The research was supported by the National Natural Science 
Foundation of China (NSFC.61841704).

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
<ol>
	<li>Recruit participants who have at least 1 year of meditative experience in religiously chanting Amit&#257;bha 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.</li>
	<li>Inform the participants about the purpose of the study and the procedures involved.</li>
	<li>Ensure that the participants are not depressed using the Beck Depression Inventory.</li>
	<li>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.</li>
	<li>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.</li>
</ol>
2. EEG data acquisition and analysis
<ol>
	<li>Set up the 128-channel EEG and multiple physiological data recording equipment according to the manufacturer&#39;s instructions.</li>
	<li>Ensure participants thoroughly wash their hair before starting the EEG/ECG data acquisition.</li>
	<li>Ask the participants to relax and sit comfortably.</li>
	<li>Introduce the experiment to the participants.</li>
	<li>Ask the participant to do a trial version of the experiment.</li>
	<li>Acquire EEG data while each participant is under three different conditions: chanting Amit&#257;bha Buddha, chanting Santa Claus, and resting state, all with eyes closed. Record 10 min of EEG data in each condition.</li>
	<li>Save and label the data appropriately for each participant.</li>
	<li>Ensure that the data is stored securely with backups.</li>
</ol>
3. MRI data acquisition
<ol>
	<li>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.</li>
	<li>Begin with a T1-weighted scanning sequence with the following parameters: Field of View (FoV) = 256 mm &#215; 150 mm &#215; 240 mm, acquisition matrix = 256 &#215; 256, Repetition Time (TR) = 15 ms, Echo Time (TE) = 3.26 ms, flip angle = 25&#176;, slice thickness = 1.5 mm, number of slices = 100, voxel resolution (x,y,z,) = 0.94 mm &#215; 1 mm &#215; 1.5 mm.</li>
	<li>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 &#215; 140 mm &#215; 230 mm, acquisition matrix = 64 &#215; 64, TR = 2000 ms, TE = 30 ms, flip angle = 90&#176;, number of slices = 32, slice thickness = 3 mm, and slice gap = 1.5 mm.</li>
	<li>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.</li>
</ol>
4. Physiological data acquisition
<ol>
	<li>Set up the physiological data acquisition system for collecting cardiac, respiratory, and other physiological data according to the manufacturer&#39;s instructions. 
	NOTE: EEG and physiological data were acquired at the same time.</li>
	<li>Attach three ECG electrodes on the left and right hands and left foot, respectively.</li>
	<li>Set up two breath belts on the participant, one for measuring breast breath and another for measuring abdominal breath.</li>
	<li>Monitor the Galvanic Skin Response (GSR) and oxygen saturation levels of the participants.</li>
	<li>Begin the data acquisition together with EEG data collection while each participant is under three conditions: chanting Amit&#257;bha Buddha, chanting Santa Claus, and resting state, all with eyes closed.</li>
	<li>Save and label the data appropriately for each participant. Ensure the data is stored securely and is good enough for further analysis.</li>
</ol>
5. EEG data analysis
<ol>
	<li>Process and analyze the EEG data with appropriate software. Here, an open-source software EEGLAB was used.</li>
	<li>Load the data into the program. Click on File &#62; Load Existing Dataset.</li>
	<li>Resample the data from 1000 Hz to 250 Hz by clicking Tools &#62; Change Sampling Rate.</li>
	<li>Filter the data with a finite impulse response (FIR) filter with a passband of 0.1-100 Hz by clicking Tools &#62; Filter the Data &#62; Basic FIR Filter.</li>
	<li>Filter the data again with a notch filter with a stopband of 47-53 Hz to remove alternating current noise. Click on Tools &#62; Filter the Data and select the option Notch filter the data instead of pass band.</li>
	<li>Inspect the data visually to eliminate artifacts such as eye and muscle movements by clicking Plot &#62; Channel Data (scroll). 
	NOTE: Designated electro-oculography (EOG) channels can be used as a reference.</li>
	<li>Inspect the data visually to note bad channels for removal.</li>
	<li>Apply spherical interpolation based on the surrounding channels to reconstruct the bad channels by clicking Tools &#62; Interpolate Electrodes and select from the data channels.</li>
	<li>Execute the independent component analysis (ICA) using the runica algorithm. For this, click on Tools &#62; Run ICA.</li>
	<li>Remove independent components (ICs) that correspond to eye movement, muscle noise, and line noise from the data by clicking Tools &#62; Reject Data using ICA &#62; Reject Components by Map.</li>
	<li>Reconstruct the data using the remaining Ics by clicking on Tools &#62; Remove Components.</li>
	<li>Click on Tools &#62; Filter the Data &#62; Basic FIR Filter to filter the data with a 47 Hz low pass filter.</li>
	<li>Estimate the similarity of ICs and group them into functionally equivalent clusters using the STUDY functions of EEGLAB. Click on File &#62; Create Study &#62; Using all Loaded Datasets.</li>
	<li>Generate the dipole locations of each IC using the DIPFIT2 function. Click on Tools &#62; Locate Dipole using DIPFIT 2.x &#62; Autofit.</li>
	<li>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 &#62; PCA Clustering &#62; Build Preclustering Array.</li>
	<li>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 &#62; Edit/Plot Clusters.</li>
	<li>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.</li>
</ol>
6. fMRI data analysis
<ol>
	<li>Preprocess the fMRI data using the Leipzig Image Processing and Statistical Inference Algorithms software (LIPSIA 2.2.7).
	<ol>
		<li>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).</li>
	</ol>
	</li>
	<li>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.</li>
	<li>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).</li>
</ol>
7. ECG and other physiological data analysis
<ol>
	<li>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.</li>
	<li>Detrend the IBI data and compute the time/frequency domain features of the HRV (heart rate variability) using the open-source toolbox HRVAS.</li>
	<li>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.</li>
	<li>Estimate the power of selected frequency bands using the Lomb-Scargle periodogram method.</li>
	<li>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.</li>
	<li>Compute other physiological data, such as breath intervals for each participant and each condition, using analysis software.</li>
	<li>Use the findpeaks function in the analysis software to detect the peak of the respiratory curve.</li>
	<li>Differentiate the inspiratory and expiratory periods, and then compute the breath rate.</li>
	<li>Compare the difference between conditions using one-way repeated measures ANOVA and post hoc tests.</li>
</ol>

