Physiological Biomarkers of Emotional Well-Being in Experienced Ashtanga Yoga practitioners

(1)

Well-Being in Experienced Ashtanga Yoga

practitioners

(2)

The Effects of Ashtanga Yoga on Emotional Well-Being

Acknowledgements

When having a completed research report in hand, the collective effort, which led to its creation, can often become blurred. Therefore, immeasurable appreciation and deepest gratitude for the help and support are extended to the following individuals who in one way or another contributed in making this research possible.

In addition, the active practice of positive emotions, in this case gratitude, is also known to increase RSA, which in turn has been reported to further increase self-reported social connectedness and positive emotions, a phenomenon termed the upward spirals of the heart (Kok & Fredrickson, 2010 ; Kok et al., 2013 ). It seems only fitting to include an acknowledgement section in this research report.

Mona Irrmischer. Without Mona’s day-to-day supervision on this project, ability to problem-solve the multitude of issues (big and small) encountered along the way, emotional and technical support and incredible hard-set discipline and focus to stick to deadlines, a project of this magnitude would have been unachievable. Mona did not only make this project possible but she also made this project with all of its bumps in the road a wonderful and enjoyable experience. It takes a special skill of supervision to bring out the best in people by pushing, but to also have the emotional intelligence to know when to relax in this approach when it is needed.

Klaus Linkenkaer-Hansen. The Principle Investigator of the Neuronal Oscillations and Cognition lab at the VU University whose pragmatic, easy-going and open-minded supervision fitted this project so remarkably well and who gave us the space and opportunity to carry out such an extensive project on a practice, which not much is known about scientifically. Usually, Yoga research studies are done in low-budget labs in India with whatever small funds can be obtained for Yoga research, which is only very recently gaining popularity as a research area in the West. Not often are Yoga research projects carried out in a state-of-the-art lab with measurements on high-density EEG and peripheral physiology on a variety of behavioral tasks.

Lucia Talamini, The Principle Investigator of the Sleep & Memory Lab of the University of Amsterdam, who very often took time out of her busy schedule to offer her expert advice on various crucial components of this research project including but not limited to the experimental design and choice for statistical analyses, in addition to expressing her critical scientific attitude when discussing research findings, the lending of laboratory equipment, offering moral support and having taught me the many of the ins and outs of scientific writing and thinking with previous projects (not that I always stick to them but that is not the case in point).

NBT Support Team: Sonja Simpraga, Filipa Borges, Simon Houtman & Simon-Shlomo Poil, the NBT support team who was always happy (and sometimes just a little bit less so) to debug all of the issues encountered with NBT during the transitional phases of this software package. A special thanks to Sonja and Simon Houtman who also programmed whatever was additionally needed and hacked NBT whenever workarounds had to be quickly found and to Filipa for teaching me the basics of NBT software data analyses.

(3)

VU-AMS staff, in particular Menaka Viswanathan and Jarik den Hartog who were happy to teach us how to use the VU-AMS and its pertaining software package VU-DAMS and were there to patiently assist us whenever we ran into their office with a crisis related to this device Claudia Pradella, The program director of Delight Yoga Amsterdam and founder of the Ashtanga Yoga School Amsterdam who played an integral role in participant recruitment (17 out of 18 of the subjects were students from the Ashtanga Yoga Yoga school of Delight Yoga Amsterdam) and in advising on the experimental design and participant inclusion criteria. In addition, Claudia underwent the 8 extensive lab-hours as a participant herself and was always happy to provide her insights into our findings from a practitioner’s perspective with 13 years of (almost) daily Ashtanga Yoga practice experience.

Ashtanga Yoga Practitioners, All 18 Ashtanga Yoga practitioners who happily spent 8 hours in an enclosed lab, and in particular cycled on a stationary bike for 75 minutes, in the name of science. Such dedication and desire to make more people aware of this beautiful practice deserves a very special mention. In addition, a special thanks is warranted to Jurre Twijnstra who aside from participating also actively contributed to participant recruitment. Sri K. Patthabi-Jois and the lineage of Ashtanga Yoga Teachers, the founder of Ashtanga Yoga and the person who brought it to the west and the succession of teachers before and after him. Without his dedication in teaching this practice to the west, we would have neither had the practice nor subjects to measure.

(4)

Table of Contents

Abstract 5

The Effects of Ashtanga Yoga on Emotional Well-being: In the Search of 6 Physiological Biomarkers

Materials & Methods 8

Results 13

Conclusion and Discussion 18

References 20

Appendix I: Ashtanga Yoga Questionnaire 23

Appendix II: Elaboration on Questionnaire Construction 28

Appendix III: Experimental Protocol 31

Appendix IV: Public Outreach Activities Based on this Report 35 Appendix V: Methods Sections of the Entire Research Project 37

(5)

Abstract

Yoga practices have been growing in popularity as many people are turning to Yoga for its purported positive effects on well-being and stress-relief. However, systematic evidence into the physiology related to improvements of well-being as a result of Yoga is lacking. This study investigated the effects of Yoga on trait- and state-level well-being, physiology and resting-state cognition in 18 Experienced Ashtanga Yoga practitioners who were randomly assigned to practice Yoga or cycle in the lab. They returned for a second day of measurements for the remaining condition after a minimum of three days. Practitioners were measured during eyes-closed rest using high-density electroencephalography, electrocardiography and impedance cardiography immediately before and 30 minutes after completing an 85-minute Yoga or cycling session. Additionally, trait-level emotional well-being and resting state cognition, as measured by the RAND-36 and the Amsterdam Resting State questionnaire respectively, was also investigated. Ashtanga Yoga practitioners reported greater trait-level well-being and also had high baseline Respiratory Sinus Arrhythmia (RSA) and low baseline frontal Individual Alpha Frequency (IAF) values as compared to normative data and previous studies on the normal population. Relating to the Yoga- and cycling-intervention, RSA increased after Yoga as compared to after cycling. In addition, Ashtanga Yoga led to a decrease of Discontinuity of Mind and Sleepiness as compared to cycling in resting state cognition. However, there was no difference between Yoga and cycling for frontal IAF. Nonetheless, frontal IAF was inversely correlated to trait well-being. These results offer supporting evidence for the well-being promoting qualities of Ashtanga Yoga.

(6)

Introduction

The search for biomarkers of stress and well-being remains a challenging quest for researchers and clinicians alike. For many years, the research field focused on creating animal models of stress (Sutanto & Kloet, 1994). However, in the last years, with the up-rise of interest in mindfulness-related neuroplasticity, greater investigation is undergoing in human models of well-being in mindfulness practitioners (Brown & Ryan, 2003). For example, Matthieu Ricard, colloquially referred to as the happiest man on earth, is claimed to have an abnormally large capacity for joy as a result of meditation and has been extensively studied scientifically (Grinde, 2010; Smith, 2004). Also, many mindfulness-based-stress-reduction interventions have been applied within the clinical field to improve emotional well-being and decrease stress (Grossmann et al., 2004).

The present research investigated the effects of Ashtanga Vinyasa Yoga, a mindfulness-based practice, on trait- and state-level emotional well-being. Ashtanga Vinyasa Yoga, hereinafter referred to as Ashtanga Yoga, is a style of yoga codified and popularized by Sri K. Pattabhi Jois, which is based on a standardized set of Yoga postures and is often promoted as a modern-day form of classical Indian Yoga (Jois, 2010). The beneficial effects of Yoga on subjective emotional well-being have been extensively documented before (Berger et al., 2008; Hartfiel et al., 2011; Dunn, 2009). However, these studies are primarily based on self-reports and the corresponding biomarkers have been less well investigated.

