Effects of Yoga Respiratory Practice (Bhastrika pranayama) on Anxiety, Affect, and Brain Functional Connectivity and Activity: A Randomized Controlled Trial
- 1Brain Institute, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
- 2Onofre Lopes University Hospital, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
- 3Department of Biophysics and Pharmacology, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
- 4Department of Radiology, Medical School, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- 5Hospital Israelita Albert Einstein, São Paulo, Brazil
- 6Sports Center, University of São Paulo (CEPE-USP), São Paulo, Brazil
Pranayama refers to a set of yoga breathing exercises. Recent evidence suggests that the practice of pranayama has positive effects on measures of clinical stress and anxiety. This study explored the impact of a Bhastrika pranayama training program on emotion processing, anxiety, and affect. We used a randomized controlled trial design with thirty healthy young adults assessed at baseline and after 4 weeks of pranayama practices. Two functional magnetic resonance imaging (MRI) protocols were used both at baseline and post-intervention: an emotion task as well as a resting-state acquisition. Our results suggest that pranayama significantly decreased states of anxiety and negative affect.
The practice of pranayama also modulated the activity of brain regions involved in emotional processing, particularly the amygdala, anterior cingulate, anterior insula, and prefrontal cortex. Resting-state functional MRI (fMRI) showed significantly reduced functional connectivity involving the anterior insula and lateral portions of the prefrontal cortex. Correlation analysis revealed that changes in connectivity between the ventrolateral prefrontal cortex and the right anterior insula were associated with changes in anxiety. Although it should be noted that these analyses were preliminary and exploratory, it provides the first evidence that 4 weeks of B. pranayama significantly reduce the levels of anxiety and negative affect, and that these changes are associated with the modulation of activity and connectivity in brain areas involved in emotion processing, attention, and awareness. The study was registered at https://www.ensaiosclinicos.gov.br/rg/RBR-2gv5c2/(RBR-2gv5c2).
Introduction
Yoga is a system of practices with ancestral roots in India (1). It is defined as Chitta Vritti Nirodhah—the cessation of the whirlwinds of the mind—which is better understood in contemporary language as a tool to calm the mind (2). The Yoga Sutras of Patañjali systematized it a set of eight practices, also called Ashtanga Yoga or Yoga of the eight limbs (1, 2): yamas (abstentions), niyamas (observances), asanas (postures), prāṇāyāma (control of breath), pratyāhāra (withdrawal of senses), dhāraṇā (concentration), dhyāna (meditation), and samādhi (oneness). The breathing practices are called prāṇāyāma, which is a Sanskrit word for prana (vital energy) and ayama (control). It refers to a series of voluntary controlled breathing exercises that manipulate the respiratory frequency, inhalation (puraka), retention (kumbhaka), exhalation (rechaka), and body locks (bandhas) (3).
The practice of pranayama influences many physiological variables. Evidence suggests that its practice produces a positive impact on the cardiorespiratory system (4–7), where slow-paced breathing leads to reduced heart rate and decreased systolic and diastolic blood pressure (8), while fast breathing leads to less robust, but consistent increase in heart rate (9–12). In fact, a previous study observed that the practice of the Bhastrika pranayama with low respiratory rate decreased significantly both the systolic and diastolic blood pressure, with a modest decrease in heart rate (10). Furthermore, changes in heart rate variability (HRV) also support the notion that the practice of pranayama improves respiratory function and cardiac sympathovagal balance, which are important psycho-physiological stress-related variables (13, 14).
A number of studies support significant positive effects of different yoga practices on anxiety and depression (15, 16) but very few have explored the impact of the practice of pranayama on neurophysiological, psychological and psychiatric variables, although evidence suggests improved self-regulation, positive mood, reduced stress, and anxiety (4, 5, 17). A study evaluating the effects of fast and slow pranayama on perceived stress and cardiovascular parameters in young students observed a significant and comparable decrease in the perceived stress scores in both types of pranayama practices, while cardiovascular parameters were changed only after the slow-paced pranayama (18). Furthermore, evidence suggests that yoga programs that include pranayama result in reducing anxiety in humans (19, 20), and a recent feasibility study found evidence of the positive impact of pranayama in patients with treatment-resistant generalized anxiety disorder (21).
