Short-term meditation training alters brain activity and sympathetic responses at rest, but not during meditation

Although more people are engaging in meditation practices that require specialized training, few studies address the issues associated with nervous activity pattern changes brought about by such training. For beginners, it remains unclear how much practice is needed before objective physiological changes can be detected, whether or not they are similar across the novices and what are the optimal strategies to track these changes. To clarify these questions we recruited individuals with no prior meditation experience. The experimental group underwent an eight-week Taoist meditation course administered by a professional, while the control group listened to audiobooks. Both groups participated in audio-guided, 34-min long meditation sessions before and after the 8-week long intervention. Their EEG, photoplethysmogram, respiration, and skin conductance were recorded during the mediation and resting state periods. Compared to the control group, the experimental group exhibited band-specific topically organized changes of the resting state brain activity and heart rate variability associated with sympathetic system activation. Importantly, no significant changes were found during the meditation process prior and post the 8-week training in either of the groups. The absence of notable changes in CNS and ANS activity indicators during meditation sessions, for both the experimental and control groups, casts doubt on the effectiveness of wearable biofeedback devices in meditation practice. This finding redirects focus to the importance of monitoring resting state activity to evaluate progress in beginner meditators. Also, 16 h of training is not enough for forming individual objectively different strategies manifested during the meditation sessions. Our results contributed to the development of tools to objectively monitor the progress in novice meditators and the choice of the relevant monitoring strategies. According to our findings, in order to track early changes brought about by the meditation practice it is preferable to monitor brain activity outside the actual meditation sessions.

Meditation is known to have a number of beneficial effects.Multiple studies suggest that meditation can lead to improvements in attention, concentration, creativity, and problem-solving 1 , and enhances the ability to implement proactive and reactive cognitive control processes 2 .Various meditation techniques were also shown to induce positive effects on memory, fluency and cognitive flexibility 3 .Meditation programs can moderately reduce negative dimensions of psychological stress 4 .It is known that experienced meditators in a resting state, unlike novices, are calmer and have improved emotional stability 5 , which is supported by the objectively measured brain activity.Meditators show decreased activity in the regions related to discursive thoughts and elevated activity in the regions related to response inhibition and attention 6,7 .There is also evidence of positive effects of meditation on physical health, such as beneficial impact upon cardiovascular functioning [8][9][10] .Meditation was shown to modulate inflammatory gene expression, improving microvascular function 11 , cell-mediated immunity and reducing biological aging effects 12 .Thus, meditation based interventions are promising avenues towards preventing a range of pathological conditions, improving depression symptoms 13 , reducing the risk of age-related neurodegeneration 14 and cerebrovascular diseases.

Resting state
We utilized a cluster-based permutation test to investigate variations in EEG power during the resting state, across various frequency bands, before and after the intervention.First, the post-intervention average resting EEG power across the frequency bands for each channel were normalized to the corresponding pre-intervention values to determine the relative change.Such an analysis was performed for the eyes-open and eyes-closed resting state separately.
As shown in Fig. 1A,B our findings suggest that, primarily, the meditator group exhibited significant clusters, which may indicate changes in neural activity as a result of the meditation training.
Specifically, our findings demonstrate that meditators showed significant clusters in the theta band in the occipito-parietal area in the eyes-open condition (p = 0.007).Theta power averaged within this cluster exhibited an increase from 1.41 ± 0.678 in the pre-intervention resting state to 1.85 ± 1.172 after the training (z-statistic = 2.22, effect size = 0.758, p = 0.024, according to Wilcoxon test).In the eyes-closed condition we found three significant clusters.The first one was located in the frontal area (p = 0.019).The second cluster included three electrodes in the right parietal area (p = 0.026).The last one was located in the occipital area (p = 0.021).The Wilcoxon signed-rank test revealed increase of theta power after meditation training in all three of them (frontal cluster: increase from 3.36 ± 2.29 to 4.62 ± 3.77, z-statistic = 2.93, effect size = 1.0, p < 0.001; right parietal cluster: increase from 2.16 ± 1.44 to 3.15 ± 2.58, z-statistic = 2.67, effect size = 0.909, p = 0.005; occipital cluster: increase from 2.89 ± 2.29 to 4.11 ± 3.29, z-statistic = 2.76, effect size = 0.939, p = 0.003).
Post-intervention changes in EEG power in other frequency bands did not reach statistical significance based on the cluster-based permutation test.
Analysis didn't find significant clusters in the control group.

