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Biological Psychology

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Dissociating meditation proficiency and experience dependent EEG changesduring traditional Vipassana meditation practice

Ratna Jyothi Kakumanua,1, Ajay Kumar Naira,1, Rahul Venugopala, Arun Sasidharana,Prasanta Kumar Ghoshb, John P. Johnc, Seema Mehrotrad, Ravindra Panthe, Bindu M. Kuttya,⁎

a Department of Neurophysiology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru 560029, Karnataka, IndiabDepartment of Electrical Engineering, Indian Institute of Science (IISc), Bengaluru 560012, Karnataka, Indiac Department of Psychiatry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru 560029, Karnataka, Indiad Department of Clinical Psychology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru 560029, Karnataka, Indiae Department of Buddhist Philosophy, Nava Nalanda Mahavihara, Nalanda 803111, Bihar, India

A R T I C L E I N F O

Keywords:Vipassana meditationEEGPermutation entropyFractal dimensionsNeural plasticity

A B S T R A C T

Meditation, as taught by most schools of practice, consists of a set of heterogeneous techniques. We wanted toassess if EEG profiles varied across different meditation techniques, proficiency levels and experience of thepractitioners. We examined EEG dynamics in Vipassana meditators (Novice, Senior meditators and Teachers)while they engaged in their traditional meditation practice (concentration, mindfulness and loving kindness in astructured manner) as taught by S.N. Goenka.

Seniors and Teachers (vs Novices) showed trait increases in delta (1–4 Hz), theta-alpha (6–10 Hz) and low-gamma power (30–40 Hz) at baseline rest; state-trait increases in low-alpha (8–10 Hz) and low-gamma powerduring concentrative and mindfulness meditation; and theta-alpha and low-gamma power during loving-kind-ness meditation. Permutation entropy and Higuchi fractal dimension measures further dissociated high profi-ciency from duration of experience as only Teachers showed consistent increase in network complexity frombaseline rest and state transitions between the different meditation states.

1. Introduction

Meditation is an umbrella term for a set of heterogeneous techni-ques that engage several different neurocognitive processes and typi-cally induce beneficial effects on brain and behavior (Boccia, Piccardi,& Guariglia, 2015; Fox et al., 2016; Nair, Sasidharan, John, Mehrotra, &Kutty, 2017). A recent attempt at classifying meditative practices basedon cognitive mechanisms acknowledged that many meditative practicesmight span multiple categories (Dahl, Lutz, & Davidson, 2015). As anexample, mindfulness based meditation has aspects of concentration,mindfulness and loving kindness (Manuello, Vercelli, Nani, Costa, &Cauda, 2016) and thus straddles the attentional, constructive and de-constructive families (Dahl et al., 2015).

We had three considerations while undertaking the present study.Since mindfulness might mean several different things (Davidson &Kaszniak, 2015), the first consideration was to specify the context under

which a study is carried out. In particular, the traditional practice andphilosophical position underlying the meditative practice needs to bearticulated (Awasthi, 2013). EEG studies examining the neurophy-siology of mindfulness based meditation techniques have found con-sistent changes in theta and alpha power (Cahn & Polich, 2006; Lomas,Ivtzan, & Fu, 2015). This might imply that there are at least somecommon mechanisms underlying these meditation techniques as theyfinally involve some aspect of mindfulness (Manuello et al., 2016). Onthe other hand, it is possible that a context based study of mindfulnessmeditation might reveal a more nuanced understanding of the neuro-physiological underpinnings of the different meditative techniques.

The second consideration was that long term practice has a traitinfluence on meditation state (Davidson & Kaszniak, 2015). Severalstudies examine the influence of duration of practice on meditationstate by comparing EEG changes in long term meditators in comparisonto novice meditators. The challenge is that there is wide variability in

https://doi.org/10.1016/j.biopsycho.2018.03.004Received 10 September 2017; Received in revised form 5 March 2018; Accepted 5 March 2018

⁎ Corresponding author at: Department of Neurophysiology, National Institute of Mental Health and Neuro Sciences (NIMHANS), P.B. No. 2900, Dharmaram P.O, Hosur Main Road,Bengaluru 560029, Karnataka, India.

1 These authors contributed equally to this work.

E-mail addresses: jyothi.maanu@gmail.com (R.J. Kakumanu), ajay.nimhans@gmail.com (A.K. Nair), rahul.nimhans@gmail.com (R. Venugopal),arunsasi84@gmail.com (A. Sasidharan), prasantag@gmail.com (P.K. Ghosh), jpjinc@yahoo.com (J.P. John), drmehrotra_seema@yahoo.com (S. Mehrotra),panthr2011@gmail.com (R. Panth), bindu.nimhans@gmail.com (B.M. Kutty).

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the operational definition of novice and non-novice meditators (Lomaset al., 2015) and sometimes the duration of practice that is categorizedas ‘experienced’ by one study could be categorized as ‘short term’ byanother study (Lomas et al., 2015; Nair et al., 2017). A further com-plication is that proficiency in meditation is often conflated withduration of practice (as a reasonable approximation), but there can besystematic differences in practice in those who teach meditation (ascompared to those who just practice for a long time) that can lead todifferent meditation proficiency levels.

