-back task under three different conditions, it was observed that daytime naps enhanced the positive activation of the frontoparietal network as well as increased the negative activation of the default network, thus optimizing working memory performance to some extent. The range of positive activation was relatively extensive in the RW state, whereas it was significantly reduced in the SD state and in between in the Nap state. One-way repeated-measures ANOVA confirmed that brain regions with significant activation differences in all three states were concentrated in the cerebellum, right insula, left thalamus, precuneus, superior temporal gyrus, and middle temporal gyrus. Post hoc analyses went even further to reveal two main neural mechanisms underlying daytime naps in improving working memory.

During the execution of working memory tasks in the awake state, the cerebellum, right insula, and left thalamus exhibit positive activation properties. However, under SD, the positive activation in these regions is significantly reduced, particularly in the thalamus, where the activation shifts from positive to markedly negative. Specifically, SD suppresses the positive activation in these brain regions, manifesting as the inhibition of rhythmic firing in the thalamus and impairing the functional coupling between the thalamus and the dorsal attention network (DAN). Following daytime napping, the activation levels in these regions partially recover, with intensities intermediate between the awake and SD states. This observation is consistent with previous findings on thalamic activation changes after SD (Chee et al., 2006b).Napping may contribute to the preservation of neuronal homeostasis through modulation of GABAergic neurotransmission within the thalamic reticular nucleus (Luskin et al., 2025, Jones, 2020). This mechanism aligns with the synaptic homeostasis hypothesis (SHY), which posits that sleep serves to enhance neural plasticity (Tononi and Cirelli, 2014), facilitate clearance of cerebral metabolic waste, and consequently optimize functional efficiency of the brain.

Furthermore, the thalamus plays a critical role in regulating the sleep-wake cycle and gating mechanisms for information transmission (Coulon et al., 2012). After napping, the thalamus partially recovers from fatigue, enabling more efficient filtering of important information, suppression of irrelevant stimuli, and improvements in attention and working memory performance. The thalamus’s balancing role aligns with the “sleep-dependent synaptic homeostasis hypothesis (SHY) “ (Tononi et al., 2023), suggesting that napping partially reverses the metabolic imbalance induced by SD through localized network resetting rather than global recovery. The right insula exhibits a similar activation pattern, with reduced positive activation after SD and partial recovery following a brief daytime nap, though the activation intensity remains intermediate between the RW and SD states.

The right insula is a core region of the salience network, which plays a critical role in detecting and responding to stimuli. Working memory tasks are complex high-level tasks closely related to attention, information storage and recognition, and executive functions. Napping enhances the role of the right insula within the executive control network, leading to partial recovery of working memory performance. Studies from a functional connectivity perspective have also indicated that the insula and anterior cingulate cortex (ACC) are closely functionally connected (Zhang et al., 2021). After SD, the effective connectivity between these regions decreases (Schneider et al., 2020). Following napping, the insula recovers, enhancing ACC function and improving attention, alertness, and cognitive flexibility (Seamans and Floresco, 2022) while also enhancing the ability to recognize and respond to task-relevant stimuli. These findings are broadly consistent with the results of this study. In recent years, the cerebellum has been recognized not only for its role in motor coordination and balance but also for its involvement in cognitive tasks. For example, studies have shown that attention, working memory, and language fluency are closely associated with cerebellar function (Abderrakib et al., 2022). In this study, the negative activation of the cerebellum following SD may indicate suppressed neural activity, while its recovery of neural excitability after a brief nap, accompanied by improvements in working memory performance, provides potential evidence for the cerebellum’s significant role in cognitive tasks.