Meditation
To investigate the effects of the intervention on the dynamics of power changes during the meditation process, a full-fledged space-time cluster-based permutation test was conducted.However, no significant clusters were observed.Although no difference between either two groups or pre-and post-intervention within a group was detected, we were still able to observe interesting, distinct and reproducible profiles of changes in EEG based band power indices for different indicators that we demonstrated in Fig. 2. In this figure we compared the average pre-and post-intervention values averaged across all electrodes in separate frequency bands.Before averaging, www.nature.com/scientificreports/ the values of each stage were normalized to the value at rest to show the changes of EEG power during meditation.For example, we could observe a clearly U-shaped profile in alpha rhythm dynamics, which coincides with results from Volodina et al., 2021 16 , observed in one of the meditator groups.

Resting state. Eyes-open condition
Using the Linear Mixed Effects model we found a significant interaction of the time point and group factors for the autonomic balance index (ABI), which indicates the ratio between the sympathetic and parasympathetic activity (estimate = 0.19, z = 2.90, p = 0.03), the stress index (SI), that reflects the stress of the cardiovascular system (estimate = 0.0002, z = 3.09, p = 0.028) and the vegetative rhythm indicator (VRI), which assesses the vegetative balance (estimate = 0.007, z = 3.08, p = 0.028).See "Materials and Methods" section for the mathematical expressions used to calculate these values and the appropriate references.All p-values were adjusted for multiple comparisons.The post-hoc Wilcoxon signed-rank tests revealed a significant increase in ABI (p = 0.003) SI (p = 0.003) and VRI (p = 0.004) in the meditator, but not in the control group.See the corresponding error-bars in Fig. 3.

Resting state.Eyes-closed condition
The Linear Mixed Effects model did not reveal significant interaction of time point and group factors for markers of the autonomic nervous system activity during the eyes-closed resting state, in either the meditator or control group.There was no interaction of group and time point factors for respiration rate and GSR during either the eyes-open or eyes-closed resting state as well.
There was also no effect of intervention on the respiration rate.
To summarize, we observed that after training, the meditator group showed a trend towards an increase in EEG power across a wide frequency range during meditation, but there were no significant changes in the dynamics of the EEG and PPG indices in the process of meditation after the course.We also did not find any division