The third consideration was that most EEG studies examine powerspectral decompositions using Fourier transforms. These techniquesassume statistical stationarity of the EEG time series over short inter-vals. While this might be a suitable approximation, it is known that EEGtime series is nonlinear, noisy and non-stationary in nature (Stam,2005). Therefore, it might be valuable to examine these dynamicalsystems using non-linear measures of complexity such as permutationentropy and fractal dimensions. These key concepts are briefly in-troduced in the next two paragraphs.

Permutation entropy (PE) is a natural complexity measure of anytime series that is calculated by assigning symbols to patterns ofneighboring time point sequences and then examining the informationentropy (a measure derived from probability) of the symbol dynamicsover the course of the full time series (Bandt & Pompe, 2002). The twodefining parameters of PE are order and delay (Riedl, Muller, & Wessel,2013). Order is the number of data points taken in one set to generatethe symbol. If the order is n, there can be n! symbols. Delay is the extentof shift from the first data point after which the next symbol is gener-ated. If the time series is well sampled, a time delay of 1 is an appro-priate default. Thus, in a time series, an order of 3 will group datapoints 1–3 into one set which will be represented as one symbol. Adelay of 1 implies that data points 2–4 will form the next set. For atutorial on computing the ordinal patterns from time series, please see(Unakafova & Keller, 2013).

Higuchi’s fractal dimension (HFD) is another measure of the com-plexity of a time series that captures the space filling ability of the datapoints and reflects the self-similarity property of a fractal time series(Higuchi, 1988). For a detailed review of HFD analysis, please see(Kesić & Spasić, 2016).

We have previously examined the influence of Vipassana meditationpractice on sleep in a series of whole night polysomnography studies(Maruthai et al., 2016; Nagendra, Maruthai, & Kutty, 2012; Nagendraet al., 2017; Pattanashetty et al., 2010; Sulekha, Thennarasu,Vedamurthachar, Raju, & Kutty, 2006) finding that senior meditatorshad enhanced slow wave sleep and REM states, increased REM activity,evidence of better sleep architecture preservation even with aging, andenhanced parasympathetic activity during sleep. Overall, these studiespoint out to the neuroplastic changes due to long term practice ofmeditation. In the present study, we address the aforementioned threeconsiderations: context of traditional complex meditation practice, re-levance of proficiency and duration of experience, and examination oflinear and non-linear EEG measures.

Vipassana meditation is an ancient Buddhist mindfulness basedpractice that comprises of several meditation techniques. A popularschool of Vipassana meditation (as taught by S.N. Goenka in the tra-dition of Sayagyi U Ba Khin) uses a structured module with threecomponents of guided meditation (Fig. 1) – focused attention on breath(Anapana), focused attention with awareness of the impermanence ofsensations (Vipassana, often called ‘mindfulness’) and radiating goodwill to oneself and others (Metta, often called ‘loving-kindness’ medi-tation). At an introductory level, the basic method of Vipassana medi-tation is taught as a ten-day residential course during which a ‘code ofdiscipline’ is prescribed. Attendance of several such ten-day courses isrequired before a novice practitioner becomes eligible for advancedtraining that is imparted during ‘long retreats’, which are intensemeditative sessions lasting between 20 and 90 days. The long retreatsprovide senior practitioners with exposure to the philosophical basis of

these practices. Senior practitioners can then volunteer to become fulltime teachers who then get frequent and detailed exposures to thetheoretical underpinnings of the technique and become eligible toconduct meditation courses for others. Teachers can thus have similaryears of meditation experience as senior practitioners but have higherproficiency due to their focused training and practice.

We recorded 128 channel EEG from three groups of Vipassanameditators (novices, senior meditators and teachers) while they en-gaged in their traditional structured meditation practice. We then ex-amined power spectral changes as well as complexity based measuresacross the different meditation components.

2. Methods

2.1. Participants

Participants were recruited with permission from VipassanaResearch Institute (VRI), Igatpuri, India. Notices about the study wereput up in the long course conducting centers across India. Study detailswere published in the VRI newsletter. Participation criteria (age range30–65) were as follows: Novice practitioners (2 or 3 ten-day courseswith less than 3 years of practice); Senior practitioners (at least onelong retreat with daily practice of more than 7 years); Teachers (in-structors of Vipassana courses at meditation centers with a daily prac-tice of more than 7 years and have undergone several long retreats).Participants with neurological or psychological disorders, history ofsubstance abuse, on psychiatric or neurological medication, or practi-cing any other forms of meditation other than Vipassana were excludedfrom the study.

We recruited three groups of age, gender, education and socio-economic status matched participants (n=68) of which EEG data fromone novice participant could not be analysed: Novice practitioners(Nov; n=24; 12 females; age 48.4 ± 10.1; meditation years:2.2 ± 0.9 with max 3 years; meditation hours: 1080.1 ± 599.9);Senior practitioners (Sen; n=22; 11 females; age 54.2 ± 12.6; medi-tation years: 13.0 ± 4.4; meditation hours: 10,364 ± 5229); andTeachers (Tea; n=21; 10 females; age 51.8 ± 12.2; meditation years:16.3 ± 5.3; meditation hours: 15,349 ± 9307). There were no sig-nificant differences in terms of age, gender, education or income (asexpected) and there was a significant difference between groups interms of years of practice (F(2,64) = 79.39; p=0.000) and hours ofmeditation experience (F(2,64) = 32.93; p=0.000). The mean differ-ence and 95% confidence intervals (CI) for the between group differ-ences for years of meditation experience (Sen vs Nov: 10.8 (8.0, 13.6)p=0.000, Tea vs Nov: 14.1(11.2, 16.9) p=0.000 and Tea vs Sen: 3.2(0.3, 6.1) p=0.025) and hours of meditation experience (Sen vs Nov:−9283.7 (5024, 13,543) p=0.000, Tea vs Nov: 14,269 (9957, 18,581)p=0.000 and Tea vs Sen: 4985.6 (583, 9388) p=0.023) showed thatthese groups were clearly different in terms of overall meditation ex-perience. Seniors and Teachers had overlaps in terms of years of ex-perience but Teachers had more hours of meditation experience as wellas higher proficiency due to their focused training in the theory andformal roles of teaching meditation.

