Transcutaneous Acupoint Electrical Stimulation Ameliorates Working Memory Impairment in Nap-deprived Interns: The Mediating Role of Augmented Brain-Heart Interaction

Transcutaneous Acupoint Electrical Stimulation Ameliorates Working Memory Impairment in Nap-deprived Interns: The Mediating Role of Augmented Brain-Heart Interaction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcutaneous Acupoint Electrical Stimulation Ameliorates Working Memory Impairment in Nap-deprived Interns: The Mediating Role of Augmented Brain-Heart Interaction Jieyi Fan, Liang Wang, Ze Lv, Chengfei Li, Yikai Pan, Yuan Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8925154/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background ​ Working memory (WM) decrement due to circadian dips and sleep restriction is a prevalent issue among interns, potentially impacting clinical decision-making. Non-invasive neuromodulation techniques, such as transcutaneous electrical acupoint stimulation (TEAS), offer a promising approach to mitigate cognitive fatigue. This study investigates the efficacy and underlying neurophysiological mechanism of TEAS at the Yintang (EX-HN3) acupoint in counteracting WM impairment induced by nap deprivation. Methods We enrolled 90 interns with habitual napping behavior, who were randomly assigned to one of three groups: normal control group (NC), nap deprivation group (ND), and nap deprivation group receiving TEAS intervention (ND+TEAS). Cognitive performance was assessed using a 3-back WM task at baseline (12:00) and post-intervention (15:00). Concurrently, prefrontal cerebral hemodynamics and autonomic nervous system activity were monitored via functional near-infrared spectroscopy (fNIRS) and heart rate variability (HRV), respectively. Subjective cognitive load was evaluated using the NASA-Task Load Index (NASA-TLX). Results Compared to the NC group, the ND group exhibited significant deterioration in WM accuracy and speed, accompanied by reduced prefrontal cortex (PFC) activation and attenuated parasympathetic activity (reflected by decreased HRV high-frequency power). The ND+TEAS group demonstrated a reversal of these effects, showing superior WM performance, enhanced PFC oxygenation, and increased vagally-mediated HRV indices relative to the ND group. Crucially, mediation analysis revealed that the improvement in WM performance following TEAS was mediated by its effect on augmenting PFC activation, which in turn was associated with increased parasympathetic tone. Conclusions TEAS at the Yintang acupoint effectively alleviates nap deprivation-induced WM impairment. The mechanism appears to involve the enhancement of parasympathetic nervous activity and the subsequent facilitation of prefrontal cortical function. These findings provide novel experimental evidence for the brain-heart interaction as a pathway for cognitive enhancement and position TEAS as a viable, non-invasive strategy to bolster cognitive resilience in populations susceptible to circadian fatigue, such as clinical staff. Transcutaneous acupoint electrical stimulation Working Memory Heart rate variability Prefrontal brain activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Working memory (WM), a core component of executive function, is essential for clinical reasoning, decision-making, and procedural execution in high-stakes medical environments[ 1 , 2 ]. For interns and other healthcare professionals, acute cognitive fatigue and WM decrement are prevalent challenges, often stemming from circadian rhythm disruptions, sleep restriction, and irregular shift patterns[ 3 ]. These impairments not only affect individual well-being but also pose potential risks to patient safety[ 4 ]. Therefore, identifying safe and effective strategies to bolster cognitive resilience in this population is of paramount clinical importance. Daytime napping is a widespread behavior adopted as a countermeasure against fatigue[ 5 , 6 ]. Among habitual nappers, the forced deprivation of a regular nap has been established as a valid experimental model to induce transient cognitive fatigue and WM deficits[ 6 , 7 ], mirroring the cognitive challenges faced by medical staff after night shifts or during prolonged duty hours[ 8 ]. Such deprivation leads to measurable declines in cognitive performance, particularly in tasks requiring sustained attention and WM[ 3 , 7 ]. The autonomic nervous system (ANS), which regulates involuntary physiological functions such as heart rate, consists of the parasympathetic (relaxation-promoting) and sympathetic (arousal-promoting) branches. The dynamic balance between these two branches governs cardiac regulation. Heart rate variability (HRV) serves as a key non-invasive marker of this autonomic balance, reflecting an individual's capacity for self-regulation and physiological adaptability [ 9 , 10 ]. Higher HRV is generally associated with protective effects for cognitive function, whereas lower HRV is correlated with cognitive decline [ 11 , 12 ]. Autonomic dysfunction has been observed in individuals with mild cognitive impairment, suggesting a link between cognitive impairment and abnormal autonomic regulation [ 13 ]. It also represents an early manifestation of neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease [ 14 , 15 ]. The HF-HRV is associated with performance in cognitive tasks such as cognitive inhibitory control and executive function[ 16 , 17 ]. Additionally, in cognitive fatigue research, HF-HRV can predict cognitive task performance [ 12 ]. Thus, high HRV and increased HF reflect a flexible and adaptive physiological system that promotes homeostasis. Conversely, low HRV and diminished HF indicate a system less adept at adapting to external stressors[ 18 – 20 ]. The function of ANS is under the control of the central autonomic network (CAN)[21, 22]. A central component of the CAN is the medial prefrontal cortex (mPFC), which plays a critical role in exerting top-down cognitive control over autonomic and emotional processes[ 12 , 23 ]. The CAN modulates autonomic function through key brainstem nuclei: the dorsal motor nucleus of vagus nerve mediates parasympathetic output, while the caudal ventrolateral medulla regulates sympathetic tone. The sympathetic pathway is activated in response to homeostatic challenges from external stressors[ 24 ]. Mirroring ANS dysfunction, impairment of the CAN is also linked to cardiac diseases and cognitive decline [ 25 – 27 ]. The activation of CAN recruits higher-order cognitive regions integral to cognition, emotion, and autonomic control[ 22 , 28 ]. Functional near-infrared spectroscopy (fNIRS) studies indicate that reduced oxy-hemoglobin (HbO) concentration in the dorsolateral prefrontal cortex (DLPFC) directly reflects diminished neural activity, correlating with cognitive decline[29]. Furthermore, weakened prefrontal network connectivity is associated with deficits in specific cognitive domains[ 30 ]. Cognitive performance concurrently influences both autonomic and central nervous system function. Research on brain-heart interactions and their functional coupling is essential for understanding the physiological manifestations of homeostatic changes[ 31 , 32 ]. Functional brain-heart coupling refers to the dynamic coordination between neural and cardiac activity, reflecting their functional interdependence[33]. Transcutaneous electrical acupoint stimulation (TEAS) is a non-invasive neuromodulation technique shown to enhance cognitive function. Owing to its minimal invasiveness and favorable safety profile, TEAS is particularly suitable for modulating cognitive function in the general population. A 4-week randomized controlled trial (RCT) demonstrated that TEAS improved attention and executive function in children aged 6–12 with ADHD[ 34 ]. Furthermore, an RCT in patients with schizophrenia found that adjunctive TEAS improved working memory (WM) and information processing speed compared to pharmacotherapy alone[ 35 ]. Notably, the Yintang (EX-HN3) acupoint, located on the forehead, is strategically positioned over the frontal lobe and is traditionally used for mental calming and cognitive enhancement[ 36 ]. A growing body of evidence suggests that TEAS can also modulate ANS activity, promoting parasympathetic tone[ 37 – 39 ]. However, a critical knowledge gap remains: whether and how TEAS can acutely mitigate WM impairment induced by a common stressor like nap deprivation in interns, and whether its cognitive benefits are mediated through the restoration of brain-heart interaction. To address the above-mentioned knowledge gaps, the study investigates the acute effects of TEAS after deprivation of daytime napping on WM (measured via the 3-back task), cortical hemodynamics (measured by fNIRS), and the ANS (measured via HRV). Our primary aims were to 1) investigate among young habital nappers how nomal daytime napping and TEAS affect 3-back task performance, and the ANS following deprivation of daytime napping, and 2) examine the relationships between TEAS-induced changes in WM, cortical hemodynamics, and HRV. we hypothesized that TEAS after deprivation of daytime napping would enhance WM and cortical hemodynamics. We also hypothesized that interrupting the nap deprivation with TEAS would increase HRV, reflecting a flexible and adaptive physiological system. Furthermore, we predicted that TEAS would yield benefits for both behavioral and neurobiological outcomes. Additionally, we hypothesized that improvements in WM and ANS would be mediated by changes in cortical hemodynamics. 2. Materials and Methods 2.1 Study population A total of 95 interns from the Fourth Military Medical University First Affiliated Hospital were recruited as participants. The interns refrained from consuming any medications or beverages affecting neurological function and avoided high-intensity physical exercise for 48 hours prior to testing. All participants maintained their regular diet and daily routines and were right-handed. Before the formal experiment commenced, all interns completed the Pittsburgh Sleep Quality Index (PSQI) questionnaire to evaluate their fatigue status and sleep conditions over the preceding two weeks. Individuals who reported recent sleep disturbances or significant fatigue were excluded. Only interns with a consistent habit of daytime napping were included. The study protocol was approved by the Ethics Committee of the Fourth Military Medical University, and written informed consent was obtained from all participants prior to their enrollment. As part of a screening procedure to assess habitual napping patterns, the interns completed sleep diaries for a minimum of one week, during which they were also required to record whether they had taken a daytime nap. Individuals were classified as habitual nappers if they reported napping at least once per week, whereas non-habitual nappers were defined as those who napped less than once per week. To account for inter-individual variability in the total number of diary days completed, napping frequency was expressed as the proportion of daytime napping relative to the total number of diary days. Consequently, habitual nappers were operationally defined as individuals who took at least one nap per week. Among habitual nappers, the mean reported nap frequency was two times per week (range: 1–6 times per week). The most frequently reported nap duration was 31–60 min. The predominant nap timing occurred between 12:00 PM and 14:00 PM (noon–early afternoon). After excluding one participant with incorrectly filled or incomplete sleep diaries and four participants who had missing data across the tasks, our final sample consisted of 90 participants (mean age ± SD:22.12 ± 0.87 years). Interns were randomly assigned to three groups: nap deprivation group (ND), normal control group (NC), and nap deprivation group receiving TEAS intervention (ND+TEAS). Each group underwent 3-back task, HRV, fNIRS, and NASA Task Load Index (NASA-TLX) at 12:00 and 15:00. In ND, interns refrained from napping. They remained in the laboratory engaging in relaxed, sedentary activities, such as reading or listening to music, and avoided vigorous exercise. NC maintained their habitual napping schedule. ND+TEAS received a daily 30-minute TEAS for six consecutive days. On the seventh (test) day, they refrained from napping and instead remained in the laboratory engaging in light, sedentary activities. Additionally, on the test day, a continuous 30-minute TEAS was administered beginning at 14:30. To examine the impact of TEAS on HRV and its association with cognitive performance, electrocardiogram (ECG) recordings were obtained from participants during the 3-back task (See Fig. 1 ). NC: normal control group; ND: nap deprivation group; ND+TEAS: nap deprivation group receiving TEAS intervention 2.2 Subjective assessment The NASA-TLX is a subjective scale originally developed by the National Aeronautics and Space Administration (NASA) for aerospace applications. It is now the most widely used multidimensional instrument for assessing subjective cognitive load [ 40 , 41 ]. The NASA-TLX assesses six dimensions: mental demand (MD), physical demand (PD), temporal demand (TD), performance (Fr), effort (Ef), and frustration (Pe). Each dimension is rated on a scale from 0 to 20[ 40 ]. 2.3 Cognitive task The 3-back task required participants to recall the spatial locations of sequentially presented blue squares. Participants were instructed to indicate whether the location of the current blue square matched the location of the square presented two trials back. The task comprised five blocks. A 10-second rest period, signaled by an on-screen "Rest" prompt, separated each block. The total task duration was 8 minutes. Each target stimulus (a blue square) was displayed for 500 ms, followed by a 1000 ms blank inter-stimulus interval, resulting in a stimulus onset asynchrony (SOA) of 1500 ms [ 42 , 43 ] (See Fig. 2 ). Prior to the main task, participants read on-screen instructions. Following a self-reported understanding of the rules, participants completed a practice block. The practice required completing three sets of 16 trials each. Participants were required to achieve an accuracy rate (ACC) greater than 80% in each set before advancing to the main task. During the main task, no performance feedback was provided. The stimulus presentation order was pseudorandomized across participants. 