Decoding Brain Activity During Religious Chanting with EEG and fMRI

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<li>Sik, H. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Modulation+of+the+neurophysiological+response+to+fearful+and+stressful+stimuli+through+repetitive+religious+chanting%5BTitle%5D)+AND+%22J+Vis+Exp%22%5BJournal%5D)">Modulation of the neurophysiological response to fearful and stressful stimuli through repetitive religious chanting.</a> J Vis Exp. (181), e62960(2022).</li>
<li>Gao, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Repetitive+religious+chanting+invokes+positive+emotional+schema+to+counterbalance+fear%3A+A+multimodal+functional+and+structural+MRI+study%5BTitle%5D)+AND+%22Front+Behav+Neurosci%22%5BJournal%5D)">Repetitive religious chanting invokes positive emotional schema to counterbalance fear: A multimodal functional and structural MRI study.</a> Front Behav Neurosci. 14, 548856(2020).</li>
<li>Saarikivi, K., Chan, T. M. V., Huotilainen, M., Tervaniemi, M., Putkinen, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enhanced+neural+mechanisms+of+set+shifting+in+musically+trained+adolescents+and+young+adults%3A+converging+fMRI%2C+EEG%2C+and+behavioral+evidence%5BTitle%5D)+AND+%22Cereb+Cortex%22%5BJournal%5D)">Enhanced neural mechanisms of set shifting in musically trained adolescents and young adults: converging fMRI, EEG, and behavioral evidence.</a> Cereb Cortex. 33 (11), 7237-7249 (2023).</li>
<li>Suzuki, K., Laohakangvalvit, T., Matsubara, R., Sugaya, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Constructing+an+emotion+estimation+model+based+on+EEG%2FHRV+indexes+using+feature+extraction+and+feature+selection+algorithms%5BTitle%5D)+AND+%22Sensors+%28Basel%29%22%5BJournal%5D)">Constructing an emotion estimation model based on EEG/HRV indexes using feature extraction and feature selection algorithms.</a> Sensors (Basel). 21 (9), 2910(2021).</li>
<li>Zhang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+neural+correlates+of+amplitude+of+low-frequency+fluctuation%3A+a+multimodal+resting-state+MEG+and+fMRI-EEG+study%5BTitle%5D)+AND+%22Cereb+Cortex%22%5BJournal%5D)">The neural correlates of amplitude of low-frequency fluctuation: a multimodal resting-state MEG and fMRI-EEG study.</a> Cereb Cortex. 33 (4), 1119-1129 (2023).</li>
<li>Maruyama, Y., Ogata, Y., Martinez-Tejada, L. A., Koike, Y., Yoshimura, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Independent+components+of+EEG+activity+correlating+with+emotional+state%5BTitle%5D)+AND+%22Brain+Sci%22%5BJournal%5D)">Independent components of EEG activity correlating with emotional state.</a> Brain Sci. 10 (10), 669(2020).</li>
<li>Bigdely-Shamlo, N., Mullen, T., Kreutz-Delgado, K., Makeig, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Measure+projection+analysis%3A+a+probabilistic+approach+to+EEG+source+comparison+and+multi-subject+inference%5BTitle%5D)+AND+%22Neuroimage%22%5BJournal%5D)">Measure projection analysis: a probabilistic approach to EEG source comparison and multi-subject inference.</a> Neuroimage. 72, 287-303 (2013).</li>
<li>Harmony, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+functional+significance+of+delta+oscillations+in+cognitive+processing%5BTitle%5D)+AND+%22Front+Integr+Neurosci%22%5BJournal%5D)">The functional significance of delta oscillations in cognitive processing.</a> Front Integr Neurosci. 7, 83(2013).</li>
<li>Tompson, S., Chua, H. F., Kitayama, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Connectivity+between+mPFC+and+PCC+predicts+post-choice+attitude+change%3A+The+self-referential+processing+hypothesis+of+choice+justification%5BTitle%5D)+AND+%22Hum+Brain+Mapp%22%5BJournal%5D)">Connectivity between mPFC and PCC predicts post-choice attitude change: The self-referential processing hypothesis of choice justification.</a> Hum Brain Mapp. 37 (11), 3810-3820 (2016).</li>
<li>Lee, K. C. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aLee%2C+KC+%22The+Guide+to+Buddhist+Counseling%22">The Guide to Buddhist Counseling</a>. , Routledge, Taylor & Francis Group. London. (2022).</li>
<li>Wu, B. W. Y., Gao, J., Leung, H. K., Sik, H. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Randomized+Controlled+Trial+of+Awareness+Training+Program+%28ATP%29%2C+a+Group-Based+Mahayana+Buddhist+Intervention%5BTitle%5D)+AND+%22Mindfulness%22%5BJournal%5D)">A Randomized Controlled Trial of Awareness Training Program (ATP), a Group-Based Mahayana Buddhist Intervention.</a> Mindfulness. 10, 1280-1293 (2019).</li>
