Improved Emotional Stability in Experienced Meditators with Concentrative Meditation Based on Electroencephalography and Heart Rate Variability

Abstract

Objective:

To determine whether emotional stability distinguishes how experienced and novice meditators react to visual stimuli.

Design:

Participants practiced concentrative meditation and then responded to visual stimuli while continuing to meditate.

Participants:

Ten experienced and 10 novice meditators responded to sequences of visual stimuli after concentrative meditation.

Results:

As predicted, both groups had increased parasympathetic activities during concentrative meditation. Experienced meditators had increased low-frequency electroencephalography (EEG) rhythms in response to visual stimulation, whereas novices had increased high-frequency EEG rhythms. Correlational analyses revealed that novice meditators changed from a meditative state to a nonrelaxed state when the visual stimuli were presented, whereas experienced meditators maintained the meditative state.

Conclusion:

The study provides evidence that regular concentrative meditation can improve emotional stability and that recording physiologic responses to visual stimuli can be a good method for identifying the effects of long-term concentrative meditation practice.

Introduction

Meditation is now part of many spiritual traditions. It is used in alternative therapy as a way to improve clients' spiritual life, and for decades it has been commonly used to alleviate stress-related psychosomatic syndromes.^1

–4 There is substantial evidence on the benefits of meditation for stress,^2,4 cardiovascular disease,¹ and mental illness.³ One specific method for studying meditation practice effects is to record interactions between the brain and the autonomic nervous system (ANS). Electroencephalography (EEG) and heart rate variability (HRV) are the most frequently used indicators of the effects of short-term and long-term meditation.⁵ Long-term meditation is associated with domination of low-frequency EEG components^5
–7 and excitement of parasympathetic activity.^1,2,6
–8 The present study targeted the effects of meditation by comparing experienced and novice meditators.

Results of a preliminary study⁶ indicated that novice practitioners of Su-soku meditation attained a meditative state defined by EEG and psychological reactions. Although it is logical to assume that novice and experienced meditators have similar physiologic reactions during meditation, more research is needed to test this assumption. Thus, the first aim of the present study was to test the hypothesis that experienced and novice meditators have similar EEG and HRV patterns during meditation.

However, determining possible differences between experienced and novice meditators on physiologic responses during meditation is not easy. Research demonstrated that heartbeat perception tasks cannot distinguish between experienced meditators and matched controls.^9,10 Recently, attempts to understand the effects of meditation have examined physiologic reactions to the presentation of stimuli during meditation.^11

–16 Results have consistently demonstrated that long-term meditation can improve emotional stability, primarily by reducing arousal evoked by visual stimuli^11
–13 and auditory stimuli.¹⁴ In other studies, reduction of physiologic arousal in experienced meditators was measured by event-related EEG potentials¹⁵ and functional magnetic resonance imaging.¹⁶ The results suggest that emotional stability might be a good way to distinguish the reactions of experienced and novice meditators to stimuli. It is reasonable that physiologic arousal would be less in experienced meditators than in novice meditators. However, the previous studies^11

–16 did not compare these two groups.

The current study attempted to fill this gap by using EEG and HRV measures to compare how these two groups react to the presentation of visual stimuli. To enhance ecologic validity, stimuli similar to those participants would be likely to encounter outside the laboratory were chosen: pictures from the International Affective Picture System (IAPS).¹⁷ These normative visual stimuli have been widely used in studies of emotion, including those cited earlier.^11

–15

Accordingly, this study proposed two main hypotheses. The first hypothesis was that concentrative meditation practice would arouse low-frequency EEG components and increase parasympathetic activity in both experienced and novice meditators. The second hypothesis was that EEG and HRV would be less varied in experienced meditators than in novices presented with visual stimuli during concentrative meditation. This study appears to be the first to test these two hypotheses simultaneously.