There may be another Neuroimaging pathway account for working memory restoration: During working memory tasks in the awake state, the middle temporal gyrus, superior temporal gyrus, and precuneus exhibit negative activation (deactivation). These regions are integral components of the default mode network (DMN), a brain system typically more active during rest or internally directed cognition (Menon, 2023). Generally, when individuals focus on external tasks, the DMN competes with task-positive networks (TPNs), such as the frontoparietal network and dorsal attention network, for limited cognitive resources. During such tasks, cognitive resources are reallocated to external stimulus processing, leading to attenuated DMN activity (Tripathi and Garg, 2022). However, under SD, heightened drowsiness significantly attenuates deactivation strength in these DMN regions (Cai et al., 2021). This impairment prevents their complete “disengagement” from cognitive tasks, thereby compromising global cognitive processing efficiency. Notably, daytime napping not only restores but also enhances the deactivation strength of these regions beyond baseline awake-state levels. These findings suggest that napping enhances suppression of the DMN while potentiating the TPN, thereby optimizing externally-oriented, task-focused processing. Furthermore, napping promotes restoration of neurochemical homeostasis, particularly for neurotransmitters governing attention and arousal. Brief naps increase acetylcholine and norepinephrine concentrations, improving cognitive performance, attention, and memory (Faraut et al., 2015, Zhang et al., 2023). Post-nap neurochemical modulation may further suppress DMN deactivation beyond awake-state levels. This hyper-suppression likely reflects restored neural efficiency, enhanced neurotransmitter function, and prioritized allocation of resources to task-relevant networks, compensating for SD-induced attentional deficits and cognitive resource depletion.

In summary, the present study demonstrated that there are two main distinct neuroimaging of daytime naps in improving working memory: first, partially mitigating the cognitive impairments brought by SD through boosting the intensity of activity in attention-related brain regions such as the salience network and the thalamus; and, second, optimizing the allocation of cognitive resources by enhancing the negative activation of specific brain regions in the DMN, thereby enhancing the brain’s ability to process external tasks. These findings provide a valuable basis for understanding how SD affects cognitive performance and how to design effective interventions.

It has been well-established that daytime napping serves as an effective countermeasure against cognitive decline induced by SD (Hartzler, 2014, Leong et al., 2022). For instance, daytime napping has been shown to exert positive effects on memory functions. Typically, daytime naps encompass Stage 1 and Stage 2 sleep, with the potential inclusion of slow-wave sleep (SWS) and brief episodes of rapid eye movement (REM) sleep. Empirical evidence indicates that participants who napped exhibited superior performance in direct associative memory, relational memory formation, and visual working memory compared to those who remained awake (MacDonald et al., 2018b, Lahl et al., 2008). It is important to note that even naps of less than twenty minutes have been shown to improve memory performance (O’Donnell et al., 2018), although to a lesser extent compared to longer naps. These findings suggest that the initial stages of sleep may initiate memory consolidation processes that persist even during post-nap wakefulness. Furthermore, empirical evidence demonstrates the consolidating effects of daytime napping on declarative memory (Schoen and Badia, 1984), procedural memory (Backhaus and Junghanns, 2006), and lexical consolidation (Batterink et al., 2017).

Finally, based on the key role of SN, correlation analysis was conducted to examine the role of post-nap right insular activation in working memory improvement. Our findings revealed a significant positive correlation between enhanced right insular activation and reduced reaction times in working memory tasks following napping, suggesting that increased right insular engagement may reflect improved efficiency of inhibitory control. As a core hub of the SN, the right insula plays a pivotal role in dynamically monitoring behavioral performance and mediating neurocognitive processes, particularly in higher-order cognitive control and attentional regulation (Cauda et al., 2011, Uddin, 2015, Menon and Uddin, 2010). Notably, recent neurofeedback training studies demonstrate that targeted modulation of the right anterior insula enhances attentional alertness (Popovova et al., 2024), further corroborating its critical involvement in post-nap cognitive recovery. These results align with our proposed neuroimaging mechanism (Mechanism 1), wherein right insular activation is associated with heightened attentional focus, enhanced information storage/retrieval, and strengthened executive control. However, conflicting evidence reported a negative association between anterior insular activation and resilience during sustained attention, implying reduced activation in individuals with superior attentional recovery (Montalto et al., 2023). This discrepancy may arise from functional heterogeneity within insular subregions or task-specific demands. Future research should explore whether the functional role of right insular activation varies across distinct cognitive domains, potentially mediated by its connectivity profiles with downstream networks.