<li>Gomes, B. A., Plaska, C. R., Ortega, J., Ellmore, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+simultaneous+EEG-fMRI+study+of+thalamic+load-dependent+working+memory+delay+period+activity%5BTitle%5D)+AND+%22Front+Behav+Neurosci%22%5BJournal%5D)">A simultaneous EEG-fMRI study of thalamic load-dependent working memory delay period activity.</a> Front Behav Neurosci. 17, 1132061(2023).</li>
<li>Gao, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Repetitive+religious+chanting+modulates+the+late-stage+brain+response+to+fear-and+stress-provoking+pictures%5BTitle%5D)+AND+%22Front+Psychol%22%5BJournal%5D)">Repetitive religious chanting modulates the late-stage brain response to fear-and stress-provoking pictures.</a> Front Psychol. 7, 2055(2017).</li>
<li>Ciccarelli, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Simultaneous+real-time+EEG-fMRI+neurofeedback%3A+A+systematic+review%5BTitle%5D)+AND+%22Front+Hum+Neurosci%22%5BJournal%5D)">Simultaneous real-time EEG-fMRI neurofeedback: A systematic review.</a> Front Hum Neurosci. 17, 1123014(2023).</li>
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The authors declare that they have no competing financial interests.

Although the 128-channel EEG system used was a high-density EEG system, the spatial resolution of EEG remains relatively poor compared to fMRI, and this shortcoming also affects EEG source localization accuracy, especially when multiple brain region candidates are plausible. Thus the deeper and higher spatial resolution of MRI can significantly enhance the spatial accuracy of EEG source analysis6 and guide the selection of the most important clusters for further analysis. The present protocol utilizes multimodal neuroimaging tools, including EEG, ECG, and fMRI data acquisition and analysis methods. It demonstrates a comprehensive approach to exploring the neurophysiological correlates of religious chanting and potentially other forms of mind training. A critical step in the protocol is the application of fMRI results in the EEG source analysis. The quality of the acquired EEG data remains crucial for subsequent analysis and interpretation of the results. The use of ICA&#160;and k-means clustering in the EEG data analysis, in conjunction with fMRI results, allows for a more nuanced understanding of the data7,8. The observed modulation of delta-band power during religious chanting aligns with literature suggesting delta rhythms may regulate behavior through the synchronization of neural activity. Delta wave can foster focused attention and a potential reduction in self-referential thought associated with the default mode network. This heightened delta activity, indicative of deeply restorative states, could underpin the therapeutic effects of chanting by reinforcing cognitive and emotional processing9.
The results from this study highlight a significant increase in delta-band power during religious chanting, as compared to non-religious chanting. The fMRI results indicate a strong decrease in centrality in brain regions associated with self-related processing10 during religious chanting. The results from physiological data also demonstrate the effects of religious chanting are distinct from those of non-religious chanting and rule out other potential confounding factors, including differences due to language processing, or cardiac activity. Overall, the findings imply a promising avenue towards the clinical application of religious chanting via Buddhist counseling in order to facilitate the fostering of &#34;non-attachment&#34;1,11,12.
Limitations include that the fMRI and EEG data were acquired from different subjects13. Secondly, given the considerable variation among subjects regarding their religious chanting experience1,14, it would be preferable if all subjects had also undergone fMRI scanning. Our future research will aim to address these limitations and to further explore the neurophysiological effects of different religious and meditative practices.
Despite these limitations, this protocol is unique in combining multimodal neuroimaging and physiological measuring tools, including EEG, ECG, and fMRI data, to provide a more comprehensive view of the neurophysiological correlates of religious chanting. This multimodal neuroimaging approach allows for a deeper understanding of religious and meditative practices, which would not be possible using methods that rely solely on one single type of data15,16.

