Submission ID #: 62960
Scriptwriter Name: Shehnaz Lokhandwala
Project Page Link: https://www.jove.com/account/file-uploader?src=19202848
Title: Modulation of the
Neurophysiological Response to Fearful and Stressful Stimuli through Repetitive
Religious Chanting
Authors and
Affiliations: Hin
Hung Sik1, Georgios T. Halkias1, Chunqi Chang2,
Junling Gao1, Hang Kin Leung1, Bonnie W. Y. Wu1*
1Buddhism and Science Research Lab, Centre
of Buddhist Studies, The University of Hong Kong
2School of Biomedical Engineering, Shenzhen
University
Corresponding Authors:
Bonnie W.
Y. Wu (bonniewu@hku.hk)
Email Addresses
for All Authors:
hinhung@hku.hk
halkias@hku.hk
cqchang@szu.edu.cn
galeng@hku.hk
hank.leung@hku.hk
bonniewu@hku.hk
Author
Questionnaire
1. Microscopy: Does your protocol require the
use of a dissecting or stereomicroscope for performing a complex dissection,
microinjection technique, or something similar? No
2. Software: Does the part of your protocol being filmed
include step-by-step descriptions of software usage? Yes, all done
3. Filming location: Will the filming need to take place in
multiple locations? No
Current
Protocol Length
Number of
Steps: 31
Number of Shots: 47
Introduction
1.
Introductory
Interview Statements
Videographer:
Obtain headshots for all authors.
REQUIRED:
1.1.
Georgios T. Halkias: Chanting and praying are among the
most popular religious practices. This protocol could help scientists examine
the neurophysiological response of repetitive religious chanting using
Event-related Potentials.
1.1.1. INTERVIEW: Named talent says the
statement above in an interview-style shot, looking slightly off-camera. Suggested b-roll: 2.7
1.2.
Ven. Hin Hung Sik: The ERP
technique can differentiate between early- and late- stages of information
processing, analoging the first and second darts of mind processing in Buddhism
teachings.
1.2.1.
INTERVIEW: Named talent says the statement above in
an interview-style shot, looking slightly off-camera. Suggested b-roll: LAB MEDIA: Figure 2
OPTIONAL:
1.3.
Dr. Bonnie Wu: Following this protocol, researchers can
examine the effect of religious chanting or other traditional practices to
identify feasible ways to help people ameliorate their emotional suffering.
1.3.1.
INTERVIEW: Named talent says the statement above in
an interview-style shot, looking slightly off-camera. Suggested b-roll: 5.2.1
Introduction of Demonstrator on Camera
1.4.
Ven. Hin Hung Sik: Demonstrating the procedure will be Dr. Junling Gao, a Research Officer, and Mr. Hank Leung, a Senior Research Assistant in my Buddhism
and Science Research laboratory.
1.4.1.
INTERVIEW: Author
saying the above.
1.4.2.
The named
demonstrator(s) looks up from workbench or desk or microscope and acknowledges
the camera.
Ethics Title Card
1.5. Procedures involving human participants have
been approved by The University of Hong Kong Institutional Review Board.
Before participating in this study, all participants signed a written informed
consent form.
Protocol
2.
Affective Modulation Experiment
2.1.
To begin this study, recruit participants with
at least 1 year of experience in chanting the name of “Amitabha Buddha” [1].
2.1.1.
WIDE: Establishing shot of talent with
recruited participants.
2.2.
During the experiment, record the EEG (E-E-G) data using a 128-channel EEG system consisting
of an amplifier, headbox, EEG cap, and two desktop computers [1] and
record the ECG data using a physiological data recording system [2].
2.2.1.
Shot of the EEG recording system.
2.2.2.
Shot of the physiological data recording system.
2.3.
For showing neutral and negative pictures from
the International Affective Picture System, or IAPS (I-A-P-S), use
stimulus presentation software on a desktop computer [1].
2.3.1.
Shot of talent at the desktop computer,
opening the stimulus presentation software.
2.4.