Materials and Methods

Participants

Ten meditators (6 men and 4 women; mean age, 45 years (standard deviation [SD], 7.5 years) with 10–30 years of experience in Tibetan Nyingmapa meditation (mean duration of experience, 20.5 years; SD, 7.6 years) formed the “experienced” meditator group. Ten other meditators (5 men and 5 women; mean age, 53 years (SD, 7.0 years) with 1–5 years of experience in Tibetan Nyingmapa meditation (mean, 3.1 years; SD, 1.4 years) formed the “novice” meditator group. To ensure uniformity of meditation practice, all participants were asked to use breath counting, the most common and basic form of concentrative meditation.^6,18 Participants focused on performing slow and deep respirations, repeatedly counting these breaths silently from 1 to 10 only during the expiration cycle. All participants were free from cardiac, pulmonary, metabolic, and any other disease that could cause ANS dysfunction. All were medication-free and none were habitual drinkers or smokers. Participants were asked not to consume caffeine or alcoholic beverages for 12 hours and not to exercise for 24 hours before the experiment. They also were asked to refrain from eating and drinking anything for at least 3 hours before the experiment.

The institutional review board of Kaohsiung Medical University Chung-Ho Memorial Hospital approved this study (protocol number: KMUH-IRB-980185). All participants agreed to take the examination and signed a consent letter.

Stimuli

Stimuli consisted of 60 color pictures selected from the IAPS,¹⁷ with 10 pictures from each of 6 different content categories: snakes, nonthreatening animals, neutral people, mutilations, erotica, and neutral scenes. Specific IAPS stimuli used in the study are as follows: snakes (1010, 1019, 1050, 1052, 1090, 1110, 1111, 1113, 1114, 1120); nonthreatening animals (1440, 1460, 1463, 1530, 1540, 1590, 1610, 1710, 1750, 1920); neutral people (2019, 2191, 2214, 2215,2372, 2383, 2393, 2394, 2480, 2595); mutilations, (3000, 3051, 3060, 3068, 3069, 3071, 3100, 3101, 3266, 3400); erotica (4611, 4641, 4658, 4659, 4666, 4676, 4677, 4680, 4681, 4690); and neutral scenes (5740, 7036, 7041, 7050, 7100, 7130, 7161, 7224, 7234, 7500).

Each picture category was divided into two sets of five pictures each, statistically matched on normative ratings for arousal. The purpose of this study was to observe that the overall physiologic responses included EEG and HRV responses of different meditation experience groups to affective pictures. Therefore, separating the pictures into two arousal categories seemed a reasonable way to simplify the observation of their effect on EEG and HRV. Snakes, mutilations, and erotica were grouped as high-arousal stimuli, with IAPS normative bipolar arousal ratings (1–9 scale: 1=most calm, 9=most arousal) averaging 6.70 (SD, 0.38); nonthreatening animals, neutral people, and neutral scenes were grouped as low-arousal stimuli, with IAPS normative bipolar arousal ratings averaging 3.55 (SD, 0.89). A t-test revealed a significant difference between the two groups of stimuli (p<0.001).

Procedure

A two-phase study was designed to explore the quality of meditation and the effect of meditation on the results of visual stimulation. The first phase was adapted from that used by Lutz et al.¹⁹ An initial physiologic baseline consisted of four 60-second blocks of ongoing activity with a counterbalanced random ordering of eyes-open and eye-closed conditions within each block. After the baseline period, participants commenced meditation. Each of the three subperiods in the meditation condition consisted of a 5-minute block of meditation followed by a 2-minute block of rest. Participants were verbally instructed to begin the meditation at least 5 seconds before the start of the official meditation period.

In the second phase, the visual stimuli were introduced while the participants continued to meditate. This “stimulation” period was divided into two 9-minute blocks separated by approximately 1 minute of rest. Thirty pictures were presented in each block, 5 from each of the 6 categories (3 high-arousal and 3 low-arousal). For each trial, the picture was presented flickering for 6 seconds; then the screen was dark with a white cross in the center for 12 seconds. The picture categories were presented in counterbalanced order, meaning that the categories were seen equally often in the first and second runs. The sequence of the session components is illustrated in Figure 1.