2.4 fNIRS measurement Prefrontal cortical activity was measured using an OctaMon functional near-infrared spectroscopy (fNIRS) system (Artinis Medical Systems, Netherlands). The system employed a wireless 8-channel configuration with a source-detector separation of 3.5 cm to measure concentration changes in oxygenated (HbO) and deoxygenated (HHb) hemoglobin in the PFC microcirculation. Each channel used dual wavelengths (756 and 856 nm) and sampled at 25 Hz. Data were collected using Oxysoft software (version 3.0.52, Artinis Medical Systems). Optodes were positioned bilaterally on the forehead according to the international 10–20 system, targeting Brodmann’s areas 9 and 10 (dorsolateral and anterior prefrontal cortex) (See Fig. 3 ). The raw fNIRS data were preprocessed using the NIRS-KIT-main toolkit in MATLAB 2023a. Initially, the Data Preparation module converted .nirs files to .mat files. Subsequently, the HbO values and their corresponding time points were extracted from the .mat file. Following this, the Preprocessing module was employed for detrending. Variations in instrument sensitivity and participants' physiological conditions during the experiment may introduce trends that could affect the study results. By applying a linear regression model to eliminate these trends, alterations in the signals of interest can be observed more distinctly in the analysis. During the experiment, participants' body movements may introduce motion artifacts into the data. Motion correction can effectively reduce these artifacts, thereby enhancing data quality. We utilize the Temporal Derivative Distribution Repair (TDDR) method to correct motion artifacts. By constraining the frequency range to 0.01 Hz to 0.08 Hz, the filter effectively removes frequency components outside this interval, thereby reducing high-frequency noise and low-frequency drift. This process enhances the sensitivity of the signals of interest. A third-order Butterworth bandpass filter is employed for this filtering. The filtered data is subsequently converted to changes in HbO concentration using the modified Beer-Lambert law. Following this, the general linear model (GLM) is applied to derive beta values. Previous research has established that HbO is the most sensitive indicator of changes in experimental paradigm stimuli[ 44 – 46 ].Therefore, this study selects the beta value of HbO as a metric for evaluating brain activity, specifically focusing on the activation level of the prefrontal cortex, where elevated beta values signify increased activation in this region. 2.5 TEAS treatment Participants were randomly assigned to three groups: NC, ND and ND+TEAS. TEAS was conducted daily using small (1.5 cm) cutaneous electrode pads placed at the Yintang (GV29) point. The Yintang acupoint is situated at the inner end of the eyebrows, specifically at the midpoint where the inner ends of both eyebrows converge. The electrical stimulation was delivered at the maximum tolerable current intensity, ensuring that no muscle contractions or discomfort occurred. The parameters were configured to a dense-sparse wave with a frequency of 50 Hz, utilizing the Hwato electronic acupuncture treatment instrument (Model No. SDZ-II; Suzhou Medical Appliances, Suzhou, China). 2.7 Heart rate variability (HRV) All participants were instructed to wear an Equivital EQ02 + LifeMonitor (Equivital, Cambridge, UK) while performing cognitive tests and undergoing TEAS treatment [ 47 ]. The Equivital LifeMonitor is a vest-like wearable device that continuously measures ECG, heart rate (HR), and heart rate variability (HRV) through two channels using three electrodes. It features a sensor electronic module that resides in a cradle on the side of the LifeMonitor. The EQ02 monitoring system comprises LifeMonitor straps of various sizes, one LM 100 LifeMonitor sensor electronic module (SEM), a Bluetooth USB dongle for laptop connectivity, and Equivital Manager software, which is utilized to configure SEMs and to export and download data. In this study, SEMs were set to real-time monitoring mode. Data were stored simultaneously in the SEMs and the laptop. A LifeMonitor belt of appropriate size secured the SEM to each participant's body. Prior to skin contact, the textile-based electrodes were moistened with water. The SEMs were recharged for approximately 1 hour following a 12-hour recording period. At the conclusion of the study, the SEM data were transferred to the Equivital Manager software, where time- and date-stamped ECG readings, inter-beat intervals, and summarized vital sign data were extracted and exported for analysis. 2.8 Statistical analysis The data from this study were analyzed statistically using SPSS 25. Initially, descriptive statistical analyses were performed on subjective scale data, behavioral data, physiological data, and near-infrared spectroscopy (NIRS) data. A repeated measures ANOVA was conducted for all subjective and objective evaluation results across ND, NC, and ND+TEAS), with time (12:00, 15:00) and group as factors. Different intervention types were treated as within-subject factors, and all post-hoc multiple comparisons were adjusted using the Bonferroni method. The effect size was reported using partial ƞ² based on the results of the analysis of variance. We employed the SPSS PROCESS macro v4. for mediation analysis. Our mediation models included three sets of variables: the predictor of working memory performance (X), autonomic nervous system activity (Y), and a mediator represented by a specific fNIRS measure (M). The fNIRS measures acting as mediators were determined as the mean β values observed in fNIRS channels showing group-by-time interaction effects. A parallel mediation model was used to explore the direct impact of working memory performance (measured through RTs) on autonomic nervous system function (assessed as HRV-HFnu), as well as the indirect influence mediated by the fNIRS parameters: β values of Ch3, Ch6, and Ch8. We analyzed the mediation between the outcome and the mediator, assessing both the direct effect of X and the indirect (mediation) effect of X through β values on Y. A visual representation of the complete model is depicted in Figure S1. All regression coefficients were reported as standardized coefficients, with effects deemed significant at p < 0.05. Bootstrapped confidence intervals (5,000 runs) were used to determine the significance of indirect effects. 3. Results 3.1Baseline characteristics Table 1 summarizes the descriptive statistics and nap parameters were observed among three group (See Table 1 ). Table 1 Baseline characteristics of three groups NC ND ND+TEAS Demographics Number of Participants 30 30 30 Age (years) 24.78 ± 1.98 23.65 ± 1.37 24.12 ± 1.05 Education (years) 17.43 ± 2.01 16.39 ± 1.87 17.96 ± 1.99 Daytime napping characteristics time in bed (TIB) 66.18 ± 4.39 64.29 ± 5.22 total sleep time (TST) 58.12 ± 3.26 bedtime 12:50 − 13:00 12:45 − 13:00 12:50 − 13:00 wake time 14:00–14:10 14:00–14:10 14:00–14:10 Normal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS). 3.2 The effect of different interventions on NASA-TLX scale scores A two-factor repeated-measures ANOVA of NASA-TLX scale scores indicated significant differences in MD, Pe, and Ef. The interaction effect between testing time point and intervention condition on MD was not significant ( F [1, 88] = 2.08, p = 0.13, partial η² = 0.05 ), neither was the main effect of testing time point ( F [1, 88] = 3.09, p = 0.08, partial η² = 0.04 ). However, the main effect of intervention condition was significant ( F [1,88] = 7.25,p < 0.001,partialη²=0.15 ). Post-hoc comparisons revealed no significant differences among the three groups at baseline (12:00, p = 0.87 ). Following TEAS, MD scores were significantly lower than those in the ND (15:00, p = 0.02). Moreover, MD scores after normal napping were significantly lower than those after non-nap (15:00, p < 0.001) Table 2 The results of repeated measures ANOVA for NASA-TLX score across three groups at 12:00 and 15:00 Measures NC ND ND+TEAS F p partial ƞ 2 12:00 15:00 12:00 15:00 12:00 15:00 MD Mean ± SD 9.71 ± 4.46 9.45 ± 3.67 10.21 ± 4.05 13.42 ± 3.91 10.53 ± 4.15 9.67 ± 4.62 PD Mean ± SD 5.75 ± 2.74 5.34 ± 2.83 5.68 ± 2.25 7.03 ± 3.26 5.57 ± 2.29 5.49 ± 2.93 group*time - - - - - - 1.39 0.26 0.03 time - - - - - - 0.59 0.45 0.01 group - - - - - - 1.74 0.18 0.04 TD Mean ± SD 10.50 ± 4.41 9.80 ± 2.95 10.37 ± 5.06 10.9 ± 4.48 11.98 ± 2.66 10.85 ± 5.34 group*time - - - - - - 0.73 0.49 0.02 time - - - - - - 0.29 0.59 0.003 group - - - - - - 1.34 0.71 0.03- Pe Mean ± SD 14.22 ± 3.68 14.47 ± 2.40 13.95 ± 1.79 14.01 ± 2.60 13.12 ± 2.61 12.42 ± 1.64 group*time - - - - - - 0.26 0.77 0.01 time - - - - - - 0.83 0.37 0.01 group - - - - - - 5.95 0.004 0.12 Ef Mean ± SD 14.37 ± 3.85 12.99 ± 3.77 13.03 ± 3.79 13.41 ± 2.71 14.70 ± 2.69 12.41 ± 6.25 group*time - - - - - - 3.56 0.03 0.08 time - - - - - - 0.09 0.76 0.001 group - - - - - - 0.20 0.82 0.01 Fr Mean ± SD group*time time group 6.96 ± 2.85 - - - 7.06 ± 2.68 - - - 7.43 ± 3.20 - - - 9.85 ± 2.41 - - - 7.29 ± 3.44 - - - 6.12 ± 2.59 - - - 0.70 1.18 0.78 0.50 0.28 0.46 0.02 0.01 0.02 Mental Demand (MD), Physical Demand (PD), Temporal Demand (TD), Frustration (Fr), Effort (Ef), and Performance (Pe). The main effect of the intervention condition on Pe was significant ( F [1, 88] = 5.95, p = 0.004, partial η² = 0.12 ). In contrast, neither the interaction effect between testing time point and intervention condition ( F [1, 88] = 0.26, p = 0.77, partial η² = 0.01 ) nor the main effect of testing time point ( F [1, 88] = 0.83, p = 0.37, partial η² =0.01 ) reached significance. Post-hoc comparisons revealed no significant differences among the three groups at baseline (12:00, p = 0.12 ). Following TEAS, Pe scores were significantly lower than those in the ND group (15:00, p = 0.02 ). However, no significant difference was found between the normal napping and non-nap (15:00, p = 0.07 ). The interaction effect between testing time point and intervention condition on was significant ( F [1, 88] = 3.56, p = 0.03, partial η² = 0.07 ). In contrast, the main effects of testing time point ( F [1, 88] = 0.09, p = 0.76, partial η²=0.001 ) and intervention condition ( F [1, 88] = 0.20, p = 0.82, partial η² =0.005 ) were not statistically significant. Following TEAS, post-intervention scores were significantly lower than baseline scores (12:00 vs. 15:00, p = 0.03 ). No statistically significant differences were observed for PD, TD, or Fr (all ps > 0.05 ) (See Table 2 and Fig. 4 ). 3.3The effect of different interventions on cognitive performance An analysis was conducted on the mean reaction times (mean RTs) and ACC of the 3-back task to explore the impact of TEAS on WM. A two-factor repeated-measures analysis of variance (ANOVA) was utilized, with time (12:00 and 15:00) as the within-subject factor and intervention groups (NC, ND and ND+TEAS) as the between-subject factor (See Table 3 and Fig. 5 ). Post hoc multiple comparisons were carried out using the Bonferroni method for correction. The outcomes of the two-factor repeated-measures ANOVA for mean RTs on the 3-back test revealed that the main effect of time was not statistically significant ( F [1, 88] = 0.06, p = 0.80, partial η² = 0.001 ). However, a significant interaction effect between time and intervention condition was observed ( F [1, 88] = 11.96, p < 0.001, partial η² = 0.24 ), along with a significant main effect of the test time point ( F [1, 88] = 6.87, p = 0.002, partial η² = 0.15 ). Post hoc multiple comparisons indicated no significant variances in baseline RTs at 12:00 among the different intervention groups ( p = 0.99 ). By 15:00, the average RT for the 3-back task following TEAS was notably shorter than that following nap deprivation ( p = 0.002 ). Similarly, post a regular nap session, the average RT for the normal control group was significantly shorter than that for the ND group at 15:00 ( p = 0.011 ). Furthermore, in comparison to the baseline at 12:00, the 30-minute TEAS significantly enhanced the average RT for the 3-back task ( p = 0.004 ). Conversely, following a regular nap, there was no significant enhancement in the average RT ( p = 0.08 ). In ND, the average RT for the 3-back task at 15:00 was significantly prolonged compared to the baseline ( p < 0.001 ). The results indicated a significant interaction between time and intervention condition on 3-back task ACC ( F [1, 88] = 31.57, p < 0.001, partial η² = 0.52 ). A significant main effect was observed for the intervention group ( F [1, 88] = 4.38, p = 0.02, partial η² = 0.13 ), while the main effect of time was not significant ( F [1, 88] = 0.03, p = 0.87, partial η² =0.001 ). Post hoc comparisons revealed no significant differences in ACC at the 12:00 baseline level among intervention groups (12:00, p = 0.07 ). However, following TEA, the ACC of the 3-back task was significantly higher compared to ND (15:00, p < 0.001), and the ACC of NC was significantly higher than that of ND (15:00, p < 0.001). Compared to the baseline measurement at 12:00, the 30-minute TEAS significantly enhanced ACC on the 3-back task (12:00 vs. 15:00, p = 0.003 ). Furthermore, ACC following the nap also exhibited a significant improvement (12:00 vs. 15:00, p < 0.001 ). In contrast, participants in ND demonstrated a significantly lower ACC in completing the 3-back test at 15:00 compared to the baseline level (12:00 vs. 15:00, p < 0.001 ). Table 3 The results of repeated measures ANOVA for behavioral performance in the 3-back task across three groups at 12:00 and 15:00 Measures NC ND ND+TEAS F p partial ƞ 2 12:00 15:00 12:00 15:00 12:00 15:00 Mean RTs(ms) Mean ± SD 783.49 ± 411.21 651.44 ± 323.18 790.53 ± 252.53 1079.40 ± 411.31 795.58 ± 226.33 607.03 ± 227.75 group*time - - - - - - 11.96 < 0.001 0.24 time - - - - - - 0.06 0.80 0.001 group - - - - - - 0.06 0.80 0.001 Accuracy Mean ± SD 0.83 ± 0.13 0.93 ± 0.10 0.81 ± 0.08 0.69 ± 0.11 0.84 ± 0.07 0.96 ± 0.08 group*time - - - - - - 31.57 < 0.001 0.52 time - - - - - - 0.03 0.87 0.001 group - - - - - - 4.3 0.02 0.13 Normal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS). 3.4The effect of different interventions on HRV The statistical analysis of HFnu revealed a significant interaction between test time point and intervention condition ( F [1, 88] = 3.31, p = 0.04, partial η² = 0.10 ). The main effect of the intervention condition was not significant ( F [1, 88] = 1.31, p = 0.28, partial η² = 0.04 ), and the main effect of the time was not significant ( F [1, 88] = 0.004, p = 0.95, partial η² = 0.001 ). Post hoc comparisons indicated no significant differences in ACC at the baseline level of 12:00 among NC, ND, and ND+TEAS (12:00, p = 0.07 ). However, HFnu following TEAS was significantly higher than that of the non-nap (15:00, p = 0.047 ). HFnu following the nap also exhibited a significant improvement (15:00, p = 0.04 ). Additionally, participants in ND demonstrated a significantly lower HFnu in completing the 3-back task at 15:00 compared to the baseline level (12:00 vs. 15:00, p = 0.04 ). There were no significant interactions between test time point and intervention condition or interactions between test time point and intervention condition observed in LFnu, RMSSD, and LF/HF ( ps ≥ 0.05 ) (See Table 4 and Fig. 6 ). Z-score normalization (standardization) was applied to the data, transforming it to have a mean of zero and a standard deviation of one. All continuous numerical features of HRV underwent standardization using Z-score normalization to address the issue of varying scales among variables (See Table 4 and Fig. 6 A). Specifically, each feature had its mean value (µ) from the training set subtracted and was then divided by the corresponding standard deviation (σ) from the same set. This normalization ensured that each feature had a mean of zero and a standard deviation of one, facilitating the convergence of our model and preventing features with larger scales from dominating the learning process. Table 4 The results of repeated measures ANOVA for HRV in the 3-back task across three groups at 12:00 and 15:00 Measures NC ND ND+TEAS F p partial ƞ 2 12:00 15:00 12:00 15:00 12:00 15:00 LFnu-3-back test Mean ± SD 47.50 ± 19.85 52.70 ± 21.11 45.20 ± 23.21 44.20 ± 23.33 54.24 ± 17.71 43.26 ± 18.30 group*time - - - - - - 0.14 0.87 0.01 time - - - - - - 0.33 0.57 0.01 group - - - - - - 0.27 0.77 0.01 HFnu-3-back test Mean ± SD 52.60 ± 19.78 55.94 ± 16.23 53.31 ± 15.54 42.36 ± 22.57 50.62 ± 17.67 57.68 ± 19.69 group*time - - - - - - 3.31 0.04 0.10 time - - - - - - 0.004 0.95 0.001 group - - - - - - 1.31 0.28 0.04 RMSSD-3-back test Mean ± SD 37.05 ± 23.36 27.06 ± 11.27 34.23 ± 17.92 25.01 ± 14.16 32.21 ± 14.02 36.68 ± 13.69 group*time - - - - - - 2.67 0.08 0.08 time - - - - - - 2.98 0.09 0.05 group - - - - - - 0.83 0.44 0.03 LF/HF-3-back test Mean ± SD 1.63 ± 2.21 0.93 ± 0.74 1.00 ± 0.83 1.34 ± 1.38 1.16 ± 1.87 1.09 ± 0.60 group*time - - - - - - 0.82 0.45 0.03 time - - - - - - 1.65 0.99 0.001 group - - - - - - 0.26 0.77 0.01 Normal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS). 