There are various biomarkers of interest worth investigating to see if they are affected by Ashtanga Yoga practice, one of which is Respiratory Sinus Arrhythmia (RSA). RSA is the variance of the heart rate rhythm associated with frequency of spontaneous breathing; see Figure 1 (Porges, 2011). It has been reported that RSA is inversely related to perceived emotional stress and positively related to emotional well-being (Dishman et al., 2000; Geisler et al., 2010). The hypothesized reason is that in most situations, like other measures of homeostatic function, the greater the range of the phasic increases and decreases, the “healthier” the individual. For example, with the aging process or stress, there is an attenuation of RSA (Lipsitz et al., 1990; Sack et al. 2004). As an illustration, RSA for the general adult population has been reported to be ~50 ms (~SD < 2 ms) (Licht et al., 2008; Licht et al., 2009). Conversely, this value is lower (M = ~44 ms, ~SD < 1 ms) in clinical populations known to have a decrease of emotional well-being, such as patients suffering from Anxiety Disorders and Major Depressive disorder (Licht et al., 2008; Licht et al., 2009).

Furthermore, a relationship has been found between RSA and positive emotions (Kok &

Fredrickson, 2010 ; Kok et al., 2013). Adults who possessed high initial levels of RSA increased in self-reported social connectedness and positive emotions more rapidly than those with low initial RSA levels across time. Furthermore, increases in self-reported social connectedness and positive emotions predicted increases in RSA, independent of initial RSA level. The authors concluded that this was supporting evidence for an “upward spiral” relationship of reciprocal causality, in which RSA and psychosocial emotional well-being reciprocally and prospectively predict one another. This provides supporting evidence for RSA being a biomarker of trait-level well-being that is also sensitive to state-level changes. This also supports the idea that trait-level differences are mediated by repetitive long-term state-level changes.

Keeping in line with the search for additional biomarkers of emotional well-being and how they are impacted by Yoga, EEG biomarkers were also investigated. One of which is an increase in human frontal and mid-line theta and lower alpha power, which is thought to reflect an emotionally positive state in meditators (Aftanas & Golocheikine, 2001). In addition, neurofeedback to increase power in the high theta and low alpha frequency bandwidth (alpha/theta) has been known to enhance well-being (Boynton, 2001; Raymond et al., 2005). However, we postulated that the reason a power increase was found in these frequency bands was not as a result of these frequency bands playing any specific role in

(7)

(IAF) was investigated (Klimesch, 1999). As an illustration of the utility of this method, frontal IAF is known to be higher in PTSD patients as compared to controls, a clinical population known for their decreased well-being and low RSA values (Wahbeh & Oken, 2013; Creamer et al., 2001; Sack et al. 2004).

In addition, previous findings suggest that there is a relationship between well-being and mind-wandering (Killingsworth & Gilbert, 2010). More specifically, different qualities of mind-wandering during the resting state, such as overall comfort experienced and the level of discontinuity of mind, have been found to be related to well-being (Diaz et al., 2014). Consequently, this study also exploratively investigated how well-being, RSA and IAF relate to resting-state cognition.

Figure 1. An illustration of variance of the heart rate rhythm associated with frequency of spontaneous breathing. First

bar indicates the breathing rhythm with blue triangles indicating an exhalation peak and red triangles indicating and inhalation peak. The second bar indicates heart-rate in beats per minute calculated per interbeat interval using the following formula: (60*10^3)/interbeat interval. Fat red and blue marks indicate the peak and through of the heart-rate in beats per minute, respectively. The third bar indicates respiratory sinus arrhythmia in milliseconds. Respiratory sinus arrhythmia is computed per respiratory cycle from two interbeat intervals: The shortest interbeat interval (heart-rate peak) during an interval starting at the beginning of inspiration and ending 1000 milliseconds after the end of inspiration and the longest interbeat interval (heart-rate through) during an interval starting at the beginning of expiration and ending 1000 milliseconds after the end of expiration. Respiratory sinus arrhythmia is calculated by the subtraction of the shortest interbeat interval in milliseconds from the longest interbeat interval in milliseconds, provided that the shortest interbeat interval (highest heart-rate in beats per minute) is part of an accelerating series within the inspiratory interval and the longest interbeat interval (lowest heart-rate in beaths per minute) of a decelerating series within the expiratory interval. The Fourth bar indicates the respiratory rate per minute from the respiratory cycle time, using the following formula: (60*10^3)/respiratory cycle time in milliseconds.

Therefore, from a trait-level perspective, we hypothesized that Ashtanga Yoga practitioners would report greater trait-level emotional well-being, have higher baseline RSA values and lower baseline frontal IAF values as compared to the normal population. Given that we hypothesized that trait-level differences are primarily being driven by repetitive long-term state-level changes we predicted that Ashtanga Yoga practice would lead to an increase of RSA and a decrease of frontal IAF as compared to cycling. In addition, we hypothesized that baseline RSA would be positively correlated and baseline frontal IAF inversely correlated to self-reported trait-level emotional well-being. Consequently, we hypothesized that baseline RSA and frontal IAF would also be inversely correlated due to measuring the same construct

(8)

Materials and Methods Subject background and recruitment

Subjects were recruited through advertisements and word of mouth. The permanent committee of science and ethics of the VU University Amsterdam approved the study and informed consents were obtained for all subjects. The eligibility criterion for participation was a minimum of one year of regular Ashtanga Yoga practice (i.e., 4 times or more of practice per week). Subjects were between 23 and 49 years of age (M = 35, SD = 7.89) and consisted of 4 males and 14 females. The average years of Yoga practice was 9 years (SD = 6.19) (range 2 to 27 years). The average years of Ashtanga Yoga practice was 6 years (SD = 5.57) and ranged from 1 to 22 years of Ashtanga Yoga practice. Subjects practiced on average 5 days a week (SD = 0.77) and received €50 payment for participating.

Measurements

Eyes-Closed Rest (ECR)

To investigate both the long and short-term effects of Ashtanga Yoga practice on resting-state physiology, 5 minutes of seated eyes-closed rest was measured at four time-points (before Yoga, after Yoga, before cycling and after cycling).

Yoga Practice and Background Information Questionnaire

Subjects also completed a questionnaire after the Yoga intervention to gauge certain qualitative aspects of the practice and to obtain additional background information (see Appendix I & II for the questionnaire and its construction elaboration, respectively). This questionnaire was constructed in house and consisted of 28 items, of which 8 items were adopted from the energy and emotional well-being/mental health scales of the RAND-36 health survey (Hays & Morales, 2001). The items of the RAND-36 health survey measure trait levels of energy and emotional well-being based on self-reports on how subjects have been feeling over the last four weeks.

Amsterdam Resting-State Questionnaire (ARSQ)

To investigate thoughts and feelings during eyes-closed rest and correlate these to physiological measures, the Amsterdam Resting-State Questionnaire (ARSQ) was presented after all ECR EEG and peripheral physiology recordings. The ARSQ consisted of 30 statements presented on a computer and rated on a 5-point Likert scale from completely disagree to completely agree (Diaz et al., 2014).

Peripheral Physiology

Heart Rate (HR), Respiratory Rate, and Respiratory Sinus Arrhythmia (RSA) were measured with the Vrije Universiteit Ambulatory Monitoring System (VU-AMS). This device uses seven Ag/AgCl electrodes to record the electrocardiogram (ECG), thoracic impedance (ICG) and skin conductance. Details on the measurement procedure with the VU-AMS can be found on http://www.vu-ams.nl.

EEG

Recordings were performed using one Electrical Geodesics (EGI, Eugene, OR) EEG system (GES400 DC), using 128-EGI HydroCel channel sponge-base EEG-caps. Impedances were kept below 70 kOhm (a suitable threshold for these kinds of EEG-caps) (http://www.egi.com). Data were recorded at 1000 Hz sampling rate, with a 400 Hz low-pass (GES250) and 0.01 Hz high-pass hardware filter and with 16 Bit resolution and reference at Cz (vertex).