Religious chanting, a very popular practice in Eastern cultures, is often compared to prayer in Western societies1. Despite its prevalence, scientific research on the neural correlates of religious chanting remains rather limited. Sophisticated multimodal electrophysiological and neuroimaging techniques were utilized in this study to fill this knowledge gap and to explore the neural associations of chanting Amit&#257;bha Buddha, one of the most widespread and one of the oldest actively preserved religious traditions2,3. Repetitive religious chanting can serve as an effective technique in Buddhist counseling to help soothe one&#39;s mind from turbulent thoughts and emotions.
Given the high spatial resolution of functional magnetic resonance imaging (fMRI), it can be applied to overcome the limitations of traditional EEG studies4. Combining fMRI&#160;and electroencephalogram (EEG) source clustering via independent component analysis (ICA), the study identifies and groups independent components of brain activity across participants. This method introduces a novel strategy for identifying signals from mixed EEG sources or disparate sources across participants, which has been challenging due to differences in brain anatomy and electrode placement.
The form of repetitive religious chanting that was studied with this protocol involves repetitive recitation of the name of Amit&#257;bha Buddha. It is also a meditative practice that has been reported to elicit blissful sensations and transcendental experiences. Amongst different Buddhist practices, the practice of chanting&#160;Amit&#257;bha Buddha is&#160;simple and easily accessible. This practice promises rebirth in the Pure Land for all those who sincerely call upon this name, which has similarities to certain traditions in Western religion1,3.
Through multimodal neuroimaging, this study aims to provide a comprehensive understanding of the neural correlates of repetitive religious chanting. The protocol can contribute to the booming field of research on the neurophysiological effects of different religious and meditative practices.
The study hypothesized that repetitive religious chanting would lead to significant signal changes in brain regions responsible for self-related processes. Furthermore, given the positive emotions attributed to Amit&#257;bha Buddha, we hypothesized that emotional shifts would transpire during religious chanting. These effective changes are likely to coincide with modifications in peripheral physiological indicators, such as variations in multi-band heart rate variability (HRV) indices and respiration rate5.

<table><tbody><tr><td>3.0 T Philips MRI scanner</td><td>Philips</td><td>3.0T</td><td>MRI data acquisition device</td></tr><tr><td>EEGLAB</td><td>Swartz Center for Computational Neuroscience</td><td>13.6.5b</td><td>EEG analysis software</td></tr><tr><td>Electroencephalogram (EEG) system</td><td>Electrical Geodesics, Inc. (EGI)</td><td>GES 200</td><td>EEG acquisition device</td></tr><tr><td>HRVAS</td><td>Ramshur, J.</td><td>Version 1</td><td>Plug-in for EEGLAB to process ECG data</td></tr><tr><td>HydroCel GSN 128 channels</td><td>Electrical Geodesics, Inc. (EGI)</td><td>GSN 130</td><td>EEG cap</td></tr><tr><td>iMac 27&quot;</td><td>Apple</td><td>Version 10.8</td><td>Running the Netstation software</td></tr><tr><td>LabChart</td><td>ADInstruments</td><td>Version 8</td><td>Physiological data acquisition software</td></tr><tr><td>LIPSIA</td><td>Max-Planck-Institute for Human Cognitive and Brain Sciences</td><td>Version 2.2.7</td><td>fMRI data analysis software</td></tr><tr><td>Matlab</td><td>MathWorks</td><td>R2011a</td><td>EEGLAB is based on Matlab, statistical analysis tool for EEG data</td></tr><tr><td>Netstation</td><td>Electrical Geodesics, Inc. (EGI)</td><td>Version 3</td><td>EEG acquisition software</td></tr><tr><td>PowerLab 8/35</td><td>ADInstruments</td><td>PL3508</td><td>Physiological data acquisition hardware</td></tr><tr><td>SPSS</td><td>IBM</td><td>Version 27</td><td>Statistical analysis tool for behavior and EEG ROI data</td></tr><tr><td>Windows PC</td><td>Dell</td><td>Version 8</td><td>Running the LabChart software</td></tr></tbody></table>

religious chanting and self-related brain regions: a multi-modal neuroimaging study