Present the pictures on a monitor at 75 centimeters
from the participants’ eyes, with visual angles of 15 degrees vertically and 21
degrees horizontally [1].
2.4.1.
Shot of participant in front of the monitor at
the appropriate position.
2.5.
Use a block design for the experiment as it
may more effectively elicit emotion-related components. Provide a brief
practice run to allow the participants to familiarize themselves with each
condition [1] and use a video monitor to ensure that the participants do
not fall asleep [2].
2.5.1.
LAB MEDIA: Figure 1.
2.5.2.
Talent watching the participant through a
video monitor.
2.6.
Begin the experiment with the religious
chanting condition [1]. Ask the participants to chant four
characters of the name of “Amitabha Buddha” for 40 seconds while imagining
the Amitabha following the script in Pureland school [2].
2.6.1.
LAB MEDIA: Figure 1. Video Editor: Please emphasize the red AmiNeu and AmiNeg rows
2.6.2.
Talent asking the participant to chant the
name of “Amitabha Buddha.”
2.7.
During the first 20 seconds, show the
participants the image of Amitabha [1], and for the next 20 seconds,
show them the IAPS images [2]. Ask the participants to observe the
pictures carefully. Show each picture for approximately 1.8 to 2.2 seconds,
with an interstimulus interval of 0.4 to 0.6 seconds [3].
2.7.1.
Shot of participant chanting the name of “Amitabha
Buddha” with the image of Amitabha being shown to them visible in frame. Videographer: Capture audio when the participant is
chanting
2.7.2.
Shot of participant chanting the name of “Amitabha
Buddha” with the IAPS images being shown to them visible in frame. Videographer: Capture audio when the participant is
chanting
2.7.3.
Shot of participant chanting
and looking at the monitor, with screen visible in frame. Videographer: Capture a shot in which the picture is
changing to the next image
2.8.
After each session, allow a rest period of 20
seconds [1] to counter the potential residual effects of chanting or
picture viewing on the next session [2-TXT].
2.8.1.
LAB MEDIA: Figure 1. Video Editor: Please emphasize the 20-second grey
Rest periods.
2.8.2.
Shot of participant resting/not looking at the
monitor. TEXT: Give the participants a 10 min rest in the
middle of the 40 min experiment
2.9.
For the non-religious chanting condition [1],
ask the participants to chant four characters of the name of “Santa
Claus” for
40 seconds while imagining the Santa Claus [2].
2.9.1.
LAB MEDIA: Figure 1. Video Editor: Please emphasize the blue SanNeu and
SanNeg rows
2.9.2.
Talent asking the participant to chant the
name of “Santa Claus.”
2.10.
During the first 20 seconds, show the
participants the image of Santa Claus [1], and for the next 20 seconds,
show them the IAPS images [2].
2.10.1.
Shot of participant chanting the name of “Santa
Claus” with the image of Santa Claus being shown to them visible in frame. Videographer: Capture audio when the participant is
chanting
2.10.2.
Shot of participant chanting the name of “Santa
Claus” with the IAPS images being shown to them. Videographer:
Capture audio when the participant is chanting
2.11.
For the control condition [1], ask the
participants to keep silent for 40 seconds [2]. During the first 20 seconds,
show the participants a blank image [3], and for the next 20 seconds,
show them the IAPS images [4].
2.11.1.
LAB MEDIA: Figure 1. Video Editor: Please emphasize the green PasNeu and
PasNeg rows
2.11.2.
Talent asking the participant to keep silent.
2.11.3.
Shot of participant staying silent with a
blank picture being shown to them visible in frame.
2.11.4.
Shot of participant staying silent with the
IAPS being shown to them visible in frame
3.
EEG Data Analysis
3.1.
To process and analyze the EEG data, use the open-source
software, EEGLAB (E-E-G-lab) [1].
3.1.1.
WIDE: Talent at the computer, opening EEGLAB
software, monitor visible in frame. Videographer:
Obtain a few shots of talent clicking the mouse and typing on the keyboard to
use as b-roll throughout the video
3.2.