(9)

Interventions

Subjects underwent two interventions (experimental and control) on two separate days, with a minimum of three days in between. For the experimental intervention, subjects were instructed to practice the Ashtanga Yoga Primary Series, see Figure 2 for an illustration the Ashtanga Yoga Primary Series postures (Jois, 2010). This took on average 85 minutes. Subjects not comfortable practicing the entire primary series were permitted to shorten the practice (i.e., only half of the practice). For the control intervention, subjects were required to cycle on a stationary bike for 75 minutes with ten minutes of rest directly afterwards comparable to the rest taken in Savasana (10 minutes of rest after Yoga).

Figure 2. An illustration of the Ashtanga Vinyasa Yoga Primary Series as performed by Alex Dinse (Ashtanga Yoga Primary Series Poster, 2008).

Experimental design

Subjects were required to visit the lab twice for measurements of up to four hours each. Subjects were randomly assigned to participate in the experimental or control intervention first (randomized crossover trial with two baselines), see Figure 3 for an elaboration of the design (see Appendix III for the protocol).

Subject preparation (i.e., EEG-cap mounting and applying peripheral physiology electrodes) took up to 35 minutes followed by ECR, filling in the ARSQ and additional tasks not analyzed for this report (See Appendix V for a description of all measurements obtained). After the end of the tasks, all electrodes with the exception of the thorax electrodes were removed. Subjects were then required to either cycle or practice Yoga. After the interventions subjects were again set up for a second session of ECR and filling in the ARSQ. After the Yoga intervention, an additional questionnaire was given gauging qualitative aspects of the practice and additional background information.

(10)

Figure 3. The experimental design used. This study utilized a randomized crossover trial with two baselines where all

subjects were subjected to both the Yoga (experimental) and cycling (control) intervention.

Data Analysis

RAND-36 & The Amsterdam Resting State Questionnaire (ARSQ)

Self-reported emotional well-being was scored by summing the values of items 24, 25, 25, 26, 28 and 30 of the RAND-36 questionnaire after recoding, as suggested by the scoring instructions of Hays (2001). The ARSQ factors were computed for the 10 dimensions of resting state cognition, as suggested by Diaz et al. (2014).

All ECG data were visually inspected to confirm that R-peaks were correctly detected using the VU-DAMS software), see Figure 4 for ECG data of a representative subject. HR was then calculated by transforming interbeat intervals into a beats per minute value. For each respiratory cycle, the total cycle time between beginning of inspiration and end of expiration was extrapolated to a breaths per-minute (BPM) Respiratory Rate and RSA scoring was based on the peak-valley method, which uses the interbeat interval time series extracted from the ECG together with the respiratory signal obtained from filtered (0.1 – 0.4 Hz) thoracic impedance (Porges & Byrne, 1992; Grossman et al., 1990; de Geus et al., 1995), see Figure 1 for an elaboration of the methods used.

These computations were performed in the VU-DAMS software and exported to SPSS 20.0 for further analyses. The mean values for baseline Heart-Rate (HR), Respiratory Rate, and Respiratory Sinus Arrhythmia (RSA) were calculated for all 18 subjects by averaging over the Yoga and Cycling baseline conditions. For subjects (N = 2) whose datasets were incomplete due to data-loss, one baseline measure was taken as the mean value.

(11)

Figure 4. 40 and 10 seconds of ECG activity of a representative subject. Vertical blue lines indicate correctly scored

R-peaks.

Given the use a double baseline design, baseline corrected values were calculated for all subjects for the intervention related analyses by deducting each baseline value from its own post-measurement (i.e., Post Yoga - Baseline Yoga | Post Cycle - Baseline Cycle). Two additional subjects were removed from the analysis due to data-loss (missing yoga baseline values) (N =13).

EEG

Bad channels were removed using NBT (Poil, 2013). IAF was calculated utilizing the center of gravity method, as suggested by Klimesch (1993; 1999) using NBT (Poil, 2013). Firstly, the power spectrum was computed using the Welch method. Secondly, the Alpha peak range was determined, the 1/f baseline subtracted, a Gaussian fit applied to the peak and the confidence interval found to determine the frequency band (f1 – f2). Finally, IAF was calculated individually for each subject according to the formula: IAF = (Sum (a(f) x f))/(Sum a(f)). Power spectral estimates at frequency f are denoted a(f) and the index of summation is in the range of f1 to f2 (the individually determined frequency band).

The frequency band usually falls within the 5 to 14.99 Hz, see Figure 5 for a 5 to 14.99 Hz. filtered EEG data of a representative subject (Wahbeh & Oken, 2013). Frontal IAF was then calculated by averaging over the IAF values of the frontal EGI cap electrodes (channels 1, 2, 3, 4, 8, 9, 10, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 32, 33, 122, 123, 124, 125, 126, 127 and 128). Due to data corruption, one subject was left out of the analyses (N = 17). Baseline corrected IAF values were calculated for all subjects for the intervention related analyses by deducting each baseline value from its own post-measurement (i.e., Post Yoga - Baseline Yoga | Post Cycle - Baseline Cycle).

(12)

Figure 5. 40 and 4 seconds of 5-14.99 Hz. filtered EEG activity of a representative subject.

Preprocessing and Outlier Removal

RAND-36 & The Amsterdam Resting State Questionnaire (ARSQ)

Emotional well-being, as measured by the emotional well-being/mental health scale of the RAND-36, was calculated for all subjects (N =18, M = 80.89, SD = 10.04). There was one outlier present (Z-score > 1.96) and subsequently removed (N =17, M = 82.59, SD = 7.20). Self-reported emotional well-being was normally distributed, as tested with the Kolmogorov-Smirnov test (D (16) = 0.184, p = 0.153). For the ARSQ dimension scores, normality was not tested, outliers were not removed and the data were assumed to be non-parametric as a standard function of the statistics of the toolbox (NBT) used.

Heart rate and respiratory rate are required to calculate RSA values. For this reason, these values were also separately analyzed and tested for normality and outliers (Z-scores > 1.96) prior to RSA analyses. This was done to ensure that there were no deviations in RSA that can be ascribed to differences in heart- or respiratory-rate.

Heart Rate

There were no outliers present for baseline HR, therefore all subjects were retained (N =18. Baseline HR was normally distributed according to the Kolmogorov-Smirnov test (D (18) = 0.16, p = 0.20) and within the normal range (60 – 100 BPM) according to the American Heart Association (AHA) for a healthy resting heart-rate, (M = 67.26, SD = 9.22) (Target Heart Rates, 2015). Mean HR did no fall within an athletic range (40-60 BPM). Therefore, differences in RSA values cannot be attributed to difference in athleticism.

Respiratory Rate

Mean respiratory rate (N = 18) was 11.89 BPM (SD = 2.70). Respiratory Rate was slightly below the typical respiratory rate for a healthy adult at rest (12 – 20 BPM) (Ganong & Barrett, 2005). One outlier was present and subsequently removed. Baseline respiratory rate in BPM for the remaining subjects (N =17) was 11.45, SD = 2.11. Baseline respiratory rate did not deviate from normality, according to the Kolmogorov-Smirnov test (D (17) = 0.13, p = 0.20).