This protocol presents a multi-modal neuroimaging approach to explore the potential brain activity associated with repetitive religious chanting, a widespread form of mind training in both Eastern and Western cultures. High-density electroencephalogram (EEG), with its superior temporal resolution, allows for capturing the dynamic changes in brain activity during religious chanting. Through source localization methods, these can be attributed to various alternative potential brain region sources. Twenty practitioners of religious chanting were measured with EEG. However, the spatial resolution of EEG is less precise, in comparison to functional magnetic resonance imaging (fMRI). Thus, one highly experienced practitioner underwent an fMRI scanning session to guide the source localization more precisely. The fMRI data helped guide the selection of EEG source localization, making the calculation of K-means of the EEG source localization in the group of 20 intermediate practitioners more precise and reliable. This method enhanced EEG&#39;s ability to identify the brain regions specifically engaged during religious chanting, particularly the cardinal role of the posterior cingulate cortex (PCC). The PCC is a brain area related to focus and self-referential processing. These multimodal neuroimaging and neurophysiological results reveal that repetitive religious chanting can induce lower centrality and higher delta-wave power compared to non-religious chanting and resting state conditions. The combination of fMRI and EEG source analysis provides a more detailed understanding of the brain&#39;s response to repetitive religious chanting. The protocol contributes significantly to the research on the neural mechanisms involved in religious and meditative practices, which is becoming more prominent nowadays. The results of this study could have significant implications for developing future neurofeedback techniques and psychological interventions.

The fMRI analysis results indicated that the strongest difference in eigenvector centrality between religious and non-religious chanting was predominantly situated in the posterior cingulate cortex (PCC); see Figure 1. This finding was leveraged to evaluate and validate the selection of the EEG-independent component clustering, which similarly manifested a cluster in the vicinity of the PCC region.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66221/66221fig01.jpg" /> 
Figure 1: Multimodal neuroimaging and electrophysiological results. Eigenvector centrality mapping applied on fMRI data, revealed that the posterior cingulate cortex is the area of the brain that decreased most in centrality during religious chanting compared to non-religious chanting. This figure has been obtained with permission from Gao et al.1. <a href="https://www.jove.com/files/ftp_upload/66221/66221fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The EEG-independent component clustering analysis yielded seven distinct IC clusters, each corresponding to a source of EEG activity. Notably, one of these clusters was situated in the PCC, a finding that aligns with the fMRI results (see Figure 2).
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66221/66221fig02.jpg" /> 
Figure 2: The EEG-independent component clustering analysis also featured a cluster in the PCC. This figure has been obtained with permission from Gao et al.1. <a href="https://www.jove.com/files/ftp_upload/66221/66221fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
This particular cluster was subsequently chosen for in-depth analysis, including spectrum analysis. A one-way ANOVA revealed a significant main effect of chanting on the power of the delta frequency band (1-4 Hz, see Figure 3 and Figure 4).
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66221/66221fig03.jpg" /> 
Figure 3: The one-way ANOVA revealed a significant main effect of chanting on the power of the delta-band (1-4 Hz). This figure has been obtained with permission from Gao et al.1. <a href="https://www.jove.com/files/ftp_upload/66221/66221fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66221/66221fig04.jpg" /> 
Figure 4: Post hoc analysis of religious chanting vs. non-religious chanting conditions. The analysis showed that religious chanting induced higher delta power than the non-religious chanting condition (p = .011). <a href="https://www.jove.com/files/ftp_upload/66221/66221fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Further post hoc analysis indicated a significantly lower power of HRV during religious chanting in comparison to the no chanting condition (see Figure 5).
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66221/66221fig05.jpg" /> 
Figure 5: Post hoc analysis of no chanting resting state vs. religious chanting conditions. The analysis showed that compared to no chanting resting state, religious chanting induced lower HRV total power, lower absolute high-frequency power, and lower absolute very-low-frequency power. This figure has been obtained with permission from Gao et al.1. <a href="https://www.jove.com/files/ftp_upload/66221/66221fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The findings indicate that in comparison to non-religious chanting, there is a decrease of eigenvector centrality in the PCC, probably driven by a regional surge in endogenous delta oscillations. These functional changes are independent of peripheral cardiac or respiratory activities and are not triggered by implicit language processing. Instead, they appear to be associated with experiences of transcendental bliss and a reduction in self-centered cognition.
Supplementary File 1: Data processing pipeline 1. <a href="https://www.jove.com/files/ftp_upload/66221/Supplementary File 1.zip" target="_blank">Please click here to download this File.</a>
Supplementary File 2: Data processing pipeline 2. <a href="https://www.jove.com/files/ftp_upload/66221/Supplementary File 2.zip" target="_blank">Please click here to download this File.</a>

Author Spotlight: Exploring Dynamic Neural Changes Associated with Religious Chanting

Here, we present a protocol to investigate the neurophysiological correlates of various meditation forms, including religious chanting. This method uniquely integrates fMRI eigenvector results for region selection in electroencephalogram (EEG) source analysis using k-means clustering. The results provide an in-depth understanding of the neural processes involved in repetitive religious chanting.