To maintain a reasonable data file size, use
the EEGLAB function “pop_resample” (pop-underscore-resample). Click on Tools followed by Change sampling rate to resample the data from 1,000 Hertz to 250
Hertz [1].
3.2.1.
SCREEN: 62960_screenshot_3.2.1. 0:00-0:15
3.3.
Next, filter the data using the EEGLAB
function “pop_eegfiltnew” (pop-underscore-E-E-G-filt-new). Click
on Tools followed by Filter the data, then select Basic FIR (F-I-R) filter new-comma-default
to filter the data with a finite impulse response filter with a 0.1
to 100 Hertz passband [1].
3.3.1.
SCREEN: 62960_screenshot_3.3.1. 0:00-0:23
3.4.
To reduce the noise from the alternating current,
click on Tools followed by Filter the
data and select Notch filter the data instead of pass band. Then, filter the data with a nonlinear infinite impulse response
filter with a 47 to 53 Hertz stopband [1].
3.4.1.
SCREEN: 62960_screenshot_3.4.1. 0:00-0:21
3.5.
Next, click on Plot and then Channel data-scroll to visually inspect the data and remove strong artifacts generated by
eye and muscle movements [1]. Then, click on Tools,
Interpolate electrodes, and Select from the data channels to
reconstruct the bad channels using spherical interpolation [2].
3.5.1.
SCREEN: 62960_screenshot_3.5.1. 0:00-0:12.
3.5.2.
SCREEN: 62960_screenshot_3.5.2. 0:00-0:17,
then skip to 0:21-0:32. Video Editor: Speed up, as
necessary
3.6.
Next, click
on Tools and Run ICA to run an independent
component analysis with the open-source algorithm “runica” [1]. Then, click on Tools
again, followed by Reject data using ICA and Reject
components by map to remove the
independent components corresponding to eye movements, blinks, muscle movement,
and line noise [2].
3.6.1.
SCREEN: 62960_screenshot_3.6.1. 0:00-0:09
3.6.2.
SCREEN: 62960_screenshot_3.6.2. 0:00-0:23
3.7.
To reconstruct
the data using the remaining independent
components, click on Tools followed by Remove
components [1].
3.7.1.
SCREEN: 62960_screenshot_3.7.1. 0:00-0:12
3.8.
Next, click
on Tools followed by Filter the data and select Basic FIR filter new-comma- default to filter the data with a 30 Hertz
low-pass filter [1].
3.8.1.
SCREEN: 62960_screenshot_3.8.1. 0:00-0:15
3.9.
Then, click on Tools followed by Extract epochs to obtain ERP data by extracting and averaging time-locked epochs for
each condition with a time window of negative-200 to 0 milliseconds as the
baseline and 0 to 800 milliseconds as the ERP [1].
3.9.1.
SCREEN: 62960_screenshot_3.9.1. 0:00-0:26
3.10.
Next, click on Tools followed by
Re-reference to re-reference the ERP data with the average of the left and
right mastoid channels [1].
3.10.1.
SCREEN: 62960_screenshot_3.10.1. 0:00-0:20
3.11.
After repeating the above steps for the datasets
from all participants, define time windows for N1 and late positive potential,
or LPP, based on established theories and the current data [1].
3.11.1.
Talent defining time windows for N1 and LPP by
pointing to a screen/figure.
3.12.
Then, using a paired t-test, find the neutral versus negative picture difference at the
N1 component [1] and the LPP component among the three conditions [2].
3.12.1.
Talent at the computer, finding neutral
versus negative picture difference at the N1 component, monitor visible in
frame.
3.12.2.
Talent at the computer, finding neutral
versus negative picture difference at the LPP component, monitor visible in
frame.
3.13.
Next, perform a region-of-interest analysis on the
N1 and LPP components by averaging relevant channels to represent a region [1]. Then,
compare the difference at N1 and LPP separately, using repeated measures ANOVA
and post hoc statistics in statistical analysis software [2].
3.13.1.