(13)

RSA

One subject had a heart-arrhythmia that interfered with Peak-Valley RSA scoring and was removed. Baseline RSA (N =17) was 94.49 ms, SD = 49.64. One outlier was present and subsequently removed. Baseline RSA for the remaining subjects (N = 16) was 85.87 ms, SD = 35.79. Given that respiratory rate is invariably related to RSA, the respiratory rate outlier was also removed (N =15), M = 90.17 ms, SD = 32.48. Both baseline RSA and the baseline corrected RSA values were normally distributed, according to the Kolmogorov-Smirnov test (D (15) = 0.13, p = 0.20 (baseline); D (13) = 0.14, p = 0.20 (cycling intervention); D (13) = 0.13, p = 0.20 (Yoga intervention)). No outliers were presented in the baseline-corrected RSA values.

EEG

Baseline frontal IAF was normally distributed, as tested with the Kolmogorov-Smirnov test (D (17) = 0.18, p = 0.128) and no outliers were present. One outlier was present for baseline corrected IAF (Z-score > 1.96), who was subsequently removed. Baseline-corrected IAF was normally distributed for the Yoga intervention, D (17) = 0.12, p = 0.20 and cycling intervention, D (17) = 0.21, p = 0.06.

Results

Self-Reported Well-Being

Mean self reported emotional well-being (N = 17) was 82.59 (SD = 7.20). The normative value in a Dutch adult population (N =3000) is 76.8 (SD = 18.4), lower than in Ashtanga Yoga practitioners (Vander Zee et al., 1996). Therefore, Ashtanga Yoga practitioners score higher on self-reported emotional well-being than in the normative population, in accord with our hypothesis.

RSA of Ashtanga Yoga Practitioners

Mean baseline RSA, as calculated with the methods described above (averaging over the Yoga and cycling baseline conditions), (N =15) was 90.17 ms (SD = 32.48). Consequently, a paired to-test was used to investigate changes in RSA as a result of intervention. Relative to the respective baseline, subjects (N = 13) had an increase of RSA after yoga (M = 10.26, SD = 19.55) and a decrease after cycling (M = -16.52, SD = 30.05), t(12) = 2.29, p = 0.04, see Figure 6. Therefore, supporting evidence was found for our hypotheses that high RSA values would be found in Ashtanga Yoga practitioners and that Ashtanga Yoga—in contrast to cycling—would lead to an increase of RSA.

(14)

Figure 6. A significant difference between cycling and Yoga for Respiratory Sinus Arrhythmia (RSA) values. This figure illustrates the boxplots of the baseline corrected values of RSA for the cycling and Ashtanga Yoga conditions,

respectively. Ashtanga Yoga led to an increase of respiratory sinus arrhythmia, as compared to cycling (N = 13).

Frontal IAF of Ashtanga Yoga Practitioners

Mean baseline frontal IAF (N =17), as calculated with the methods described above (averaging over the Yoga and cycling baseline conditions) was 9.38 Hz (SD = 0.88). According to the formula of Köpruner (11.95-0.053*age), IAF for a normal age-matched population should be 10.095 Hz (Köpruner et al., 1969, cited in Klimesch 1999). To investigate changes in IAF as a result of intervention, a paired t-test was performed on the baseline corrected values of the post-measurements (N = 17). There was no significant difference in frontal IAF between baseline-corrected post-cycle (M = - 0.04, SD = 0.30) and post-Yoga (M = - 0.09, SD = 0.48) measurements, t(15) = 0.43, p = 0.67, see Figure 7. Therefore, in accord with our hypothesis, frontal IAF was low in Ashtanga Yoga practitioners as compared to a normal hypothetical age-matched population. However, no supporting evidence was found for a decrease in IAF as a result of Ashtanga Yoga practice.

(15)

Figure 7. No significant difference between Individual Alpha Frequency (IAF) values as a result of cycling and Yoga.

This figure illustrates boxplots of the baseline corrected values of frontal IAF for the cycling and Yoga condition (N = 17).

Associations Between Physiology and Well-Being

The relationship between RSA and trait-level emotional well-being, as measured by the emotional well-being/mental health scale of the RAND-36, was investigated with a Pierson Correlation analysis. There was no relationship found between RSA and emotional well-being (Rs(15) = 0.12, p = 0.66). This was not in accord with our hypothesis that RSA is

related to emotional well-being. In addition, there was no relationship found between baseline RSA and frontal IAF (Rs(16) = 0.12, p = 0.65). The relationship between baseline RSA and

frontal IAF, was investigated with a Pierson Correlation analysis. Post-hoc exploratory correlational analyses between RSA on frontal IAF per measurement time-point (pre-Yoga, post-Yoga, pre-cycle, post-cycle) also revealed no significant relationships. This was not in accord with our hypothesis that RSA and frontal IAF are inversely related due to measuring the same construct of emotional well-being. Frontal IAF and emotional well-being, however, were significantly related (Rs(16) = -0.614, p = 0.02), see Figure 8 for the scatterplot. This

provides supporting evidence of the role of Frontal IAF in emotional well-being.

Taken together we failed to reproduce previous findings on a relationship between RSA and well-being, in addition to not finding a relationship between RSA and IAF. However, an interesting correlation was found between IAF and well-being.

(16)

Figure 8. Emotional well-being is inversely related to frontal Individual Alpha Frequency (IAF). This figure illustrates

the relationship between trait-level emotional well-being, as measured by the RAND-36, and baseline IAF of the frontal region. There is an inverse correlation between self-reported emotional well-being and frontal region IAF (Rs(16) = -0.614, p = 0.02).

Exploratory Analyses

A Decrease of IAF in Other Cortical Regions

Exploratory analyses were conducted to investigate whether there were differences in IAF in other regions as a result of Yoga. There were no differences in baseline measurements for Yoga and cycling (baseline homogeneity). However, for post-measurements there was a decrease in IAF in the central brain regions. Therefore, the practice of Ashtanga Yoga appears to decrease IAF in the central regions.

(17)

Figure 9. Yoga led to a decrease of Individual Alpha Frequency (IAF) in the central regions. This figure illustrates the

effects of Yoga on IAF. No differences were found between the baseline IAF. However, there is a decrease in IAF as a result of Yoga. White circles indicate significant channels. Binomial multiple-comparison correction was performed as a lenient approach to multiple comparisons correction (Poil et al., 2011).

The Amsterdam Resting State Questionnaire (ARSQ)

By means of the Wilcoxon Ranked Sum Test, the pre- and post-measurements were tested to see if there are differences in resting state cognition as a result of Ashtanga Yoga, see Figure 10. There were no differences in the dimensions of resting cognition for the baseline values, suggesting that the thoughts and feelings probed by the ARSQ are stable within the present participant cohort on the time scale of a week. However, after Yoga there was a decrease of discontinuity of mind and sleepiness. Correlational analyses were conducted using the Spearmann’s Rank Order Correlation on how the 10 baseline (averaging over the Yoga and cycling baseline conditions) dimensions of resting-state cognition relates to well-being, RSA and frontal IAF. There were no relationships found between the 10 dimensions of resting state cognition and wellbeing, RSA or frontal IAF.

(18)

Conclusion & Discussion

This study investigated the effects of Ashtanga Yoga on trait- and state-level emotional well-being in experienced Ashtanga Yoga practitioners. Ashtanga Yoga practitioners report higher trait-level emotional well-being as compared to a normative population. Physiologically, Ashtanga Yoga practitioners also have high resting RSA, which is often interpreted as indicating high trait-level emotional well-being, self-regulation and autonomic flexibility (Dishman et al., 2000; Geisler et al., 2010; Kok & Fredrickson, 2010 ; Kok et al., 2013). RSA values obtained in this study were close to double to RSA values obtained for the normal population using the same equipment and similar experimental protocol (sitting eyes-closed rest) (Licht et al., 2008; Licht et al., 2009). Furthermore, Ashtanga Yoga practitioners have low IAF, which was hypothesized to be a biomarker of trait-level well-being (Köpruner et al., 1969, cited in Klimesch, 1999). In addition, our finding on IAF offers preliminary support for our hypothesis that previous findings implicating a role of frontal low-alpha and theta in well-being and meditation may be caused by a definitional artifact of frequency bands, whose difference in power and power-shifts are dependent on IAF (Aftanas & Golocheikine, 2001;Boynton, 2001; Raymond et al., 2005). Progressive slowing of IAF may be the underlying cause of increases in theta and low-alpha power, which for example would not as easily be found in novice meditators (controls) who may be starting off at a higher IAF but may still be undergoing a similar slowing of alpha oscillations (e.g., a decrease of 2 Hz from 13 Hz as opposed to from 9 Hz. has different implications for the frequency bands in question). However, comparisons to previous literature and normative data were not statistically tested and an age- and gender-matched control group would be required before definite conclusions can be drawn.