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

This protocol presents a multi-modal neuroimaging approach to explore the potential brain activity associated with repetitive religious chanting, a ...

religious-chanting-self-related-brain-regions-multi-modal

Research

JoVE Journal

Neuroscience

899 Views.  The University of Hong Kong. Ons onderzoek verkent de neurofysiologische correlaten van religieuze gezangen met behulp van multimodale neuroimaging-technieken. We streven ernaar om het hersengebied en de neurale processen bloot te leggen die betrokken zijn bij repetitief religieus chanten en hoe ze verschillen van niet-religieus chanten en rusttoestand. Recente studies zijn begonnen met het onderzoeken van het effect van verschillende meditatieve en religieuze praktijken op de hersenen m...

Video: Auteur in de schijnwerpers: onderzoek naar dynamische neurale veranderingen die verband houden met religieus gezang

Functional Magnetic Resonance Imaging (fMRI) with Auditory Stimulation in Songbirds

The neurobiology of birdsong, as a model for human speech, is a pronounced area of research in behavioral neuroscience. Whereas electrophysiology and molecular approaches allow the investigation of either different stimuli on few neurons, or one stimulus in large parts of the brain, blood oxygenation level dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) allows combining both advantages, i.e. compare the neural activation induced by different stimuli in the entire brain at once. fMRI in songbirds is challenging because of the small size of their brains and because their bones and especially their skull comprise numerous air cavities, inducing important susceptibility artifacts. Gradient-echo (GE) BOLD fMRI has been successfully applied to songbirds 1-5 (for a review, see 6). These studies focused on the primary and secondary auditory brain areas, which are regions free of susceptibility artifacts. However, because processes of interest may occur beyond these regions, whole brain BOLD fMRI is required using an MRI sequence less susceptible to these artifacts. This can be achieved by using spin-echo (SE) BOLD fMRI 7,8 . In this article, we describe how to use this technique in zebra finches (Taeniopygia guttata), which are small songbirds with a bodyweight of 15-25 g extensively studied in behavioral neurosciences of birdsong. The main topic of fMRI studies on songbirds is song perception and song learning. The auditory nature of the stimuli combined with the weak BOLD sensitivity of SE (compared to GE) based fMRI sequences makes the implementation of this technique very challenging.

Functional Magnetic Resonance Imaging fMRI with Auditory Stimulation in Songbirds

This article shows an optimized procedure for imaging of the neural substrates of auditory stimulation in the songbird brain using functional Magnetic Resonance Imaging (fMRI). It describes the preparation of the sound stimuli, the positioning of the subject and the acquisition and subsequent analysis of the fMRI data.

The neurobiology of birdsong, as a model for human speech, is a pronounced area of research in behavioral neuroscience. Whereas electrophysiology and ...

Behavior

Brain Imaging Investigation of the Neural Correlates of Emotional Autobiographical Recollection

Recollection of emotional autobiographical memories (AMs) is important to healthy cognitive and affective functioning 1 - remembering positive AMs is associated with increased personal well-being and self-esteem 2, whereas remembering and ruminating on negative AMs may lead to affective disorders 3. Although significant progress has been made in understanding the brain mechanisms underlying AM retrieval in general (reviewed in 4, 5), less is known about the effect of emotion on the subjective re-experience of AMs and the associated neural correlates. This is in part due to the fact that, unlike the investigations of the emotion effect on memory for laboratory-based microevents (reviewed in 6, 7-9), often times AM studies do not have a clear focus on the emotional aspects of remembering personal events (but see 10). Here, we present a protocol that allows investigation of the neural correlates of recollecting emotional AMs using functional magnetic resonance imaging (fMRI). Cues for these memories are collected prior to scanning by means of an autobiographical memory questionnaire (AMQ), therefore allowing for proper selection of emotional AMs based on their phenomenological properties (i.e., intensity, vividness, personal significance). This protocol can be used in healthy and clinical populations alike.

We present a protocol that allows investigation of the neural correlates of recollecting emotional autobiographical memories, using functional magnetic resonance imaging. This protocol can be used with both healthy and clinical participants.

Recollection of emotional autobiographical memories (AMs) is important to healthy cognitive and affective functioning 1 - remembering positive AMs is ...