It has been hypothesized that the psychobiological mechanism through which pranayama exerts its effects are mediated by the vagus nerve, through interconnections between peripheral sensory organs, the solitary nucleus, thalamus, limbic areas, and the prefrontal cortex (17, 22). Furthermore, it has been suggested that the increase of parasympathetic activity (associated with expiration time) reduces the release of hormones associated with stress (22, 23), and enhances GABA inhibition from the prefrontal cortex and insula to the amygdala, reducing its activity, and the psychological and somatic symptoms associated with stress (24, 25).
Recent studies show that yoga practices, such as meditation, are associated with emotional regulation processes (26, 27). It remains unclear, however, whether these changes occur through top-down or bottom-up strategies. The emotional regulation task used in this trial allowed us to investigate both processes. In addition, several brain regions involved in emotion regulation, as the amygdala, insula, and anterior cingulate cortex (ACC) play an important role in anxiety disorders (28), and previous evidence suggests that anxiety-prone individuals have increased activity in the bilateral amygdala and insula when compared to healthy controls (29). Furthermore, the practice of meditation, including focused on breathing, leads to optimized emotion regulation through increased acceptance and enhanced present-moment awareness (30–33) while impaired emotion regulation has been associated with depression and anxiety (34, 35).
However, the neural basis of the effects of pranayama on anxiety, mood, and emotional regulation, has been less explored. This study aimed at exploring the impact of a 30-day training program of B. pranayama on a brain network involved in emotion processing and its association with self-reported changes in affect and anxiety. We used functional magnetic resonance imaging (fMRI) to assess changes in activity and connectivity of brain networks involved with anxiety and emotion processing, and questionnaires to access states of anxiety and affect. Our hypotheses were that pranayama training would: i) decrease anxiety levels; ii) decrease negative affect levels; iii) increase positive affect levels; iv) decrease connectivity within emotion processing brain networks; v) decrease activity in the amygdala; vi) increase activity in parts of the prefrontal cortex, anterior cingulate, and insula, all involved in emotion processing and anxiety.
Materials and Methods
Participants
Participants were recruited through word-of-mouth and printed advertisements posted at the Federal University of Rio Grande do Norte campus and community. After contacting the experimenter, participants received information about the goal of the study as well as the exclusion criteria. They were informed that they could be allocated in either a group that involves pranayama practices or in a control group, depending on randomization.
Thirty volunteers were selected according to the following inclusion criteria: i) healthy young adults, between 18 and 40 years of age, naïve to the practice of pranayama. Exclusion criteria included: i) MRI contraindication, such as metal parts in the body or pregnancy; ii) chronic rhinitis, with partial or complete obstruction of one or both nostrils; iii) frequent use of bronchodilator; iv) regular use of beta-blocker, stimulants or any other substance that interferes with cardiovascular activity; and v) current diagnosis or history of neurological or psychiatric disorders. The Ethics and Research Committee of the Federal University of Rio Grande do Norte approved the study (protocol #579.226), and all subjects provided written informed consent prior to their participation in the study. This study was registered at http://www.ensaiosclinicos.gov.br/rg/RBR-2gv5c2/(RBR-2gv5c2).
Experimental Protocol
This study used a randomized controlled design with two parallel arms, and it was conducted in accordance with the consolidated standards of reporting trials (CONSORT) statement (36). Subjects were allocated in blocks of (4:4) to either a pranayama training group or to a control group, using permuted block randomization (http://www.randomization.com). Given the nature of the training, participants and researchers were not blinded. Nevertheless, all the analyses were conducted blindly. In addition, to ensure motivation, those in the control group interested in pranayama were put in a waiting list to receive the training after the study. Figure 1 shows the experimental protocol used in the study. Participants from both groups (pranayama and control) were assessed at baseline and right after a 30-days training program. Both assessments included fMRI and psychometric trait-state measures of affect and anxiety.