Participants had at least high school education, were fluent inEnglish, and the majority was from the middle income category as perIndian standards. Participants were all healthy, right handed, non-smokers and refrained from any caffeinated beverages on the day of thestudy. They were recruited from all over India. Food, accommodationand travel expenses were offered with no other kind of financial in-centives. All participants provided written informed consent as ap-proved by NIMHANS Institute Human Ethics Committee.

2.2. EEG acquisition

All EEG recordings were carried out in the forenoon (starting 9 AM),in a sound attenuated chamber of the Human Cognitive Research

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laboratory in the Department of Neurophysiology, NIMHANS. Theambient temperature was maintained at 25 °C. The participants satcomfortably in a cushioned chair with armrest and E-prime 2.0 stimuluspresentation software (Psychology Software Tools, Inc., Sharpsburg,PA, USA) was used for presenting audio instructions.

EEG data were acquired using a Geodesic EEG System 300(Electrical Geodesics, Inc., USA) with Net Amps 300 amplifier and NetStation software version 4.5.7. Appropriate sizes of HydroCel GeodesicSensor Nets with 128 channels were used. Preparatory procedures asrecommended by the vendor were carefully carried out. EEG was di-gitized with a resolution of 24 bits at a sampling rate of 1 KHz. No notchfilters were used. Impedance for all electrodes was maintained at lessthan 50 KΩ as recommended by the vendor.

The Meditation EEG protocol (Fig. 2) lasted just over one hour andconsisted of the following structure: Pre-Rest Eyes Open (RO) and EyesClosed (RC) for one minute each, alternating twice – 4min; Anapana(Ana) – 3min; Vipassana (Vipa) – 40min; Metta – 6min; Post-Rest (ROand RC) for one minute each, alternating twice – 4min. All meditativestates were in eyes closed condition. At the start of each meditativecondition, time locked audio instructions were played in the voice ofS.N. Goenka, with permissions from VRI. The participants were ex-plicitly instructed to be relaxed and to avoid meditating during the restsessions. The transcript of the instructions is provided in the Supple-mentary material (SM). At the end of the protocol, there was a de-briefing session and all participants mentioned that they were able tocomply with the instructions.

2.3. Data pre-processing and analysis

The EEG data were preprocessed with a 0.1 Hz first order high passfilter using Net Station 4.5.7 and then exported as Net Station simplebinary files. Further EEG preprocessing and analysis was done withcustom scripts using EEGLAB v13.4.4b (Delorme & Makeig, 2004) – anopen source toolbox using MATLAB version R2013a (Math Works Inc.,

Natick, MA, USA). Channel locations were set as per the 129 channelfile supplied by the vendor. Following a low pass FIR filter of 40 Hz,artifact correction and removal was done using the artifact subspacereconstruction (ASR) method implemented in the ‘clean_rawdata’plugin ver 1.2 of EEGLAB. As meditation data often contains highamplitude alpha and theta bursts (see Fig. 3), a high threshold of 20standard deviations was set for detecting artifacts in the EEG. The badchannels that were removed were interpolated, and the cleaned datadown sampled to 250 Hz and re-referenced to average.

For analyses, the last 2-min data from Anapana and middle 2-minportion of Metta were taken. The 40-min-long Vipassana data wasepoched into four equal ‘10′ minute portions (Vipa1 to Vipa4) out ofwhich the middle 2-min data from each epoch was taken for analyses.Data from the 2-min eyes closed pre-rest (R1C) condition were taken asbaseline for comparisons. The data was z-scored to minimize confoundsdue to individual variability.

Power spectral density was computed and the results of the topo-graphical distribution of delta (1–4 Hz), low-theta (4–6 Hz), high-theta(6–8 Hz), low-alpha (8–10 Hz), high-alpha (10–12 Hz), low-beta(12–15 Hz), high-beta (15–30 Hz) and low-gamma (30–40 Hz) wereused for statistical analyses. We could not evaluate high-gamma powerdue to the 40 Hz low-pass filter that was applied to achieve good qualityartifact rejection with our data. In the present paper, we focus on high-theta and low-alpha power bands.

For the complexity measures, each epoched dataset (120 s duration)was divided into sub epochs of 0.5 s yielding 240 bins per state for eachparticipant. PE and HFD were calculated for each bin and averagedacross 240 bins for each of the 129 electrodes for each subject. For PE,the order was set as 3 and the delay as 1. For HFD, the scale value of 5was chosen. Custom scripts were written in MATLAB for the non-linearanalyses and for generating scalp topography plots.

