<meta charset="utf8"></head><body>EEG 및 fMRI를 사용한 종교 성가 중 뇌 활동 디코딩

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	<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. 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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>

<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

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.

<meta charset="utf8"></head><body>저자 스포트라이트: 종교적 성가와 관련된 역동적인 신경 변화 탐구

종교적 성가와 자기 관련 뇌 영역: 다중 모드 신경 영상 연구

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

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

415 조회수.  The University of Hong Kong. 우리의 연구는 다중 모드 신경 영상 기법을 사용하여 종교 성가의 신경 생리학적 상관 관계를 탐구합니다. 우리는 반복적인 종교적 노래와 관련된 뇌 영역과 신경 과정을 밝히고 이것이 비종교적 노래 부르기 및 휴식 상태와 어떻게 다른지 밝히는 것을 목표로 합니다. 최근의 연구는 첨단 신경 영상 기법을 사용하여 다양한 명상 및 종교 행위가 뇌에...

비디오: 저자 스포트라이트: 종교적 성가와 관련된 역동적인 신경 변화 탐구

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.

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.