FIG. 1.

Sequence diagram of the procedure of overall experiment.

Data acquisition and analysis

EEG data were recorded from six electrode placements (F3, F4, C3, C4, O1, and O2) using the Nicolet™ Clinical EEG monitor (Natus Medical Inc., San Carlos, CA) with a sampling rate of 250 Hz. The electrodes were placed according to the standard 10–20 system using a standard EEG cap; reference electrodes were attached to the earlobes. Impedance was less than 10 kΩ for each electrode. Signals between 0.2 and 70 Hz were filtered by a band-pass filter, and a notch filter was applied at 60 Hz to substantially remove external noise caused by power line output.

Electrocardiography (ECG) signals were recorded from Ag/AgCl electrodes at a sampling rate of 1000 Hz using a 0.5- to 100-Hz band-pass filter manufactured by Coulbourn Instrument Lab Inc. (Whitehall, PA) V75-04. The ECG data were recorded in a LabView environment (National Instruments, Austin, TX) using a data acquisition card (NI USB-6009, National Instruments).

The recorded data were analyzed by MATLAB^® (MathWorks, Natick, MA) software. For each EEG channel, a power spectrum was computed by a fast Fourier transform every 4 seconds with 2-second overlap. The EEG frequency spectrum was divided into bands of theta1 (θ1, 4.25–6 Hz), theta2 (θ2, 6.25–8 Hz), alpha1 (α1, 8.25–10 Hz), alpha2 (α2, 10.25–12 Hz), beta (β, 12.25–30 Hz), and gamma (γ, 12.25–30 Hz). These EEG data were then averaged across meditation and rest periods for each frequency band for each channel. To assess HRV, instantaneous heart rate and R-R interval were calculated. The ECG frequency spectrum was computed by fast Fourier transform every 60 seconds with 30-second overlap, divided into two bands: low frequency (LF; 0.04–0.15 Hz) and high frequency (HF; 0.16–0.45 Hz). In the time domain, the standard deviation (SD) of normal to normal intervals (SDNN), and the square root of the mean squared difference of successive normal to normal intervals (RMSSD), were recorded. In the frequency domain, LF (reflecting both sympathetic and parasympathetic activity) and HF (reflecting parasympathetic activity only) were recorded. Their ratio (LF/HF) was then calculated as an index of ANS activity.

Because novice meditators revealed greater alpha2 activity at F4 than the experienced meditators at baseline (p=0.03) (Fig. 2), ANS activity did not differ in terms of HRV between these two groups at baseline. This difference was likely due instead to random fluctuations. To adjust for these baseline differences in alpha2 at F4, the changes in all the EEG channels and in HRV throughout the session were determined. All subsequent analyses were performed on these changed values. The MATLAB statistical analyses were preceded by a log transform of the EEG spectra. Repeated-measures analyses of variance (ANOVAs) were used to check for putative group differences in the physiologic parameters during baseline. Two-way mixed ANOVAs (2 meditation groups ×6 EEG channels) were then performed for each EEG band (theta1, theta2, alpha1, alpha2, beta, and gamma) during meditation, with channels as the repeated measure. The two groups were compared on the HRV parameters (heart rate, SDNN, RMSSD, LFn, HFn, and LF/HF) by t-test. To compare the two groups on changes in meditative state from the baseline to experimental conditions, three-way ANOVAs (two groups×three state changes [baseline to meditation, baseline to high-arousal stimulation, and baseline to low-arousal stimulation]×two channels) were performed separately for each EEG frequency band in the frontal (F3, F4), central (C3, C4) and occipital (O1, O2) lobes. Two-way ANOVAs (two groups×three states) were performed on each HRV variable. For all ANOVAs, post hoc t-tests were used for pairwise comparisons of the components of significant interactions.

