The Cognitive Mechanisms of expert Jhana Meditation
Creators: name: Mago, Jonas
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Life Sciences
subject: Neuroscience and Neurobiology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: 1.Title: The Cognitive Mechanisms of expert Jhana Meditation 2. Authors: Jonas Mago, Mark Miller, Ruben Laukkonen, Heleen Slagter, Michael Lifshitz. For further information, please contact Jonas Mago: Jonas.h.mago@gmail.com. 3. Description Background &amp; Rationale: This study is designed to examine how Jhana meditation as opposed to mindfulness of breath-ing meditation, alters cognition, neurophysiology, biomarkers, and experience within expert meditators. We will ask five distinct questions. Research Question 1: Predictive processing, meditation, and the mismatch negativity re-sponse A central computational aspect of perception is that it is realized via Bayesian inference, a pro-cess by which expectations or priors about a specific percept powerfully shape how it is per-ceived (Parr, Pezzulo, and Friston 2022). Meditation research has shown that the impact of these priors on perception can be altered through different types of meditation (Laukkonen and Slag-ter 2021; Sandved-Smith et al. 2020). In this study, we want to test how Jhana meditation changes this Bayesian inference using a hierarchical auditory oddball paradigm. We will use an adaptation of the paradigm used by Straus et al. (Strauss et al. 2015a) during their sleep study. We want to use their hierarchical oddball task to test if Jhana meditation affects mismatch nega-tivity responses (ERPs) of global violations in sounds differently in Jhana meditation than mind-fulness of breath meditation. Hypothesis: We hypothesize that Jhana meditation will lead to a reduced global MMN response compared to mindfulness of breathing meditation. During deep Jhana meditation, many medita-tors experience a fading of perception. Some even report that no sounds are consciously expe-rienced during this specific type of meditation. Following this experience, we expect that the auditory stimuli of this task are less processed by the brain than in the mindfulness of breathing condition. As a consequence, we expect the MMN response to be smaller. Research Questions 2: Physiological markers of Jhana As an exploratory aspect of this study, we will compare the physiological and neuro-physiological markers of the first four Jhanas to mindfulness of breathing meditation. We will compare these states in regards to the following metrics: Brain entropy and Lempel-Ziv of the EEG signal as a measure of flexibility, randomness, and malleability of brain states. Scale-freeness of the EEG signal as a measure for local connectivity in comparison to global connectivity And a range of physiological measures, including breathing (measured using a resistance belt), skin conductance, heart rate variability, and ECG. Hypothesis: This is an exploratory research question; we do not have a specific hypothesis. Research Questions 3: Nimitta &amp; light perception In preparation for Jhana meditation, many practitioners use a so-called nimitta as an object of concentration. A nimitta is often experienced as a bright light of varying shapes that appears in the visual field while the eyes of the meditators are closed. With this study, we will explore if the phenomenology of this nimitta relies on neurophysiological activity similar to those that are involved in the actual perception of light. Therefore, we will record EEG data while participants sit in front of a light source that repeatedly increases and decreases in brightness. In addition, we will record EEG data while participants use a nimitta as an object of concentration. During these recordings, we ask participants to press a button whenever they see the nimitta appear and stabilize. We will then train a classifier on the first dataset (subjects in front of a light) and test if this classifier can accurately predict the appearance of a nimitta in the second dataset where participants create this light experience internally. Hypothesis: We hypothesize that a decoder trained on the EEG signal of the light condition (par-ticipants looking at a light bulb) will be able to predict the timepoints of when a Nimitta is expe-rienced during the meditation condition above chance. Research Question 4: Sensory Plasticity We will test if a 10-day intensive Jhana meditation retreat alters sensory plasticity. Therefore we will measure sensory plasticity on the first day of the retreat and the final day of the retreat and do a within-subject comparison. As measures of sensory plasticity, we will use the Roving Mis-match Negativity (MMN) and the Visual LTP paradigm. These two tasks measure sensory plastic-ity by assuming two different models of perceptual learning, namely, Predictive Coding (MMN) and Hebbian plasticity (LTP) (Spriggs et al. 2018). Previous studies have found that these tasks yield results consistent with Predictive Coding and Hebbian predictions. In particular, dynamic Causal Modelling revealed that the generation of the MMN modulates forward and backward connections in the underlying network, while visual LTP only modulates forward connections. Previous studies have found that both these types of sensory plasticity are enhanced during altered states such as the psychedelic experience (Sumner, McMillan, Spriggs, Campbell, Mal-pas, Maxwell, Deng, Hay, Ponton, Kirk, et al. 2020; Sumner, McMillan, Spriggs, Campbell, Mal-pas, Maxwell, Deng, Hay, Ponton, Sundram, et al. 2020; Timmermann et al. 2018). Here, we will explore if sensory plasticity as measured by these two tasks is