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

The neural correlates of consonance and dissonance perception have been widely studied, but not the neural correlates of consonance and dissonance production. The most straightforward manner of musical production is singing, but, from an imaging perspective, it still presents more challenges than listening because it involves motor activity. The accurate singing of musical intervals requires integration between auditory feedback processing and vocal motor control in order to correctly produce each note. This protocol presents a method that permits the monitoring of neural activations associated with the vocal production of consonant and dissonant intervals. Four musical intervals, two consonant and two dissonant, are used as stimuli, both for an auditory discrimination test and a task that involves first listening to and then reproducing given intervals. Participants, all female vocal students at the conservatory level, were studied using functional Magnetic Resonance Imaging (fMRI) during the performance of the singing task, with the listening task serving as a control condition. In this manner, the activity of both the motor and auditory systems was observed, and a measure of vocal accuracy during the singing task was also obtained. Thus, the protocol can also be used to track activations associated with singing different types of intervals or with singing the required notes more accurately. The results indicate that singing dissonant intervals requires greater participation of the neural mechanisms responsible for the integration of external feedback from the auditory and sensorimotor systems than does singing consonant intervals.

The neural correlates of listening to consonant and dissonant intervals have been widely studied, but the neural mechanisms associated with production of consonant and dissonant intervals are less well known. In this article, behavioral tests and fMRI are combined with interval identification and singing tasks to describe these mechanisms.

The neural correlates of consonance and dissonance perception have been widely studied, but not the neural correlates of consonance and dissonance ...

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, Statistical Parametric Mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard Mindfulness-based Stress Reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities.

This manuscript describes how to use the wavelet entropy index to analyze high-density electroencephalography (EEG) and electrocardiography (ECG) data. We show that the irregularity of cerebral and cardiac activities became more coordinated during mindfulness-based stress reduction practice.

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One ...

Monitoring Acupuncture Effects on Human Brain by fMRI

Functional MRI is used to study the effects of acupuncture on the BOLD response and the functional connectivity of the human brain.  Results demonstrate that acupuncture mobilizes a limbic-paralimbic-neocortical network and its anti-correlated sensorimotor/paralimbic network at multiple levels of the brain and that the hemodynamic response is influenced by the psychophysical response.  Physiological monitoring may be performed to explore the peripheral response of the autonomic nerve function. This video describes the studies performed at LI4 (hegu), ST36 (zusanli) and LV3 (taichong), classical acupoints that are commonly used for modulatory and pain-reducing actions. Some issues that require attention in the applications of fMRI to acupuncture investigation are noted.

FMRI and physiological monitoring is used to study the effects of Acupuncture on the central and peripheral nervous systems. Acupuncture mobilizes a limbic-paralimbic-neocortical network, with great overlap with the default mode network, to modulate neurological activity, possibly related to its autonomic effect in the peripheral nervous system.

Functional MRI is used to study the effects of acupuncture on the BOLD response and the functional connectivity of the human brain.  Results demonstrate ...

Biology

Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy (NIRS) Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

Portable neuroimaging approaches provide new advances to the study of brain function and brain development with previously inaccessible populations and in remote locations. This paper shows the development of field functional Near Infrared Spectroscopy (fNIRS) imaging to the study of child language, reading, and cognitive development in a rural village setting of C&#244;te d&#39;Ivoire. Innovation in methods and the development of culturally appropriate neuroimaging protocols allow&#160;a first-time look into the brain&#39;s development and children&#39;s learning outcomes in understudied environments. This paper demonstrates protocols for transporting and setting up a mobile laboratory, discusses considerations for field versus laboratory neuroimaging, and presents a guide for developing neuroimaging consent procedures and building meaningful long-term collaborations with local government and science partners. Portable neuroimaging methods can be used to study complex child development contexts, including the impact of significant poverty and adversity on brain development. The protocol presented here has been developed for use in C&#244;te d&#39;Ivoire, the world&#39;s primary source of cocoa, and where reports of child labor in the cocoa sector are common. Yet, little is known about the impact of child labor on brain development and learning. Field neuroimaging methods have the potential to yield new insights into such urgent issues, and the development of children globally.

Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy NIRS Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

Portable neuroimaging approaches (functional Near Infrared Spectroscopy) provide advances to the study of the brain in previously inaccessible regions; here, rural C&#244;te d&#39;Ivoire. Innovation in methods and development of culturally-appropriate neuroimaging protocols permits novel study of the brain&#39;s development and children&#39;s learning outcomes in environments with significant poverty and adversity.

Portable neuroimaging approaches provide new advances to the study of brain function and brain development with previously inaccessible populations and in ...

