Religious Chanting and Self-Related Brain Regions: A Multi-Modal Neuroimaging Study

Ethical approval was obtained for the study from the University of Hong Kong before the experiment. All participants had signed a written consent form before attending the EEG and fMRI experiments.

1. Participant selection and preparation

1. Recruit participants who have at least 1 year of meditative experience in religiously chanting Amitābha Buddha for a minimum of 15 min per day. Ensure that the age range is between 40 to 52 years old. Estimate the number of participants by power analysis. In the current study, 21 participants were recruited.
   NOTE: Power analysis was conducted based on the effect sizes observed in pilot studies and existing literature on EEG and fMRI studies of meditation. Generally, within-subject design has a better power to differentiate different conditions, given that variation between subjects is usually larger.
2. Inform the participants about the purpose of the study and the procedures involved.
3. Ensure that the participants are not depressed using the Beck Depression Inventory.
4. Ensure that the participants are in good health and are not under the influence of any substances that may affect their physiological responses.
   NOTE: Exclusion criteria include depression, neuropsychiatric pathology, or being under the influence of alcohol or psychoactive substances. Further, MRI-specific exclusion criteria include having a pacemaker, deep brain implants, or metal implants that are not compatible with an MRI check.
5. Ensure that the participants are suitable to attend the EEG/electrocardiogram (ECG)/MRI study.
   NOTE: The EEG and MRI data were recorded separately. The duration of data collection differed between EEG and fMRI to optimize the data quality for each modality, considering the fatigue and comfort of participants. Our pilot study found that fMRI is more sensitive to demonstrating neural correlates, while EEG data usually had bad segments or other artifacts and needed a slightly longer duration to ensure enough data for final analysis. So, the EEG time was slightly longer than the MRI time. Different postures during EEG and fMRI data collection were necessary due to the constraints of each imaging modality. The conditions were randomized using a computer-generated randomization schedule to control for order effects. The sequence was not the same between EEG and fMRI.

2. EEG data acquisition and analysis

1. Set up the 128-channel EEG and multiple physiological data recording equipment according to the manufacturer's instructions.
2. Ensure participants thoroughly wash their hair before starting the EEG/ECG data acquisition.
3. Ask the participants to relax and sit comfortably.
4. Introduce the experiment to the participants.
5. Ask the participant to do a trial version of the experiment.
6. Acquire EEG data while each participant is under three different conditions: chanting Amitābha Buddha, chanting Santa Claus, and resting state, all with eyes closed. Record 10 min of EEG data in each condition.
7. Save and label the data appropriately for each participant.
8. Ensure that the data is stored securely with backups.

3. MRI data acquisition

1. Prepare a 3.0 T MRI scanner and ensure it functions properly before starting the data acquisition. Ensure that the participant is comfortable and understands the procedure before starting the scan.
2. Begin with a T1-weighted scanning sequence with the following parameters: Field of View (FoV) = 256 mm × 150 mm × 240 mm, acquisition matrix = 256 × 256, Repetition Time (TR) = 15 ms, Echo Time (TE) = 3.26 ms, flip angle = 25°, slice thickness = 1.5 mm, number of slices = 100, voxel resolution (x,y,z,) = 0.94 mm × 1 mm × 1.5 mm.
3. Obtain fMRI images with gradient echo-planar imaging (EPI) using an 8-channel SENSE head coil. Set the sequence parameters as follows: FoV = 230 mm × 140 mm × 230 mm, acquisition matrix = 64 × 64, TR = 2000 ms, TE = 30 ms, flip angle = 90°, number of slices = 32, slice thickness = 3 mm, and slice gap = 1.5 mm.
4. Start the fMRI data acquisition while each participant is under three conditions: religious chanting, non-religious chanting, and resting state. Record the data for the duration of each condition: each condition comprised 243 dynamic volumes, with a total duration of 8.1 min.

4. Physiological data acquisition

1. Set up the physiological data acquisition system for collecting cardiac, respiratory, and other physiological data according to the manufacturer's instructions.
   NOTE: EEG and physiological data were acquired at the same time.
2. Attach three ECG electrodes on the left and right hands and left foot, respectively.
3. Set up two breath belts on the participant, one for measuring breast breath and another for measuring abdominal breath.
4. Monitor the Galvanic Skin Response (GSR) and oxygen saturation levels of the participants.
5. Begin the data acquisition together with EEG data collection while each participant is under three conditions: chanting Amitābha Buddha, chanting Santa Claus, and resting state, all with eyes closed.
6. Save and label the data appropriately for each participant. Ensure the data is stored securely and is good enough for further analysis.