enhanced by 10-days of intensive Jhana meditation. Hypothesis: We hypothesize that the measures of plasticity will be higher in both tasks on the final day of the retreat compared to the first day of the retreat. Note: Please note however that we expect to be underpowered for this analysis. The studies cited above have had 20, 30, and 44 participants compared to 10 participants in our study. Fur-thermore, we expect that the effect induced by meditation is smaller than when induced by drugs as in the case of psychedelics. However, we think this will be an important and valuable study as it gives us a good effect size estimate for further studies of sensory plasticity induced by meditation. Research Question 5: Semantic distance task We will also use the semantic distance task to measure the creativity of meditators following each meditation session. We wish to test if creativity, as measured by this task, is differently affected by mindfulness of breathing and Jhana meditation. We use the semantic distance task proposed by Olson at al. (Olson et al. 2021). For this task, participants are asked to name ten unrelated words. We will then analyze the average semantic distance of these words to derive a creativity score. Hypothesis: We hypothesize higher semantic distance scores after Jhana meditation than mind-fulness of breathing meditation. Design Plan 4. Study Type: Experiment 5. Blinding: Participants and experimenters will know which condition they are in. However, during the analysis phase, researchers will be blind as to which condition and group the data is from. 6. Additional blinding: None. 7. Study Design: For research questions one and two, our study will have 1 factor: conditions. The conditions are a within-subjects factor with two main levels: mindfulness of breathing and Jhana meditation. The latter factor (Jhana meditation) is further broken down into 4 factors, representing the four discrete Jhana states. For research question three, we have a one factor design with the conditions being two within-subject factors: (1) looking at a lightbulb and (2) meditating on the Nimitta experience. For research question four, we have a one factor design with the conditions being two within-subject factors: (1) the first day of and (2) the last day of the retreat. 8. Randomization: During the experiment, every subject will be tested in two different condi-tions (mindfulness of breathing and Jhana meditation) during multiple days of the retreat. The order of these conditions will switch each day and the order of the first day is randomized. Sampling Plan 9. Existing data: No data have been collected to date, only a few pilot measurements that will not be included in the final analysis. 10. Data collection procedure Participants: We work with the meditation teacher Shaila Catherine, she invited 10 of her most experienced students who all practice Jhana meditation on a regular base to this study. Experimental procedure: The recording will take place at the Ignatius House meditation center in Atlanta, GA, USA. Data will be collected during a 10-night meditation retreat led by Shaila Catherine. Data will always be collected from two meditators in parallel. For all data collection days, participants will always participate in the same pairs and at the same time of the day. The meditators will be sitting in the same room both facing the same direction. Data will be recorded with two versatile 32-channel EEG systems by Bitbrain (https://www.bitbrain.com/neurotechnology-products/semi-dry-eeg/versatile-eeg) and two versatile biosensory by bitbrain (https://www.bitbrain.com/neurotechnology-products/biosignals/versatile-bio) . We will run two different experimental procedures. One the first and last full day of the retreat (day 1 and 9), we will collect data for the (a) LTP task, for the (b) rMMN task, and have (c) record EEG while participants look at a light that increases and decrease in brightness. This last data (c) will be used to train the classifier that is intended to find the moments where subjects experi-ence a brightness during the nimitta meditation. On day 2-4, there will be no data collection. During these days participants can fully concen-trate on their meditation. During days 5-8, we will collect data while participants do Jhana and mindfulness of breathing meditation. During these days we will record data during a mindfulness of breathing block, and during a Jhana block (order of these blocks is semi-random). During the mindfulness block, participants will do 5 minutes of mindfulness of breathing, followed by 20 minutes of mindful-ness of breathing while the hierarchical auditory oddball task is played in the background. Dur-ing the Jhana block, participants will start with 3 minutes of nimitta meditation and press a but-ton every time a nimitta appears (1 button press) and stabilizes (2 button presses). They will then do 2 minutes of access concentration, followed by 5 minutes for Jhana one, two, three, and four respectively. Following this, participants will do 20 minutes of Jhana meditation using theur deepest stable Jhana. During these 20 minutes, the hierarchical auditory oddball task will be played in the background. Recorded instructions will indicate the switch between these ele-ments of meditation. The recordings will be played using a psychopy script to ensure con-sistent timing across participants and days. overview meditation procedure LTP and rMMN procedure Hierarchical Auditory Oddball Paradigm: Subjects will undergo a passive oddball task in which auditory stimuli are presented. This task is adopted from Straus et al. (Strauss et al. 2015a) (for related literature, see