The level of anxiety was assessed by the State-Trait Anxiety Inventory (STAI), which has been translated and validated to Brazilian Portuguese (37). We also used the Positive Affect and Negative Affect Scale (PANAS) to assess positive affect (PANAS-P), such as well-being, enthusiasm, inspiration and determination, and negative affect (PANAS-N), aimed at dimensions such as fear, nervousness, and disturbance. We used the adapted and validated PANAS to Brazilian Portuguese (38, 39). Both scales were applied to assess state and trait characteristics. fMRI assessments included an emotional regulation task and a resting state (rs-fMRI) protocol, described below in detail.
The Training Program
The practice of the B. pranayama is not easy for inexperienced Yoga practitioners. Therefore, during the initial 5 days of training, participants were guided for 30 min a day for the correct practice of B. pranayama. An instructor was present during these encounters and followed a specific sequence of daily steps designed to guide participants into the correct practice. Each training session had only four participants at a time to allow careful and individualized training.
This initial period was followed by 4 weeks of regular B. pranayama practice. In order to assure a volume of practices similar to previous studies from our group (40) and to the ones focused on mindfulness meditation (41), our study was designed with five practices a week, for 4 weeks. For 3 days a week, 30 min per day, participants gathered and practice together with an instructor in supervised training classes. Besides in-person meetings, subjects were instructed to practice at home for at least two more days a week. The control group also gathered with the same frequency and duration but performed ludic cognitive activities such as crosswords, puzzles, domino, checkers, and card games, also in the presence of an instructor. Participants performed one of these activities at each encounter and were asked to alternate between activities to ascertain that each game was practiced at least one to two times during the 12 scheduled meetings. Subjects in the control group were also instructed to practice at home.
Each B. pranayama session started with a brief 2-min savasana (relaxation), followed by 5 min of asanas (pavanamuktasana, sukhasana, gomukhasana, paschimotanasana, and vakrasana) applied solely to prepare the body for the practice of pranayama, as described in Patañjali’s Yoga Sutra. Following this short preparation, the B. pranayama was performed continuously for 25 min. Sessions ended with another brief savasana (2 min).
The practice of the B. pranayama used in this study followed the description of Swami Kuvalayananda (13), according to whom, each round of the practice is composed by a set of fast breathing (kapalabhati) followed by a slow inspiration through the right nostril, a comfortable apnea done with the three bandhas (mula, jalandhara, and uddiyana) and a slow expiration through the left nostril (Surya bedhana). The relation inspiration:apnea:expiration was set according to individual comfort, varying from 1:1:2 to 1:2:2; 1:3:2, or 1:4:2—apnea never exceeded four times the inspiration time, and expiration was set to constantly correspond to twice the inspiration time.
Each kapalabhati consists of a series of 30 rapid self-paced expirations generated by contractions of the rectus abdominis muscle. Contrary to natural breathing, the kapalabhati inspiration is passive while expiration is active. One-cycle of Surya bedhana is composed of a slow inspiration through the right nostril, followed by apnea and by a longer, yet comfortable expiration through the left nostril. The suggested ratio between inspiration:apnea:expiration followed the traditional description of 1:4:2. Inspiration, apnea, and expiration times were set individually, according to one’s capacity and comfort. Therefore, when the 1:4:2 ratio was felt uncomfortable, volunteers were instructed to decrease apnea duration to one of the following alternative ratios: 1:3:2; 1:2:2, or 1:1:2. During periods of apnea, practitioners were also instructed to execute three bandhas, also called locks: jalandhara bandha, uddyiana bandha, and mula bandha. Jalandhara bandha is attained by pressing the chin against the jugular notch, with both nostrils closed with the fingers, uddyiana bandha by a chest expansion after jalandhara bandha, followed by perineum contraction, called mula bandha. Participants were instructed to perform 30 cycles of the B. pranayama at each encounter.
Magnetic Resonance Imaging
Imaging Acquisition
Images were acquired in a 1.5-T MRI scanner (HDxt, General Electric, USA). Functional MRI datasets were acquired with the following EPI sequence parameters: repetition time (TR) = 2000 ms; echo time (TE) = 35 ms; flip angle = 60˚; field of view (FOV) = 24 cm; matrix = 64 × 64; slice thickness = 3 mm; gap = 0.3 mm; number of slices = 35; volumes = 165 (emotion processing) and 213 (resting state). High-resolution T1-weighted images were acquired with the following FSPGR BRAVO sequence: TR = 12.7 ms; TE = 5.3 ms; flip angle = 60˚; FOV = 24 cm; matrix = 320 × 320; slice thickness = 1.0 mm; number of slices = 128.