Given the cross-sectional design of this study, the differences in RSA and IAF could be ascribed to already existing individual trait-level differences independent of Ashtanga Yoga practice. Nevertheless, when comparing the physiological effects of Yoga and cycling, RSA increased after Yoga practice while this pattern was not present for cycling. This provides supporting evidence for a positive causal role of Ashtanga Yoga on state-level well-being, as measured by RSA, in addition to providing supporting evidence for the idea that trait-level differences are primarily being driven by repetitive long-term state-level changes. However, RSA was not related to self-reported emotional well-being over the last four weeks or IAF. Previous research has indeed indicated that RSA is both a biomarker of state- and trait-level emotional well-being, which may produce a confounding effect (Thayer et al., 2012; Kok & Fredrickson, 2010 ; Kok et al., 2013). Potentially, RSA may be a more sensitive biomarker for state- as opposed to trait-level emotional well-being. Future research should investigate the effects of Yoga on self-reported state-level emotional well-being.

Conversely, Frontal IAF was related to self-reported emotional well-being over the last four weeks but did not differ between the cycling and Yoga intervention. It could be that IAF is a more stable trait-level biomarker of emotional well-being that is less subject to immediate change, in this case one Yoga practice. An idea for future research would be to conduct a longitudinal study where novice Ashtanga Yoga practitioners are measured across multiple practices to see if there are longer-term differences occurring in IAF. However, exploratory analyses did reveal a change in IAF in more central regions. This location, however, may be caused by volume conduction and performing CSD transformation on future analyses may lead to a greater understanding of the source of this brain activity (Kayser & Tenke, 2015).

Pertaining to resting state cognition, Yoga led to a decrease of discontinuity of mind and sleepiness. This could be describing the state of mind that is known to occur with meditation as well, known as relaxed alertness (Telles et al., 1995). Even though the subject reports feeling more awake, they still report experiencing a calmer mental state, which could contribute to well being. Nevertheless, there were no correlations found between the

(19)

then using the appropriate statistical analyses. This might lead to the discovery of additional dimensions of resting state cognition being affected by Yoga and these may correlate to the variables of well-being.

An additional suggestion for future research relates to how IAF was calculated for this study. Namely, there are two methods of calculating IAF: The peak frequency and center of gravity method (Klimesch et al., 1993). In this report, the center of gravity method was used, as this is the standard function in NBT. Usually however, when choosing between the two methods, it is important to consider the type of mental activity that will be studied. It is advised that if alpha frequency is studied during a resting period, a pronounced alpha peak can be expected and peak frequency would be the more appropriate measure to reflect alpha activity (Klimesch et al., 1993). The center of gravity method is preferably used for subjects performing a difficult mental task as this would lead alpha power to drop drastically and as a consequence, no or only a rudimentary broad "peak" will be observed. Indeed, previous eyes-closed-rest studies on frontal IAF utilized the peak alpha frequency method (Angelakis et al., 2004; Laufs et al., 2003; Laufs et al., 2006; Wahbeh & Oken, 2013). Therefore, additional analyses should be carried out using the peak frequency method to be better able to compare these results to previous studies.

Nevertheless, IAF being correlated to emotional well-being is a novel finding, of which the underlying mechanisms are not yet known. Given that Ashtanga Yoga is known to increase Gamma-Amino-Butyric-Acid (GABA) levels, it may be that IAF is modulated by GABA levels (Streeter et al., 2007). Furthermore, previous studies have also found a correlation between post-trauma GABA plasma levels and PTSD (Vaiva et al., 2004; Vaiva et al., 2006). Low post-trauma GABA plasma levels were a predictive factor in the development of acute PTSD, which has been previously related to high IAF (Wahbeh & Oken, 2013). In addition, many disorders, which are known to detrimentally affect emotional well-being, are hypothesized to be caused by an imbalance of GABA (Stahl, 2011). Potentially, IAF may be an easily accessibly biomarker for diagnosing and potentially predicting many clinical disorders such as insomnia and anxiety in their prodromal phase before behavioral symptoms develop. This in turn can pave the way for greater research in clinical neuroscience and EEG neurofeedback on how to approach and revert clinical disorders, which from a Psychiatric perspective have yet to be entirely understood.

Further research is warranted to investigate the individual differences in alpha frequency. One research idea is to train subjects to lower IAF by means of neurofeedback to see if this positively affects well-being. Previous studies done on increasing alpha/theta power, which may be indirectly lowering IAF, indicate that altering emotional well-being through neurofeedback is possible (Boynton, 2001; Raymond et al., 2005). Another idea for future research is to investigate whether there is a direct relationship between IAF and emotional well-being or whether it is mitigated by other more endophenotypic variables. For example, disorders such as PTSD and insomnia, which are known to negatively affect well-being, are linked to hyperarousal and can be treated with Yoga-Therapy interventions (Cabral et al., 2011; Khalsa, 2004; Van der Kolk, 2006; Stahl 2011). In this case, it may be that the IAF is a CNS biomarker of the hyperarousal endophenotype, which in turn modulates subjective emotional well-being.

The findings obtained above illustrate the importance of investigating mindfulness practitioners, in this case Ashtanga Yoga practitioners, and thus considering the entire spectrum of emotional well-being, rather than strictly focusing on the clinical and normal

(20)

References

Aftanas, L. I., & Golocheikine, S. A. (2001). Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: high-resolution EEG investigation of meditation. Neuroscience letters, 310(1), 57-60. Angelakis, E., Lubar, J. F., Stathopoulou, S., & Kounios, J. (2004). Peak alpha frequency: an

electroencephalographic measure of cognitive preparedness. Clinical Neurophysiology, 115(4), 887-897.

Ashtanga Yoga Primary Series Poster. (2008). Retrieved June 24, 2015, from

http://www.dailycupofyoga.com/2008/11/09/ashtanga-yoga-primary-series-poster/. Berger, D. L., Silver, E. J., & Stein, R. E. (2008). Effects of yoga on inner-city children's

well-being: a pilot study. Alternative Therapies in Health and Medicine, 15(5), 36-42. Boynton, T. (2001). Applied research using alpha/theta training for enhancing creativity and

well-being. Journal of Neurotherapy, 5(1-2), 5-18.

Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: mindfulness and its role in psychological well-being. Journal of personality and social psychology, 84(4), 822. Cabral, P., Meyer, H. B., & Ames, D. (2011). Effectiveness of yoga therapy as a complementary treatment for major psychiatric disorders: a meta-analysis. The primary care companion to CNS disorders, 13(4).

Creamer, M., Burgess, P., & McFarlane, A. C. (2001). Post-traumatic stress disorder: findings from the Australian National Survey of Mental Health and Well-being. Psychological medicine, 31(07), 1237-1247.

Diaz, B. A., Van Der Sluis, S., Benjamins, J. S., Stoffers, D., Hardstone, R., Mansvelder, H. D., ... & Linkenkaer-Hansen, K. (2014). The ARSQ 2.0 reveals age and personality effects on mind-wandering experiences. Frontiers in psychology, 5.