also: (Bekinschtein et al. 2009; Chennu et al. 2013; Dehaene et al. 2015; Diaconescu et al. 2014; El Karoui et al. 2015; Faugeras et al. 2012; Kompus et al. 2020; Strauss et al. 2015b; Uhrig et al. 2016; Wacongne et al. 2011). During this hierarchical auditory oddball task, participants will listen to trials of five tones. For 80% of these trials, a se-quence of this type is played: ABABA (same letters representing same tones). This is the global standard series. During the remaining 20% of trials, the final tone of this series is altered, mak-ing it this: ABABB. This is the global oddball trial. The 5 tones of each trial are separated by a 50ms break. Trials are separated by a inter-trial-interval of 700-1000ms. The evoked response of the final tone of both the global standard and global oddball task will be compared. The sounds played are vowels a and o. Which of these sounds is mapped to the A and B loca-tion of the trials is semi-randomized across measuring days. Psychopy will be used to display the stimuli. LTP and Roving MMN task: For the LTP task, we will use the exact set-up as in (Sumner, McMillan, Spriggs, Campbell, Mal-pas, Maxwell, Deng, Hay, Ponton, Kirk, et al. 2020). For the roving MMN we will use the param-eters as previously used by (Sumner, McMillan, Spriggs, Campbell, Malpas, Maxwell, Deng, Hay, Ponton, Sundram, et al. 2020). Semantic Distance Task: We will analyze the data as done in (Olson et al. 2021). We will take the first seven valid words that subjects report and calculate the average semantic distance based on those. We will use the code written by Olson et al., to compute the semantic distance. Phenomenological ratings: After each block of only Jhana or only mindfulness of breathing meditation, participants will be asked a range of phenomenological questions. These questions (a) allow us to test if partici-pants were able to perform the right meditation at the right time and (b) allow us to correlate the empirical findings with the experiential depth of meditative states. 11. Sample size: Participants will be ten expert meditators Variables 12. Manipulated variables Independent Variable: The type of meditation (Mindfulness of breathing, Nimitta, Jhana 1, Jhana 2, Jhana 3, Jhana 4). 13. Measured variables Dependent variables: Oddball effect: measured by MMN amplitude Light-like quality of Nimitta meditation: measured by decoding accuracy between Nimitta meditation and restfulness while looking at a bright light. Changes in neurophysiological plasticity: measured by changes in the amplitude of the LTP task performed at the beginning and end of the entire retreat. 14 Analysis Plan EEG Recording: EEG signals will be recorded at 512 Hz using the 10-20 international system with the mobile 32-semi-dry system by Bitbrain. An electrode on the ear lobe will serve as a reference. The ground is located at Afz. Preprocessing: Data processing will be performed using MNE Python (Gramfort et al. 2014). Pre-processing will be done using custom-written scripts. We will annotate bad data segments using a semi-automatic artefact rejection method. We will interpolate any electrodes for which the recorded data exceeds 100 uV (indicating low impedance and high noise). We will interpolate up to 6 electrodes/channels based on sur-rounding electrodes before we reject participants for any analysis that relies on all electrodes. For ERP analyses that rely on only one electrode, we will only include participants were the sig-nal is good for this electrode. For the main analysis, we will bandpass filter the data with a high-pass filter at 0.3 Hz and a low-pass filter at 45 Hz. We will furthermore apply a notch filter in 50/60Hz and 80Hz to avoid electrical noise and the DC component of the recording devices. We will perform an ICA to find components of the data that result from a source different to neurophysiological activity (Gramfort et al. 2014). ICA analysis will be performed on bandpass filtered data using a high pass of 1Hz and a low pass of 30Hz. We will then pack project the ICA weights to the data that was band-pass filtered at 0.3Hz and 40Hz. We will bandpass filter the data from 1Hz to 30Hz to perform an ICA analysis. We will then back project the resulting weights ERP Analysis: For ERP analysis, a baseline correction will be performed using a 50 ms pre-stimulus period, and epochs will be averaged over participants and conditions (Jhana , Mindful-ness). ERPs will be analyzed for Fz electrodes. We will evaluate mean ERP component activity rather than peaks (which are sensitive to noise). For the MMN mean ERP components, we will average for the section 100ms to 250ms after stimulus onset, for the P3 component, we will average the section 250ms – 500ms after stimulus onset. We will also consider a more modern regression based approaches to ERP estimation which might be able to improve the precision of ERP features (e.g. https://www.unfoldtoolbox.org/). We will use a mixed effects models to evaluate differences in ERP features across states (jhana, mindfulness). We expect that the MMN component will be stronger in the mindfulness condi-tion compared to the jhana condition. Decoding: Nimitta analysis: We will train a classifier on data that is recorded while participants sit with eyes closed under two conditions: (A) while a bright light shines on their closed eyes and (B) while there is just the normal brightness of the room. Training a classifer on this data will give us a set of weights that we will use to decode representations of lights during Meditation (nimitta light experience present vs. absent). State analysis. 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