Emotion Processing Protocol
All subjects had normal or corrected to normal vision. Immediately before scanning, they received a training session to ensure task compliance, using another set of images for stimuli. During the fMRI emotion processing task, programmed using Psychopy v.1.79 (42), subjects viewed a series of images with different emotion valence (neutral or negative) and were asked to rate the emotional impact of these images using a 5-point Likert scale (very negative, negative, neutral, positive, very positive). Responses were recorded via a five-button fiber optic response pad (Current Designs, Philadelphia, USA).
We used pictures from the International Affective Picture System (IAPS) (43) dataset, from which we selected 72 pictures with negative valence and 36 with neutral valence. Images were classified using the valence and arousal scores: negative images had low valence and high arousal scores and neutral images had medium valence and low arousal scores. During negative images presentation, participants were instructed to either observe passively the image or to try to reappraise it, depending on the instruction displayed previous to the image on the screen. In this context, reappraisal describes the attempt to attribute a new meaning to the arousing stimulus in order to reduce its emotional impact (44, 45). A list of possible cognitive reappraisal strategies was presented to the participants beforehand, such as thinking that the pictures were not of a real scene, imagining that the image was from a movie, or imagining a happy ending to the situation depicted on the scene. In addition, we explicitly asked participants to avoid closing their eyes or distracting themselves from the picture. Three conditions served to form 3 fMRI predictors: i) passively looking at neutral pictures (NEU); ii) passively looking at negative pictures (NEG); iii) reappraisal of negative pictures (REAP).
During each fMRI session, subjects were scanned in three separate runs (~5.5 min each), comprising 18 trials, 6 for each condition (NEU, NEG, REAP). Each trial lasted 18 s: 2 s for instructions (LOOK or REAPPRAISE), followed by 6 s of picture presentation, 6 s for the button response, and 4 s of a fixation cross presented at the center of the screen (Figure S1).
fMRI Emotion Processing Analysis
The fMRI emotion processing task was analyzed using SPM12 (Statistical Parametric Mapping, UK). The first three volumes were discarded to allow for T1 stabilization. Preprocessing steps included head motion correction, slice timing correction, spatial smoothing [8-mm full width at half maximum (FWHM) Gaussian kernel], and a high-pass filter (128 s). EPI images were coregistered to each subject’s anatomical scan, normalized into standardized MNI space and resampled to voxels of 2 mm3. Serial autocorrelations were accounted for by a first-order autoregressive model (AR1). In addition, motion artifact was examined using the Artifact Detection Toolbox (ART) and volumes with a global signal deviation superior to three SD from the mean signal or in which the difference in frame displacement (FD, a composite measure of movement) between two consecutive volumes exceeded 1 mm, were considered as outlier volumes. The entire run was excluded if more than 15% of all volumes behaved as outliers.
A first-level fixed-effects model was used for each subject and each session. Five regressors corresponding to the three conditions (NEU, NEG, REAP), instruction, and rating periods were modeled using a boxcar function convolved with a canonical hemodynamic response function. Periods of fixation cross were defined as the baseline. The model also included six motion parameters and outlier volumes as regressors of no interest. Images contrasts were calculated using t-statistics for: i) NEG, ii) REAP, iii) NEG vs. REAP.
