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Vipassana Meditation and Brain

Oscillatory Entropy

Milena Engel

11097477

Supervisor: MA MSc. Evert Boonstra, Dr. Ruben Laukkonen July 27th,2020

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Table of Contents

ABSTRACT... 3 ACKNOWLEDGEMENTS ... 3 INTRODUCTION ... 4 BACKGROUND ... 4

THE ENTROPIC BRAIN THEORY ... 5

METHOD ... 7

DATA ANALYSIS ... 8

RESULTS ... 9

DISCUSSION AND LIMITATIONS ... 9

REFERENCES ... 11

Table of Figures

FIGURE 1 ... 7

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Abstract

According to existing literature, Vipassana meditation can change the entropic levels of brain oscillations. In this paper, we aimed to study how a meditations retreat affects entropy in EEG oscillations during a resting state and a meditative state. We aimed to replicate pre-existing studies based on the entropic brain theory that show that meditation increases entropy. We used pre-existing EEG data of 21 participants collected before and after a meditation retreat and calculated multiscale sample entropy, multiscale permutation entropy and the Higuchi Fractal Dimension of the following four conditions 1) Resting state pre-retreat (RPR) 2) meditation state pre-retreat (MPR) 3) resting state post-retreat (RP) 4) meditation state post-retreat (MP). We performed a repeated measures ANOVA to calculate differences between conditions. Contrary to our hypotheses, we found no significant effects between conditions.

Keywords: entropy, meditation, brain oscillations, resting state, meditation retreat, multiscale sample entropy, multiscale permutation entropy, Higuchi Fractal Dimension

Acknowledgements

I would like to especially thank Evert Boonstra and Ruben Laukkonen for their excellent guidance, encouragement, constant willingness to listen and discuss my ideas and in general for contributing to my understanding of this subject.

I would also like to thank all members of my internship and my friend Moritz Menzel for spending hours theorizing with me about meditation and entropy and for being a constant source of inspiration. This assignment could not have been completed without their support.

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Introduction

Background

History of Vipassana Meditation

The term "Vipassana" comes from Pali, the ancient language of Theravada literature and can be loosely translated to "insight" meditation, whereby Vi means "into" or "through" and "Passana" means perceiving and seeing (Braun, 2014). Within scientific literature, meditation is often operationalized as a set of cognitive processes and techniques to modulate attention, perception, emotions and

breathing. Prior literature on Buddhist meditation has extracted secular derivatives of most mediation techniques in the form of Focused Attention (FA) (concentrative meditation) and Open Monitoring (OM) (mindfulness meditation) (Lutz et al, 2008). FA meditation encourages a voluntary tuning or deployment of attention on an object and OM meditation entails a disengagement of attention or non-reactivity to monitor the momentary high-level or low-level patterns of sensory experiences (Lutz et al, 2008). Vipassana meditation specifically incorporates both FA and OM. The idea of Vipassana meditation is to deconstruct ordinary conceptions of "reality" and transformqualities and reactive emotions associated to one’s self by means of a rigorous mental training and focused analysis of the mind and the body. Ultimately, the aim is to recognize their true nature, as being impersonal mental and physical features (Dhammas) devoid of a self-identification (Krishnamurthy, 2017).

The default mode network

According to previous research on several types of Buddhist meditation, the interplay between attention and executive control is a key ingredient for self-related processing (Marzetti et al., 2014). In recent years, an abundance of research has consistently shown that expert meditators can control and access different layers of cognitive processes by self-inducing changes in brain oscillations and altering the resting state network (RSN), also known as the default mode network (DMN). (Lutz et al, 2008; Dor Zidermann et al., 2013). The DMN is a well-defined network that reflects how the brain acts during rest and self-referential free and voluntary thought about the past, present or future (Whitfield-Gabrieli & Ford, 2012). Anatomically, the core regions of the DMN include the medial prefrontal cortex (MPFC), posterior cingulate/restrospenial cortex (PCC/RSp), left and right inferior parietal lobules (IPLs), including the hippocampus as well as the medial temporal lobe (MTL) (Whitfield-Gabrieli & Ford, 2012). In healthy subjects, these regions typically show greater activation during rest and are suppressed during goal-directed and attention-demanding tasks (Whitfield-Gabrieli & Ford, 2012). In

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5 contrast to healthy subjects, however, Vipassana meditation has been found to increase the

suppression of the activity in the DMN, in the prefrontal cortex, a region particularly associated with mind wandering (Bauer et al., 2019). In addition, while activity in the DMN tends to decrease during meditation, connectivity between the DMN and the central executive network (CEN) tends to increase (Bauer et al., 2019).

