Poster No:
1985
Submission Type:
Abstract Submission
Authors:
Shigeyuki Ikeda1, Sho Tsukawaki1, Takayuki Nozawa1
Institutions:
1University of Toyama, Toyama, Toyama
First Author:
Co-Author(s):
Introduction:
Viewing emotional videos is known to synchronize our brain activity. In most cases, the Pearson correlation coefficient has been used to derive multisubject similarity measures of brain activity data. However, this method is limited in identifying the frequency bands where neural synchronization occurs. This study used wavelet transform coherence (WTC) (Torrence and Compo 1998; Grinsted, Moore, and Jevrejeva 2004) to evaluate the extent to which emotional video viewing synchronizes multisubject brain activity in time-frequency space. We hypothesized that videos with strong emotions (i.e., arousal and valence) would increase inter-subject coherence (ISC). This study compared ISC between high and low arousal, as well as between high and low valence videos.
Methods:
This study was approved by the Institutional Review Board at the University of Toyama and was conducted in accordance with the Declaration of Helsinki. The subjects included 17 Japanese, healthy, university or postgraduate students (14 men and 3 women, mean age: 20.9 ± 0.93 years). We obtained written informed consent from all subjects. Brain activity during viewing videos was measured using WOT-HS (Hitachi High-Technologies, Japan), a wearable functional near-infrared spectroscopy (fNIRS) device with 34 channels covering the prefrontal and temporal lobes. Forty 60-second emotional videos were retrieved from YouTube. Nine of the 17 subjects viewed the videos while their brain activity was recorded. After each video, their emotional state was quantified by the self-assessment manikin questionnaire, where they rated valence and arousal levels on a 9-point Likert scale. Based on these ratings, we selected 12 videos eliciting strong or weak emotional responses: 3 each for high arousal, low arousal, high valence, and low valence. The remaining 8 subjects watched these 12 videos, and their brain activity was also recorded.
The fNIRS signals from individual channels were preprocessed using linear detrending. Skin blood-flow signals recorded with short source-detector distance channels were regressed out to extract cortical blood-flow components. Each channel's signals were then z-normalized using the mean and standard deviation from a 20-second resting period provided at the start of the experiment. Finally, the 34 channels were averaged into five brain regions: medial prefrontal, bilateral lateral prefrontal, and bilateral temporal regions.
ISC for all pairs of the 17 subjects in each brain region was computed using WTC, which provides coherence values between two time series as a function of period (the inverse of frequency) and time. Coherence values for each period were time-averaged over the 60-second video duration. The time-averaged coherence values were then averaged across three videos for each emotion. To cover a broad frequency range and account for wavelet transform edge effects, we focused on the coherence values for each period between 1.33 and 21.9 s (i.e., 0.046–0.75 Hz). Differences in ISC for each period between high and low emotions were assessed using a two-tailed paired-sample permutation test (10,000 iterations). Statistical significance was determined by applying a false discovery rate correction (q ≤ 0.05) to comparisons across all periods for each emotion in individual brain regions.
Results:
Unexpectedly, ISC for low valence was significantly greater than for high valence in the medial prefrontal (0.15–0.18 and 0.29–0.35 Hz), right lateral prefrontal (0.12–0.17 and 0.26–0.35 Hz), and right temporal regions (0.05–0.08 and 0.29–0.37 Hz). In contrast, high arousal showed significantly greater ISC than low arousal in the right temporal region (0.05–0.09 and 0.37–0.44 Hz).
Conclusions:
Contrary to our expectations, low valence showed greater ISC than high valence. Furthermore, significant results were observed in the frequency range above 0.2 Hz, which, in previous studies, has been considered non-neuronal activity.
Emotion, Motivation and Social Neuroscience:
Emotional Perception
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Other Methods
Novel Imaging Acquisition Methods:
NIRS 1
Perception, Attention and Motor Behavior:
Perception: Visual
Keywords:
Cerebral Blood Flow
Cognition
Near Infra-Red Spectroscopy (NIRS)
NORMAL HUMAN
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
Other, Please specify
-
fNIRS
Provide references using APA citation style.
Grinsted, A., J. C. Moore, and S. Jevrejeva. 2004. “Application of the Cross Wavelet Transform and Wavelet Coherence to Geophysical Time Series.” Nonlinear Processes in Geophysics 11 (5/6): 561–66.
Torrence, Christopher, and Gilbert P. Compo. 1998. “A Practical Guide to Wavelet Analysis.” Bulletin of the American Meteorological Society 79 (1): 61–78.
No