SCREEN:
To be uploaded by Authors: Performing ROI analysis Videographer: Please film the screen for this shot as backup
3.13.2.
Talent at computer, performing repeated
measures ANOVA, monitor visible in frame.
4.
ERP Source Analysis
4.1.
Use the SPM open-source software to perform
the ERP source analysis [1].
4.1.1.
WIDE: Talent at the computer, opening SPM
software, monitor visible in frame.
4.2.
Link the coordinate system of the EEG cap sensor
to that of a standard structural MRI image by landmark-based co-registration. In SPM, click on Batch, then SPM,
M/EEG (M-slash-EEG),
Source reconstruction, and Head model specification [1].
4.2.1.
SCREEN: 62960_screenshot_4.2.1. 0:00-0:14
4.3.
Next, perform forward computation to calculate
the effect of each dipole on the cortical mesh imposed on the EEG sensors. Under
the same Batch Editor, click on SPM, then M/EEG, Source reconstruction,
and Source inversion [1].
4.3.1.
SCREEN: 62960_screenshot_4.3.1. 0:00-0:12
4.4.
To perform the inverse reconstruction, use the
greedy search-based multiple sparse priors algorithm in the third step. Choose MSP-GS for the Inversion
type in the Source Inversion window [1].
4.4.1.
SCREEN: 62960_screenshot_4.4.1. 0:00-0:10.
4.5.
Determine the difference between conditions
using general linear modeling in SPM. After setting the significance level to p (single letter
‘P’) less than 0.05, under Batch Editor, click on SPM, then Stats and Factorial design
specification [1].
4.5.1.
SCREEN: 62960_screenshot_4.5.1. 0:00-0:12
5.
ECG Data and Behavioral Assessment Analysis
5.1.
To process and analyze the ECG data, use
physiological and data processing software [1]. To calculate the mean
scores for each condition, in EEGLAB, click
on Tools followed by FMRIB Tools and Detect QRS events [2].
5.1.1.
WIDE: Talent at
the computer, opening physiological and data processing software, monitor
visible in frame.
5.1.2.
SCREEN: 62960_screenshot_5.1.2. 0:00-0:12
5.2.
For behavioral assessment analysis, ask the
participants to rate their belief in the efficacy of chanting the subject’s
name on a 1 to 9 scale, where 1 is considered the weakest and 9 the strongest [1].
5.2.1.
Shot of participant rating their beliefs in the
efficacy of chanting.
Results
6. Results: Effective Modulation of Late Positive
Potential (LPP) by Repetitive Religious Chanting
6.1. Results for participants’ belief in chanting [1]
revealed an average score of 8.16 for “Amitabha Buddha” [2], 3.26 for “Santa Claus” [3], and 1.95 for the blank control condition [4].
6.1.1. LAB MEDIA:
Supplementary Table 1
6.1.2. LAB MEDIA:
Supplementary Table 1. Video Editor: Please emphasize
the mean score ‘8.16’ for the Amitabha Figure
6.1.3. LAB MEDIA:
Supplementary Table 1. Video Editor: Please emphasize
the mean score ‘3.26’ for the Santa Claus Figure
6.1.4. LAB MEDIA:
Supplementary Table 1. Video Editor: Please emphasize
the mean score ‘1.95’ for the Blank Figure
6.2. The
representative channel of the Parietal lobe demonstrated [1] that the chanting
conditions had different effects on the early [2] and late processing of
neutral and negative pictures [3],
showing the time window of N1 and LPP,
respectively [4].
6.2.1. LAB MEDIA:
Figure 2.
6.2.2. LAB MEDIA:
Figure 2. Video Editor: Please emphasize the N1 box
6.2.3. LAB MEDIA:
Figure 2. Video Editor: Please emphasize the LPP box
6.2.4. LAB MEDIA:
Figure 2.
6.3. The ERP results showed [1] an
increased N1 while viewing the fearful pictures in the three chanting
conditions [2]. The negative images induced stronger central brain
activities than neutral images, and the increases are comparable in the three
conditions [3].