5. EEG data analysis

1. Process and analyze the EEG data with appropriate software. Here, an open-source software EEGLAB was used.
2. Load the data into the program. Click on File > Load Existing Dataset.
3. Resample the data from 1000 Hz to 250 Hz by clicking Tools > Change Sampling Rate.
4. Filter the data with a finite impulse response (FIR) filter with a passband of 0.1-100 Hz by clicking Tools > Filter the Data > Basic FIR Filter.
5. Filter the data again with a notch filter with a stopband of 47-53 Hz to remove alternating current noise. Click on Tools > Filter the Data and select the option Notch filter the data instead of pass band.
6. Inspect the data visually to eliminate artifacts such as eye and muscle movements by clicking Plot > Channel Data (scroll).
   NOTE: Designated electro-oculography (EOG) channels can be used as a reference.
7. Inspect the data visually to note bad channels for removal.
8. Apply spherical interpolation based on the surrounding channels to reconstruct the bad channels by clicking Tools > Interpolate Electrodes and select from the data channels.
9. Execute the independent component analysis (ICA) using the runica algorithm. For this, click on Tools > Run ICA.
10. Remove independent components (ICs) that correspond to eye movement, muscle noise, and line noise from the data by clicking Tools > Reject Data using ICA > Reject Components by Map.
11. Reconstruct the data using the remaining Ics by clicking on Tools > Remove Components.
12. Click on Tools > Filter the Data > Basic FIR Filter to filter the data with a 47 Hz low pass filter.
13. Estimate the similarity of ICs and group them into functionally equivalent clusters using the STUDY functions of EEGLAB. Click on File > Create Study > Using all Loaded Datasets.
14. Generate the dipole locations of each IC using the DIPFIT2 function. Click on Tools > Locate Dipole using DIPFIT 2.x > Autofit.
15. Use k-means clustering to create IC clusters based on similar dipole locations (weight: 2/3) and power spectrum (weight: 1/3). Click on Study > PCA Clustering > Build Preclustering Array.
16. Repeat the clustering procedure ten times, each time with a different k parameter setting. Identify the k parameter setting that generates distinctive yet reasonable clusters. Click on Study > Edit/Plot Clusters.
17. Perform spectrum analysis and conduct a one-way ANOVA on all major frequency bands using the ICs from a specific cluster of interest.
    NOTE: Pay particular attention to the posterior cingulate cortex (PCC), as this area has been found to decrease in centrality due to a regional increase in the endogenous generation of delta oscillations during religious chanting.

6. fMRI data analysis

1. Preprocess the fMRI data using the Leipzig Image Processing and Statistical Inference Algorithms software (LIPSIA 2.2.7).
   1. Perform preprocessing that includes signal intensity normalization, movement correction, spatial normalization to MNI space,spatial smoothing with Full Width at Half Maximum (FWHM) = 6 mm, and temporal highpass filtering with a cutoff frequency of 1/90 Hz to remove low frequency drifts in the fMRI time series. Refer to these steps in data processing pipeline 1 (Supplementary File 1).
2. Remove covariates of no interest, such as global signal fluctuations and movement parameters, by regressing them out of the data for each scanning sequence that corresponds to each of the three conditions.
3. Finally, investigate whole-brain functional connectomics by applying Eigenvector Centrality Mapping (ECM), a graph theory method that identifies the most influential nodes within a network, and the ECM images from two conditions are subtracted from one another to produce the resulting contrast image. Refer to these steps in data processing pipeline 2 (Supplementary File 2).

7. ECG and other physiological data analysis

1. Clean raw ECG and other physiological data via a Butterworth band pass filter and extract the inter-beat-interval (IBI) after replacing outliers with spline interpolation.
2. Detrend the IBI data and compute the time/frequency domain features of the HRV (heart rate variability) using the open-source toolbox HRVAS.
3. Set frequency ranges for the VLF to 0-0.04 Hz, LF to 0.04-0.15 Hz, and HF to 0.15-0.4 Hz.
4. Estimate the power of selected frequency bands using the Lomb-Scargle periodogram method.
5. Subject the derived HRV metrics to statistical tests using one-way repeated measures ANOVA and post hoc tests to assess differences between conditions. Set the alpha level of significance at 0.05.
6. Compute other physiological data, such as breath intervals for each participant and each condition, using analysis software.
7. Use the findpeaks function in the analysis software to detect the peak of the respiratory curve.
8. Differentiate the inspiratory and expiratory periods, and then compute the breath rate.
9. Compare the difference between conditions using one-way repeated measures ANOVA and post hoc tests.