FIG. 2.

Means and standard errors for log-transformed electroencephalography power spectrum values for the two groups for each frequency band at each channel during baseline.

Results

EEG power spectra and HRV indices during baseline

Two-way repeated-measures ANOVAs on the EEG power spectra revealed significant effects of group for the alpha2 bands (Table 1). Post hoc analyses for each channel revealed significant differences between groups for in alpha2 at F4 (p=0.03). Mean log-transformed EEG power spectra with standard errors for both groups at each channel for each frequency band during baseline are shown in Figure 2. For HRV, the t-tests revealed no significant differences between groups during baseline (Table 2).

Table 1.

Results (p-Values) from Two-way Analyses of Variance for Each Electroencephalography Frequency Band During Baseline

Variable	Theta1	Theta2	Alpha1	Alpha2	Beta	Gamma
Group	0.47	0.77	0.22	0.01^*	0.97	0.41
Channel	0.00^**	0.00^**	0.00^**	0.00^**	0.45	0.07
Group×channel	0.87	0.95	0.79	0.28	0.63	0.54

p<0.05.

p<0.01.

Table 2.

Mean (Standard Deviation) Heart Rate Variation Indices and p-Values from t-Tests Comparing the Two Groups During Baseline

Variable	Experienced meditators	Novice meditators	p-Value
Heart rate	80.7 (3.7)	83.2 (2.3)	0.98
SDNN	42.9 (3.8)	52.8 (5.2)	0.22
RMSSD	45.6 (4.7)	59.8 (8.0)	0.23
LF	0.3 (0.03)	0.4 (0.07)	0.57
HF	0.3 (0.04)	0.3 (0.04)	0.80
LF/HF ratio	1.7 (0.5)	2.2 (0.7)	0.46

SDNN, standard deviation of normal to normal intervals; RMSSD, square root of the mean squared difference of successive normal to normal intervals; LF, low frequency; HF, high frequency.

Changes in EEG and HRV indices from baseline to meditation and visual stimulation

Three-way repeated-measures ANOVAs on differences in EEG power spectra revealed significant interactions for certain frequency bands in the frontal, central and occipital lobes (Table 3). Post hoc analyses for each brain area revealed the following significant between-group differences in the following: (1) alpha2 (F[1,119]=7.83; p<0.01), beta (F[1119]=13.48; p<0.01) and gamma (F[1,119]=7.31; p<0.01) for high-arousal pictures in frontal; (2) alpha1 (F[1,119]=4.55; p<0.05), alpha2 (F[1,119]=9.82; p<0.01], beta (F[1,119]=17.18; p<0.01) and gamma (F[1,119]=9.80; p<.01) for low-arousal pictures in frontal; (3) alpha2 (F[1,119]=16.30; p<0.01) and gamma (F[1,119]=11.08; p<0.01) for high-arousal pictures in central; (4) alpha2 (F[1,119]=19.57; p<0.01) and gamma (F[1,119]=12.69; p<0.01) for low-arousal pictures in central; (5) beta (F[1,119]=11.73; p<0.01) and gamma (F[1,119]=13.62; p<0.01) for high-arousal pictures in occipital; (6) theta2 (F[1,119]=3.93; p<0.05), beta (F[1,119]=12.74; p<.01), and gamma (F[1,119]=16.00; p<0.01) for low-arousal pictures in occipital. Figure 3 gives post hoc t-tests comparing log-transformed EEG power spectra between baseline and experimental conditions for each frequency band for each channel.

FIG. 3.

Means and standard errors for log-transformed electroencephalography power spectrum values for each frequency band at each channel for the three conditions. (*p<0.05; **p<0.01; ***p<0.001.)

Table 3.