Dishman, R. K., Nakamura, Y., Garcia, M. E., Thompson, R. W., Dunn, A. L., & Blair, S. N. (2000). Heart rate variability, trait anxiety, and perceived stress among physically fit men and women. International Journal of Psychophysiology, 37(2), 121-133.

Dunn, K. D. (2009). The effectiveness of hatha yoga on symptoms of anxiety and related vulnerabilities, mindfulness, and psychological wellbeing in female health care employees.

Field, A. (2009). Discovering statistics using SPSS. Sage publications.

Ganong, W. F., & Barrett, K. E. (2005). Review of medical physiology (Vol. 21). eNew York New York: McGraw-Hill Medical.

Geisler, F. C., Kubiak, T., Siewert, K., & Weber, H. (2013). Cardiac vagal tone is associated with social engagement and self-regulation. Biological psychology, 93(2), 279-286. de Geus, E. J., Willemsen, G. H., Klaver, C. H., & van Doornen, L. J. (1995). Ambulatory

measurement of respiratory sinus arrhythmia and respiration rate. Biological psychology, 41(3), 205-227.

Grinde, B. (2010). An evolutionary perspective on happiness as understood in the tradition of Tibetan Buddhism. Open Behavioral Science Journal, 4, 31-36.

Grossman, P., Beek, J. V., & Wientjes, C. (1990). A comparison of three quantification methods for estimation of respiratory sinus arrhythmia. Psychophysiology, 27(6), 702-714.

Grossman, P., Niemann, L., Schmidt, S., & Walach, H. (2004). Mindfulness-based stress reduction and health benefits: A meta-analysis. Journal of psychosomatic research, 57(1), 35-43.

Hartfiel, N., Havenhand, J., Khalsa, S. B., Clarke, G., & Krayer, A. (2011). The effectiveness of yoga for the improvement of well-being and resilience to stress in the workplace. Scandinavian journal of work, environment & health, 70-76.

Hays, R. D., & Morales, L. S. (2001). The RAND-36 measure of health-related quality of life. Annals of medicine, 33(5), 350-357.

(21)

Kayser, J., & Tenke, C. E. (2015). Issues and considerations for using the scalp surface Laplacian in EEG/ERP research: a tutorial review. International Journal of Psychophysiology.

Khalsa, S. B. S. (2004). Treatment of chronic insomnia with yoga: a preliminary study with sleep–wake diaries. Applied psychophysiology and biofeedback, 29(4), 269-278. Killingsworth, M. A., & Gilbert, D. T. (2010). A wandering mind is an unhappy mind.

Science, 330(6006), 932-932.

Klimesch, W., Schimke, H. A. N. N. E. S., & Pfurtscheller, G. (1993). Alpha frequency, cognitive load and memory performance. Brain topography, 5(3), 241-251.

Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain research reviews, 29(2), 169-195.

Kok, B. E., & Fredrickson, B. L. (2010). Upward spirals of the heart: Autonomic flexibility, as indexed by vagal tone, reciprocally and prospectively predicts positive emotions and social connectedness. Biological psychology, 85(3), 432-436.

Kok, B. E., Coffey, K. A., Cohn, M. A., Catalino, L. I., Vacharkulksemsuk, T., Algoe, S. B., ... & Fredrickson, B. L. (2013). How positive emotions build physical health perceived positive social connections account for the upward spiral between positive emotions and vagal tone. Psychological science, 24(7), 1123-1132.

Van der Kolk, B. A. (2006). Clinical implications of neuroscience research in PTSD. Annals of the New York Academy of Sciences, 1071(1), 277-293.

Laufs, H., Kleinschmidt, A., Beyerle, A., Eger, E., Salek-Haddadi, A., Preibisch, C., & Krakow, K. (2003). EEG-correlated fMRI of human alpha activity. Neuroimage, 19(4), 1463-1476.

Laufs, H., Holt, J. L., Elfont, R., Krams, M., Paul, J. S., Krakow, K., & Kleinschmidt, A. (2006). Where the BOLD signal goes when alpha EEG leaves. Neuroimage, 31(4), 1408-1418.

Licht, C. M., de Geus, E. J., Zitman, F. G., Hoogendijk, W. J., van Dyck, R., & Penninx, B. W. (2008). Association between major depressive disorder and heart rate variability in the Netherlands Study of Depression and Anxiety (NESDA). Archives of General Psychiatry, 65(12), 1358-1367.

Licht, C. M., de Geus, E. J., van Dyck, R., & Penninx, B. W. (2009). Association between anxiety disorders and heart rate variability in The Netherlands Study of Depression and Anxiety (NESDA). Psychosomatic medicine, 71(5), 508-518.

Poil, S. S., Jansen, R., van Aerde, K., Timmerman, J., Brussaard, A. B., Mansvelder, H. D., & Linkenkaer‐Hansen, K. (2011). Fast network oscillations in vitro exhibit a slow decay of temporal auto‐correlations. European Journal of Neuroscience, 34(3), 394-403. Poil, S. (2013). Neurophysiological Biomarkers of cognitive decline: from criticality to

toolbox.

Porges, S. W., & Byrne, E. A. (1992). Research methods for measurement of heart rate and respiration. Biological psychology, 34(2), 93-130.

Porges, S. W. (2011). The Polyvagal Theory: Neurophysiological Foundations of Emotions, Attachment, Communication, and Self-regulation (Norton Series on Interpersonal Neurobiology). WW Norton & Company.

Raymond, J., Varney, C., Parkinson, L. A., & Gruzelier, J. H. (2005). The effects of alpha/theta neurofeedback on personality and mood. Cognitive brain research, 23(2),

(22)

Stahl, S. M. (2011). Stahl's essential psychopharmacology: neuroscientific basis and practical applications. Cambridge university press.

Streeter, C. C., Jensen, J. E., Perlmutter, R. M., Cabral, H. J., Tian, H., Terhune, D. B., ... & Renshaw, P. F. (2007). Yoga Asana sessions increase brain GABA levels: a pilot study. The journal of alternative and complementary medicine, 13(4), 419-426. Sutanto, W., & De Kloet, E. R. (1994). The use of various animal models in the study of

stress and stress-related phenomena. Laboratory Animals, 28(4), 293-306.

Target Heart Rates (2015). Retrieved 27th of May 2015 from http://www.heart.org/HEARTORG/GettingHealthy/PhysicalActivity/FitnessBasics/Ta rget-Heart-Rates_UCM_434341_Article.jsp

Telles, S., Nagarathna, R., & Nagendra, H. R. (1995). Autonomic changes during" OM" meditation. Indian journal of physiology and pharmacology, 39, 418-420.

Thayer, J. F., Åhs, F., Fredrikson, M., Sollers, J. J., & Wager, T. D. (2012). A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neuroscience & Biobehavioral Reviews, 36(2), 747-756.

Vaiva, G., Thomas, P., Ducrocq, F., Fontaine, M., Boss, V., Devos, P., ... & Goudemand, M. (2004). Low posttrauma GABA plasma levels as a predictive factor in the development of acute posttraumatic stress disorder. Biological psychiatry, 55(3), 250-254.

Vaiva, G., Boss, V., Ducrocq, F., Fontaine, M., Devos, P., Brunet, A., ... & Thomas, P. (2006). Relationship between posttrauma GABA plasma levels and PTSD at 1-year follow-up. The American journal of psychiatry, 163(8), 1446-1448.