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

The Translational Brain Mapping Program at the University of Rochester is an interdisciplinary effort that integrates cognitive science, neurophysiology, neuroanesthesia, and neurosurgery. Patients who have tumors or epileptogenic tissue in eloquent brain areas are studied preoperatively with functional and structural MRI, and intraoperatively with direct electrical stimulation mapping. Post-operative neural and cognitive outcome measures fuel basic science studies about the factors that mediate good versus poor outcome after surgery, and how brain mapping can be further optimized to ensure the best outcome for future patients. In this article, we describe the interdisciplinary workflow that allows our team to meet the synergistic goals of optimizing patient outcome and advancing scientific understanding of the human brain.

This article provides an overview of a multi-modal brain mapping program designed to identify regions of the brain that support critical cognitive functions in individual neurosurgery patients.

The Translational Brain Mapping Program at the University of Rochester is an interdisciplinary effort that integrates cognitive science, neurophysiology, ...

Combining Behavior and EEG to Study the Effects of Mindfulness Meditation on Episodic Memory

Although there has been recent interest in how mindfulness meditation can affect episodic memory as well as brain structure and function, no study has examined the behavioral and neural effects of mindfulness meditation on episodic memory. Here we present a protocol that combines mindfulness meditation training, an episodic memory task, and EEG to examine how mindfulness meditation changes behavioral performance and the neural correlates of episodic memory. Subjects in a mindfulness meditation experimental group were compared to a waitlist control group. Subjects in the mindfulness meditation experimental group spent four weeks training and practicing mindfulness meditation. Mindfulness was measured before and after training using the Five Facet Mindfulness Questionnaire (FFMQ). Episodic memory was measured before and after training using a source recognition task. During the retrieval phase of the source recognition task, EEG was recorded. The results showed that mindfulness, source recognition behavioral performance, and EEG theta power in right frontal and left parietal channels increased following mindfulness meditation training. In addition, increases in mindfulness correlated with increases in theta power in right frontal channels. Therefore, results obtained from combining mindfulness meditation training, an episodic memory task, and EEG reveal the behavioral and neural effects of mindfulness meditation on episodic memory.

Here we present a protocol for combining mindfulness meditation training, an episodic memory task, and EEG to understand the behavioral and neural effects of mindfulness meditation on episodic memory.

Although there has been recent interest in how mindfulness meditation can affect episodic memory as well as brain structure and function, no study has ...

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each other, mostly using simultaneous-recording signals of brains (namely hyperscanning) with fNIRS. In fNIRS hyperscanning studies, IBS has been commonly assessed through the wavelet transform coherence (WTC) method because of its advantage on expanding time series into time-frequency space where oscillations can be seen in a highly intuitive way. The observed IBS can be further validated via the permutation-based random pairing of the trial, partner, and condition. Here, a protocol is presented to describe how to obtain brain signals via fNIRS technology, calculate IBS through the WTC method, and validate IBS by permutation in a hyperscanning study. Further, we discuss the critical issues when using the above methods, including the choice of fNIRS signals, methods of data preprocessing, and optional parameters of computations.&#160;In summary, using the WTC method and permutation is a potentially standard pipeline for analyzing IBS in fNIRS hyperscanning studies, contributing to both the reproducibility and reliability of IBS.

The dynamics between coupled brains of individuals have been increasingly represented by inter-brain synchronization (IBS) when they coordinate with each ...

Modulation of the Neurophysiological Response to Fearful and Stressful Stimuli Through Repetitive Religious Chanting

In neuropsychological experiments, the late positive potential (LPP) is an event-related potential (ERP) component that reflects the level of one's emotional arousal. This study investigates whether repetitive religious chanting modulates the emotional response to fear- and stress-provoking stimuli, thus leading to a less responsive LPP. Twenty-one participants with at least one year of experience in the repetitive religious chanting of "Amitabha Buddha" were recruited. A 128-channel electroencephalography (EEG) system was used to collect EEG data. The participants were instructed to view negative or neutral pictures selected from the International Affective Picture System (IAPS) under three conditions: repetitive religious chanting, repetitive nonreligious chanting, and no chanting. The results demonstrated that viewing the negative fear- and stress-provoking pictures induced larger LPPs in the participants than viewing neutral pictures under the no-chanting and nonreligious chanting conditions. However, this increased LPP largely disappeared under repetitive religious chanting conditions. The findings indicate that repetitive religious chanting may effectively alleviate the neurophysiological response to fearful or stressful situations for practitioners.

The present event-related potential (ERP) study provides a unique protocol for investigating how religious chanting can modulate negative emotions. The results demonstrate that the late positive potential (LPP) is a robust neurophysiological response to negative emotional stimuli and can be effectively modulated by repetitive religious chanting.

In neuropsychological experiments, the late positive potential (LPP) is an event-related potential (ERP) component that reflects the level of one's ...

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

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