3.5 The effect of different interventions on fNIRS brain activation This study utilized a two-way repeated-measures analysis of variance (ANOVA) to examine the impact of TEAS on cerebral activity indicators while performing 3-back task after non-nap. Variations in β values among three groups during 3-back task were assessed. To evaluate task-state activation levels indicative of the prefrontal cortex (PFC), individual two-way repeated-measures ANOVA were conducted on each of the eight channels. The interaction effect outcomes for each channel are detailed in Table 6 and Fig. 7 . For the Ch3, the interaction effect between the intervention condition and time was significant ( F [1, 88] = 4.51, p = 0.014, partial η² = 0.11 ). In contrast, neither the main effect of time ( F [1, 88] = 0.18, p = 0.67, partial η² = 0.002 ) nor the main effect of the intervention condition ( F [1, 88] = 0.05, p = 0.96, partial η²=0.001 ) reached significance. Post-hoc multiple comparisons indicated a significant difference in β values during the 3-back task after 30 minutes of TEAS when compared to the baseline at 12:00 (12:00 vs. 15:00, p = 0.02 ). However, no significant difference was observed after one hour of normal napping (12:00 vs. 15:00, p = 0.20 ). Additionally, without any intervention following nap deprivation, β values did not show a significant difference (12:00 vs. 15:00, p = 0.17 ). The interaction effect between intervention condition and test time point was significant for the Ch6 ( F [1, 88] = 7.41, p < 0.001, partial η² = 0.17 ). Neither the main effect of time ( F [1, 88] = 0.29, p = 0.59, partial η² = 0.004 ) nor the main effect of intervention condition ( F [1, 88] = 0.25, p = 0.78, partial η²=0.007 ) reached significance. Post-hoc multiple comparisons revealed a significant difference in β values during the 3-back task after 30 minute TEAS compared to the baseline at 12:00 (12:00 vs. 15:00, p = 0.004 ). However, no significant difference was observed after one hour of normal nap (12:00 vs. 15:00, p = 0.09 ). Furthermore, without any intervention following nap deprivation, β values exhibited no significant difference (12:00 vs. 15:00, p = 0.09 ). The interaction effect between intervention condition and time was significant for the Ch8 ( F [1, 88] = 6.40, p = 0.003, partial η² = 0.15 ). However, the main effects of time ( F [1, 88] = 0.25, p = 0.62, partial η² = 0.003 ) and intervention condition ( F [1, 88] = 0.001, p = 0.99, partial η² =0.001 ) were not significant. Post-hoc multiple comparisons revealed a significant difference in β values during the 3-back task after 30 minutes TEAS compared to the baseline at 12:00 (12:00 vs. 15:00, p = 0.007 ). Conversely, no significant difference was observed after one hour of normal nap (12:00 vs. 15:00, p = 0.10 ). Additionally, without any intervention following nap deprivation, β values exhibited no significant difference (12:00 vs. 15:00, p = 0.12 ). For hemodynamic response in HbO (i.e., β value), there were no significant interactions between test time point and intervention condition or interactions between test time point and intervention condition observed ( ps ≥ 0.05 ) (See Table 5 ). Table 5 The results of repeated measures ANOVA for β values in the 3-back task across three groups at 12:00 and 15:00 Channel NC ND ND+TEAS F p partial ƞ 2 12:00 15:00 12:00 15:00 12:00 15:00 Ch1 Mean ± SD 0.18 ± 0.07 0.14 ± 0.01 0.15 ± 0.12 0.13 ± 0.01 0.17 ± 1.55 0.19 ± 1.60 group*time - - - - - - 1.62 0.20 0.04 time - - - - - - 1.45 0.23 0.02 group - - - - - - 0.61 0.55 0.02 Ch2 Mean ± SD 0.14 ± 0.09 0.18 ± 0.01 0.18 ± 0.13 0.18 ± 0.01 0.13 ± 1.56 0.17 ± 1.55 group*time - - - - - - 2.43 0.10 0.06 time - - - - - - 2.12 0.14 0.03 group - - - - - - 1.37 0.26 0.04 Ch3 Mean ± SD 0.15 ± 0.06 0.21 ± 0.01 0.20 ± 0.15 0.21 ± 0.01 0.26 ± 1.55 0.31 ± 1.54 group*time - - - - - - 4.51 0.014 0.11 time - - - - - - 0.18 0.67 0.002 group - - - - - - 0.05 0.96 0.001 Ch4 Mean ± SD 0.18 ± 0.07 0.16 ± 0.01 0.20 ± 0.11 0.16 ± 0.01 0.19 ± 1.54 0.18 ± 1.56 group*time - - - - - - 1.89 0.16 0.05 time - - - - - - 1.72 0.19 0.02 group - - - - - - 1.11 0.33 0.03 Ch5 Mean ± SD 0.08 ± 0.08 0.17 ± 0.01 0.11 ± 0.01 0.17 ± 0.01 0.14 ± 1.58 0.26 ± 1.56 group*time - - - - - - 2.81 0.07 0.07 time - - - - - - 0.11 0.74 0.002 group - - - - - - 0.15 0.86 0.004 Ch6 Mean ± SD 0.27 ± 0.06 0.18 ± 0.01 0.28 ± 0.08 0.18 ± 0.01 0.41 ± 1.49 0.27 ± 1.55 group*time - - - - - - 7.409 < 0.001 0.17 time - - - - - - 0.29 0.59 0.004 group - - - - - - 0.25 0.78 0.01 Ch7 Mean ± SD 0.18 ± 0.16 0.22 ± 0.01 0.14 ± 0.07 0.12 ± 0.01 0.13 ± 1.53 0.17 ± 1.53 group*time - - - - - - 2.67 0.08 0.07 time - - - - - - 2.48 0.12 0.03 group - - - - - - 1.19 0.31 0.03 Ch8 Mean ± SD 0.22 ± 0.06 0.21 ± 0.01 0.21 ± 0.09 0.21 ± 0.01 0.32 ± 1.53 0.31 ± 1.54 group*time - - - - - - 6.403 0.003 0.15 time - - - - - - 0.25 0.62 0.003 group - - - - - - 0.001 0.99 0.001 Normal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS). 3.6 Mediation Effects of the fNIRS Measures The mediation model indicated a significant relationship between WM performance and autonomic nervous system activity ( c = -0.17, p < 0.001 ), with the direct effect of WM performance being significant but smaller than the total effect ( c' = -0.12, p < 0.01 ), leading to shorter reaction times with higher HFnu during the 3-back task. An indirect effect was observed for the β values of Ch6 in the mediation model, showing that better WM performance was linked to a larger β value ( a = -0.01, p = 0.045 ). Additionally, the fNIRS parameter associated with WM performance correlated with higher HFnu ( b = 8.72, p = 0.0101 ). These findings suggest that following TEAS, improved WM performance, along with higher vagus nerve activity, mediated HRV by enhancing the β value of Ch6. However, the β values of Ch3 ( b = -0.28, p = 0.72 ) and Ch8 ( b = -2.18, p = 0.26 ) did not exhibit a significant mediation effect (See Fig. 8 ). 4. Discussion The findings supported our hypotheses that TEAS applied at the Yintang acupoint enhanced the ANS and measures of WM and that these effects were mediated by improvements in cortical hemodynamics in interns. Compared to those who were deprived of daytime napping, those who received TEAS showed significant improvement in WM performance. We also noted significant increases in the ANS (i.e., HRV) and cortical activation in regions associated with WM. Moreover, the mediation analysis suggested that changes in HRV (i.e., HF) may influence performance via alterations in cortical oxygenation activation during a 3-back task. These findings advance our understanding of TEAS as a neuromodulatory tool and underscore the critical role of brain-heart interaction in cognitive resilience. 4.1 Influence of TEAS on WM In terms of behavioral data, the results showed that among interns, TEAS led to significant improvements in a 3-back task, as evidenced by shorter RT and higher ACC. The findings support and extend prior research emphasizing that TEAS benefits WM, which is essential for attention and processing speed, reasoning and problem-solving, and verbal learning and language[ 28 ]. Moreover, our study adds to previous research by suggesting that TEAS resulted in more pronounced improvements in WM following non-nap compared to participants without napping. Notably, recent research indicates that the practical implementation of TEAS has been explored and confirmed as feasible for cognitive dysfunction associated with neurodegenerative diseases, such as mild cognitive impairment (MCI) and Alzheimer's disease (AD)[ 34 , 48 , 49 ]. However, research on its potential for cognitive enhancement in healthy participants remains insufficient[ 50 ]. Furthermore, participants exhibited slower RT and lower ACC without habitual napping, underscoring the negative consequences of the absence of a nap on cognitive function. These findings corroborate the existing literature demonstrating the protective effects of moderate daytime napping on cognitive performance [ 6 , 51 ]. Overall, our results underscore the significant impact of TEAS on multiple aspects of executive performance and highlight the potential advantages of integrating TEAS into practical settings characterized by mental fatigue (e.g., daytime nap deprivation) to alleviate the detrimental consequences of prolonged wakefulness on cognitive function. 4.2 Influence of TEAS on autonomic nervous system Our research suggests that engaging in TEAS following non-nap led to significant increases in parasympathetic nervous activity, as indicated by variations in HFnu. Nap deprivation was associated with a decrease in parasympathetic nervous activity (HFnu). These findings support the notion that TEAS can enhance vagal tone and improve cognitive regulation capabilities. The results align with the existing literature demonstrating the correlation between an elevated parasympathetic nervous system and enhanced cognitive control, emotional regulation, and health maintenance [ 10 ]. Our study further demonstrated that participants without napping showed a significant decrease in HFnu, accompanied by increased sympathetic activity. Previous research has shown that during cognitive tasks performed under sleep deprivation, participants exhibit reduced HRV-HF, diminished vagus nerve, and heightened sympathetic activity [ 52 ]. These alterations reflect compensatory mechanisms aimed at maintaining cognitive performance, accompanied by increased sympathetic nervous system activity[ 53 ]. This perspective is supported by prominent heart-centered theories such as the Neurovisceral Integration Model and Polyvagal Theory[ 54 , 55 ]. However, several studies have reported conflicting results, indicating activation of the parasympathetic system following sleep deprivation. This discrepancy may stem from individual attempts to maintain wakefulness, which could lead to reduce in HFnu, resulting in sympathetic activation and an elevation in LFnu[ 56 ]. Notably, the TEAS condition resulted in greater changes in HRV than the daytime napping condition. This finding indicates that under the physiological context of sympathetic hyperactivation and ANS imbalance induced by nap deprivation, the parasympathetic regulatory response to TEAS becomes more pronounced. This observation aligns with the theory of compensatory autonomic regulation, which proposes that neuromodulatory interventions can elicit a heightened physiological compensatory response when basal autonomic homeostasis is compromised[ 57 , 58 ]. Nap deprivation disrupts autonomic function, manifesting as persistent sympathetic hyperactivation and parasympathetic suppression. Within this altered autonomic milieu, TEAS may amplify neuromodulatory effects via a threshold-lowering effect[ 59 ]. To preserve homeostasis, the organism mounts a stronger compensatory parasympathetic activation in response to identical stimulation[ 60 , 61 ]. This mechanism reflects the individual adaptive capacity to enhance parasympathetic tone when autonomic equilibrium is perturbed, consistent with evidence that neuromodulation strategies (e.g., TEAS and taVNS) can restore autonomic balance by reinforcing vagal activity under conditions of sympathetic dominance[ 62 , 63 ]. 4.3 Influence of TEAS on Cortical Hemodynamics Compared with nap deprivation, TEAS was associated with enhanced cortical activation, particularly in the PFC, specifically in the DLPFC and orbitofrontal cortex (OFC) regions during a 3-back task, which correlates with improved WM, indicating a significant relationship between brain activity and cognitive performance. This suggests that TEAS may enhance neural efficiency, enabling better cognitive performance in the absence of daytime napping. Previous studies have emphasized that TEAS influences the PFC involved in WM by modulating hippocampal synaptic plasticity[ 34 ]. fNIRS findings showed that TEAS led to significant activation of the DLPFC and OFC. Elevated β-values reflect enhanced neural efficiency and motivational integration after TEAS under deprivation of napping. Specifically, the activation of the DLPFC indicates increased resource allocation for WM maintenance and manipulation[ 64 , 65 ], while OFC engagement may support outcome valuation and error monitoring to optimize behavioral adaptation[ 66 ]. Critically, TEAS may augment cortico-subcortical circuits (e.g., prefrontal-striatal pathways), thereby strengthening the DLPFC-OFC synergy for efficient executive control[ 65 ]. These hemodynamic changes serve as biomarkers for cognitive improvement, as evidenced by the correlation between β-value increases and clinical recovery in memory-related disorders[67, 68]. Stimulation of the Yintang (EX-HN3) acupoint may modify the interaction between the DLPFC and OFC to impact WM [ 69 ]. 