Significant Interaction (p-Values) from Three-Way Analyses of Variance for Each Electroencephalography Frequency Band in Three Brain Areas During Experimental Condition

Variable	Theta1	Theta2	Alpha1	Alpha2	Beta	Gamma
Frontal (F3, F4)
Group×state	0.53	0.71	0.03^*	0.00^**	0.00^**	0.01^*
Central (C3, C4)
Group×state	0.37	0.21	0.01^*	0.00^**	0.11	0.03^*
Occipital (O1, O2)
Group×state	0.09	0.04^*	0.88	0.89	0.00^*	0.00^*

p<0.05.

p<0.01.

Two-way repeated-measures ANOVAs on the HRV indices revealed significant interactions between groups and states for RMSSD (F[2,178]=5.19; p<0.01) and HF (F[2,178]=3.06; p<0.05). For the HRV indices, t-tests revealed significant between-group differences on heart rate, SDNN, and RMSSD for both high- and low-arousal pictures and on HF for low-arousal pictures (Table 4). Figure 4 illustrates the mean between group differences on the HRV indices during baseline and meditation.

FIG. 4.

Means and standard errors of heart rate variation parameters for experienced and novice meditators in the three conditions. (*p<0.05; **p<0.01.) HR, heart rate; HF, high frequency; LF, low frequency; RMSSD, square root of the mean squared difference of successive normal to normal intervals; SDNN, standard deviation of normal to normal intervals.

Table 4.

Mean (Standard Deviation) Heart Rate Variation Indices and p-Values from t-Tests Comparing the Two Groups in the Three Conditions During Meditation

	Experienced meditators			Novice meditators			p-Value
Variable	Meditation	High-arousal	Low-arousal	Meditation	High-arousal	Low-arousal	Meditation	High-arousal	Low-arousal
HR	−1.2 (0.8)	−2.1 (4.0)	−2.7 (3.9)	−1.1 (0.6)	−10.7 (5.3)	−11.1 (5.3)	0.91	0.03^*	0.03^*
SDNN	4.0 (4.8)	−0.08 (3.8)	−2.6 (4.3)	−1.9 (4.3)	−16.8 (5.3)	−15.7 (5.3)	0.12	0.00^**	0.00^**
RMSSD	4.5 (6.5)	0.6 (4.2)	−0.5 (6.0)	−1.1 (6.7)	−23.6 (7.5)	−22.3 (7.6)	0.30	0.00^**	0.00^**
LF	−0.04 (0.03)	0.06 (0.06)	0.04 (0.05)	−0.03 (0.03)	0.01 (0.06)	0.01 (0.06)	0.83	0.33	0.49
HF	0.04 (0.02)	0.10 (0.04)	0.14 (0.03)	0.04 (0.03)	0.05 (0.04)	0.05 (0.04)	0.75	0.07	0.00^**
LF/HF	−0.5 (0.3)	−0.3 (0.4)	−0.5 (0.4)	−0.7 (0.3)	−0.8 (0.6)	−0.9 (0.6)	0.36	0.23	0.34

p<0.05.

p<0.01.

Correlations between EEG and HRV in response to visual stimulation

For estimating the association between EEG and HRV in response to visual stimulation, the parameters with significant group difference were selected. The selected EEG parameters were the differences in EEG power spectra for alpha1, alpha2, beta, and gamma at F3, F4, C3, and C4. The integration of the LF components (alpha1 and alpha2), based on the arithmetic mean, was labeled alpha1+alpha2; the corresponding integration of the HF components (beta and gamma) was labeled beta+gamma. For HRV, HF was selected. Because the responses of SDNN and RMSSD had similar trends, the integration of SDNN and RMSSD, based on the arithmetic mean, was labeled \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}$$\overline{{ \rm SDNN} + { \rm RMSSD}}$$ \end{document} .

The correlations between EEG and HRV in response to visual stimulation for each arousal level are shown in Table 5. For experienced meditators, the LF EEG components are positively correlated with HF. For novice meditators, the HF EEG components are negatively correlated with HF and \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}$$\overline{{ \rm SDNN} + { \rm RMSSD}}$$ \end{document} .