Surrogate data based statistical tests were carried out with non-parametric permutation based two-way ANOVA using 2000 randompartitions and False Discovery Rate (FDR) correction for multiple

Fig. 1. Outline of the traditional Vipassana meditation module. This is the sequence of meditative practices followed in the tradition of Sayagyi U Ba Khin. The salient features of eachpractice and the expected outcomes are represented. The notation used in this paper is: Anapana, Vipassana and Metta.

Fig. 2. Meditation EEG Protocol. RO=Rest Eyes Open, RC=Rest Eyes Closed. The Pre-Rest and Post-Rest conditions lasted four minutes each. Arrows indicate the time points whenaudio instructions were provided for the next condition. The durations indicated for each condition do not include the time for instructions. Anapana had instructions for focusedattention on breath. Vipassana had instructions for being mindfully aware of bodily sensations and Metta had instructions for radiating goodwill to be in a state of loving-kindness.

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comparisons. Statistical significance level was set at p < 0.05.The median values of all electrodes for each power band for each

state were used for creating the median plots and for correlating withmeditation experience (in hours), HFD and PE.

3. Results

We present the salient results in this section. Since we had four timepoints in the Vipassana condition, we use Vipa2 as a representativecondition in this section and present the results of all time points in theVipassana condition in the SM figures. The summary of all the powerspectral changes (including post hoc analyses) can be found inSupplementary material Table 1.

3.1. State changes due to meditation

As compared to eyes-closed pre-rest (R1C), each group (Nov, Senand Tea) showed global increases in each power band (delta, theta,alpha and low-beta) during each meditation condition (Ana, Vipa andMetta). These changes demonstrate that the meditation protocol eli-cited robust state (state-trait in the case of long term meditators)changes in each group. The post-rest condition (R2C) also showed theafter-effect of the hour long meditation session with global power in-creases, even though the participants had explicit instructions not tomeditate during the rest condition.

Fig. 3 shows representative Raw EEG traces of Nov and Tea.Fig. 4 (and Supplementary material Fig. 1) shows median power

spectral values for the three groups as they went through the timecourse of the meditation protocol. All the groups showed higher poweracross the different bands during Post-Rest than in Pre-Rest but thesevalues were lower than the preceding meditative conditions. Supple-mentary material Figs. 2 and 3 show the comparison between Pre-Restand Post-Rest for both eyes open and eyes closed states.

3.2. Trait changes due to long term meditation experience

Comparison of pre-rest (R1C) between the groups revealed traitdifferences between the groups in the delta, high-theta, low-alpha andlow-gamma bands (Fig. 5, first row in every sub-panel). Post hoc ana-lyses revealed that there were no differences between Sen and Tea butthey both had higher low-alpha and low-gamma power as compared toNov (Supplementary material Table 1). Thus, the differences in restingstate power at baseline are attributable to the effects of long termmeditation experience.

3.3. State-Trait changes due to long term meditation experience

State-trait interactions (trait differences attributable to long term

practice as well as state changes during the meditation session) werevisible in low-alpha band for Ana, Vipa2, Metta and R2C conditions(Fig. 5, 2nd row in each plot). The power spectra for the Vipa condition(Supplementary material Fig. 4) showed differing state-trait changesacross the four time points. Supplementary material Fig. 5 shows re-duction in power in R2C as compared to the meditative states as well asstate-trait effects leading to condition differences even in the R2Ccondition.

Post hoc between-group analyses revealed that there were no dif-ferences between Sen and Tea in any power band for any of the med-itation conditions and that Nov group had lower low-alpha power andhigher low-gamma power as compared to both Sen and Tea in all theconditions. It is important to note that the higher low-gamma power inNov had a bitemporal distribution in all the conditions, suggestive ofpossible high frequency artifacts from the temporalis muscle(Goncharova, McFarland, Vaughan, & Wolpaw, 2003). Since the Novgroup had power increases during meditation, these analyses highlightthat long term practice enabled further low-alpha power increases andlesser beta and gamma power increases during meditation. It is note-worthy that there were widespread high-theta differences between Novand Tea in all the conditions, but not between Nov and Sen or Sen andTea. Similarly Nov had higher low-gamma as compared to Tea in theR1O and R2O whereas there were no differences in low-gamma be-tween Nov and Sen or Sen and Tea. These results indicate that the Sengroup was intermediate group between Nov and Tea in terms of theirEEG profile of meditation proficiency and/or experience.

3.4. Higuchi fractal dimension changes during meditation

Analysis of HFD data showed that Tea and Nov had increased HFDcomplexity (as compared to R1C) during Vipassana meditation ascompared to rest (Fig. 6A). Sen did not show any HFD differences in anymeditation state.

There were between-group differences for all the meditation con-ditions and post hoc analyses revealed that there were no differencesbetween Sen and Tea in terms of HFD while Nov had significantlyhigher HFD values than both Sen and Tea for each state. This resultsuggests a non-linear ‘U’ shaped relationship in terms of complexityincreases during meditation with meditation proficiency as Sen was anintermediate group with no significant differences in complexity.

3.5. Permutation entropy changes during meditation

Interestingly, Tea showed increased PE complexity (as compared toR1C) during the various meditation conditions (Ana, Vipa and Metta) ascompared to rest but there were no differences between R1C and R2C(Figs. 6B and 7 ). Nov showed increased PE complexity from baselineonly during Vipassana meditation. Sen did not show any complexitydifferences in any meditation condition.