Discussion
In this study we focussed on the effects of meditation training in beginners, and thoroughly explored the changes in the ANS and CNS activity.The CNS activity was evaluated using electroencephalography (EEG), while a combination of respirometry (RESP), photoplethysmography (PPG), and electrodermal activity measurement www.nature.com/scientificreports/ was employed to assess the ANS activity.We aimed at simulating a real-life scenario in which contemporary urban inhabitants engage in meditation practices.The meditator group underwent a 16-session Taoist meditation training, each lasting an hour, over an 8-week period.The subjects were instructed not to practice meditation at home, to ensure the consistency of the intervention.It was important for the participants to receive offline training under the guidance of an experienced instructor, who had the ability to adjust the preparatory exercises and body position.Working with an experienced instructor minimizes the risk of undesirable consequences from meditation practice 24 .The control group spent an equivalent amount of time attending offline group sessions where the participants listened to audio books.Overall, as revealed by our study,Taoist meditation training had a significant effect on the baseline physiological indicators during resting state intervals, but did not affect the meditation process itself.More specifically, the results of the study showed an increase in theta and alpha power during the eyes-open and eyes-closed resting state after the intervention in the meditator group but not in the control group.These could be interpreted as the signs of relaxed alertness state 15 observed in the group of meditators.The results also showed changes in the heart rate variability indices associated with the increased sympathetic activity and alertness of the body (such as autonomic balance index, vegetative rhythm indicator, and stress index) during resting state with open eyes in the meditator group after the intervention.
The meditator group showed an increase in theta and alpha power during resting state.Such changes observed in the meditator group after the intervention are typical changes and have been previously reported in several studies of meditation 15,25 .One of the interpretations states that the increased alpha fluctuations reflect engagement in the internally directed tasks 26 .
In Takahashi's EEG based study, alpha power increased significantly during meditation, reflecting processes such as anticipation and attention 18 .More specifically, in our study, we observed increased alpha power in frontal, central, and parietal regions.It is known that frontal alpha activity can help to monitor and predict changes in some behavioral indices 27 and the asymmetry of the alpha range of EEG in frontal areas may be related to emotional reactivity 28 .Based on the fact that high alpha activity in healthy individuals is negatively correlated with activation of the "negative emotion zone" in semantic emotional space 29 , we can then hypothesize that the increase of alpha band power in the resting state condition observed in meditators, may be indicative of an improved emotional state with longer meditation practice.Regarding cognitive processes, there are studies that confirm that it is the enhanced and synchronized alpha activity in the frontal area that is involved in higher brain processes during cognitive tasks 30 , higher alpha power in the right parietal cortex reflects focused internal attention during complex cognitive tasks, such as idea generation and mental imagery 31 .Also, alpha activity increases during working memory load 32 .Moreover, some studies have shown a correlation between the increased sensorimotor alpha rhythm power and bodily manifestations.For example, a study using biofeedback to reduce muscle tension found a link between increased alpha rhythm and muscle relaxation 33 .In other studies, increased cortical alpha rhythm accompanied more efficient sensorimotor interactions 34,35 .These findings, in combination with our observations, speak about the increased cognitive alertness and muscular relaxation in our meditators.
Theta power has been linked to attention and arousal 36,37 , memory 38 , affective cognitive processing mechanisms 39 , regulation of focused attention 40 , conscious awareness 41 , sustained attention, and mental effort 42 .In our study, we also observed increased theta power in the frontal, central, and occipital regions.Like increased frontal alpha, which may indicate increased cognitive load and improved psycho-emotional state, there are similar results related to increased theta power.Increased frontal theta activity is associated with increased effort to maintain high levels of performance 37 , mental tasks 43,44 , is related to attention concentration 45 , and may be a potential mechanism of cognitive control 46 .Frontal theta activity may be an EEG correlate of mood-related emotional processing 47 , associated with emotionally positive "blissful" experience and internalized attention 48 , and may be associated with reduction of anxiety symptoms in patients with generalized anxiety disorder 49 .As for other brain areas, it has been found that during cognitive tasks in the occipital region, the theta EEG frequency band exhibits increased power compared to baseline 50 .Theta band oscillations in the hippocampus were also shown to be crucial for temporal encoding/decoding of active neuronal ensembles and modification of synaptic weights 51 .The simultaneous increase of alpha and theta power has been associated with "relaxed vigilance" 52 .Studies have shown that the most reliable effects of a positive emotional state and internalized attention during meditation are reflected by increased local theta power and lower alpha power 48 .In another study, significant increases in theta and alpha power activity were found under meditation conditions when averaged across all brain regions, but in contrast to our results, alpha activity was found to be significantly higher in posterior compared to frontal regions 53 .Our results mostly corroborate findings reported by Nuhus et al. 54 , who found in the experimental mindfulness meditation group the meditation induced an increase of theta power in the right frontal and left parietal regions.This may be interpreted as the presence of a positive effect of meditation on the activation of memory related brain networks.
Our results also showed changes in heart rate variability indices known to be associated with increased sympathetic activity.The interplay of heightened alpha and theta power, along with increased sympathetic nervous system activity found in our study, may suggest a unique balance between alertness and relaxation in the participants.It is worth noting that there is inconclusive data from previous studies regarding sympathetic activation as a result of meditation training.There is a common belief that meditation leads to a decreased breathing frequency 55 and the increased parasympathetic activity 18,56 , leading to relaxation.However, there is also evidence that meditation involves both sympathetic and parasympathetic systems, with a balanced coordination of both systems needed to maintain the meditative state.One of the studies revealed two different physiological strategies of meditation, characterized by distinct physiological changes 16 .The effects induced by meditation on the autonomic nervous system were found to depend on the meditation type [57][58][59]  www.nature.com/scientificreports/ the sympathetic system first and later the parasympathetic system 60 .Further research, with a longer meditation intervention, could cast some additional light onto this hypothesis.Furthermore, we aimed to investigate the feasibility of identifying a personalized physiological strategy that could assist novice meditators in entering the meditative state.However, our analyses did not reveal significant differences in physiological measures changes during the meditation session.The trajectory of such changes was unaffected by the intervention.Thus, we could not make conclusions about the existence of a predisposition to certain physiological strategies of meditation, at least in the early stages of learning meditation.Based on our results, the 16 h of instructed meditation training over 8 weeks, appeared to be not enough for obtaining a stable physiological effect on the meditation process itself.This implies that longer intervention periods are needed to better understand the physiological effects of meditation and develop more effective meditation training methods.However, even such relatively short intervention, as used in our study, resulted in changes in the activity of the central and autonomic nervous system notable during the resting state.The elucidated, in this study, changes in the objective parameters of CNS and ANS functioning may indicate improved attention, particularly attention to internal cues, the rise of alertness and the increase of memory related brain networks activation.
In addition to the results presented in the manuscript, our statistical analysis revealed a trend towards an increase of delta range activity among meditators and a decreased delta band power in control group.Such results are consistent with previous findings by Faber et al. 61 , who also reported an increase in post-meditation delta power.Many researchers, however, consider delta waves as a rhythm associated with cognitive processes 62 .Several studies have found links between delta activity and engagement in internal attention processes 63,64 as well as with homeostasis and basal metabolic rate 65,66 .In our study, the trend of the increased delta rhythm power appears to coincide with sympathetic nervous system activation.We interpret this as signs of the activation of homeostatic and brain-body interaction processes in meditators.Tonic sympathetic activity has been shown to support resting metabolic rate in healthy adults in previous studies 67 , and increased brain-body interaction have been reported in the experienced meditators in several papers 68,69 .
Several limitations should be considered when interpreting the findings of this study.
Here, we have only investigated the effects of Taoist meditation training, and it is unclear whether these findings can be generalized to other types of meditation.Future research should explore the specificity of physiological changes in different types of meditation and investigate how Taoist meditation relates to other forms of this self-regulation practice.We have also limited our analysis to exploring the effects within each quantitative measure and did not examine the interplay between these measures, which constitutes an interesting and potentially fruitful future research direction.In this study, we did not use the specific questionnaires reflecting the success of mindfulness meditation such as MAAS 70,71 .
The additional important limitation of the present study is the modest sample size which may affect the validity of the conclusions, especially regarding negative findings, when no changes in regional EEG power were observed between the two groups of subjects.Where there were positive results, we have included effect sizes that appeared to be reasonably large and resulted in test power values on the order of 0.8, which we have also specified for each of the tests.In order to reassure our negative findings, additional experiments are required with a greater number of subjects.To conduct these and maintain the number of participants over a prolonged study duration, detailed questionnaires need to be completed, and significant monetary compensation has to be considered, to minimize the drop-out rate of subjects over the course of such a long experiment.