Vander Zee, K. I., Sanderman, R., Heyink, J. W., & de Haes, H. (1996). Psychometric qualities of the RAND 36-Item Health Survey 1.0: a multidimensional measure of general health status. International journal of behavioral medicine, 3(2), 104-122. Wahbeh, H., & Oken, B. S. (2013). Peak high-frequency HRV and peak alpha frequency

(23)

Appendix I:

(24)

Astanga Yoga Questionnaire

1. How well were you able to focus on your Bandhas during your practice? 1. Not at all

2. Not very well 3. Moderately 4. Very well 5. Perfectly

2. How well were you able to focus on your Ujayyi breath? 1. Not at all

3. How well were you able to focus on your Drishtis? 1. Not at all

4. How well was the overall focus of your practice? 1. Not at all

5. How flexible were you during your practice? 1. Not at all

2. Not very 3. Moderately 4. Very 5. Perfectly

6. How well were you able to maintain your balance during your practice? 1. Not at all

2. Not very 3. Moderately 4. Very 5. Perfectly

7. How was the quality of your focus during your practice?  Effortful

 Effortless

8. How many years have you been practicing Yoga?

(25)

12. Do you have experience with formal sitting meditation?  Yes

 No

13. If yes, how many minutes do you meditate on average per week? 14. Which phase of your menstrual cycle are you on?

 Not Applicable

 Follicular phase (1 to 14 days since the first day of your last period)  Luteal phase (15 days or more since the first day of the last period) 15. Do you use hormonal forms of birth control?

 Yes  No

16. What is your monthly alcohol intake?  I don’t drink

 Less than 5 drinks a month  5 to 10 drinks a month  More than 10 drinks a month 17. Do you smoke?

 Yes  No

18. How often have you recalled your dreams recently (in the past several months)?  Almost every morning

 Several times a week  About once a week

 Two or three times a month  About once a month  Less than once a month  Never

19. Lucid dreams are dreams in which the dreamer is aware that he or she is dreaming and can often consciously influence dream content. How often do you experience lucid dreams?

 Several times a week  About once a week  About 2-3 times a month  About once a month  About 2-4 times a year  About once a year

(26)

 All of the Time  Most of the Time  A Good Bit of the Time  Some of the Time  A Little of the Time  None of the Time

21. Have you been a very nervous person?  All of the Time

 Most of the Time  A Good Bit of the Time  Some of the Time  A Little of the Time  None of the Time

22. Have you felt so down in the dumps that nothing could cheer you up?  All of the Time

 Most of the Time  A Good Bit of the Time  Some of the Time  A Little of the Time  None of the Time

23. Have you felt calm and peaceful?  All of the Time

 Most of the Time  A Good Bit of the Time  Some of the Time  A Little of the Time  None of the Time 24. Did you have a lot of energy?

25. Have you felt downhearted and blue?  All of the Time

 Most of the Time  A Good Bit of the Time  Some of the Time  A Little of the Time  None of the Time 26. Did you feel worn out?

(27)

 A Little of the Time  None of the Time

27. Have you been a happy person?  All of the Time

 Most of the Time  A Good Bit of the Time  Some of the Time  A Little of the Time  None of the Time 28. Did you feel tired?

(28)

Appendix II:

Elaboration on Questionnaire

Construction

(29)

Motivation for Ashtanga Yoga Questionnaire Construction:

An additional questionnaire was constructed in order to gain greater understanding of the qualitative aspects of participant Yoga practice and to obtain additional background information. The rationale and background of the questions is elaborated upon below.

Questionnaire Items:

Item 1 through 3 measure the 3 most important aspects of the Ashtanga Yoga practice, which claim to make it meditative (5-point Likert scale):

Bandhas (1) Ujjayi breath (2) Dristi (3)

Given that these items measure the meditative components of the Ashtanga Yoga practice, it might be of interest to add these in as covariates to the post-practice measurements.

Item 4 through 6 measure additional qualitative aspects of the Astanga practice (5-point Likert scale):

Overall focus (4) Flexibility (5) Balance (6)

The justification for these items is due to the idea that anecdotally this practice is undertaken 6 times for the opportunity to study the state the mind resides daily in and to observe its fluctuations. The idea behind this is that a distracted mind will lead a decrease of physical balance and flexibility and also to a diminished capacity for focused attention during the practice. These scores might be of interest to correlate both to pre- and post practice measurements.

Item 7 measures the qualitative aspect of the focus during the Yoga practice utilizing a binary scale (effortful, effortless). It is argued that after years to decades of practice, the practitioner realizes an effortless quality to his or her practice. Given that the neural mechanisms for an effortful as opposed to effortless practice may be different, it might lead to disparate results (Garrisson et al., 2013). Therefore, this item takes this potential differentiation in practice quality into account.

Item 8 and 9 gauge Yoga practice length. Item 10 and 11 gauge Yoga practice intensity.

Item 12 and 13 gauges whether the subject in addition to a Yoga practice also has a meditation practice.

Item 14 and 15 measure hormonal levels of female subjects. GABA levels are known to change over the menstrual cycle with a decline during the luteal phase and an increase during the follicular phase (Griffiths et al., 2005; Epperson et al., 2002; Streeter et a., 2007). As menstrual stage could affect brain GABA levels, menstrual and contraceptive histories will be obtained. This is in accord to the Methods of Streeter et al., (2007) who used this measurement as a covariate in a study that measured GABA levels using MRS in Astanga

(30)

Item 18 and 19 are of personal interest as previous studies have shown that meditators have greater dream recall and are more likely to be lucid dreamers (Reed, 1978; Schredler & Erlacher, 2004). It is of interest to see if more advanced practitioners experience greater dream recall and lucid dreaming abilities.

Item 20 through 28 are from the energy and emotional well-being/mental health scale of the RAND-36 to see if previous resting state findings can be replicated in Yoga practitioners as well as to gauge their mental health (Diaz et al., 2014; Hays & Morales, 2001)

References

Diaz, B. A., Van Der Sluis, S., Benjamins, J. S., Stoffers, D., Hardstone, R., Mansvelder, H. D., ... & Linkenkaer-Hansen, K. (2014). The ARSQ 2.0 reveals age and personality effects on mind-wandering experiences. Frontiers in psychology, 5.

Elias, A. N., & Wilson, A. F. (1995). Serum hormonal concentrations following transcendental meditation-potential role of gamma aminobutyric acid. Medical hypotheses, 44(4), 287-291.

Epperson CN, Haga K, Mason GF, et al. Cortical gamma- aminobutyric acid levels across the menstrual cycle in healthy women and those with premenstrual dysphoric disorder: A proton magnetic resonance spectroscopy study. Arch Gen Psychiatry 2002;59:851–858. Garrison, K. A., Santoyo, J. F., Davis, J. H., Thornhill IV, T. A., Kerr, C. E., & Brewer, J. A. (2013). Effortless awareness: using real time neurofeedback to investigate correlates of posterior cingulate cortex activity in meditators' self-report. Frontiers in human neuroscience, 7.

Griffiths J, Lovick T. Withdrawal from progesterone increases expression of alpha4, beta1 and delta GABA(A) receptor sub- units in neurons in the periaqueductal gray matter in female Wistar rats. J Comp Neurol 2005;486:89–97.

Hays, R. D., & Morales, L. S. (2001). The RAND-36 measure of health-related quality of life. Annals of medicine, 33(5), 350-357.

Mason, G. F., Petrakis, I. L., de Graaf, R. A., Gueorguieva, R., Guidone, E., Coric, V., ... & Krystal, J. H. (2006). Cortical gamma-aminobutyric acid levels and the recovery from ethanol dependence: preliminary evidence of modification by cigarette smoking. Biological psychiatry, 59(1), 85-93.

Schredl, M., & Erlacher, D. (2004). Lucid dreaming frequency and personality. Personality and Individual Differences, 37(7), 1463-1473.