There were between-group differences for all the meditation con-ditions. Post hoc analyses revealed that there were no differences be-tween Sen and Tea in terms of PE in any condition while Nov hadsignificantly larger PE values than both Sen and Tea in every condition.The group differences are more apparent when examining the medianPE and HFD values across all conditions (Fig. 8) which also shows theincreased processing by all groups during Metta.

3.6. Correlations between power spectra, meditation experience andcomplexity values

Median power-spectra and hours of meditation experience werepositively correlated in the theta-alpha band (especially pre-rest andpost-rest conditions) and negatively correlated with delta, beta andgamma bands. On the other hand, power spectra and both complexitymeasures were negatively correlated in the theta-alpha band and po-sitively correlated with delta, beta and gamma bands. However, the

Fig. 3. Representative EEG traces of Novice (A) and Teachers (B) during meditation.

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number of significant correlations was more for the complexity mea-sures that for the meditation experience. Fig. 9 summarizes all thesignificant correlations.

4. Discussion

Our study results reveal several distinctive state-trait changesamong Vipassana mindfulness meditators. Long term practice (as in thecase of Sen and Tea) yields trait differences at baseline rest conditionsseen via both linear and non-linear methods. Complexity based mea-sures reveal a further nuance that there are proficiency based trait-statedifferences as Teachers showed heightened information processing andfractal characteristics during meditative states (as compared to rest)whereas Senior meditators did not show these effects. The value ofstudying the Vipassana mindfulness based meditation as an entiremodule was revealed as (compared to rest) Anapana and Vipassanawere marked by state-trait differences in delta, low-alpha, high-betaand low-gamma power but no group differences in high-theta power.Metta, on the other hand, was marked by differences in high-theta, low-alpha and low-gamma power but showed no group differences in high-beta power. Metta also required the highest amount of informationprocessing and complexity among all the states. We discuss the possibleimplications of these changes in this section, but before that, we ex-amine our results in the light of the three considerations we had forundertaking this study.

Our first consideration was to examine the context in which ameditation study is carried out. Vipassana (as taught by S.N. Goenka)has a structure in which multiple meditation techniques are practiced ina specific progressive sequence − starting with concentration onbreath, moving on to mindful attention to sensations (potentiallyyielding insight into impermanence of things) and ending with a lovingkindness meditation in which goodwill is generated towards the selfand others. This structure is designed for specific outcomes (see Fig. 1)at each step. It is a useful exercise to study a particular meditationtechnique in isolation as is common practice. Several authors haveemphasized the need to place these meditation studies in the traditionalcontext (Dahl et al., 2015; Josipovic & Baars, 2015). We have

previously examined the ability of meditators to rapidly shift betweenrest and meditation states (Nair et al., 2017) based on the traditionalpractice of Rajayoga meditators of performing one minute meditations.In this context a couple of new studies have attempted to examine thecumulative effects of doing multiple meditative techniques in a de-signed sequence (DeLosAngeles et al., 2016; Schoenberg, Ruf,Churchill, Brown, & Brewer, 2018) as- studying different techniques inisolation does not provide this value. Our approach of following anexact traditional sequence in the protocol contributes in this directionand is ecologically a more valid study than examining any meditationcomponent in isolation. On the other hand, there have been attempts toclassify meditation groups into higher abstractions − such as con-sidering all Buddhism-related groups together as distinct from Hin-duism-related groups (Tomasino, Chiesa, & Fabbro, 2014) or indeed allmeditation types together (Hinterberger, Schmidt, Kamei, & Walach,2014). These approaches are valuable as they provide insight into whatis common between the varieties of techniques in terms of EEG profilesor outcomes (Cahn & Polich, 2006; Sedlmeier et al., 2012) but they donot provide insight into how any particular meditation approach iseffective in its traditional context. Our study provides a novel con-tribution in this latter direction.

Our second consideration was to examine trait influences on state,in particular due to amount of meditation experience as well as theirproficiency profiles. In our study, we used only experienced meditators.Even the Nov group had considerable experience and indeed otherstudies have classified people with about 1000 h of meditation as highlyexperienced − see for example: (Hinterberger et al., 2014). The Senand Tea groups had over 10 years of regular meditation practice andmore than 10,000 h of meditation experience. These groups would havetypically been clubbed as one expert meditation group. However, fromthe traditional Vipassana viewpoint, these groups are different as theTea group has exposure to more theory and focused practice as well as arole specific effect as they are formally involved in teaching meditation.We therefore examined these groups separately and found them to havesimilarities and differences. We suggest that future studies could con-sider examining proficiency differences in addition to duration ofpractice while determining groups of expert meditators.

Fig. 4. Median power spectral changes across the meditation protocol. R1C=Pre-Rest with eyes closed. Ana=Anapana meditation. Vipa2=Vipassana meditation 2nd time point.R2C=Post-Rest with eyes closed. Nov=Novice meditators. Sen= Senior meditators. Tea=Teachers of meditation.

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Fig. 5. EEG Topography Comparisons: Pre-Rest vs Anapana, Vipa2 and Metta. Average topography plot using z-score normalized values for each subject. Row and column labelled ‘Diff’show statistically significant differences across conditions – red dots indicate electrode locations (p < 0.05 using permutation based two-way ANOVA with 2000 random partitions, FDRcorrected). R1C=Pre-Rest with eyes closed. Ana=Anapana meditation. Vipa2=Vipassana meditation 2nd time point. Nov=Novice meditators. Sen= Senior meditators.Tea=Teachers of meditation. The various panels show the plots for corresponding frequency bands (1–4 Hz, 6–8 Hz, 8–10 Hz and 30–40 Hz). (For interpretation of the references tocolour in this figure legend, the reader is referred to the web version of this article.)