Conclusion
The course of Taoist meditation, consisting of 16 h of training over 8 weeks, caused topically well organized changes in brain activity (increase in theta and alpha power), and markers of autonomic nervous system activity which indicate sympathetic activation.The effect was primarily observed in the resting state.There were no significant changes in the dynamics of indicators during the meditation session, which could be due to the insufficient duration of the training.Since we did not observe changes in the physiological indicators during the meditation process, we could not draw conclusions about the existence of a predisposition to one of the meditation strategies.Further, studies with increased duration of training are needed to answer this question.
As illustrated, during the meditation process we could observe clear stage-by-stage variations in the band power which forms frequency-specific oscillatory power profiles.However, we found no changes between these profiles for the experimental and control groups.This conceptually complicates the development of assistive devices aimed at "guiding" novice meditators during the meditation process.Accordingly, and based on our results, the focus in creating such digital assistants should be shifted towards monitoring neurophysiological activity during time intervals outside of the meditation session.As apparent from Fig. 3 these changes occur not only in the EEG derived parameters but are also detectable based on the markers of the ANS activity, which can be readily measured with a range of wearable devices, which renders hope for a rapid translation of our results into practical applications.

Subjects
The study included 28 participants who underwent pretesting, with 25 participants included in the final analysis.Three participants left the experiment due to the occurrence of undesirable side effects.The final sample included a 12-person group (ranging in age from 20 to 37 years, with three men and nine women and a mean age of 28.08 ± 5.45), and a 13-person control group (ranging in age from 21 to 38 years, with four men and nine women and a mean age of 27.69 ± 5.68).Inclusion Criteria: participants in both the experimental and control groups were selected from individuals aged 20 to 40 years.None of the participants in either group had any prior experience with meditation, and this criterion aimed to prevent any confounding effects from previous www.nature.com/scientificreports/exposure.Also, participants should be without diagnosed mental illnesses or brain disorders and not taking drugs that affect the central nervous system, such as antidepressants or sedatives.Exclusion Criteria: inability to attend sessions and the occurrence of negative psychological effects (anxiety, problems, etc.) during meditation practice sessions.Before the experiment, we analyzed various physiological parameters, as well as gender and age to ensure the meditators and the control group were similar in all indicators.The experiment was conducted in accordance with the declaration of Helsinki.Participation in the study was voluntary.All participants provided written informed consent, approved by The HSE University Committee on Inter-University Surveys and Ethical Assessment of Empirical Research in accordance with the Declaration of Helsinki.All experimental protocols were approved by The HSE University Committee on Inter-University Surveys and Ethical Assessment of Empirical Research in accordance with the Declaration of Helsinki.