The resting and meditative brain

While meditation can modulate the activity in specific brain regions associated with the DMN, meditation practice can also affect the neural oscillation patterns in these regions. For example, a more recent study by Lardonne et al. (2018) investigated the long-term effects of Vipassana meditation and found that compared to controls meditators showed a higher degree of theta band connectivity in the right hippocampal region of the brain. In fact, research has shown that generally during FA and OM meditation, recordings tend to elicit an increase in anterior theta oscillations (Cahn et al.,2010; Lee et al., 2018). However, only FA meditation tends to show changes in posterior theta activity (Lee et al., 2018). In addition, several studies, where the meditative state was compared to the resting state of expert meditators, showed a decrease in frontal delta (1–4 Hz) power during

meditation, as well as significant increase in parietal-occipital gamma (35–45 Hz) power (Cahn et al.,2010; Braboszcz et al., 2017;Lee et al., 2018). In addition, a systematic review of brain oscillations underlying meditation found that while FA and OM are related to alpha activity increases in posterior brain regions, only FA shows a bilateral increase in alpha power in anterior regions (Lee et al., 2018). OM, in contrast, tends to show a decrease in alpha activity on the anterior left hemisphere of the brain (Lee et al., 2018). Indeed, Vipassana meditation (here both FA and OM are practiced) has been shown to increase gamma and alpha activity in posterior as well as anterior regions (Braboszcz et al., 2017; Lee et al., 2018). Lastly, while Cahn et al., (2010) found a decrease in frontal delta (1–4 Hz) during meditation there is little evidence that both delta as well as beta oscillations decrease in long-term Vipassana meditators (Cahn et al, 2010; Lee et al., 2018).

The Entropic Brain Theory

From recent research on consciousness and psychedelic drugs, Robin Cahart Harris and colleagues developed the novel entropic brain theory (Carhart-Harris, 2018). The theory states that the subjective quality, or qualia, of an experience, can be indexed by the magnitude of entropy measured from spontaneous brain activity using EEG or MEG (Carhart-Harris, 2018). Importantly, the theory is heavily

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6 influenced by neuroimaging studies on psilocybin and LSD and has only recently been applied to meditation (Vivot et al. 2020; Milliere et al., 2018). The main aim of the entropic brain theory is to provide a theoretical framework based on entropy to explain the underlying mechanism of altered state of consciousness (Carhart-Harris, 2018). In this endeavour and in line with Karl Friston’s free energy principle, Carhart-Harris argues that increased entropy is synonymous with increased

uncertainty and in an information theoretical sense takes the approach that increased entropy means increased complexity and vice versa (Carhart-Harris, 2018). EBT holds that psychedelic drugs tune the brain towards criticality (a zone where a complex dynamical system is in a shifting state between complete order and disorder) (Carhart-Harris et al., 2014). Systems in a state of criticality have three signature characteristics: a) they have a maximal repertoire of dynamic substates and are at a maximum of metastable states b) they are maximally sensitive to both intrinsic end extrinsic

perturbation, c) they are sensitive to cascade-like processes that will change or shift the whole system (Carhart-Harris et al., 2014). A key concept within EBT is that quantity of entropy can reliably

distinguish between different brain states, and that there are upper and lower limits in the entropy index in which consciousness can be lost (Carhart-Harris, 2018). One mechanistic explanation is that through self-organized activity in the DMN network (and associated entropy/uncertainty/disorder minimization) a coherent sense of self and ego emerges, which psychedelic drugs and meditation supress (Carhart-Harris, 2018). Hereby, EBT takes the stance that the mind is a quintessential part of physical information which can be decoded. Empirically, several studies have shown that entropy increases during content-rich psychedelic experiences (Schartner et al., 2017). There is also evidence that entropy is reliably lowered in reduced states of consciousness (Schartner et al., 2015; Chen-Chih et al., 2016). In fact, Shartner and colleagues used the Lempel Ziv complexity exponent to show that an anesthetized state is correlated with decreased entropy (Schartner et al., 2015). While there are arguably several differences between a meditative and a psychedelically induced altered state of consciousness, one similarity is often thought to lie in the mode of induction of the altered quality of conscious experience and “ego-dissolution” (Milliere et al., 2018). In fact, Milliere et al. (2018) argue that “ego dissolution may be a non-linear phenomenon that only occurs after a critical inflection point has been reached“ (p .2) Hence, a recent study by Vivot et al. (2020) investigated whether the

quantity of sample entropy of spontaneous brain oscillations is positively correlated with meditation and found that indeed specifically vipassana meditation increases the entropy of gamma and alpha oscillations compared to controls (Vivot et al, 2020). However, an earlier paper by Vyšata et al. ( 2014) found the exact opposite when looking and permutation entropy (Vyšata et al., 2014). Hence, the relationship between meditation, entropy and complexity still warrants further investigation.

