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

<ol>
	<li>Gao, J., Leung, H. K., Wu, B. W. Y., Skouras, S., Sik, H. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+neurophysiological+correlates+of+religious+chanting.">The neurophysiological correlates of religious chanting.</a> Sci Rep. 9 (1), 4262 (2019).</li><li>Sik, H. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modulation+of+the+neurophysiological+response+to+fearful+and+stressful+stimuli+through+repetitive+religious+chanting.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Repetitive+religious+chanting+invokes+positive+emotional+schema+to+counterbalance+fear:+A+multimodal+functional+and+structural+MRI+study.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhanced+neural+mechanisms+of+set+shifting+in+musically+trained+adolescents+and+young+adults:+converging+fMRI,+EEG,+and+behavioral+evidence.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Constructing+an+emotion+estimation+model+based+on+EEG/HRV+indexes+using+feature+extraction+and+feature+selection+algorithms.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+neural+correlates+of+amplitude+of+low-frequency+fluctuation:+a+multimodal+resting-state+MEG+and+fMRI-EEG+study.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Independent+components+of+EEG+activity+correlating+with+emotional+state.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measure+projection+analysis:+a+probabilistic+approach+to+EEG+source+comparison+and+multi-subject+inference.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+functional+significance+of+delta+oscillations+in+cognitive+processing.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Connectivity+between+mPFC+and+PCC+predicts+post-choice+attitude+change:+The+self-referential+processing+hypothesis+of+choice+justification.">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://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Guide to Buddhist Counseling. , (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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Randomized+Controlled+Trial+of+Awareness+Training+Program+(ATP),+a+Group-Based+Mahayana+Buddhist+Intervention.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+simultaneous+EEG-fMRI+study+of+thalamic+load-dependent+working+memory+delay+period+activity.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Repetitive+religious+chanting+modulates+the+late-stage+brain+response+to+fear-and+stress-provoking+pictures.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simultaneous+real-time+EEG-fMRI+neurofeedback:+A+systematic+review.">Simultaneous real-time EEG-fMRI neurofeedback: A systematic review.</a> Front Hum Neurosci. 17, 1123014 (2023).</li><li>Abreu, R., Nunes, S., Leal, A., Figueiredo, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Physiological+noise+correction+using+ECG-derived+respiratory+signals+for+enhanced+mapping+of+spontaneous+neuronal+activity+with+simultaneous+EEG-fMRI.">Physiological noise correction using ECG-derived respiratory signals for enhanced mapping of spontaneous neuronal activity with simultaneous EEG-fMRI.</a> Neuroimage. 154, 115-127 (2017).</li></ol>

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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&#34;</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

786 Views.  The University of Hong Kong. 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.

Video: Author Spotlight: Exploring Dynamic Neural Changes Associated with Religious Chanting

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains technically challenging. One approach, Activation-Induced Manganese-enhanced MRI (AIM MRI), has been used successfully to map neuronal activity in rodents 1-5. In AIM MRI, Mn2+ acts a calcium analog and accumulates in depolarized neurons 6,7. Because Mn2+ shortens the T1 tissue property, regions of elevated neuronal activity will enhance in MRI. Furthermore, Mn2+ clears slowly from the activated regions; therefore, stimulation can be performed outside the magnet prior to imaging, enabling greater experimental flexibility. However, because Mn2+ does not readily cross the blood-brain barrier (BBB), the need to open the BBB has limited the use of AIM MRI, especially in mice.
One tool for opening the BBB is ultrasound. Though potentially damaging, if ultrasound is administered in combination with gas-filled microbubbles (i.e., ultrasound contrast agents), the acoustic pressure required for BBB opening is considerably lower. This combination of ultrasound and microbubbles can be used to reliably open the BBB without causing tissue damage 8-11.
Here, a method is presented for performing AIM MRI by using microbubbles and ultrasound to open the BBB. After an intravenous injection of perflutren microbubbles, an unfocused pulsed ultrasound beam is applied to the shaved mouse head for 3 minutes. For simplicity, we refer to this technique of BBB Opening with Microbubbles and UltraSound as BOMUS 12. Using BOMUS to open the BBB throughout both cerebral hemispheres, manganese is administered to the whole mouse brain. After experimental stimulation of the lightly sedated mice, AIM MRI is used to map the neuronal response.
To demonstrate this approach, herein BOMUS and AIM MRI are used to map unilateral mechanical stimulation of the vibrissae in lightly sedated mice 13. Because BOMUS can open the BBB throughout both hemispheres, the unstimulated side of the brain is used to control for nonspecific background stimulation. The resultant 3D activation map agrees well with published representations of the vibrissae regions of the barrel field cortex 14. The ultrasonic opening of the BBB is fast, noninvasive, and reversible; and thus this approach is suitable for high-throughput and/or longitudinal studies in awake mice.