Experimental protocol
The design of the experiment is shown in Fig. 4. For eight weeks, participants attended 1 h long group meetings twice a week, during which they engaged in a course of Taoist meditation with a qualified instructor (experimental group) or listened to audiobooks (control group, audiobooks: "A Warm Cup on a Cold Day-How Physical Sensations Affect Our Solutions", Talma Lobel and "Healthy Brain, Happy Life: A Personal Program to Activate Your Brain and Do Everything Better", Wendy Suzuki).Both groups took the course/lectures in the same auditorium, in the evening for 1 h, 2 days per a week.The meditation courses were on Tuesdays and Thursdays.The sessions with the control group were on Mondays and Wednesdays.The meditation lesson practically repeated the audio instruction used on the physiological testing (see Supplementary materials).
All participants were required to undergo physiological testing before and after the course.During such testing, the experimenter placed the EEG cap, PPG, GSR, and RESP sensors and measured physiological indicators during the resting state (4 min: 2 min eyes-open + 2 min eyes-closed) and during the Taoist meditation guided by audio instruction, delivered through the earphones (34 min).
Meditation protocol during testing.Prior to the meditation session, all the participants read the text of the meditation and could ask questions.The audio instruction was presented by an experienced meditation teacher and consisted of 16 stages.The end of each audio instruction marked the beginning of a new stage of meditation, and each stage lasted for about 2 min.For the subsequent statistical analysis we merged meditation intervals into the following 6 stages: Resting state before meditation; The full text of meditation guidelines can be found in the Supplementary materials.

EEG
The EEG data was recorded using a 30-channel wireless EEG system (SmartBCI, Mitsar, Russia) with a sampling rate of 250 Hz.The digital averaged ear signal served as a reference for all EEG data channels.The EEG data was precisely synchronized with the audio instruction.A Python script was used to simultaneously run the audio instruction and to collect EEG data.
The PPG sensor was placed on the subject's index finger of the right hand.The following filters were used: 4th order 0.5 Hz high-pass filter, 4th order 10 Hz low-pass Butterworth filter, notch filter at 50 Hz.
The GSR sensors were placed on the subject's first phalanges of the ring and index fingers of the left hand.The following filters were applied to the GSR signals: 4th order 10 Hz low-pass Butterworth filter, notch filter 50 Hz.
The respirogram was measured with a thermometric sensor of nasal respiration placed under the nose, whose signal was filtered in the (0.05-10) Hz range using the 4th order Butterworth filter and a notch filter centered at 50 Hz.

EEG
The EEG data underwent preprocessing, including bandpass filtering with a lower cut-off of 1 Hz and an upper cut-off at 40 Hz.A 50 Hz notch filter was also applied to suppress the powerline interference.Independent Component Analysis yielded 29 components, from which eye movement and muscular components were excluded.On average, five components were eliminated.Preprocessing was performed using NFBLab software 72 .

EEG
The EEG processing was performed the same way as in the previous Taoist meditation study 16 .The Welch method was applied to compute the EEG spectral power with 1 s Hanning window with 50% overlap.Scipy.signal.welchfunction was used.The power spectral density was then summarized by a simple averaging within the following frequency bands: Delta (1-4) Hz, Theta (4-8) Hz, Alpha (8-12) Hz, Beta (12-30) Hz, and Gamma (30-40) Hz.
MNE-python was used for all subsequent data processing.