6.3.1. LAB MEDIA:
Figure 3.
6.3.2. LAB MEDIA:
Figure 3. Video Editor: Please emphasize the entire Negative-Pictures
column
6.3.3. LAB MEDIA:
Figure 3. Video Editor: Please emphasize the entire Negative-Neutral
(t-test) column
6.4. The ERP also demonstrated an increased
LPP [1] in the non-religious chanting [2] and no-chanting
conditions [3]. However, the
LPP induced by fearful pictures is barely visible when the participants chant
Amitabha Buddha’s name [4].
6.4.1. LAB MEDIA:
Figure 4.
6.4.2. LAB MEDIA:
Figure 4. Video Editor: Please emphasize the Negative-Neutral
(t-test) column for ‘Chanting Santa Claus’
6.4.3. LAB MEDIA:
Figure 4. Video Editor: Please emphasize the Negative-Neutral
(t-test) column for ‘Passive View’
6.4.4. LAB MEDIA:
Figure 4. Video Editor: Please emphasize the Negative-Neutral
(t-test) column for ‘Chanting Amitabha’
6.5. A region-of-interest
analysis [1] revealed that the differences in the N1 component were similar across the three
conditions [2]; however, the difference in the LPP component is much
smaller in the religious chanting condition [3] than in the non-religious
chanting condition [4] and the silent viewing condition [5].
6.5.1. LAB MEDIA:
Figure 5.
6.5.2. LAB MEDIA:
Figure 5. Video Editor: Please emphasize all three N1 bars
6.5.3. LAB MEDIA:
Figure 5. Video Editor: Please emphasize the brown LPP bar
6.5.4. LAB MEDIA:
Figure 5. Video Editor: Please emphasize the green LPP bar
6.5.5. LAB MEDIA:
Figure 5. Video Editor: Please emphasize the blue LPP bar
6.6. Source
analysis revealed that when compared
with neutral pictures [1], negative pictures induce more parietal activation
in the non-religious chanting
condition [2] and no chanting condition [3]. In contrast, this
negative picture-induced activation largely disappears in the religious
chanting condition [4].
6.6.1. LAB MEDIA:
Figure 6.
6.6.2. LAB MEDIA:
Figure 6. Video Editor: Please emphasize the yellow brain
regions in the ‘Chanting Santa Claus ’ row
6.6.3. LAB MEDIA:
Figure 6. Video Editor: Please emphasize the yellow brain
regions in the ‘Passive View’ row
6.6.4. LAB MEDIA:
Figure 6. Video Editor: Please emphasize the ‘Chanting
Amitabha’ row
6.7. A
significant change in the heart rate was detected [1] between the
fearful and neutral pictures in the non-religious [2] and no-chanting
conditions [3]. However, no
such difference was found in the religious chanting condition [4].
6.7.1. LAB MEDIA:
Figure 7.
6.7.2. LAB MEDIA:
Figure 7. Video Editor: Please emphasize the difference
(‘p=0.002’) between SanNeu and SanNeg
6.7.3. LAB MEDIA:
Figure 7. Video Editor: Please emphasize the difference
(‘p=0.032’ ) between PasNeu and PasNeg
6.7.4. LAB MEDIA:
Figure 7. Video Editor: Please emphasize the no difference
(‘p=0.888’ ) between AmiNeu and AmiNeg
Conclusion
7.
Conclusion Interview Statements
7.1.
Dr Junling Gao:
This same protocol can be used in functional neuroimaging studies to
reveal more specifically the
brain regions involved in religious chanting.
7.1.1.
INTERVIEW: Named talent says the statement above in
an interview-style shot, looking slightly off-camera.
7.2.
Ven. Hin Hung Sik:
This study demonstrates a method for examining how repetitive
religious chanting or other similar practices can influence the
neurophysiological response and help reduce the suffering induced by negative
stimuli.
7.2.1.
INTERVIEW: Named talent says the statement above in
an interview-style shot, looking slightly off-camera. Suggested b-roll: LAB MEDIA: Figure 4
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