Table 5.

Correlations Between Electroencephalography Power Spectra and Heart Rate Variation Indices During Visual Stimulation for the Two Groups in the Two Arousal Conditions

	Experienced meditators				Novice meditators
	High-arousal		Low-arousal		High-arousal		Low-arousal
Variable	\documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}$$\overline{{ SDNN} + { RMSSD}}$$ \end{document}	HF	\documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}$$\overline{{ SDNN} + { RMSSD}}$$ \end{document}	HF	\documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}$$\overline{{ SDNN} + { RMSSD}}$$ \end{document}	HF	\documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}$$\overline{{ SDNN} + { RMSSD}}$$ \end{document}	HF
alpha1+alpha2	−0.03	0.36^*	0.16	0.36^*	0.24	0.27	0.22	0.02
beta+gamma	−0.23	0.01	−0.01	−0.20	−0.43^*	−0.49^**	−0.50^**	−0.45^*

p<0.05.

p<0.01.

In sum, in response to evoked emotion, the physiologic activity in novice meditators changed from the baseline period to the meditation period. Specifically, the dominant EEG components changed from LF to HF (Fig. 3), and the HRV indices SDNN and RMSSD decreased (Figure 4). The HF EEG power differences from baseline to visual stimulation were negatively correlated with the HRV indices SD and HF (Table 5). The physiologic responses of the experienced meditators differed from those of the novice meditators in all three experimental conditions. For the experienced group, the LF EEG components consistently dominated (Fig. 3); the HRV indices LF and HF increased and LF/HF decreased (Fig. 4). In addition, EEG alpha was positively correlated with the differences in HF differentia from baseline to visual stimulation (Table 5).

Discussion

As predicted, during meditation, both experienced and novice meditators revealed similar physiologic changes from baseline. Brain activity during the meditation period was characterized by a significant increase in the LF components and a decrease in the HF components of the EEG power spectra. EEG alpha increased in the frontal and central regions, and EEG theta increased in the frontal, central, and posterior regions. These results are consistent with those from previous studies.^5,6,18 Parasympathetic activity increased and sympathetic activity decreased, represented by an increase in HF and decrease in LF and LF/HF. This dominance of parasympathetic activity is also consistent with the results of previous studies.^6,18,20,21 EEG results and HRV did not significantly differ between experienced and novice meditators. Both groups showed an increase in EEG theta and alpha and a decrease in beta and gamma. Thus, the findings support the study's first hypothesis, that meditation increases the LF components of the EEG and increases parasympathetic activity in both experienced and novice meditators. These results further suggest that participants experienced an altered state of consciousness during meditation.

In contrast, the two meditator groups had different physiologic reactions to the visual stimuli. For experienced meditators, the stimuli produced a sustained increase in alpha in the frontal and central regions of the brain and a sustained reduction in gamma was in the central region. For the novices, alpha, beta, and gamma decreased in all the recorded channels; sympathovagal activity was characterized by significant reductions in HRV, SDNN, and RMSSD in the time domain. Previous studies have shown that increases in gamma indicate event-related responses to the performance of emotionally evocative cognitive tasks. Emotional arousal has been associated with low gamma power in low-arousal states in normal adults.^11,22 Furthermore, HF EEG components, especially gamma, are associated with universal information processing.²³ On the other hand, an investigation of ANS activity revealed enhanced sympathetic and reduced parasympathetic activity under stress.²⁴ Likewise, the current results for EEG and HRV indicate that the novice meditators' states of consciousness became more stressful in response to the visual stimuli, reflecting a decrease in emotional stability. Similarly, other studies have shown less physiologic arousal in experienced meditators in response to stimuli.^11,15,16,25 Furthermore, the questionnaire data suggest that meditation can improve emotional stability² and reduce negative mood states, such as depression, tension, fatigue, and anxiety,²⁶ as well as pain reduction.²⁵

A strength of the study was the supplementing of questionnaire data with objective evidence on the continuous dominance of parasympathetic activity during visual stimulation in experienced meditators. The study also suggests that emotional stability in the face of moderate emotional arousal can be improved through regular meditation. The results indicate that experienced meditators are more emotionally stabile than novice meditators, thereby providing objective physiologic evidence for the proposition that regular meditation can improve emotional stability. Accordingly, the findings supported study hypothesis that the physiologic responses of experienced meditators are less varied than those of novice meditators in response to visual stimuli.