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Our third consideration was the use of both linear and non-linearapproaches to study EEG profiles. Linear methods (power spectra) havebeen the dominant mode of EEG analysis and have yielded a lot ofinsight into similarities and differences between meditation techniques(Cahn & Polich, 2006; Lomas et al., 2015). In our study too, powerspectral changes could help distinguish between different meditationstates as well as show some differences between groups. However, non-linear methods provide additional insight by examining different as-pects such as complex information processing or fractal geometry ordimensional complexity. These methods have found applications inwidely varying domains such as patterns in seismic activity, stockmarket fluctuations, heart rate changes and EEG analysis (Aftanas &Golocheikine, 2002, 1998; Gao et al., 2016; Lutzenberger, Elbert,Birbaumer, Ray, & Schupp, 1992). In our study, we found that em-ploying two different non-linear methods (fractal dimensions and per-mutation entropy) provided different insights and so we suggest thatfuture EEG meditation studies could also benefit from using complexitymeasures. We now discuss the EEG profiles of the three groups understudy.

Firstly, all the groups were able to make shift from rest to a medi-tation state as evidenced by global power increases in each meditationcondition as compared to rest and these increases were different for thevarious conditions and the three groups. Several meditation studieshave found changes in these different power bands (see (Lomas, Ivtzan,& Fu, 2015) for a review) but most studies have focused on one or twobands. The most common reports have been for changes in theta-alphaand gamma bands and our study provides further evidence in that di-rection. The robust global enhancements across all these power bandsshow that these meditation techniques induce distinct states of con-sciousness that are vividly different from different stages of sleep, restand relaxed wakefulness or active task performance. Further, these

states don’t show a monotonic increase in power with passage of time.Instead, there are differences in how the power changes across thevarious conditions and for the different groups showing an interactionbetween technique and proficiency levels. Additionally, the powerchanges were also observed in Nov group (although not at the samelevel as the long-term groups), indicating that this group was also ex-perienced enough in each meditation technique. While there is a stronglikelihood of spillover effects of each meditation condition on the next,there were different spectral and complexity profiles in the differentmeditation conditions that support the notion that these techniqueshave clear distinctions in terms of neural processing.

Tea and Sen (vs Nov) showed enhanced low-alpha power in allmeditative states (Ana, Vipa and Metta). Cortical alpha activity is in-hibitory and routes information by functionally blocking off the task-irrelevant pathways (Brefczynski-lewis, Lutz, Schaefer, Levinson, &Davidson, 2007; Jensen & Mazaheri, 2010; Klimesch, Sauseng, &Hanslmayr, 2007). Thus, enhanced low-alpha power during meditationsupports the state of sustained attention on the selected object byblocking off irrelevant information. Ana and Vipa are cognitively in-tense meditative states (Gross & Thompson, 2007) as they have a spe-cific attentional component whereas Metta has both a cognitive andaffective component as it is emotionally intense (Goenka, 1987) and theenhanced high-theta changes only in this condition supports its affec-tive role (Aftanas & Golocheikine, 2001). While it is possible that thedifferences in theta power could be attributed to differences in durationof the practice (Ana was 3min, Vipa was 40min and Metta was 6min)or that it is because Metta was towards the end of the practice, it isunlikely to be so. As is evident from Fig. 4, Tea had a very differentprofile of power spectral changes in high-theta and low-alpha power ascompared to the other two groups suggesting proficiency linked dif-ferences rather than differences in duration of the technique being

Fig. 6. Changes in Higuchi Fractal Dimensions andPermutation Entropy during Vipassana meditation.Average topography plot using z-score normalizedvalues for each subject. Row and column labelled‘Diff’ show statistically significant differences acrossconditions – red color indicates areas with significantchanges (p < 0.05 using permutation based twoway ANOVA with 2000 random partitions, FDRcorrected). R1C=Pre-Rest with eyes closed.Vipa=Vipassana meditation. Nov=Novice medi-tators. Sen=Senior meditators. Tea=Teachers ofmeditation. Panel A: Changes in Higuchi FractalDimensions (HFD). Panel B: Changes in PermutationEntropy (PE). (For interpretation of the references tocolour in this figure legend, the reader is referred tothe web version of this article.)

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practiced. Nov had enhanced delta and low-gamma power as comparedto the long term groups. It has been suggested that increased delta isassociated with enhanced internal processing (Lomas et al., 2015) andincreased low-gamma with enhanced active state (Cahn, Delorme,Polich, Diego, & Jolla, 2010). It is thus plausible that Nov had a moreinternally engaged effortful state of consciousness as compared to thelong term groups.