PPG
Heart rate variability indices were analyzed in the same way as in the article 16 .Briefly, indices are defined as follows: RR is the interval between successive PPG peaks, ME is the Median RR, AME is the amplitude of median, SD is the standard deviation.
1.The heart beats were detected with a commonly used peak detection algorithm implemented in routine 73 .The heart rate (HR) values were calculated using a 20-s long moving sliding window with a 5-s stride.Heart rate variability (HRV) analysis is one of the commonly used methods to appraise the activity of the sympathetic and parasympathetic nervous system.2. HRV was calculated using pulse-to-pulse (RR) time series using a 60-s sliding window, with a stride of 30 s (30 s overlap).Then the obtained values within each meditation stage were averaged.The HRV-based metrics included the time domain and frequency domain indices.3. RMSSD is the "sequential difference mean squared", the square root of the mean of the squared successive differences between neighboring NNs (beat-to-beat intervals).High RMSSD values indicate high parasympathetic activation.4. Autonomic balance index (ABI) indicates the ratio between the activity of the sympathetic and parasympathetic divisions of the autonomic nervous system 74 (1).

Vegetative rhythm indicator (VRI) assesses the vegetative balance (2)
. A lower VRI indicates greater activity in the parasympathetic nervous system, which suggests a shift towards a more balanced state of the autonomic nervous system 75 .
6. Stress index (SI), which is a geometric measure of HRV that reflects the stress experienced by the cardiovascular system 76 (3).High SI values indicate reduced variability and high sympathetic activation of the heart.www.nature.com/scientificreports/GSR A zero-phase high-pass 4th order Butterworth filter with a cutoff frequency of 0.05 Hz was applied to the data to remove the slow trend and the number of spontaneous reactions was measured, defined as signal fluctuations with an amplitude greater than one standard deviation of the signal calculated for each individual subject during the entire recording.A sliding window with a length of 20 s with 5-s strides was used.

RESP
A peak detection algorithm was applied to the recorded breathing data.The respiratory rate was calculated using a moving window with a length of 20 s (strides of 5 s), all stages were then averaged.The respiration amplitude was calculated as the average difference between the upper and lower signal envelopes.

EEG
To determine the statistical significance of the changes in EEG power between the pre-and post-training conditions in the meditator and control group, we performed a cluster-level permutation paired t-test 77 .
The analysis was conducted using the mne.stats.permutation_cluster_1samp_testfunction with 1024 permutations.The average number of neighbors per electrode was 5.1, with a range from 3 to 8. We performed this test separately for the meditators and control groups.Average EEG power within the electrodes comprising significant clusters was calculated and the Wilcoxon signed-rank test was performed for obtained values to estimate the effect size.
During the analysis of EEG signal dynamics within the meditation process, all values were standardized to the baseline measurement obtained during eyes-closed conditions.We then examined changes in EEG power across various stages of meditation in comparison to the baseline.To determine whether the dynamics during meditation were influenced by the intervention, a cluster-based permutation test was employed.

PPG, GSR, and RESP data
Firstly, we analyzed the interaction of "group" (control/meditator) and "time point" (pre-/post-training) factors using the Linear Mixed Effects model 78 for resting state data and interaction for "group", "time point", and "meditation stage" factors for meditation data.The analysis was performed utilizing the statsmodels.formula.api.mixedlm function.Fixed effects included group (meditator/control) and time point (pre-/post-intervention).Participant ID was included as a random effect to account for individual differences and minimize their impact on the results.For indicators with a significant interaction of factors after the FDR correction procedure 79 , the data before and after the intervention were compared using the Wilcoxon test.P-values obtained for the Wilcoxon test were also adjusted for multiple comparisons using the FDR correction procedure.

Figure 1 .
Figure 1.Analysis of variations in EEG power during the resting state across various frequency bands, before and after the intervention, using a cluster-based permutation test.The results are presented as topographic maps of the t-statistic.The upper panels (A and B) show the results in the meditator group.The lower panels (C and D) show the results in the control group.The channels comprising each statistically significant (p < 0.05) cluster are marked with white circles.For significant clusters, the barplots are presented showing the average spectral power density over that frequency band within the cluster ± the standard deviation before and after the intervention.

Figure 2 .
Figure 2. Changes in EEG power in the process of meditation compared to eyes-closed resting state averaged across all the channels.The green line marks the pre-intervention condition and the blue line marks the postintervention condition.The data are presented as the mean ± 95% confidence interval.The x-axis corresponds to the meditation stages.Stage 0 corresponds to the eyes-closed resting state condition.

Figure 3 .
Figure 3.The differences between groups in PPG data during the eyes-open resting state (A is the Autonomic balance index, B is the Stress index, C is the Vegetative rhythm indicator).Data are presented as median ± interquartile range.* indicates significant (p < 0.01) differences between the meditator group and the control group according to the Wilcoxon signed-rank tests followed by FDR correction.