The two groups of participants showed similar correlation levels for EEG and HRV while responding to the affective pictures. For experienced meditators, alpha brain activity was significantly positively correlated to parasympathetic activity. This result indicates that the experienced meditators maintained a calm and stable mental state during this period. On the contrary, HF brain activity was significantly negatively correlated with parasympathetic activity in novice meditators during this period, indicating that they were nervous. Regardless of whether the pictures were high or low arousal, the experienced meditators revealed significantly less physiologic disturbance than the novice meditators. Previous studies by the current authors have also found this difference.²⁷

Although the physiologic activity of experienced and novice meditators was concordant during the meditation period, experienced meditators achieved higher emotional stability than novice meditators in response to evoked emotion. This result demonstrates that the altered state of consciousness in experienced meditators can be sustained even in the presence of such stimuli. On the other hand, in novice participants, the meditative state was temporary and observed only during the meditation session. Improvement of emotional stability may be a consequence of regular meditation. On the other hand, in novice meditators, meditation practice was accompanied by temporary alternations in physiology (EEG and HRV). The results suggest that assessment of the physiologic responses to the types of stimuli one experiences outside the laboratory can be used to verify the effects of meditation practice, although further confirmatory investigation is necessary.

The present study has several limitations. The possibility that HRV might be affected by the menstrual cycle^28,29 was not excluded in the present study. To check for this, a post hoc analysis was done to see whether there were significant sex differences at baseline. HRV indices, including heart rate, SDNN, RMSSD, LF, HF, and LH/HF, did not differ. Therefore, this concern can be disregarded for this study. Because a small sample of only a certain type of meditator was used, the results cannot be generalized to all meditators.

The study also included only one type of meditation, namely, concentrative meditation. Previous studies have shown that focused-attention tasks elicit EEG activity in the theta and alpha frequency ranges,^6,18,30 so it is not surprising that the current study likewise found that concentrated meditation enhanced theta and alpha rhythms. However, some investigators found different physiologic responses to meditation in experienced meditators.^31,32 Other studies found that EEG gamma was enhanced in experienced meditators doing compassion meditation, a more complex and advanced technique than concentrated meditation.^19,33,34 Thus, examination of the effects of compassion meditation is suggested for future research.

Finally, the present results cannot show the direction of causality. For example, did extensive meditation lead to enhanced emotional stability, or are emotionally stable people more likely than others to engage in long-term meditation? Longitudinal studies are needed to confirm causal models of the effects of meditation practice.

In conclusion, the present study provides evidence for a long-term effect of meditation on emotional processes in the brain. The study also demonstrates that meditation practice can improve attention (as reflected by EEG activity) and parasympathetic activity. The results suggest that meditation practice creates changes in brain and ANS function, especially insofar as they enhance cognitive activity that promotes emotional stability.

Footnotes

Acknowledgments

The authors are especially indebted to Dr. Chung-Yao Hsu of Kaohsiung Medical University and the staff of the Sleep Disorders Center at the affiliated Chung-Ho Memorial Hospital for their support of this study. The authors also thank the Tibetan Nyingmapa Kathok Organization for supplying the participants for our study. The work was supported by the National Science Council of Taiwan (contracts NSC 100-2221-E-006-160 and NSC 101-2221-E-006-221-MY3).

Author Disclosure Statement

No competing financial interests exist.

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