The enhanced theta-alpha power and reduced low-gamma power atrest in long term meditators indicate the trait differences that are likelydue to neuroplastic changes although as mentioned in the limitationsbelow, we cannot rule out the possibility that the differences in low-gamma power could be due to muscle artifacts. Several studies havedocumented a variety of structural and functional changes in the brainfollowing long term practice of mindfulness meditation (Tang, Holzel, &Posner, 2015). It has been suggested that Nov might employ ‘top-down’emotion regulation strategies whereas experts might use a ‘bottom-up’strategy as a consequence of long-term practice (Chiesa, Serretti, &Christian, 2013). Since normal controls have self-referential processing

by default (Gusnard, Akbudak, Shulman, & Raichle, 2001), these traitchanges could suggest decreased self-referential processing and en-hanced objective stance towards oneself and others. It has been sug-gested that anterior theta and alpha in Sahaja yoga meditators couldreflect positive emotions and internalized attention (Aftanas &Golocheikine, 2001). In the present study, the long-term groups showedenhanced central and posterior theta-alpha power across the threedifferent meditation techniques (Ana, Vipa and Metta). While Sahajayoga has strong focus on internalized attention and bliss, the threetechniques in the Vipassana meditation module in our study (Goenkatradition) focus on non-judgmental observation or awareness whilepaying attention (as a starting point) on breath (Ana), being mindful ofbodily sensations (Vipa) or radiating goodwill (Metta). It is noteworthythat there are other traditions of Vipassana meditation where the focusis open awareness without focus on any specific object. Thus, the dif-fering results between Aftanas et al. and our study are likely to be dueto differences in the meditation technique being studied. While positiveresults from studies on Vipassana meditators have usually been attrib-uted to mindfulness alone, it is important to reiterate that the Vipassanameditative program has several distinct components including atten-tion, mindfulness and loving-kindness (Ivanovski & Malhi, 2007).

We used two different complexity measures in our study. Neuronalassemblies inside the brain are coupled at varying levels and they os-cillate at varying frequencies based on the task and nature of functionalcoupling. The inherent nature and interactions of the dynamical systemis the source of all the complex patterns and behaviors that emerge fromit. Multiple systems doing totally different information processing canproduce similar patterns and same system can generate a plethora ofpatterns by a small change in the initial conditions (Wolfram, 1983).

Fig. 7. Changes in Permutation Entropy during Anapana and Metta meditation. Averagetopography plot using z-score normalized values for each subject. Row and column la-belled ‘Diff’ show statistically significant differences across conditions – red color in-dicates areas with significant changes (p < 0.05 using permutation based two wayANOVA with 2000 random partitions, FDR corrected). R1C=Pre-Rest with eyes closed.Ana=Anapana meditation. PE: Permutation Entropy. Nov=Novice meditators.Sen= Senior meditators. Tea=Teachers of meditation. Panel A: Changes in PE duringAnapana meditation. Panel B: Changes in PE during Metta meditation. (For interpretationof the references to colour in this figure legend, the reader is referred to the web versionof this article.)

Fig. 8. HFD and PE values for all rest and meditation states. Median HFD and PE valuesfor each group. R1C=Pre-Rest with eyes closed. Ana=Anapana. Vipa=Vipassana.R2C=Post-Rest with eyes closed. HFD: Higuchi Fractal Dimensions. PE: PermutationEntropy. Nov=Novice meditators. Sen= Senior meditators. Tea=Teachers of medita-tion. Panel A: Median HFD values across rest and meditation states. Panel B: Median PEvalues across rest and meditation states.

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Each nonlinear parameter reveals one aspect of the dynamical system.Permutation entropy represents the Shannon information of the dis-tribution of order patterns. It gives a measure of how ordered andpredictable the system is from the time series data. Fractal dimensionsrepresent the geometrical pattern in a time series at multiple scales.

Both the complexity measures clearly discriminated between noviceand long term meditators. Nov had significantly higher informationprocessing as reflected by PE measures. Novice meditators are in a moreeffortful stage as they make efforts to reach and maintain the medita-tive states while the long term groups are in a relatively effortless stateof meditation due to the trait benefits of long term practice (Tang,Rothbart, & Posner, 2012). While both Sen and Tea were similar andclearly proficient in practice (there were no significant differences be-tween these groups), only Tea showed complexity increases across themeditation states while Sen did not. The correlational summary (Fig. 9)indicated that there was an inverse relationship between duration ofmeditation experience and complexity changes. Specifically, highcomplexity was positively correlated with delta, beta and low-gammaand negatively correlated with theta-alpha and conversely, long termexperience was negatively correlated with delta, beta and low-gammaand positively correlated with theta-alpha. However, there were somepower spectral differences (Supplementary material Table 1) that wereonly between Nov and Tea (such as high-theta during Metta, high-betaduring R1C, low-gamma during R1O and R2O) and some other differ-ences that were only between Nov and Sen (such as high-beta duringAna) that show that there were minor differences between Sen and Teathat did not reach statistical significance. Qualitatively too (Fig. 4) Senand Tea showed some differences. Overall, these suggest that both thelong term groups had considerably experience and thus were similar intheir overall profile, but there were differences that can be attributed tothe conscious attention to detail and accuracy in practice due to Tea’srole in teaching meditation. On the other hand, Nov and Tea were verydifferent from each other but showed similar increased informationprocessing (unlike Sen) during the various meditation conditions. This‘U’ shaped relationship between information processing and proficiencylevels is not unprecedented as it was found that there was an inverted‘U’ shaped relationship between regional brain activation during sus-tained attention and meditation experience (Brefczynski-lewis et al.,2007). We found that all the groups had highest global average HFDand PE values during Metta which relates with others and has an af-fective component that distinguishes it from Ana and Vipa conditionsthat have a more internally focused attention component. Remarkably,the complexity measures between post-rest and pre-rest were not dif-ferent for any group even though the power spectra showed a hugeinfluence of the intervening hour long meditation state. This demon-strates that the meditators were actually at rest and not meditating (asinstructed) during post-rest but that the functional changes due to theearlier meditative state continued to exert an influence on the overall