The effect of pranayama was inspected in the following region of interest (ROI), previously implicated in emotional processing (46, 47): ACC, amygdala, anterior insula, orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (dlPFC), dorsomedial prefrontal cortex (dmPFC), ventrolateral prefrontal cortex (vlPFC), and ventromedial prefrontal cortex (vmPFC). With the exception of vmPFC (48), and the anterior insula (49) all other ROI were obtained directly from the WFU PickAtlas (50) in SPM12 (Figure S2). Mean β-values for each ROI were extracted from each subject’s contrast using MarsBaR (SPM12).
rs-fMRI Data Acquisition and Analysis
The resting-state fMRI acquisition lasted for ~7.1 min, and subjects were instructed to lie down with their eyes closed, avoiding to move or fall asleep. Resting-state data were analyzed using a functional connectivity (fc) toolbox (CONN, https://www.nitrc.org/projects/conn). The first three volumes were discarded to allow for T1 equilibrium. Preprocessing steps included head motion correction, slice timing correction, and spatial smoothing (8-mm FWHM Gaussian kernel). Functional and structural images were normalized to the MNI template. Quality control included motion artifacts inspection with ART. Outlier volumes were considered when the global signal deviated more than three standard deviations (SD) from the mean signal or when the difference in FD between two consecutive volumes exceeded 0.5 mm. Denoising step included: CompCor method (to remove physiological and other sources of noise); use of six motion parameters and its derivatives as regressors of no interest; scrubbing and a band-pass filter (0.01–0.1Hz).
We explored the same ROI used in the emotion processing task. A 16 × 16 correlation matrix was generated by computing Pearson’s coefficients between the averaged time-series for every pair of ROI.
Statistical Analysis
Demographic variables were compared using the independent Student’s t-test when continuous, or chi-square test when categorical. To evaluate the effects of the B. pranayama on anxiety and affect, and β-values changes, we used a repeated-measures ANOVA (RM-ANOVA) with two factors: intervention (pranayama × control) and time (baseline × post-intervention). In cases where we found significant interactions, within groups post hoc analyses were conducted using the dependent Student’s t-test. To further investigate the relationship between fMRI changes and the effects of B. pranayama, Pearson’s correlation analyses were conducted to explore associations between changes in β-values from baseline to post-intervention (Δβ= βafter–βbefore) and changes in STAI and PANAS scores from baseline to post-intervention. Also, correlation coefficients were estimated only when significant interaction was found. We set the statistical threshold at p < 0.05 and used Cohen’s d to estimate effect sizes. Statistical analyses were performed using GraphPad Prism version 7.00 (GraphPad Software, La Jolla CA, USA). Since the trial adheres to the CONSORT statement, between-group baseline differences of primary outcomes should not be reported (51).
Results
The participants recruited for our study were healthy young adults (25.1 ± 4.3 years old; 15 women), mostly university students. There were no significant between-groups differences (control × pranayama) with respect to age (t28 = 1.41; p = 0.16), gender (χ2 = 0.13; p = 0.71), years of education (χ2 = 2.91; p = 0.23), and household income (χ2 = 0.57; p = 0.90). Detailed demographic and clinical characteristics are presented in Table 1. Figure S3 shows the CONSORT flow diagram for the trial.
Effects of B. pranayama on Anxiety and Affect
One subject from the pranayama group did not attend the second evaluation due to health issues. Boxplots were used for outlier identification (values above or below 1.5 times the interquartile range), which resulted in the exclusion of one subject from the pranayama group in the STAI and two from the control group in the PANAS-N (Figure S4).
Figure 2 shows changes in the STAI state and trait scores for both groups (pranayama and control) from baseline to post-intervention. We observed an interaction effect (intervention × time) in state of anxiety (F1,26 = 4.30; p = 0.048, Cohen’s d = 0.81), with significant decreased levels in the pranayama group after intervention (t12 = 3.01; p = 0.01; Figure 2A). No significant interaction was observed in trait anxiety (F1,27 = 2.13; p = 0.16; Figure 2B).
Figure 3 shows the observed changes in PANAS state and trait scores for both groups before and after intervention. We observed significant interaction effect (intervention × time) in the state of negative affect (state PANAS-N, F1.25 = 8.56; p = 0.007, Cohen’s d = 1.17; Figure 3A), with a significant decrease within the pranayama group (t13 = 3.43; p = 0.004; Figure 3A). States of positive affect were also significantly changed, with a significant interaction (intervention × time) effect (PANAS-P; F1,27 = 5.91; p = 0.02, Cohen’s d = 0.93; Figure 3B). For trait PANAS, we found a significant interaction (intervention × time) effect in PANAS-P (F1,27 = 7.35; p = 0.012, Cohen’s d = 1.04; Figure 3D), and a trend for PANAS-N (F1,27 = 3.78; p = 0.06; Cohen’s d = 0.78; Figure 3C).