A technique is described for broadly opening the blood-brain barrier in the mouse using microbubbles and ultrasound. Using this technique, manganese can be administered to the mouse brain. Because manganese is an MRI contrast agent that accumulates in depolarized neurons, this approach enables imaging of neuronal activity.

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains ...

Growing Neural Stem Cells from Conventional and Nonconventional Regions of the Adult Rodent Brain

Recent work demonstrates that central nervous system (CNS) regeneration and tumorigenesis involves populations of stem cells (SCs) resident within the adult brain. However, the mechanisms these normally quiescent cells employ to ensure proper functioning of neural networks, as well as their role in recovery from injury and mitigation of neurodegenerative processes are little understood. These cells reside in regions referred to as &#34;niches&#34; that provide a sustaining environment involving modulatory signals from both the vascular and immune systems. The isolation, maintenance, and differentiation of CNS SCs under defined culture conditions which exclude unknown factors, makes them accessible to treatment by pharmacological or genetic means, thus providing insight into their in vivo behavior. Here we offer detailed information on the methods for generating cultures of CNS SCs from distinct regions of the adult brain and approaches to assess their differentiation potential into neurons, astrocytes, and oligodendrocytes in vitro. This technique yields a homogeneous cell population as a monolayer culture that can be visualized to study individual SCs and their progeny. Furthermore, it can be applied across different animal model systems and clinical samples, being used previously to predict regenerative responses in the damaged adult nervous system.

Neural stem cells harvested from the adult brain are increasing utilized in applications ranging from the basic research of nervous system development to exploring potential clinical applications in regenerative medicine. This makes rigorous control in the isolation and culturing conditions used to grow these cells critical to sound experimental outcomes.

Recent work demonstrates that central nervous system (CNS) regeneration and tumorigenesis involves populations of stem cells (SCs) resident within the ...

The Olfactory System as a Model to Study Axonal Growth Patterns and Morphology In Vivo

The olfactory system has the unusual capacity to generate new neurons throughout the lifetime of an organism. Olfactory stem cells in the basal portion of the olfactory epithelium continuously give rise to new sensory neurons that extend their axons into the olfactory bulb, where they face the challenge to integrate into existing circuitry. Because of this particular feature, the olfactory system represents a unique opportunity to monitor axonal wiring and guidance, and to investigate synapse formation. Here we describe a procedure for in vivo labeling of sensory neurons and subsequent visualization of axons in the olfactory system of larvae of the amphibian Xenopus laevis. To stain sensory neurons in the olfactory organ we adopt the electroporation technique. In vivo electroporation is an established technique for delivering fluorophore-coupled dextrans or other macromolecules into living cells. Stained sensory neurons and their axonal processes can then be monitored in the living animal either using confocal laser-scanning or multiphoton microscopy. By reducing the number of labeled cells to few or single cells per animal, single axons can be tracked into the olfactory bulb and their morphological changes can be monitored over weeks by conducting series of in vivo time lapse imaging experiments. While the described protocol exemplifies the labeling and monitoring of olfactory sensory neurons, it can also be adopted to other cell types within the olfactory and other systems.

The Olfactory System as a Model to Study Axonal Growth Patterns and Morphology In Vivo

We describe a protocol for in vivo labeling of olfactory sensory neurons by electroporation and subsequent confocal laser-scanning or multiphoton microscopy to visualize neuronal morphology and its development over time.

The olfactory system has the unusual capacity to generate new neurons throughout the lifetime of an organism. Olfactory stem cells in the basal portion of ...