4.4 The Association Between Acute Physical Exercise-Induced Changes in Retinal Microvasculature, Cortical Hemodynamics, and Cognitive Performance The results of the mediation analysis indicated that the association between HRV and RT was partially mediated by PFC activation. In the present study, HRV, which plays a pivotal role in cognitive enhancement, was used as an independent variable, specifically referring to changes during the 3-back task, while PFC activation was selected as a mediator, serving as a key intermediary that transmits the influence of the ANS on cognitive performance[ 70 , 71 ].PFC activation acts as an indicator of neural activity and functional connectivity, demonstrating the capacity of the brain to allocate resources and coordinate activity across cortical regions. Our observation that changes in cortical hemodynamics mediate the relationship between ANS and cognitive performance. PFC activation is directly involved in WM and cognitive control processes. The dlPFC and OFC are robustly activated during a 3-back task, and their activity levels are closely associated with improvements in RT. During the 3-back task, PFC activation significantly enhances the ACC and speed of cognitive performance. Increased dlPFC activation is negatively correlated with faster RT, indicating that this region shortens RT by optimizing conflict monitoring and decision-making processes[72–74]. Moreover, fluctuations in PFC activation account for interindividual differences in cognitive performance. This indicates that reduced dlPFC activity is linked to greater RT variability, consistent with a decline in the stability of cognitive control[ 75 , 76 ]. Hence, the PFC likely acts as a mediator, integrating autonomic inputs, such as HRV, to adjust cognitive resource allocation and thereby modulate behavioral performance. These findings suggest that HRV changes indirectly regulate RT by influencing prefrontal neural activity. HRV reflects vagal tone and physiological arousal, whereas the prefrontal cortex (e.g., dlPFC and OFC) serves as a key hub connecting autonomic states with higher-order cognition. In studies combining HRV monitoring and fMRI, higher HRV predicts enhanced prefrontal activation, which in turn translates into faster RT during affect-attention control tasks[ 77 , 78 ]. This suggests that increased HRV, reflecting better autonomic regulation, optimizes cognitive performance by augmenting prefrontal resources, such as conflict detection or response inhibition[ 79 , 80 ]. This mechanism is particularly prominent in the 3-back task. HRV fluctuations lead to acceleration or delay in RT through corresponding changes in the PFC (e.g., dlPFC) activity[72, 78]. Conclusion The research demonstrates that TEAS significantly enhances WM in the context of non-nap among interns, who routinely face circadian disruption, sleep restriction, and cognitive overload. The acute cognitive fatigue induced in our model mirrors the mental state often encountered during extended duties. Our results propose TEAS as a practical, non-invasive, and time-efficient intervention that could be deployed to rapidly bolster cognitive readiness and mitigate fatigue-related errors in clinical settings. This intervention regulates specific neural activities in the prefrontal cortex, leading to substantial improvements in cognitive behavioral performance. Additionally, it promotes a dynamic balance between the sympathetic and vagus nerves, enhances HRV, and increases adaptive capacity to external environmental changes. High vagus nerve-mediated HRV indicates the predominance of parasympathetic regulation over cardiac function, characterized by strong neuro-visceral integration, a more adaptable autonomic nervous system, and a tighter coupling between brain and heart. This neuro-visceral integration optimizes resource allocation in the prefrontal cortex, facilitating rapid adaptation to task demands. Importantly, these findings affirm that TEAS is an effective intervention for enhancing WM and mitigating mental fatigue, providing a foundation for targeted interventions that address both the brain's functional changes underlying cognitive performance and individual differences in autonomic nervous regulation. Abbreviations ACC, accuracy AD, Alzheimer's disease DLPFC, dorsolateral prefrontal cortex fNIRS, functional near-infrared spectroscopy HbO, oxygenated hemoglobin HRV, heart rate variability MCI, mild cognitive impairment mPFC, medial prefrontal cortex NASA-TLX, NASA task load index OFC, orbitofrontal cortex PFC, prefrontal cortex PSQI, pittsburg sleep quality index RT, reaction time TEAS, transcutaneous electrical acupoint stimulation WM, working memory Declarations Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of the Fourth Military Medical University (Ethical number: KY20243597-1), and written informed consent was obtained from all participants prior to their enrollment. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Funding The study was funded by Equipment Technology Foundation Project (145BZB2100101353X), National Natural Science Foundation of China (No. 82570613), Innovation Capacity Support Program of Shaanxi Province (No. 2025JC-GXPT-036), China National Postdoctoral Program for Innovative Talents and Medical Science (No. BX20250458) and Technology R&D Program (2025JSKY18). Conflict of Interest The authors declare no conflict of interest. Authors’ contributions Conceptualization, X.S. and G.Y.; methodology, Z.L.; software, L.W.; validation, J.F., Y.W. and C.L.; formal analysis, J.F.; investigation, Y.W.; resources, Y.P. and Y.W.; data curation, S.L.; writing—original draft preparation, J.F.; writing—review and editing, L.W.; visualization, Y.W.; supervision, Y.G.; project administration, X.S. and Y.G.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript. 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Biological psychiatry Cognitive neuroscience and neuroimaging 2025, 10(10):1078-1092. 75.Mansouri FA, Buckley MJ, Tanaka K: The neural substrate and underlying mechanisms of executive control fluctuations in primates. Progress in neurobiology 2022, 209:102216. 76.Qiu Y, Dou H, Becker B, He Z, Mei Y, Lei Y: Behavioral and neural dysfunctions in reward- related cognitive control among adolescents with major depressive disorder. Psychological medicine 2025, 55:e298. 77.Corcoran AW, Le Coz A, Hohwy J, Andrillon T: When your heart isn't in it anymore: cardiac correlates of task disengagement. Communications biology 2025, 8(1):1646. 78.Yoo HJ, Nashiro K, Min J, Cho C, Mercer N, Bachman SL, Nasseri P, Dutt S, Porat S, Choi P et al : Multimodal neuroimaging data from a 5-week heart rate variability biofeedback randomized clinical trial. Scientific data 2023, 10(1):503. 79.Zhu S, Liu Q, Zhang X, Zhou M, Zhou X, Ding F, Zhang R, Becker B, Kendrick KM, Zhao W: Transcutaneous auricular vagus nerve stimulation enhanced emotional inhibitory control via increasing intrinsic prefrontal couplings. International journal of clinical and health psychology : IJCHP 2024, 24(2):100462. 80.Tan G, Adams J, Donovan K, Demarest P, Willie JT, Brunner P, Gorlewicz JL, Leuthardt EC: Does vibrotactile stimulation of the auricular vagus nerve enhance working memory? A behavioral and physiological investigation. Brain stimulation 2024, 17(2):460-468. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8925154","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596203043,"identity":"aff7336d-4dd8-4c41-9f23-4284eaed6fbe","order_by":0,"name":"Jieyi Fan","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jieyi","middleName":"","lastName":"Fan","suffix":""},{"id":596203044,"identity":"7894395b-81eb-4ce3-be11-ac29527877df","order_by":1,"name":"Liang Wang","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Wang","suffix":""},{"id":596203045,"identity":"0145b561-c901-4398-9d50-d92b4e2ca765","order_by":2,"name":"Ze Lv","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ze","middleName":"","lastName":"Lv","suffix":""},{"id":596203046,"identity":"92d506b3-3d0c-4649-8b75-dae16240ceb8","order_by":3,"name":"Chengfei Li","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chengfei","middleName":"","lastName":"Li","suffix":""},{"id":596203048,"identity":"8e8449ea-ce6b-4078-a2c6-58fa08b9a923","order_by":4,"name":"Yikai Pan","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yikai","middleName":"","lastName":"Pan","suffix":""},{"id":596203049,"identity":"fe45f102-71d6-483a-9f90-7c802df29a3a","order_by":5,"name":"Yuan Wang","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Wang","suffix":""},{"id":596203052,"identity":"96e8be21-7908-4020-a78a-fdb545161322","order_by":6,"name":"Shuhan Li","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuhan","middleName":"","lastName":"Li","suffix":""},{"id":596203053,"identity":"ff6c8a47-fdbe-43c2-ae59-c5cce643d7e9","order_by":7,"name":"Yongchun Wang","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongchun","middleName":"","lastName":"Wang","suffix":""},{"id":596203054,"identity":"1a94fd93-d5e2-4e9b-9089-3a9129ba9a7d","order_by":8,"name":"Yuan Gao","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Gao","suffix":""},{"id":596203055,"identity":"99e55b84-d836-4a9a-bc2a-4066f9492b2b","order_by":9,"name":"Xiqing Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYLCCBAYJBgbmA0BWhYScPPFa2BKArDMWxoYNRFsF0sLYVpHIcICAQvkZucckHpRZ5Mm7cadu5p0nkcDYwPzw0Q08Wgxu5CUbJJyTKDY8xrvtNu82iTx2BjZj4xx8WiRyDB8ktkkkbpzfC9ZSzNjAwyaNT4v8jByDA2AtbSBb5kgkNhwgoIXhBtSW+WwgLQ1EaDE488YY5JfEDUAtN+cckzA2bCbgF/n2HDPJH2V1ifOBDrvxpqZOTp69+eFjvA4DAzagdQdgHGaCyqFa5BuIUjkKRsEoGAUjEQAAe1VLFwpnhhEAAAAASUVORK5CYII=","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiqing","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2026-02-20 11:09:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8925154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8925154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103387299,"identity":"8aad16a7-b41f-4d24-b37d-4be018e92b19","added_by":"auto","created_at":"2026-02-25 06:59:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":932587,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the experimental procedure.\u003c/p\u003e\n\u003cp\u003eNC: normal control group; ND: nap deprivation group; ND+TEAS: nap deprivation group receiving TEAS intervention\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8925154/v1/c2b1a4ddbfdfd00b90302fdf.png"},{"id":103387302,"identity":"d2871521-e6bf-499c-b961-7c03b067d554","added_by":"auto","created_at":"2026-02-25 06:59:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":405144,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow path of the 3-back\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8925154/v1/81ef6d7fc99c208861914c93.png"},{"id":103387300,"identity":"2b24ca20-c222-450b-ae7c-02fdf5b2f910","added_by":"auto","created_at":"2026-02-25 06:59:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53316,"visible":true,"origin":"","legend":"\u003cp\u003eOptode placement on the forehead.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8925154/v1/24792c4263ef711a2f1dea84.png"},{"id":103387306,"identity":"59f75c3f-6380-4a9b-b56c-6cfe3b9b9b3d","added_by":"auto","created_at":"2026-02-25 06:59:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":195510,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in NASA-TLX scale scores among three groups at 12:00 and 15:00. (A) Mental Demand (MD), (B)Physical Demand (PD), (C)Temporal Demand (TD), (D)Frustration (Fr), (E) Effort (Ef), and (F)Performance (Pe).\u003cem\u003e \u003c/em\u003eNormal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS).\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt;0.05,\u003c/em\u003e\u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt;0.01,\u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt;0.001\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8925154/v1/e6a3b3bddd2077fc6766df9e.png"},{"id":103387301,"identity":"3258eb0e-274b-4729-9656-2a35f9914cb2","added_by":"auto","created_at":"2026-02-25 06:59:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1091244,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in reaction time (A) and accuracy (B) among three groups during the 3-back test. The upper and lower lines of the box in a box-violin plot represent the upper and lower quartiles respectively. Normal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS).\u003cem\u003e \u003c/em\u003e\u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt;0.01,\u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt;0.001\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8925154/v1/49da3167cfd8fdede15cc782.png"},{"id":103387305,"identity":"e69ff7de-f503-469a-a8d8-ad76d5d1787f","added_by":"auto","created_at":"2026-02-25 06:59:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":212888,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in LFnu、HFnu、RMSSD and LF/HF among three groups during the 3-back task. The upper and lower lines of the box in a box-violin plot represent the upper and lower quartiles, respectively. Normal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS).\u003csup\u003e *\u003c/sup\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt;0.01,\u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt;0.001\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8925154/v1/265e8a75715d88baaacb1c60.png"},{"id":103387303,"identity":"7527faa6-4fd3-4640-9539-63580113509c","added_by":"auto","created_at":"2026-02-25 06:59:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1118182,"visible":true,"origin":"","legend":"\u003cp\u003eThe graphs show corresponding changes in group-average HbO Beta values for each group and time.（A）Channel map of brain regions that showed interaction of group and time affecting HbO activation in the PFC. （B）The main effect of time on HbO activation in the PFC. （C）The main effect of group on HbO activation in the PFC.（D）The β values of Ch3 among three groups during the 3-back task. （E）The β values of Ch3 among three groups during the 3-back task. （F）The β values of Ch3 among three groups during the 3-back task.\u003cem\u003e \u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt;0.05\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8925154/v1/5c11507cfc6aa7e2b57f85e4.png"},{"id":103507375,"identity":"8297ded9-0f57-429b-8506-e2a3a6c8eb7f","added_by":"auto","created_at":"2026-02-26 13:41:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":260504,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the mediation analysis are presented as standardized coefficients. The total effect is reported in brackets and the direct effect above it. The mediators depicted were related to β values of Ch3, Ch6 and Ch8. Note. The total effect is reported in brackets. \u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e\u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt; 0.01\u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u0026lt; 0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8925154/v1/6c00d438593ba678759d589e.png"},{"id":104397527,"identity":"a047092f-bb4b-475c-82ea-979d1ac42c5b","added_by":"auto","created_at":"2026-03-11 11:50:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6078381,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8925154/v1/6de12c43-edbd-44a2-9db5-c2f85b092116.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcutaneous Acupoint Electrical Stimulation Ameliorates Working Memory Impairment in Nap-deprived Interns: The Mediating Role of Augmented Brain-Heart Interaction","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWorking memory (WM), a core component of executive function, is essential for clinical reasoning, decision-making, and procedural execution in high-stakes medical environments[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For interns and other healthcare professionals, acute cognitive fatigue and WM decrement are prevalent challenges, often stemming from circadian rhythm disruptions, sleep restriction, and irregular shift patterns[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These impairments not only affect individual well-being but also pose potential risks to patient safety[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, identifying safe and effective strategies to bolster cognitive resilience in this population is of paramount clinical importance.