Several limitations of this study warrant careful consideration. First, the demographically restricted cohort, consisting exclusively of young male college students, limits the generalizability of our findings to populations with broader clinical or demographic characteristics. Future studies should endeavor to include more diverse sample populations—such as shift workers and older adults—to evaluate potential population-specific effects. Second, the three-phase cross-sectional design (including resting wakefulness, post-sleep deprivation, and post-nap assessments) does not allow for the characterization of temporal dynamics in cognitive trajectories. Moreover, nap architecture is known to be significantly influenced by circadian rhythm. Consequently, longitudinal monitoring of cognitive decline and recovery kinetics is necessary to identify critical inflection points that may inform targeted interventions. Third, although task-based fMRI is effective in capturing regional brain activation patterns, it cannot establish causal relationships within network-level mechanisms. The integration of multimodal approaches—such as simultaneous EEG-fMRI or pharmaco-genetic manipulations—could help elucidate the neurochemical and oscillatory determinants underlying sleep deprivation effects. Finally, the study did not assess the specific contributions of different sleep stages (e.g., NREM vs. REM) to post-nap recovery, nor did it examine potential genetic modulators of plasticity within the salience network and thalamus. Future research combining polysomnography with multi-omics profiling may help clarify stage-dependent and genetically stratified recovery mechanisms.

5. Conclusions

In this study, we used task-state functional magnetic resonance to find that SD and daytime naps mainly affected the cerebellum, thalamus, and brain regions constituting the salience network and the DMN from the neuroimaging perspective. Daytime napping partially restores working memory performance by enhancing the degree of positive activation in the cerebellum, thalamus, and salience network, as well as increasing the negative activation of the DMN. The present study not only helps us better to understand the complex relationship between sleep and cognitive function but also provides a scientific basis for designing interventions against cognitive deficits brought about by SD, which has good theoretical and practical significance.

CRediT authorship contribution statement

Yuanqiang Zhu: Methodology. Yan Li: Methodology. Jiaxi Peng: Methodology. Lingli Zeng: Methodology. Jun Jiang: Writing – original draft. Wei He: Writing – review & editing. ouyang anping: Writing – original draft. Qianqian Dong: Writing – review & editing. Xinxin Lin: Investigation. Lin Wu: Investigation. Peng Fang: Writing – review & editing. Lingling Wang: Investigation. Xuqian Diao: Investigation. Qiutao Yan: Investigation.

Clinical trial number

not applicable.

Institutional review board statement

The study was conducted following the Declaration of Helsinki and approved by the Institutional Review Board of First Affiliated Hospital of Fourth Military Medical University (KY20183115–1). Prior to participating in the study, each participant provided written informed consent.

Disclaimer/publisher’s note

The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of the editor(s). The editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.

Informed consent

Written informed consent was obtained from all individual participants included in the study. Participants were fully informed of potential risks (e.g., fatigue during 30 h sleep deprivation) and their right to withdraw at any time.

Safety protocols

Continuous physiological monitoring (ECG, blood pressure) was implemented during sleep deprivation.

A physician was on-call throughout the experiment to address adverse events.

Post-study recovery sleep (8 h) was mandatory prior to discharge.

Ethical approval

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of First Affiliated Hospital of Fourth Military Medical University (KY20183115–1) and conducted in accordance with the Declaration of Helsinki.

Funding

This work was funded by the National Natural Science Foundation of China (No.32471081), Shaanxi Provincial Natural Science Basic Research Program (2024JC-YBQN-0209), National Key Laboratory of Unmanned Aerial Vehicle Technology (No. WR202420–2) and Clinical Medicine and X Research Center Project (LHJJ24XL03).

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors gratefully acknowledge the editors and anonymous experts for their constructive feedback and valuable comments, which will greatly improve the quality of this work.

Contributor Information

Qianqian Dong, Email: qiamqian@163.com.

Wei He, Email: afmmuhw@163.com.

Peng Fang, Email: fangpeng@fmmu.edu.cn.

Data availability

The datasets generated and analyzed during this study are not publicly available due to ethical restrictions and privacy concerns related to participant confidentiality. However, de-identified data may be made available upon reasonable request for academic purposes, subject to approval by the institutional ethics committee and compliance with data protection agreements. Researchers interested in accessing the data should contact the corresponding author to initiate the request process.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated and analyzed during this study are not publicly available due to ethical restrictions and privacy concerns related to participant confidentiality. However, de-identified data may be made available upon reasonable request for academic purposes, subject to approval by the institutional ethics committee and compliance with data protection agreements. Researchers interested in accessing the data should contact the corresponding author to initiate the request process.

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