Streeter, C. C., Jensen, J. E., Perlmutter, R. M., Cabral, H. J., Tian, H., Terhune, D. B., ... & Renshaw, P. F. (2007). Yoga Asana sessions increase brain GABA levels: a pilot study. The journal of alternative and complementary medicine, 13(4), 419-426.

(31)

Appendix III:

(32)

PROTOCOL ASHTANGA YOGA EXPERIMENT Subject Number: ……….

Name: ………... Gender: ……… Date: ………. Time: ………. Lunar cycle: Class 1 / Class 2 / Class 3

Length of Yoga practice: ………..Length of Cycling:……… Lay out:

GSR, ICG and EKG electrodes, alcohol pads, EEG cap, Yoga mat and supplies, and informed consent form.

Turn on heater. Turn on speakers.

Ask to read and sign informed consent form (5 minutes) Pre-Measurements: +/-60 minutes

Set up electrodes (25 minutes): Hair washed?

Measure head circumference and cross. Soak designated cap.

Clean skin on designated spots with alcohol pads.

Apply ICG and EKG electrodes (Both Vu-Dams and PIB). Mount EEG cap.

Switch to Screen 1 Check impedance. Plastic wrap head. Open Log.

Connect Vu-Dams cables

Apply GSR electrodes on the volar surface of the medial phalanges of the index and middle finger on the non-dominant hand. Fill with white conductivity gel. Ask to keep hand resting on cushion (standardized hand placement) for the duration of the tasks.

Adjust chin rest: Straight spine

Open Vu-Dams program and check signal. Start Vu-Dams recording.

Switch to screen 2.

Open OpenSesame from desktop: Run YogiesModifiedEnglishArsq task. Turn off lights in room. Leave small lights on.

(33)

Tasks (27 minutes) Turn on lights in room.

Remove electrodes (5 minutes): Stop Vu-Dams recording.

Take memory card and batteries into experimenter room.

Remove all electrodes with the exception of EKG and ICG electrodes. Wash white conductivity gel out of GSR electrodes.

Don’t rinse cap.

Turn off lights in room. Leave small lights on. Experimental Intervention (+/- 90 minutes):

Provide Yoga mat, Savasana blanket, eye pillow, and a water spray bottle. Ask subject to practice the Astanga-Vinyasa Yoga Primary Series on the provided Yoga mat.

Control Intervention (75 minutes):

Ask the subject to cycle on the stationary bike for 75 minutes at a similar level of physical effort as their Astanga practice (i.e., HR of 95 beats per minute). Have 10 minutes of rest afterwards.

During Practice: Charge batteries.

Transfer data from memory card to computer Delete data on memory card.

Post-Measurements (+/-60 minutes) Turn on lights in room.

Set up electrodes (20 minutes): Soak designated cap.

Mount EEG cap. Switch to Screen 1 Check impedance. Plastic wrap head. Open Log.

Connect Vu-Dams cables

Apply GSR electrodes on the volar surface of the medial phalanges of the index and middle finger on the non-dominant hand. Ask to keep hand resting on cushion (standardized hand placement) for the duration of the tasks.

Adjust chin rest: Straight spine

Open Vu-Dams program and check signal. Start Vu-Dams recording.

Switch to screen 2.

(34)

Tasks (27 minutes)

ECR + ARSQ (10 minutes)

SFM task (10 minutes)

CTET task (7 minutes)

Astanga Questionnaire after Yoga (5 minutes)

Pranayama (if participant wants to) (10 minutes)

Manually start a recording in NetStation.

Ask participant to take a one minute pause in between Pranayama practices in the order below:

Samma Vritti

1st_{Ashtanga Pranayama}

2nd_{Ashtanga Pranayama}

Remove electrodes (5 minutes):

Remove all electrodes with the exception of EKG and ICG electrodes. Total Length Paradigm: +/- 3.5 hours

After experiment: Clean EEG cap and mat

Notes: ……… ……… ……… ……… ……… ……… ……… ……… ………

(35)

Appendix IV:

Public Outreach Activities Based on this

Report

(36)

Presentations Given Based on this Research Report:

International Conference on Yogatherapy and Research (2015) of the Network Yoga Therapy Organization. (3rd of June, 2015)

http://networkyogatherapy.org/international-conference-on-yogatherapy-and-research Here I presented the preliminary findings of this study for ~150 people coming from the clinical (Psychology, Psychiatry and Medicine) and Yoga therapy (Yoga teachers working with Yoga therapy) fields.

Delight Yoga Amsterdam (11th of June, 2015).

Here I presented the findings of this research- report for ~40 Yoga teachers, staff and the founders of Delight Yoga Amsterdam, an Ashtanga based Yoga School in Amsterdam. http://delightyoga.nl/

CNCR INF Internship Talks 2015 (24th of June, 2015)

Here I presented the findings of this research-report for the CNCR staff as part of the internship talks.

Popular Science Articles Based on this Research Report: A yet to be published article in the Psychiatry magazine Deviant. http://www.tijdschriftdeviant.nl

Contact information of the Journalist: Annemarie Huiberts (annemarie_h@live.nl).

(37)

Appendix V:

Methods Sections of the Entire Research

Project

(38)

Methods Sections of the Entire Research Project

In addition to the data that were analyzed and reported in detail in this internship report, several other paradigms were recorded. In the following, the complete Materials and Methods of the data recorded as part of this internship are described

Materials and Methods

Subject background and recruitment

Subjects were recruited through advertisements and word of mouth. The permanent committee of science and ethics of the VU University Amsterdam approved the study and informed consents were obtained for all participants. The eligibility criterion for participation was a minimum of one year of regular Ashtanga Yoga practice (i.e., 4 times or more of practice per week). Subjects were between 23 and 49 years of age (M=35, SD=7.89) and consisted of 4 males and 14 females. The average years of Yoga practice was 9 years (SD=6.19) (range 2 to 27 years). The average years of Ashtanga Yoga practice was 6 years (SD=5.57) and ranged from 1 to 22 years of Ashtanga Yoga practice. Participants practiced on average 5 days a week (SD=0.77). Subjects received €50 payment for participating. Research Instruments

Eyes-Closed Rest (ECR)

To investigate both the long and short-term effects of Ashtanga Yoga practice on resting-state physiology, 5-minutes of seated eyes-closed was measured at four time-points (before Yoga, after Yoga, before cycling and after cycling).

Amsterdam Resting-State Questionnaire (ARSQ)

To investigate thoughts and feelings during rest and correlate these to physiological measures, the Amsterdam Resting-State Questionnaire (ARSQ) was presented after all ECR measures. The ARSQ consisted of 55 statements presented on a computer and rated on a 5-point Likert scale from completely disagree to completely agree (Diaz et al., 2014).

Yoga Practice and Background Information Questionnaire

Subjects completed a questionnaire after the Yoga intervention to gauge certain qualitative aspects of the practice and to obtain additional background information (for details on the questionnaire and its construction, see Supplemental Information).

Continuous Temporal Expectancy Task (CTET):

Participants completed a sustained attention task (O'Connell et al., 2009), which was designed to measure lapses in attention such as mind wandering through the number and timing of errors the participants make. The task consist of a centrally presented stimulus (photos of different flowers), shown at regular intervals (600 ms), resulting in a continuous stream of pictures. Participants are asked to monitor the temporal duration of each stimulus and to identify when a stimulus is presented longer (1200 ms) than the standard duration. Longer durations occurred semi randomly (every 4th_{to 10}th_{stimuli), resulting in a total of 90 targets.}

The stimuli were made of naturalistic pictures taken from the International Affective Picture System (IAPS;Lang et al.(1999 )), with pictures specifically chosen for their low arousal values. Additionally, the color brightness and -saturation, and size of the scenes were standardized to decrease stimulus perception dependent differences.