\u003c/p\u003e \u003cp\u003eDaytime napping is a widespread behavior adopted as a countermeasure against fatigue[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among habitual nappers, the forced deprivation of a regular nap has been established as a valid experimental model to induce transient cognitive fatigue and WM deficits[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], mirroring the cognitive challenges faced by medical staff after night shifts or during prolonged duty hours[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Such deprivation leads to measurable declines in cognitive performance, particularly in tasks requiring sustained attention and WM[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe autonomic nervous system (ANS), which regulates involuntary physiological functions such as heart rate, consists of the parasympathetic (relaxation-promoting) and sympathetic (arousal-promoting) branches. The dynamic balance between these two branches governs cardiac regulation. Heart rate variability (HRV) serves as a key non-invasive marker of this autonomic balance, reflecting an individual's capacity for self-regulation and physiological adaptability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Higher HRV is generally associated with protective effects for cognitive function, whereas lower HRV is correlated with cognitive decline [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Autonomic dysfunction has been observed in individuals with mild cognitive impairment, suggesting a link between cognitive impairment and abnormal autonomic regulation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It also represents an early manifestation of neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The HF-HRV is associated with performance in cognitive tasks such as cognitive inhibitory control and executive function[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, in cognitive fatigue research, HF-HRV can predict cognitive task performance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, high HRV and increased HF reflect a flexible and adaptive physiological system that promotes homeostasis. Conversely, low HRV and diminished HF indicate a system less adept at adapting to external stressors[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR19\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe function of ANS is under the control of the central autonomic network (CAN)[21, 22]. A central component of the CAN is the medial prefrontal cortex (mPFC), which plays a critical role in exerting top-down cognitive control over autonomic and emotional processes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The CAN modulates autonomic function through key brainstem nuclei: the dorsal motor nucleus of vagus nerve mediates parasympathetic output, while the caudal ventrolateral medulla regulates sympathetic tone. The sympathetic pathway is activated in response to homeostatic challenges from external stressors[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Mirroring ANS dysfunction, impairment of the CAN is also linked to cardiac diseases and cognitive decline [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR26\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The activation of CAN recruits higher-order cognitive regions integral to cognition, emotion, and autonomic control[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Functional near-infrared spectroscopy (fNIRS) studies indicate that reduced oxy-hemoglobin (HbO) concentration in the dorsolateral prefrontal cortex (DLPFC) directly reflects diminished neural activity, correlating with cognitive decline[29]. Furthermore, weakened prefrontal network connectivity is associated with deficits in specific cognitive domains[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Cognitive performance concurrently influences both autonomic and central nervous system function. Research on brain-heart interactions and their functional coupling is essential for understanding the physiological manifestations of homeostatic changes[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Functional brain-heart coupling refers to the dynamic coordination between neural and cardiac activity, reflecting their functional interdependence[33].\u003c/p\u003e \u003cp\u003eTranscutaneous electrical acupoint stimulation (TEAS) is a non-invasive neuromodulation technique shown to enhance cognitive function. Owing to its minimal invasiveness and favorable safety profile, TEAS is particularly suitable for modulating cognitive function in the general population. A 4-week randomized controlled trial (RCT) demonstrated that TEAS improved attention and executive function in children aged 6\u0026ndash;12 with ADHD[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, an RCT in patients with schizophrenia found that adjunctive TEAS improved working memory (WM) and information processing speed compared to pharmacotherapy alone[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Notably, the Yintang (EX-HN3) acupoint, located on the forehead, is strategically positioned over the frontal lobe and is traditionally used for mental calming and cognitive enhancement[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A growing body of evidence suggests that TEAS can also modulate ANS activity, promoting parasympathetic tone[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR38\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, a critical knowledge gap remains: whether and how TEAS can acutely mitigate WM impairment induced by a common stressor like nap deprivation in interns, and whether its cognitive benefits are mediated through the restoration of brain-heart interaction.\u003c/p\u003e \u003cp\u003eTo address the above-mentioned knowledge gaps, the study investigates the acute effects of TEAS after deprivation of daytime napping on WM (measured via the 3-back task), cortical hemodynamics (measured by fNIRS), and the ANS (measured via HRV). Our primary aims were to 1) investigate among young habital nappers how nomal daytime napping and TEAS affect 3-back task performance, and the ANS following deprivation of daytime napping, and 2) examine the relationships between TEAS-induced changes in WM, cortical hemodynamics, and HRV. we hypothesized that TEAS after deprivation of daytime napping would enhance WM and cortical hemodynamics. We also hypothesized that interrupting the nap deprivation with TEAS would increase HRV, reflecting a flexible and adaptive physiological system. Furthermore, we predicted that TEAS would yield benefits for both behavioral and neurobiological outcomes. Additionally, we hypothesized that improvements in WM and ANS would be mediated by changes in cortical hemodynamics.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eA total of 95 interns from the Fourth Military Medical University First Affiliated Hospital were recruited as participants. The interns refrained from consuming any medications or beverages affecting neurological function and avoided high-intensity physical exercise for 48 hours prior to testing. All participants maintained their regular diet and daily routines and were right-handed. Before the formal experiment commenced, all interns completed the Pittsburgh Sleep Quality Index (PSQI) questionnaire to evaluate their fatigue status and sleep conditions over the preceding two weeks. Individuals who reported recent sleep disturbances or significant fatigue were excluded. Only interns with a consistent habit of daytime napping were included. The study protocol was approved by the Ethics Committee of the Fourth Military Medical University, and written informed consent was obtained from all participants prior to their enrollment.\u003c/p\u003e \u003cp\u003eAs part of a screening procedure to assess habitual napping patterns, the interns completed sleep diaries for a minimum of one week, during which they were also required to record whether they had taken a daytime nap. Individuals were classified as habitual nappers if they reported napping at least once per week, whereas non-habitual nappers were defined as those who napped less than once per week. To account for inter-individual variability in the total number of diary days completed, napping frequency was expressed as the proportion of daytime napping relative to the total number of diary days. Consequently, habitual nappers were operationally defined as individuals who took at least one nap per week. Among habitual nappers, the mean reported nap frequency was two times per week (range: 1\u0026ndash;6 times per week). The most frequently reported nap duration was 31\u0026ndash;60 min. The predominant nap timing occurred between 12:00 PM and 14:00 PM (noon\u0026ndash;early afternoon). After excluding one participant with incorrectly filled or incomplete sleep diaries and four participants who had missing data across the tasks, our final sample consisted of 90 participants (mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD:22.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87 years).\u003c/p\u003e \u003cp\u003eInterns were randomly assigned to three groups: nap deprivation group (ND), normal control group (NC), and nap deprivation group receiving TEAS intervention (ND+TEAS). Each group underwent 3-back task, HRV, fNIRS, and NASA Task Load Index (NASA-TLX) at 12:00 and 15:00. In ND, interns refrained from napping. They remained in the laboratory engaging in relaxed, sedentary activities, such as reading or listening to music, and avoided vigorous exercise. NC maintained their habitual napping schedule. ND+TEAS received a daily 30-minute TEAS for six consecutive days. On the seventh (test) day, they refrained from napping and instead remained in the laboratory engaging in light, sedentary activities. Additionally, on the test day, a continuous 30-minute TEAS was administered beginning at 14:30. To examine the impact of TEAS on HRV and its association with cognitive performance, electrocardiogram (ECG) recordings were obtained from participants during the 3-back task (See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNC: normal control group; ND: nap deprivation group; ND+TEAS: nap deprivation group receiving TEAS intervention\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Subjective assessment\u003c/h2\u003e \u003cp\u003eThe NASA-TLX is a subjective scale originally developed by the National Aeronautics and Space Administration (NASA) for aerospace applications. It is now the most widely used multidimensional instrument for assessing subjective cognitive load [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The NASA-TLX assesses six dimensions: mental demand (MD), physical demand (PD), temporal demand (TD), performance (Fr), effort (Ef), and frustration (Pe). Each dimension is rated on a scale from 0 to 20[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Cognitive task\u003c/h2\u003e \u003cp\u003eThe 3-back task required participants to recall the spatial locations of sequentially presented blue squares. Participants were instructed to indicate whether the location of the current blue square matched the location of the square presented two trials back. The task comprised five blocks. A 10-second rest period, signaled by an on-screen \"Rest\" prompt, separated each block. The total task duration was 8 minutes. Each target stimulus (a blue square) was displayed for 500 ms, followed by a 1000 ms blank inter-stimulus interval, resulting in a stimulus onset asynchrony (SOA) of 1500 ms [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e43\u003c/span\u003e] (See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Prior to the main task, participants read on-screen instructions. Following a self-reported understanding of the rules, participants completed a practice block. The practice required completing three sets of 16 trials each. Participants were required to achieve an accuracy rate (ACC) greater than 80% in each set before advancing to the main task. During the main task, no performance feedback was provided. The stimulus presentation order was pseudorandomized across participants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 fNIRS measurement\u003c/h2\u003e \u003cp\u003ePrefrontal cortical activity was measured using an OctaMon functional near-infrared spectroscopy (fNIRS) system (Artinis Medical Systems, Netherlands). The system employed a wireless 8-channel configuration with a source-detector separation of 3.5 cm to measure concentration changes in oxygenated (HbO) and deoxygenated (HHb) hemoglobin in the PFC microcirculation. Each channel used dual wavelengths (756 and 856 nm) and sampled at 25 Hz. Data were collected using Oxysoft software (version 3.0.52, Artinis Medical Systems). Optodes were positioned bilaterally on the forehead according to the international 10\u0026ndash;20 system, targeting Brodmann\u0026rsquo;s areas 9 and 10 (dorsolateral and anterior prefrontal cortex) (See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe raw fNIRS data were preprocessed using the NIRS-KIT-main toolkit in MATLAB 2023a. Initially, the Data Preparation module converted .nirs files to .mat files. Subsequently, the HbO values and their corresponding time points were extracted from the .mat file. Following this, the Preprocessing module was employed for detrending. Variations in instrument sensitivity and participants' physiological conditions during the experiment may introduce trends that could affect the study results. By applying a linear regression model to eliminate these trends, alterations in the signals of interest can be observed more distinctly in the analysis. During the experiment, participants' body movements may introduce motion artifacts into the data. Motion correction can effectively reduce these artifacts, thereby enhancing data quality. We utilize the Temporal Derivative Distribution Repair (TDDR) method to correct motion artifacts. By constraining the frequency range to 0.01 Hz to 0.08 Hz, the filter effectively removes frequency components outside this interval, thereby reducing high-frequency noise and low-frequency drift. This process enhances the sensitivity of the signals of interest. A third-order Butterworth bandpass filter is employed for this filtering. The filtered data is subsequently converted to changes in HbO concentration using the modified Beer-Lambert law. Following this, the general linear model (GLM) is applied to derive beta values. Previous research has established that HbO is the most sensitive indicator of changes in experimental paradigm stimuli[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR45\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e46\u003c/span\u003e].Therefore, this study selects the beta value of HbO as a metric for evaluating brain activity, specifically focusing on the activation level of the prefrontal cortex, where elevated beta values signify increased activation in this region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 TEAS treatment\u003c/h2\u003e \u003cp\u003eParticipants were randomly assigned to three groups: NC, ND and ND+TEAS. TEAS was conducted daily using small (1.5 cm) cutaneous electrode pads placed at the Yintang (GV29) point. The Yintang acupoint is situated at the inner end of the eyebrows, specifically at the midpoint where the inner ends of both eyebrows converge. The electrical stimulation was delivered at the maximum tolerable current intensity, ensuring that no muscle contractions or discomfort occurred. The parameters were configured to a dense-sparse wave with a frequency of 50 Hz, utilizing the Hwato electronic acupuncture treatment instrument (Model No. SDZ-II; Suzhou Medical Appliances, Suzhou, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Heart rate variability (HRV)\u003c/h2\u003e \u003cp\u003eAll participants were instructed to wear an Equivital EQ02\u0026thinsp;+\u0026thinsp;LifeMonitor (Equivital, Cambridge, UK) while performing cognitive tests and undergoing TEAS treatment [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The Equivital LifeMonitor is a vest-like wearable device that continuously measures ECG, heart rate (HR), and heart rate variability (HRV) through two channels using three electrodes. It features a sensor electronic module that resides in a cradle on the side of the LifeMonitor. The EQ02 monitoring system comprises LifeMonitor straps of various sizes, one LM 100 LifeMonitor sensor electronic module (SEM), a Bluetooth USB dongle for laptop connectivity, and Equivital Manager software, which is utilized to configure SEMs and to export and download data. In this study, SEMs were set to real-time monitoring mode. Data were stored simultaneously in the SEMs and the laptop. A LifeMonitor belt of appropriate size secured the SEM to each participant's body. Prior to skin contact, the textile-based electrodes were moistened with water. The SEMs were recharged for approximately 1 hour following a 12-hour recording period. At the conclusion of the study, the SEM data were transferred to the Equivital Manager software, where time- and date-stamped ECG readings, inter-beat intervals, and summarized vital sign data were extracted and exported for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data from this study were analyzed statistically using SPSS 25. Initially, descriptive statistical analyses were performed on subjective scale data, behavioral data, physiological data, and near-infrared spectroscopy (NIRS) data. A repeated measures ANOVA was conducted for all subjective and objective evaluation results across ND, NC, and ND+TEAS), with time (12:00, 15:00) and group as factors. Different intervention types were treated as within-subject factors, and all post-hoc multiple comparisons were adjusted using the Bonferroni method. The effect size was reported using partial ƞ\u0026sup2; based on the results of the analysis of variance.