New Methods to Study Gustatory Coding

The sense of taste allows animals to detect chemicals in the environment, giving rise to behaviors critical for survival. When Gustatory Receptor Neurons (GRNs) detect tastant molecules, they encode information about the identity and concentration of the tastant as patterns of electrical activity that then propagate to follower neurons in the brain. These patterns constitute internal representations of the tastant, which then allow the animal to select actions and form memories. The use of relatively simple animal models has been a powerful tool to study basic principles in sensory coding. Here, we propose three new methods to study gustatory coding using the moth Manduca sexta. First, we present a dissection procedure for exposing the maxillary nerves and the subesophageal zone (SEZ), allowing recording of the activity of GRNs from their axons. Second, we describe the use of extracellular electrodes to record the activity of multiple GRNs by placing tetrode wires directly into the maxillary nerve. Third, we present a new system for delivering and monitoring, with high temporal precision, pulses of different tastants. These methods allow the characterization of neuronal responses in vivo directly from GRNs before, during and after tastants are delivered. We provide examples of voltage traces recorded from multiple GRNs, and present an example of how a spike sorting technique can be applied to the data to identify the responses of individual neurons. Finally, to validate our recording approach, we compare extracellular recordings obtained from GRNs with tetrodes to intracellular recordings obtained with sharp glass electrodes.

We present three new methods to study gustatory coding. Using a simple animal, the moth Manduca sexta (Manduca), we describe a dissection protocol, the use of extracellular tetrodes to record the activity of multiple gustatory receptor neurons, and a system for delivering and monitoring precisely timed pulses of tastants.

The sense of taste allows animals to detect chemicals in the environment, giving rise to behaviors critical for survival. When Gustatory Receptor Neurons ...

Stereotactic Atlas-Guided Laser Capture Microdissection of Brain Regions Affected by Traumatic Injury

The ability to isolate specific brain regions of interest can be impeded in tissue disassociation techniques that do not preserve their spatial distribution. Such techniques also potentially skew gene expression analysis because the process itself can alter expression patterns in individual cells. Here we describe a laser capture microdissection (LCM) method to selectively collect specific brain regions affected by traumatic brain injury (TBI) by using a modified Nissl (cresyl violet) staining protocol and the guidance of a rat brain atlas. LCM provides access to brain regions in their native positions and the ability to use anatomical landmarks for identification of each specific region. To this end, LCM has been used previously to examine brain region specific gene expression in TBI. This protocol allows examination of TBI-induced alterations in gene and microRNA expression in distinct brain areas within the same animal. The principles of this protocol can be amended and applied to a wide range of studies examining genomic expression in other disease and/or animal models.

We describe the use of laser capture microdissection to obtain samples of distinct cell populations from different brain regions for gene and microRNA analysis. This technique allows the study of differential effects of traumatic brain injury in specific regions of the rat brain.

The ability to isolate specific brain regions of interest can be impeded in tissue disassociation techniques that do not preserve their spatial ...

Use of Synaptic Zinc Histochemistry to Reveal Different Regions and Laminae in the Developing and Adult Brain

Characterization of anatomical and functional brain organization and development requires accurate identification of distinct neural circuits and regions in the immature and adult brain. Here we describe a zinc histochemical staining procedure that reveals differences in staining patterns among different layers and brain regions. Others have utilized this procedure not only to reveal the distribution of zinc-containing neurons and circuits in the brain, but also to successfully delineate areal and laminar boundaries in the developing and adult brain in several species. Here we illustrate this staining procedure with images from developing and adult ferret brains. We reveal a zinc-staining pattern that serves as an anatomical marker of areas and layers, and can be reliably used to distinguish visual cortical areas in the developing and adult visual cortex. The main goal of this protocol is to present a histochemical method that allows the accurate identification of layers and regions in the developing and adult brain where other methods fail to do so. Secondarily, in conjunction with densitometric image analysis, this method allows one to assess the distribution of synaptic zinc to reveal potential changes throughout development. This protocol describes in detail the reagents, tools, and steps necessary to successively stain frozen brain sections. Although this protocol is described using ferret brain tissue, it can easily be adapted for use in rodents, cats, or monkeys as well as in other brain regions.

We describe a histochemical procedure that reveals characteristic laminar and areal zinc staining patterns in different brain regions. The zinc-staining pattern may be used in conjunction with other anatomical markers to reliably distinguish layers and regions in the developing and adult brain.

Characterization of anatomical and functional brain organization and development requires accurate identification of distinct neural circuits and regions ...