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7 In the current study we aim to further investigate the relationship between Vipassana meditation, entropy and complexity. Specifically, we will study how a meditation retreat affects entropy in EEG oscillations during a resting-state and meditation state. We also aim to replicate previous work by comparing the resting state with the meditation state. To understand the influence that meditation has on entropy and complexity, we will use the framework provided by the entropic brain theory to evaluate and assess our findings, which will be guided by the following research questions:

1. To what extent does complexity as measured by the HFD differ between pre and post retreat for resting state and meditating participants?

2. To what extent does entropy as measured by multi scale sample entropy and multi scale permutation entropy differ between pre and post retreat for resting and meditation states?

3. Do differences in entropy increase between resting state and meditation increase after a meditation retreat?

Method

Sample

We will use existing EEG data previously collected by the Sapienza University of Rome. 21

participants, male and female, between the ages 28 and 57 were recruited to participate in the study. Participants meditation experience varied between 10 to 1000 hours of experience.

Figure 1

Experimental Design

Note. Data was collected from a simple pre-/post

retreat resting state/meditation task. The pre/post retreat resting state was 3 (eyes open) + 3 (eyes closed) minutes long followed by a 4 min meditation session.

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Procedure

A 1-64-channel Cognionics EEG wet-system was used to collect the EEG data. Electrodes were placed according to the easycap- M11 system (www.easycap.de).EEG resting state data was recorded before a 6-day retreat followed by a 4-minute meditation session. EEG resting state data was also recorded after the 6-day retreat followed by a 4-minute meditation session (see Figure1).

Pre-processing

All acquired data was preprocessed with a custom written script with MATLAB software (Mathworks) and based primarily on the EEGLAB toolbox. Since the meditation was eyes-closed, we only used the eyes-closed resting sate data for our analysis. The same preprocessing criteria was applied for all subjects and conditions.

Data was re-referenced to the average of all electrodes. Prior to cleaning the data, we excluded the accelerometer outputs (electrodes 65-69) which track head motion and were unnecessary for our analysis. A high-pass filter at 0.1 Hz and low pass filter at 40 Hz was applied using a bandpass FIR Filter and EEG data artifacts were removed using the EEG plugin "clean_rawdata" (see:

https://github.com/sccn/clean_rawdata). We used a threshold of 20 standard deviations to detect artifacts in the EEG. The bad channels that were removed were interpolated, and the cleaned data was down sampled to 250 Hz and re-referenced to average. For the analysis we selected the middle 1-min portions of all datasets.

After preprocessing we calculated the time frequencies by applying a Fourier transform from MATLAB toolboxes. We extracted the delta (1-4Hz), theta (4-7 Hz), alpha (8-12Hz) and beta(12.5-30Hz), delta bands of each channel for each participant and applied a Higuchi Fractal Dimension (HFD)

measurement, multiscale sample entropy (MSE) and a multiscale permutation entropy (MPE) measurement to the frequencies. These were taken from the MATLAB file exchange.

Data Analysis

For data analysis, the software JASP (JASP Team, 2020) was used. Entropy and complexity scores in the alpha, beta, delta and theta range were compared across all conditions. Importantly, we averaged the entropy measures in each bandwidth across all channels, as we were not interested in specific brain regions. We performed a repeated measure two-way within subject ANOVA with four factors: 1) Resting state pre-retreat (RPR) 2) meditation state pre-retreat (MPR) 3) resting state post-retreat

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9 (RP) 4) meditation state post-retreat (MP). To check for sphericity, we used a Mauchly’s test and we applied the Greenhouse Geisser correction if the condition of sphericity was not met.

Results

To test whether there was a difference in MSE levels before and after the meditation retreat in both the meditation and resting state conditions, we performed a repeated measures ANOVA. Contrary to our expectations MSE levels did not differ significantly between conditions in the delta (F(df)=.781, p=.516), theta (F(df)=1.056, p=.386) , alpha (F(df) = 1.443, p = .255) and beta (F(df)=0.657, p=582) bands. Similar, the results for the HFD revealed no significant differences across conditions in the delta (F(df)=2.219, p=.112), theta(F(df)=1.556, p=.226), alpha ((F(df)=0.971, p=.423) and beta ((F(df)= 0.477, p=.699) frequency ranges. Lastly, we also found no significant effects for the MPE measure in any of the frequency bands delta ((F(df)=1.073, p=.379), theta ((F(df)=0.631, p=.598), alpha

((F(df)=2.589, p=.076) and beta (F(df)=1.88, p=0.159).