\u003c/p\u003e \u003cp\u003eWe employed the SPSS PROCESS macro v4. for mediation analysis. Our mediation models included three sets of variables: the predictor of working memory performance (X), autonomic nervous system activity (Y), and a mediator represented by a specific fNIRS measure (M). The fNIRS measures acting as mediators were determined as the mean β values observed in fNIRS channels showing group-by-time interaction effects. A parallel mediation model was used to explore the direct impact of working memory performance (measured through RTs) on autonomic nervous system function (assessed as HRV-HFnu), as well as the indirect influence mediated by the fNIRS parameters: β values of Ch3, Ch6, and Ch8. We analyzed the mediation between the outcome and the mediator, assessing both the direct effect of X and the indirect (mediation) effect of X through β values on Y. A visual representation of the complete model is depicted in Figure S1. All regression coefficients were reported as standardized coefficients, with effects deemed significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Bootstrapped confidence intervals (5,000 runs) were used to determine the significance of indirect effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1Baseline characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the descriptive statistics and nap parameters were observed among three group (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of three groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND+TEAS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDaytime napping characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime in bed (TIB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.29\u0026thinsp;\u0026plusmn;\u0026thinsp;5.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etotal sleep time (TST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebedtime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:50\u0026thinsp;\u0026minus;\u0026thinsp;13:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12:45\u0026thinsp;\u0026minus;\u0026thinsp;13:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12:50\u0026thinsp;\u0026minus;\u0026thinsp;13:00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewake time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14:00\u0026ndash;14:10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14:00\u0026ndash;14:10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14:00\u0026ndash;14:10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNormal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The effect of different interventions on NASA-TLX scale scores\u003c/h2\u003e \u003cp\u003eA two-factor repeated-measures ANOVA of NASA-TLX scale scores indicated significant differences in MD, Pe, and Ef. The interaction effect between testing time point and intervention condition on MD was not significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;2.08, p\u0026thinsp;=\u0026thinsp;0.13, partial η\u0026sup2; = 0.05\u003c/em\u003e), neither was the main effect of testing time point (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;3.09, p\u0026thinsp;=\u0026thinsp;0.08, partial η\u0026sup2; = 0.04\u003c/em\u003e). However, the main effect of intervention condition was significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1,88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;7.25,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001,partialη\u0026sup2;=0.15\u003c/em\u003e). Post-hoc comparisons revealed no significant differences among the three groups at baseline (12:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.87\u003c/em\u003e). Following TEAS, MD scores were significantly lower than those in the ND (15:00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). Moreover, MD scores after normal napping were significantly lower than those after non-nap (15:00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of repeated measures ANOVA for NASA-TLX score across three groups at 12:00 and 15:00\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eND+TEAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003epartial \u003cem\u003eƞ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.71\u0026thinsp;\u0026plusmn;\u0026thinsp;4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.21\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.37\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.85\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.03\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.41\u0026thinsp;\u0026plusmn;\u0026thinsp;6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003cp\u003etime\u003c/p\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003cp\u003e1.18\u003c/p\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e0.28\u003c/p\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMental Demand (MD), Physical Demand (PD), Temporal Demand (TD), Frustration (Fr), Effort (Ef), and Performance (Pe).\u003c/p\u003e \u003cp\u003eThe main effect of the intervention condition on Pe was significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;5.95, p\u0026thinsp;=\u0026thinsp;0.004, partial η\u0026sup2; = 0.12\u003c/em\u003e). In contrast, neither the interaction effect between testing time point and intervention condition (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.77, partial η\u0026sup2; = 0.01\u003c/em\u003e) nor the main effect of testing time point (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.83, p\u0026thinsp;=\u0026thinsp;0.37, partial η\u0026sup2; =0.01\u003c/em\u003e) reached significance. Post-hoc comparisons revealed no significant differences among the three groups at baseline (12:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.12\u003c/em\u003e). Following TEAS, Pe scores were significantly lower than those in the ND group (15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.02\u003c/em\u003e). However, no significant difference was found between the normal napping and non-nap (15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.07\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThe interaction effect between testing time point and intervention condition on was significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;3.56, p\u0026thinsp;=\u0026thinsp;0.03, partial η\u0026sup2; = 0.07\u003c/em\u003e). In contrast, the main effects of testing time point (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.09, p\u0026thinsp;=\u0026thinsp;0.76, partial η\u0026sup2;=0.001\u003c/em\u003e) and intervention condition (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.20, p\u0026thinsp;=\u0026thinsp;0.82, partial η\u0026sup2; =0.005\u003c/em\u003e) were not statistically significant. Following TEAS, post-intervention scores were significantly lower than baseline scores (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.03\u003c/em\u003e). No statistically significant differences were observed for PD, TD, or Fr (all \u003cem\u003eps\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/em\u003e) (See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3The effect of different interventions on cognitive performance\u003c/h2\u003e \u003cp\u003eAn analysis was conducted on the mean reaction times (mean RTs) and ACC of the 3-back task to explore the impact of TEAS on WM. A two-factor repeated-measures analysis of variance (ANOVA) was utilized, with time (12:00 and 15:00) as the within-subject factor and intervention groups (NC, ND and ND+TEAS) as the between-subject factor (See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Post hoc multiple comparisons were carried out using the Bonferroni method for correction. The outcomes of the two-factor repeated-measures ANOVA for mean RTs on the 3-back test revealed that the main effect of time was not statistically significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.06, p\u0026thinsp;=\u0026thinsp;0.80, partial η\u0026sup2; = 0.001\u003c/em\u003e). However, a significant interaction effect between time and intervention condition was observed (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;11.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u0026sup2; = 0.24\u003c/em\u003e), along with a significant main effect of the test time point (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;6.87, p\u0026thinsp;=\u0026thinsp;0.002, partial η\u0026sup2; = 0.15\u003c/em\u003e).\u003c/p\u003e \u003cp\u003ePost hoc multiple comparisons indicated no significant variances in baseline RTs at 12:00 among the different intervention groups (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.99\u003c/em\u003e). By 15:00, the average RT for the 3-back task following TEAS was notably shorter than that following nap deprivation (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e). Similarly, post a regular nap session, the average RT for the normal control group was significantly shorter than that for the ND group at 15:00 (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.011\u003c/em\u003e). Furthermore, in comparison to the baseline at 12:00, the 30-minute TEAS significantly enhanced the average RT for the 3-back task (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.004\u003c/em\u003e). Conversely, following a regular nap, there was no significant enhancement in the average RT (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.08\u003c/em\u003e). In ND, the average RT for the 3-back task at 15:00 was significantly prolonged compared to the baseline (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThe results indicated a significant interaction between time and intervention condition on 3-back task ACC (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;31.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u0026sup2; = 0.52\u003c/em\u003e). A significant main effect was observed for the intervention group (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;4.38, p\u0026thinsp;=\u0026thinsp;0.02, partial η\u0026sup2; = 0.13\u003c/em\u003e), while the main effect of time was not significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.03, p\u0026thinsp;=\u0026thinsp;0.87, partial η\u0026sup2; =0.001\u003c/em\u003e). Post hoc comparisons revealed no significant differences in ACC at the 12:00 baseline level among intervention groups (12:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.07\u003c/em\u003e). However, \u003cem\u003efollowing TEA, the ACC of the 3-back task was significantly higher compared to ND (15:00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the ACC of NC was significantly higher than that of ND (15:00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eCompared to the baseline measurement at 12:00, the 30-minute TEAS significantly enhanced ACC on the 3-back task (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.003\u003c/em\u003e). Furthermore, ACC following the nap also exhibited a significant improvement (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e). In contrast, participants in ND demonstrated a significantly lower ACC in completing the 3-back test at 15:00 compared to the baseline level (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of repeated measures ANOVA for behavioral performance in the 3-back task across three groups at 12:00 and 15:00\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eND+TEAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003epartial \u003cem\u003eƞ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean RTs(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e783.49\u0026thinsp;\u0026plusmn;\u0026thinsp;411.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e651.44\u0026thinsp;\u0026plusmn;\u0026thinsp;323.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e790.53\u0026thinsp;\u0026plusmn;\u0026thinsp;252.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1079.40\u0026thinsp;\u0026plusmn;\u0026thinsp;411.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e795.58\u0026thinsp;\u0026plusmn;\u0026thinsp;226.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e607.03\u0026thinsp;\u0026plusmn;\u0026thinsp;227.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e31.