brain state.While the study makes several contributions to the literature as

discussed above, there are several limitations that need to be taken intoaccount. Firstly, we distinguished the three groups in terms of durationand proficiency based on their years of experience (Nov vs others) andformal roles (Tea vs Sen). It would be valuable to formally examineproficiency using a questionnaire that is designed to consider the tra-ditional theoretical framework of these practitioners. Such studies arelacking in the literature. While the EEG profiles in our study supportedthe categorization into three groups, having a formal proficiency scorewould help correlate these values. Secondly, we used scalp topo-graphical differences with 129 electrodes while examining powerspectral changes. In our view, this is better than averaging a few re-gional electrodes and suggesting frontal or parietal activation etc.However, it would be useful to carry out source localization analysis(Canuet et al., 2011) and examine activation in different brain regionsduring the various conditions. Such an approach can also be achievedusing fMRI studies (Fox et al., 2016) but EEG approaches are less in-trusive and better suited for mimicking a traditional meditative prac-tice. Thirdly, the gamma band has prominently featured in manymeditation studies (Cahn, Delorme, & Polich, 2010; Fell, Axmacher, &Haupt, 2010), but we had noise limitations due to which we had tofilter out this band to a large extent. We could only focus on the low-gamma band in this study and which yielded some valuable informa-tion but we cannot rule out confounds due to muscle artifacts especiallyfor Nov. It might be possible to use different artifact removal ap-proaches that still allow examination of changes across the full extent ofthe gamma band. Fourthly, since our study employed a fixed order anddiffering durations of meditation techniques to closely follow the tra-ditional practice, order effects are to be expected. This does not allow usto interpret our findings related to Vipassana and Metta portions asbeing specific to these states. Also, the rest eyes open and closed stateswere of one minute duration each and were combined to get twominute epochs that were used for comparison with two minute epochsduring the various meditative states. A possible confound is that EEGpower in the low frequency bands increase with eyes closed state. Thishowever, did not seem to be the case in our study as we observed powerincreases from pre-rest state to the meditative states, differing powerchanges across the three meditative states and finally power decreases(as compared to meditative states) in the post-rest state (Fig. 4 andSupplementary material Fig. 5). Future studies could consider havingthe participants to undergo a control protocol (on a separate day) of thesame overall duration but with instructions for suitable thought en-gagement. Fifthly, we used global averages of the two different com-plexity measures when we provided results of the median differencesfor HFD and PE between the groups. The scalp topographies of thecomplexity measures indicate regional differences which would havebeen lost when using global averages. Finally, since this was a cross-

Fig. 9. Correlation values for all rest and meditation conditions with meditation experience and complexity measures. Median power spectral values across all electrodes for each powerband and each condition were used for calculating the correlations. Significant negative correlations are shown in blue and to the left of the dashed lines whereas positive correlations areshown in red to the right of the dashed lines. R1C=Pre-Rest with eyes closed. Ana=Anapana. Vipa1 to Vipa4=Vipassana for the 4 time points. R2C=Post-Rest with eyes closed. TheGreek alphabets from left to right represent the frequency bands delta (1–4Hz), low-theta (4–6 Hz), high-theta (6–8 Hz), low-alpha (8–10 Hz), high-alpha (10–12 Hz), low-beta(12–15Hz), high-beta(15–30Hz) and low-gamma(30–40 Hz). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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sectional study, we cannot attribute all the observed group differencesto the duration and proficiency of meditation practice or even rule outthe possibility that there may be other pre-disposing factors that leadpractitioners to engage in intensive meditation practice and therebybecome more proficient. Nevertheless, these limitations do not detractmuch from our overall finding that it is possible to use EEG profilesusing linear and non-linear methods to dissociate between extent ofmeditation experience and proficiency of practice.

5. Conclusion

Our study demonstrates the value of studying meditation within thetraditional context of the practitioners. The study shows that bothduration of practice and role based proficiency of practice influence thebrain states that can be seen in the EEG profiles. Finally, our studydemonstrates the value of using both linear and non-linear methodsthat can complement and supplement the results to provide a fullerunderstanding of brain changes during meditation. We suggest thatfuture EEG studies on meditators could incorporate these considera-tions to examine the brain mechanisms underlying these practices inthe quest for using such practices for therapeutic or performance en-hancement benefits.

Acknowledgements

This study has been funded by Cognitive Science Research Initiative,Department of Science & Technology (DST-CSI), Government of India,New Delhi (Grant: SR/CSI/63/2011 to B.M.K). The study was furthersupported (for data analysis and manuscript writing) by Science andTechnology of Yoga and Meditation, Department of Science &Technology (DST-SATYAM), Government of India, New Delhi (Grant:SR/SATYAM/18/2015 to B.M.K). We are grateful to VRI (VipassanaResearch Institute, Global Pagoda, Mumbai, India) for giving us per-mission and logistical support for recruitment of meditators; andNIMHANS for providing facilities support. We thank the two anon-ymous reviewers for their valuable inputs. Finally, we thank theVipassana meditators who were generous with their time and sincereparticipation.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in theonline version, at https://doi.org/10.1016/j.biopsycho.2018.03.004.

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