Effects of B. pranayama on fMRI
fMRI analysis was conducted in 13 subjects from each group. One subject from the pranayama group did not attend the second fMRI session due to health issues and three subjects were excluded due to excessive head motion artifact, two from the control group (C8 and C12) and one from pranayama group (P8).
Emotion Processing Task—Behavioral Results
Scores attributed to the emotional impact of each image were analyzed using an ANOVA with two within-subjects factors: condition (NEU, NEG, REAP) and time (baseline and post-intervention), and one-factor between-subjects: intervention (pranayama and control). We observed a main effect for condition (F2,24 = 96.72; p < 0.0001) and post hoc comparisons, corrected by the Sidak test, showed significant differences between conditions NEG × NEU (p < 0.0001) and NEG × REAP (p < 0.0001). There were no significant differences in REAP × NEU condition (p = 0.73). No effects of time, intervention or interactions between them were found.
Emotion Processing Task: fMRI Results
Effects of the B. pranayama were analyzed with an RM-ANOVA with a time factor (baseline and post-intervention) and an intervention factor (control and pranayama) in each condition (NEG, REAP, and NEG > REAP).
Figure 4 shows the results in the NEG condition (Figure S5 shows the main effect for this condition). We observed a significant interaction in the right amygdala (F1,24 = 5.24; p = 0.03, Cohen’s d = 0.93; Figure 4A), the left anterior insula (F1,24 = 11.67, p = 0.002, Cohen’s d = 1.39; Figure 4B), and in the right anterior insula (F1,24 = 13.88; p = 0.001, Cohen’s d = 1.52; Figure 4C). Within-group analysis in the pranayama group showed significant increased activity after intervention in bilateral anterior insula (right: t12 = 3.01; p = 0.011; left: t12 = 2.62; p = 0.023).
Correlation analysis revealed a significant association between state of negative affect and changes in the activity of the right amygdala (r = 0.59, p = 0.034; Figure 4D), left anterior insula (r = 0.67; p = 0.012; Figure 4E), and right anterior insula (r = 0.72; p = 0.005; Figure 4F). These associations revealed that subjects in the pranayama group with increased activity in these areas had the least decreased state of negative affect. We found no significant correlation between BOLD signal changes and behavior on the emotional regulation task.
Figure 5 presents the results for the REAP condition (Figure S6 shows the main effect for this condition). We observed significant interaction in the left vmPFC (F1,24 = 5.52; p = 0.027; Cohen’s d = 0.95; Figure 5A) and right ACC (F1,24 = 7.42, p = 0.012; Cohen’s d = 1.11; Figure 5B), with significant increase within the pranayama group (t12 = 2.37; p = 0.035). Figure 5C shows significant interaction in the right anterior insula (F1,24 = 10.38; p = 0.004, Cohen’s d = 1.31) with respect to the NEG-REAP condition.
Resting-State fMRI
Figure 6A shows the effects of the intervention on fc. The right anterior insula and the right vlPFC were the two regions with the most consistent fc changes both with other ROIs and also with each other. The anterior insula showed significant fc changes with the following areas: right vlPFC (F1,27 = 4.72; p = 0.03; Cohen’s d = 0.83), left vlPFC (F1,27 = 4.73; p = 0.03; Cohen’s d = 0.83), right dmPFC (F1,27 = 7.07; p = 0.01; Cohen’s d = 1.02), left dmPFC (F1,27 = 5.17; p = 0.03, Cohen’s d = 0.87), and right ACC (F1,27 = 4.47; p = 0.04, Cohen’s d = 0.86). We observed significant interaction (intervention × time) fc between the vlPFC and the following areas: right vmPFC (F1,27 = 8.00; p = 0.008; Cohen’s d = 1.08), left vmPFC (F1,27 = 4.82; p = 0.003; Cohen’s d = 0.84), right dlPFC (F1,27 = 6.54; p = 0.01; Cohen’s d = 0.98), and right dmPFC (F1,27 = 8.57; p = 0.006; Cohen’s d = 1.12).