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNormal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4The effect of different interventions on HRV\u003c/h2\u003e \u003cp\u003eThe statistical analysis of HFnu revealed a significant interaction between test time point and intervention condition (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;3.31, p\u0026thinsp;=\u0026thinsp;0.04, partial η\u0026sup2; = 0.10\u003c/em\u003e). The main effect of the intervention condition was not significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;1.31, p\u0026thinsp;=\u0026thinsp;0.28, partial η\u0026sup2; = 0.04\u003c/em\u003e), and the main effect of the time was not significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.004, p\u0026thinsp;=\u0026thinsp;0.95, partial η\u0026sup2; = 0.001\u003c/em\u003e). Post hoc comparisons indicated no significant differences in ACC at the baseline level of 12:00 among NC, ND, and ND+TEAS (12:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.07\u003c/em\u003e). However, HFnu following TEAS was significantly higher than that of the non-nap (15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.047\u003c/em\u003e). HFnu following the nap also exhibited a significant improvement (15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.04\u003c/em\u003e). Additionally, participants in ND demonstrated a significantly lower HFnu in completing the 3-back task at 15:00 compared to the baseline level (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.04\u003c/em\u003e). There were no significant interactions between test time point and intervention condition or interactions between test time point and intervention condition observed in LFnu, RMSSD, and LF/HF (\u003cem\u003eps\u0026thinsp;\u0026ge;\u0026thinsp;0.05\u003c/em\u003e) (See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZ-score normalization (standardization) was applied to the data, transforming it to have a mean of zero and a standard deviation of one. All continuous numerical features of HRV underwent standardization using Z-score normalization to address the issue of varying scales among variables (See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Specifically, each feature had its mean value (\u0026micro;) from the training set subtracted and was then divided by the corresponding standard deviation (σ) from the same set. This normalization ensured that each feature had a mean of zero and a standard deviation of one, facilitating the convergence of our model and preventing features with larger scales from dominating the learning process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of repeated measures ANOVA for HRV in the 3-back task across three groups at 12:00 and 15:00\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eND+TEAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003epartial \u003cem\u003eƞ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFnu-3-back test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.50\u0026thinsp;\u0026plusmn;\u0026thinsp;19.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.70\u0026thinsp;\u0026plusmn;\u0026thinsp;21.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.20\u0026thinsp;\u0026plusmn;\u0026thinsp;23.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.20\u0026thinsp;\u0026plusmn;\u0026thinsp;23.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.24\u0026thinsp;\u0026plusmn;\u0026thinsp;17.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.26\u0026thinsp;\u0026plusmn;\u0026thinsp;18.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFnu-3-back test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.60\u0026thinsp;\u0026plusmn;\u0026thinsp;19.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.94\u0026thinsp;\u0026plusmn;\u0026thinsp;16.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.31\u0026thinsp;\u0026plusmn;\u0026thinsp;15.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.36\u0026thinsp;\u0026plusmn;\u0026thinsp;22.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.62\u0026thinsp;\u0026plusmn;\u0026thinsp;17.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.68\u0026thinsp;\u0026plusmn;\u0026thinsp;19.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD-3-back test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.05\u0026thinsp;\u0026plusmn;\u0026thinsp;23.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.06\u0026thinsp;\u0026plusmn;\u0026thinsp;11.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.23\u0026thinsp;\u0026plusmn;\u0026thinsp;17.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.01\u0026thinsp;\u0026plusmn;\u0026thinsp;14.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.21\u0026thinsp;\u0026plusmn;\u0026thinsp;14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.68\u0026thinsp;\u0026plusmn;\u0026thinsp;13.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF/HF-3-back test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNormal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The effect of different interventions on fNIRS brain activation\u003c/h2\u003e \u003cp\u003eThis study utilized a two-way repeated-measures analysis of variance (ANOVA) to examine the impact of TEAS on cerebral activity indicators while performing 3-back task after non-nap. Variations in β values among three groups during 3-back task were assessed. To evaluate task-state activation levels indicative of the prefrontal cortex (PFC), individual two-way repeated-measures ANOVA were conducted on each of the eight channels. The interaction effect outcomes for each channel are detailed in Table\u0026nbsp;6 and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor the Ch3, the interaction effect between the intervention condition and time was significant (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;4.51, p\u0026thinsp;=\u0026thinsp;0.014, partial η\u0026sup2; = 0.11\u003c/em\u003e). In contrast, neither the main effect of time (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.67, partial η\u0026sup2; = 0.002\u003c/em\u003e) nor the main effect of the intervention condition (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.05, p\u0026thinsp;=\u0026thinsp;0.96, partial η\u0026sup2;=0.001\u003c/em\u003e) reached significance. Post-hoc multiple comparisons indicated a significant difference in β values during the 3-back task after 30 minutes of TEAS when compared to the baseline at 12:00 (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.02\u003c/em\u003e). However, no significant difference was observed after one hour of normal napping (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.20\u003c/em\u003e). Additionally, without any intervention following nap deprivation, β values did not show a significant difference (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.17\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThe interaction effect between intervention condition and test time point was significant for the Ch6 (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;7.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u0026sup2; = 0.17\u003c/em\u003e). Neither the main effect of time (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.29, p\u0026thinsp;=\u0026thinsp;0.59, partial η\u0026sup2; = 0.004\u003c/em\u003e) nor the main effect of intervention condition (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;0.78, partial η\u0026sup2;=0.007\u003c/em\u003e) reached significance. Post-hoc multiple comparisons revealed a significant difference in β values during the 3-back task after 30 minute TEAS compared to the baseline at 12:00 (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.004\u003c/em\u003e). However, no significant difference was observed after one hour of normal nap (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.09\u003c/em\u003e). Furthermore, without any intervention following nap deprivation, β values exhibited no significant difference (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.09\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThe interaction effect between intervention condition and time was significant for the Ch8 (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;6.40, p\u0026thinsp;=\u0026thinsp;0.003, partial η\u0026sup2; = 0.15\u003c/em\u003e). However, the main effects of time (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;0.62, partial η\u0026sup2; = 0.003\u003c/em\u003e) and intervention condition (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e[1, 88]\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.99, partial η\u0026sup2; =0.001\u003c/em\u003e) were not significant. Post-hoc multiple comparisons revealed a significant difference in β values during the 3-back task after 30 minutes TEAS compared to the baseline at 12:00 (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.007\u003c/em\u003e). Conversely, no significant difference was observed after one hour of normal nap (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.10\u003c/em\u003e). Additionally, without any intervention following nap deprivation, β values exhibited no significant difference (12:00 vs. 15:00, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.12\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eFor hemodynamic response in HbO (i.e., β value), there were no significant interactions between test time point and intervention condition or interactions between test time point and intervention condition observed (\u003cem\u003eps\u0026thinsp;\u0026ge;\u0026thinsp;0.05\u003c/em\u003e) (See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of repeated measures ANOVA for β values in the 3-back task across three groups at 12:00 and 15:00\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChannel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eND+TEAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003epartial \u003cem\u003eƞ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15:00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup*time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNormal control group (NC), Nap deprivation group (ND) and Nap deprivation group receiving TEAS intervention (ND+TEAS).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Mediation Effects of the fNIRS Measures\u003c/h2\u003e \u003cp\u003eThe mediation model indicated a significant relationship between WM performance and autonomic nervous system activity (\u003cem\u003ec = -0.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e), with the direct effect of WM performance being significant but smaller than the total effect (\u003cem\u003ec' = -0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e), leading to shorter reaction times with higher HFnu during the 3-back task. An indirect effect was observed for the β values of Ch6 in the mediation model, showing that better WM performance was linked to a larger β value (\u003cem\u003ea = -0.01, p\u0026thinsp;=\u0026thinsp;0.045\u003c/em\u003e). Additionally, the fNIRS parameter associated with WM performance correlated with higher HFnu (\u003cem\u003eb\u0026thinsp;=\u0026thinsp;8.72, p\u0026thinsp;=\u0026thinsp;0.0101\u003c/em\u003e). These findings suggest that following TEAS, improved WM performance, along with higher vagus nerve activity, mediated HRV by enhancing the β value of Ch6. However, the β values of Ch3 (\u003cem\u003eb = -0.28, p\u0026thinsp;=\u0026thinsp;0.72\u003c/em\u003e) and Ch8 (\u003cem\u003eb = -2.18, p\u0026thinsp;=\u0026thinsp;0.26\u003c/em\u003e) did not exhibit a significant mediation effect (See Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe findings supported our hypotheses that TEAS applied at the Yintang acupoint enhanced the ANS and measures of WM and that these effects were mediated by improvements in cortical hemodynamics in interns. Compared to those who were deprived of daytime napping, those who received TEAS showed significant improvement in WM performance. We also noted significant increases in the ANS (i.e., HRV) and cortical activation in regions associated with WM. Moreover, the mediation analysis suggested that changes in HRV (i.e., HF) may influence performance via alterations in cortical oxygenation activation during a 3-back task. These findings advance our understanding of TEAS as a neuromodulatory tool and underscore the critical role of brain-heart interaction in cognitive resilience.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Influence of TEAS on WM\u003c/h2\u003e \u003cp\u003eIn terms of behavioral data, the results showed that among interns, TEAS led to significant improvements in a 3-back task, as evidenced by shorter RT and higher ACC. The findings support and extend prior research emphasizing that TEAS benefits WM, which is essential for attention and processing speed, reasoning and problem-solving, and verbal learning and language[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, our study adds to previous research by suggesting that TEAS resulted in more pronounced improvements in WM following non-nap compared to participants without napping. Notably, recent research indicates that the practical implementation of TEAS has been explored and confirmed as feasible for cognitive dysfunction associated with neurodegenerative diseases, such as mild cognitive impairment (MCI) and Alzheimer's disease (AD)[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, research on its potential for cognitive enhancement in healthy participants remains insufficient[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Furthermore, participants exhibited slower RT and lower ACC without habitual napping, underscoring the negative consequences of the absence of a nap on cognitive function. These findings corroborate the existing literature demonstrating the protective effects of moderate daytime napping on cognitive performance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Overall, our results underscore the significant impact of TEAS on multiple aspects of executive performance and highlight the potential advantages of integrating TEAS into practical settings characterized by mental fatigue (e.g., daytime nap deprivation) to alleviate the detrimental consequences of prolonged wakefulness on cognitive function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Influence of TEAS on autonomic nervous system\u003c/h2\u003e \u003cp\u003eOur research suggests that engaging in TEAS following non-nap led to significant increases in parasympathetic nervous activity, as indicated by variations in HFnu. Nap deprivation was associated with a decrease in parasympathetic nervous activity (HFnu). These findings support the notion that TEAS can enhance vagal tone and improve cognitive regulation capabilities. The results align with the existing literature demonstrating the correlation between an elevated parasympathetic nervous system and enhanced cognitive control, emotional regulation, and health maintenance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Our study further demonstrated that participants without napping showed a significant decrease in HFnu, accompanied by increased sympathetic activity. Previous research has shown that during cognitive tasks performed under sleep deprivation, participants exhibit reduced HRV-HF, diminished vagus nerve, and heightened sympathetic activity [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. These alterations reflect compensatory mechanisms aimed at maintaining cognitive performance, accompanied by increased sympathetic nervous system activity[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This perspective is supported by prominent heart-centered theories such as the Neurovisceral Integration Model and Polyvagal Theory[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. However, several studies have reported conflicting results, indicating activation of the parasympathetic system following sleep deprivation. This discrepancy may stem from individual attempts to maintain wakefulness, which could lead to reduce in HFnu, resulting in sympathetic activation and an elevation in LFnu[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, the TEAS condition resulted in greater changes in HRV than the daytime napping condition. This finding indicates that under the physiological context of sympathetic hyperactivation and ANS imbalance induced by nap deprivation, the parasympathetic regulatory response to TEAS becomes more pronounced. This observation aligns with the theory of compensatory autonomic regulation, which proposes that neuromodulatory interventions can elicit a heightened physiological compensatory response when basal autonomic homeostasis is compromised[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Nap deprivation disrupts autonomic function, manifesting as persistent sympathetic