Discussion and Limitations

Contrary to our expectations, the present study shows that the MSE, HFD and MPE does not

significantly change between conditions in the delta, theta, alpha and beta bands. There are number of practical and theoretical limitations which may explain these results.

One of the main limitations of the study is that people varied a lot in their meditation experience, between 10 to 1000 hours. In fact, most of the participants were not expert meditators. Recent studies on this topic suggest that in order to self-induce changes in brain states and approach criticality, subjects must be very practiced and controlled. Hence, our results offer good insight into how meditation affects the entropy levels in ordinary, hobby and less practiced meditators, which are arguably most people. An interesting aspect for future studies would be to look at optimum levels, or at which level of expertise (hours of practice) meditation practitioners can voluntarily change their entropic brain levels.

Another major point of discussion is that after preprocessing we aimed to obtain 2 minutes worth of clean data per dataset. Since, however most datasets did not have clean data lengths of 2 minutes, we settled for 1-minute long data lengths to increase our power. Previous studies who have found

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10 al., 2020). A potential solution to improve our results could have been to epoch our data into several small epochs and compare these epochs with one another. In our case, we did not do this, because our data was already very short, and we thought that dividing our data into further very small segments would change the inherent patterns of the data and manipulate our end results. While previous studies have applied short epochs to their data, we aimed at replicating studies who have not. And interesting addition to entropy research would be to investigate the implications of epoching on overall entropy levels. In general, however, we suggest that studies aim for cleaner data lengths.

In addition, while we used MSE and MPE scripts, we only looked at scale timescale 1 for both entropy measures. Future studies should investigate multiple scales to shine light on potential entropy changes between different time scales. We also used available entropy scripts form MATLAB. In order to understand what the code or scripts are doing with the data, a profound basis in mathematics and software coding is important. Thus, it was difficult for the authors of this study to detect whether the scripts were in fact mathematically correct and measuring what was wanted.

On a theoretical level, however, there are also inherent issues with the term “entropy” which

becomes clearer when moving from physics to information theory. The term entropy was first coined by Rudolf Clausius in 1865 to describe a force in a closed system that irreversibly changes hat in thermodynamics and whereby a small account of heat is always lost. Our paper, however, focused on a different concept of entropy. We used Shannon’s concept of entropy which is based on Boltzmann’s entropy and represents the uncertainty or randomness of a signal and its precise distribution. Like in physics, entropy in an informational theoretical sense, is the force that brings about chaos, uncertainty and randomness. Therefore, to know that a cognitive system has entropy, studies often measure chaos, disorder, variance, randomness or uncertainty or neuronal signals. Importantly, however, chaos and entropy should not be confused under the disguise of synonymy. They are not the same thing. Chaos, variance or uncertainty can be measured in various mathematical ways and depend on the topic of interest. If entropy is the umbrella term for chaos, variance, uncertainty and disorder of a system, it becomes difficult to compare results between studies. Future studies in this field should be clearer with the terminology and measurements they use and emphasize that what they are really measuring is chaos, variance, uncertainty or disorder under the assumption that their results say something about entropy..

In addition to studying the effects of meditation on entropy, we were also interested in studying the relationship between entropy and complexity. We used MSE and MPE to study entropy and the HFD to study complexity. Mathematically, however, the HFD is very similar to the entropy measures used in

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11 this study. In short, HFD (D= log N/log M) measures the copies (or fractals) in a system that scale with the decrease of the size of a microstate and calculates the nonlinearity and the density or detail of the self-similarity of a system. Here, self-similarity refers to the statistical properties of the individual parts of a system which are like the properties of the system at large. While sample entropy and the permutation entropy do not measure the extension of a system in each direction, they also focus on the amount of self-similarity in a system. Hence, if self-similarity increases with sample entropy or permutation entropy, it is likely that self-similarity increases with the HFD or vice versa. It seems that while sample entropy and permutation entropy are grounded in information theory, the Higuchi fractal dimension is more often applied in mathematics to calculate complex systems. Essentially, they are based on similar principles, but stem from different backgrounds. Hence, with these calculations, complexity and entropy tend to mean the same thing: self-similarity or variance. Mathematically, however, the exact relationship between complexity and entropy is still not understood and future studies in this field should keep the inherent correlation in mind when investigating a potential correlation between these measurements.

While our research did not support our hypotheses, it did however provide new grounds in

understanding the potential skill or practice necessary to voluntarily access different brain states and change the entropy levels in brain oscillations. Future entropy projects should keep our limitations in mind to ensure better quality of research and results.

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