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 ...

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

Concurrent brain recordings of two or more interacting persons, an approach termed hyperscanning, are gaining increasing importance for our understanding of the neurobiological underpinnings of social interactions, and possibly interpersonal relationships. Functional near-infrared spectroscopy (fNIRS) is well suited for conducting hyperscanning experiments because it measures local hemodynamic effects with a high sampling rate and, importantly, it can be applied in natural settings, not requiring strict motion restrictions. In this article, we present a protocol for conducting fNIRS hyperscanning experiments with parent-child dyads and for analyzing brain-to-brain synchrony. Furthermore, we discuss critical issues and future directions, regarding the experimental design, spatial registration of the fNIRS channels, physiological influences and data analysis methods. The described protocol is not specific to parent-child dyads, but can be applied to a variety of different dyadic constellations, such as adult strangers, romantic partners or siblings. To conclude, fNIRS hyperscanning has the potential to yield new insights into the dynamics of the ongoing social interaction, which possibly go beyond what can be studied by examining the activities of individual brains.

The present protocol describes how to conduct fNIRS hyperscanning experiments and analyze brain-to-brain synchrony. Further, we discuss challenges and possible solutions.

Concurrent brain recordings of two or more interacting persons, an approach termed hyperscanning, are gaining increasing importance for our understanding ...

Collection of Frozen Rodent Brain Regions for Downstream Analyses

As our understanding of neurobiology has progressed, molecular analyses are often performed on small brain areas such as the medial prefrontal cortex (mPFC) or nucleus accumbens. The challenge in this work is to dissect the correct area while preserving the microenvironment to be examined. In this paper, we describe a simple, low-cost method using resources readily available in most labs. This method preserves nucleic acid and proteins by keeping the tissue frozen throughout the process. Brains are cut into 0.5&#8211;1.0 mm sections using a brain matrix and arranged on a frozen glass plate. Landmarks within each section are compared to a reference, such as the Allen Mouse Brain Atlas, and regions are dissected using a cold scalpel or biopsy punch. Tissue is then stored at -80 &#176;C until use. Through this process rat and mouse mPFC, nucleus accumbens, dorsal and ventral hippocampus and other regions have been successfully analyzed using qRT-PCR and Western assays. This method is limited to brain regions that can be identified by clear landmarks.

This procedure describes the collection of discrete frozen brain regions to obtain high-quality protein and RNA using inexpensive and commonly available tools.

As our understanding of neurobiology has progressed, molecular analyses are often performed on small brain areas such as the medial prefrontal cortex ...

Dissection and Isolation of Murine Glia from Multiple Central Nervous System Regions

The methods presented here demonstrate laboratory procedures for the dissection of four different regions of the central nervous system (CNS) from murine neonates for the isolation of glial subpopulations. The purpose of the procedure is to dissociate microglia, oligodendrocyte progenitor cells (OPCs), and astrocytes from cortical, cerebellar, brainstem, and spinal cord tissue to facilitate further in vitro analysis. The CNS region isolation procedures allow for the determination of regional heterogeneity among glia in multiple cell culture systems. Rapid CNS region isolation is performed, followed by the mechanical removal of meninges to prevent meningeal cell contamination of glia. This protocol combines gentle tissue dissociation and plating on a specified matrix designed to preserve cell integrity and adherence. Isolating mixed glia from multiple CNS regions provides a comprehensive analysis of potentially heterogenous glia while maximizing the use of individual experimental animals. Additionally, following dissociation of regional tissue, mixed glia are further divided into multiple cell types including microglia, OPCs, and astrocytes for use in either single cell type, cell culture plate inserts, or co-culture systems. Overall, the demonstrated techniques provide a comprehensive protocol of broad applicability for careful dissection of four individual CNS regions from murine neonates and includes methods for the isolation of three individual glia cell types to examine regional heterogeneity in any number of in vitro cell culture systems or assays.

Here we present a protocol for in vitro isolation of multiple glial cell populations from a mouse CNS. This method allows for the segregation of regional microglia, oligodendrocyte precursor cells, and astrocytes to study the phenotypes of each in a variety of culture systems.

The methods presented here demonstrate laboratory procedures for the dissection of four different regions of the central nervous system (CNS) from murine ...