hyperactivation and parasympathetic suppression. Within this altered autonomic milieu, TEAS may amplify neuromodulatory effects via a threshold-lowering effect[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. To preserve homeostasis, the organism mounts a stronger compensatory parasympathetic activation in response to identical stimulation[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This mechanism reflects the individual adaptive capacity to enhance parasympathetic tone when autonomic equilibrium is perturbed, consistent with evidence that neuromodulation strategies (e.g., TEAS and taVNS) can restore autonomic balance by reinforcing vagal activity under conditions of sympathetic dominance[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Influence of TEAS on Cortical Hemodynamics\u003c/h2\u003e \u003cp\u003eCompared with nap deprivation, TEAS was associated with enhanced cortical activation, particularly in the PFC, specifically in the DLPFC and orbitofrontal cortex (OFC) regions during a 3-back task, which correlates with improved WM, indicating a significant relationship between brain activity and cognitive performance. This suggests that TEAS may enhance neural efficiency, enabling better cognitive performance in the absence of daytime napping. Previous studies have emphasized that TEAS influences the PFC involved in WM by modulating hippocampal synaptic plasticity[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. fNIRS findings showed that TEAS led to significant activation of the DLPFC and OFC. Elevated β-values reflect enhanced neural efficiency and motivational integration after TEAS under deprivation of napping. Specifically, the activation of the DLPFC indicates increased resource allocation for WM maintenance and manipulation[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e65\u003c/span\u003e], while OFC engagement may support outcome valuation and error monitoring to optimize behavioral adaptation[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Critically, TEAS may augment cortico-subcortical circuits (e.g., prefrontal-striatal pathways), thereby strengthening the DLPFC-OFC synergy for efficient executive control[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. These hemodynamic changes serve as biomarkers for cognitive improvement, as evidenced by the correlation between β-value increases and clinical recovery in memory-related disorders[67, 68]. Stimulation of the Yintang (EX-HN3) acupoint may modify the interaction between the DLPFC and OFC to impact WM [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4 The Association Between Acute Physical Exercise-Induced Changes in Retinal Microvasculature, Cortical Hemodynamics, and Cognitive Performance\u003c/h2\u003e \u003cp\u003eThe results of the mediation analysis indicated that the association between HRV and RT was partially mediated by PFC activation. In the present study, HRV, which plays a pivotal role in cognitive enhancement, was used as an independent variable, specifically referring to changes during the 3-back task, while PFC activation was selected as a mediator, serving as a key intermediary that transmits the influence of the ANS on cognitive performance[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e71\u003c/span\u003e].PFC activation acts as an indicator of neural activity and functional connectivity, demonstrating the capacity of the brain to allocate resources and coordinate activity across cortical regions. Our observation that changes in cortical hemodynamics mediate the relationship between ANS and cognitive performance.\u003c/p\u003e \u003cp\u003ePFC activation is directly involved in WM and cognitive control processes. The dlPFC and OFC are robustly activated during a 3-back task, and their activity levels are closely associated with improvements in RT. During the 3-back task, PFC activation significantly enhances the ACC and speed of cognitive performance. Increased dlPFC activation is negatively correlated with faster RT, indicating that this region shortens RT by optimizing conflict monitoring and decision-making processes[72\u0026ndash;74]. Moreover, fluctuations in PFC activation account for interindividual differences in cognitive performance. This indicates that reduced dlPFC activity is linked to greater RT variability, consistent with a decline in the stability of cognitive control[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Hence, the PFC likely acts as a mediator, integrating autonomic inputs, such as HRV, to adjust cognitive resource allocation and thereby modulate behavioral performance.\u003c/p\u003e \u003cp\u003eThese findings suggest that HRV changes indirectly regulate RT by influencing prefrontal neural activity. HRV reflects vagal tone and physiological arousal, whereas the prefrontal cortex (e.g., dlPFC and OFC) serves as a key hub connecting autonomic states with higher-order cognition. In studies combining HRV monitoring and fMRI, higher HRV predicts enhanced prefrontal activation, which in turn translates into faster RT during affect-attention control tasks[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. This suggests that increased HRV, reflecting better autonomic regulation, optimizes cognitive performance by augmenting prefrontal resources, such as conflict detection or response inhibition[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. This mechanism is particularly prominent in the 3-back task. HRV fluctuations lead to acceleration or delay in RT through corresponding changes in the PFC (e.g., dlPFC) activity[72, 78].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe research demonstrates that TEAS significantly enhances WM in the context of non-nap among interns, who routinely face circadian disruption, sleep restriction, and cognitive overload. The acute cognitive fatigue induced in our model mirrors the mental state often encountered during extended duties. Our results propose TEAS as a practical, non-invasive, and time-efficient intervention that could be deployed to rapidly bolster cognitive readiness and mitigate fatigue-related errors in clinical settings. This intervention regulates specific neural activities in the prefrontal cortex, leading to substantial improvements in cognitive behavioral performance. Additionally, it promotes a dynamic balance between the sympathetic and vagus nerves, enhances HRV, and increases adaptive capacity to external environmental changes. High vagus nerve-mediated HRV indicates the predominance of parasympathetic regulation over cardiac function, characterized by strong neuro-visceral integration, a more adaptable autonomic nervous system, and a tighter coupling between brain and heart. This neuro-visceral integration optimizes resource allocation in the prefrontal cortex, facilitating rapid adaptation to task demands. Importantly, these findings affirm that TEAS is an effective intervention for enhancing WM and mitigating mental fatigue, providing a foundation for targeted interventions that address both the brain's functional changes underlying cognitive performance and individual differences in autonomic nervous regulation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACC, accuracy\u003c/p\u003e\n\u003cp\u003eAD, Alzheimer\u0026apos;s disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDLPFC, dorsolateral prefrontal cortex\u003c/p\u003e\n\u003cp\u003efNIRS, functional near-infrared spectroscopy\u003c/p\u003e\n\u003cp\u003eHbO, oxygenated hemoglobin\u003c/p\u003e\n\u003cp\u003eHRV, heart rate variability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMCI, mild cognitive impairment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emPFC, medial prefrontal cortex\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNASA-TLX, NASA task load index\u003c/p\u003e\n\u003cp\u003eOFC, orbitofrontal cortex\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePFC, prefrontal cortex\u003c/p\u003e\n\u003cp\u003ePSQI, pittsburg sleep quality index\u003c/p\u003e\n\u003cp\u003eRT, reaction time\u003c/p\u003e\n\u003cp\u003eTEAS, transcutaneous electrical acupoint stimulation\u003c/p\u003e\n\u003cp\u003eWM, working memory\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of the Fourth Military Medical University (Ethical number: KY20243597-1), and written informed consent was obtained from all participants prior to their enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by Equipment Technology Foundation Project (145BZB2100101353X), National Natural Science Foundation of China (No. 82570613), Innovation Capacity Support Program of Shaanxi Province (No. 2025JC-GXPT-036), China National Postdoctoral Program for Innovative Talents and Medical Science (No. BX20250458) and Technology R\u0026amp;D Program (2025JSKY18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, X.S. and G.Y.; methodology, Z.L.; software, L.W.; validation, J.F., Y.W. and C.L.; formal analysis, J.F.; investigation, Y.W.; resources, Y.P. and Y.W.; data curation, S.L.; writing—original draft preparation, J.F.; writing—review and editing, L.W.; visualization, Y.W.; supervision, Y.G.; project administration, X.S. and Y.G.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants for their involvement in the study as well as the researchers involved in coordinating data collection for this project.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Maguire T, Furness T, Olasoji M, Willetts G, Levett-Jones T: Investigating the Suitability of\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethe Forensic Mental Health Nursing Clinical Reasoning Cycle for Nurses Working in Generalist Mental Health Settings. \u003cem\u003eInternational journal of mental health nursing\u0026nbsp;\u003c/em\u003e2025, 34(1):e13481.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Mouchabac S, Conejero I, Lakhlifi C, Msellek I, Malandain L, Adrien V, Ferreri F, Millet B, Bonnot\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eO, Bourla A\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: Improving clinical decision-making in psychiatry: implementation of digital phenotyping could mitigate the influence of patient\u0026apos;s and practitioner\u0026apos;s individual cognitive biases. \u003cem\u003eDialogues in clinical neuroscience\u0026nbsp;\u003c/em\u003e2021, 23(1):52-61.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Li J, Cao Y, Ou S, Jiang T, Wang L, Ma N: The effect of total sleep deprivation on working\u003c/p\u003e\n\u003cp\u003ememory: evidence from diffusion model. \u003cem\u003eSleep\u0026nbsp;\u003c/em\u003e2024, 47(2).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003eAyres-de-Campos D: A wider agreement is needed on basic intrapartum concepts.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAmerican journal of obstetrics and gynecology\u0026nbsp;\u003c/em\u003e2023, 228(5s):S994-s996.\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp;\u0026nbsp;Souabni M, Hammouda O, Romdhani M, Trabelsi K, Ammar A, Driss T: Benefits of Daytime\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNapping Opportunity on Physical and Cognitive Performances in Physically Active Participants: A Systematic Review. \u003cem\u003eSports medicine (Auckland, NZ)\u0026nbsp;\u003c/em\u003e2021, 51(10):2115-2146.\u003c/p\u003e\n\u003cp\u003e6.\u0026nbsp; \u0026nbsp;\u0026nbsp;Mesas AE, N\u0026uacute;\u0026ntilde;ez de Arenas-Arroyo S, Martinez-Vizcaino V, Garrido-Miguel M, Fern\u0026aacute;ndez-\u003c/p\u003e\n\u003cp\u003eRodr\u0026iacute;guez R, Bizzozero-Peroni B, Torres-Costoso AI: Is daytime napping an effective strategy to improve sport-related cognitive and physical performance and reduce fatigue? 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A behavioral\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eand physiological investigation. \u003cem\u003eBrain stimulation\u0026nbsp;\u003c/em\u003e2024, 17(2):460-468.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transcutaneous acupoint electrical stimulation, Working Memory, Heart rate variability, Prefrontal brain activity","lastPublishedDoi":"10.21203/rs.3.rs-8925154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8925154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cb\u003e​\u003c/b\u003e Working memory (WM) decrement due to circadian dips and sleep restriction is a prevalent issue among interns, potentially impacting clinical decision-making. Non-invasive neuromodulation techniques, such as transcutaneous electrical acupoint stimulation (TEAS), offer a promising approach to mitigate cognitive fatigue. This study investigates the efficacy and underlying neurophysiological mechanism of TEAS at the Yintang (EX-HN3) acupoint in counteracting WM impairment induced by nap deprivation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe enrolled 90 interns with habitual napping behavior, who were randomly assigned to one of three groups: normal control group (NC), nap deprivation group (ND), and nap deprivation group receiving TEAS intervention (ND+TEAS). Cognitive performance was assessed using a 3-back WM task at baseline (12:00) and post-intervention (15:00). Concurrently, prefrontal cerebral hemodynamics and autonomic nervous system activity were monitored via functional near-infrared spectroscopy (fNIRS) and heart rate variability (HRV), respectively. Subjective cognitive load was evaluated using the NASA-Task Load Index (NASA-TLX).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to the NC group, the ND group exhibited significant deterioration in WM accuracy and speed, accompanied by reduced prefrontal cortex (PFC) activation and attenuated parasympathetic activity (reflected by decreased HRV high-frequency power). The ND+TEAS group demonstrated a reversal of these effects, showing superior WM performance, enhanced PFC oxygenation, and increased vagally-mediated HRV indices relative to the ND group. Crucially, mediation analysis revealed that the improvement in WM performance following TEAS was mediated by its effect on augmenting PFC activation, which in turn was associated with increased parasympathetic tone.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTEAS at the Yintang acupoint effectively alleviates nap deprivation-induced WM impairment. The mechanism appears to involve the enhancement of parasympathetic nervous activity and the subsequent facilitation of prefrontal cortical function. These findings provide novel experimental evidence for the brain-heart interaction as a pathway for cognitive enhancement and position TEAS as a viable, non-invasive strategy to bolster cognitive resilience in populations susceptible to circadian fatigue, such as clinical staff.\u003c/p\u003e","manuscriptTitle":"Transcutaneous Acupoint Electrical Stimulation Ameliorates Working Memory Impairment in Nap-deprived Interns: The Mediating Role of Augmented Brain-Heart Interaction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 06:59:14","doi":"10.21203/rs.3.rs-8925154/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1dc44f52-678e-4b9f-ae75-55992cd3a8c1","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T13:42:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 06:59:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8925154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8925154","identity":"rs-8925154","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}