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. 2013;8(4):e57111.
doi: 10.1371/journal.pone.0057111. Epub 2013 Apr 3.

Gum chewing inhibits the sensory processing and the propagation of stress-related information in a brain network

Affiliations

Gum chewing inhibits the sensory processing and the propagation of stress-related information in a brain network

Hongbo Yu et al. PLoS One. 2013.

Abstract

Stress is prevalent in human life and threatens both physical and mental health; stress coping is thus of adaptive value for individual's survival and well-being. Although there has been extensive research on how the neural and physiological systems respond to stressful stimulation, relatively little is known about how the brain dynamically copes with stress evoked by this stimulation. Here we investigated how stress is relieved by a popular coping behavior, namely, gum chewing. In an fMRI study, we used loud noise as an acute stressor and asked participants to rate their feeling of stress in gum-chewing and no-chewing conditions. The participants generally felt more stressful when hearing noise, but less so when they were simultaneously chewing gum. The bilateral superior temporal sulcus (STS) and the left anterior insula (AI) were activated by noise, and their activations showed a positive correlation with the self-reported feeling of stress. Critically, gum chewing significantly reduced the noise-induced activation in these areas. Psychophysiological interaction (PPI) analysis showed that the functional connectivity between the left AI and the dorsal anterior cingulate cortex (dACC) was increased by noise to a lesser extent when the participants were chewing gum than when not chewing gum. Dynamic causality modeling (DCM) demonstrated that gum chewing inhibited the connectivity from the STS to the left AI. These findings demonstrate that gum chewing relieves stress by attenuating the sensory processing of external stressor and by inhibiting the propagation of stress-related information in the brain stress network.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Sequence of events in a trial for functional MRI scanning.
Figure 2
Figure 2. Experienced stress before the presentation of noise (SVAS-5, left) and after the presentation of noise (SVAS-20, right) as a function of noise presentation and gum chewing in the fMRI session.
* p<0.05.
Figure 3
Figure 3. Brain regions revealed by the factorial and parametric models.
(A) Brain regions sensitive to noise and noise-induced stress (“Noise > NoNoise”). (B) Brain regions in which the activation level positively correlates with the ratings of the subjectively experienced level of stress. (C) and (D) The time course of BOLD signal change in the left STS and the left AI reflecting the effect of noise (“Noise > NoNoise”) in the Chew and NoChew conditions. Error bars indicate the standard error of percent signal change (±SEM). To see more clearly the activations in insula, regions illustrated here used a voxel level threshold of p<0.005 (uncorrected) and a extent threshold of 200 contiguous voxels.
Figure 4
Figure 4. Results of the psychophysiological interactions (PPI) analysis with a left AI seed.
(A) The dACC demonstrated larger increase in functional connectivity with the left AI in the NoChew conditions (NoChew_Noise > NoChew_NoNoise) than in the Chew conditions (Chew_Noise > Chew_NoNoise). (B) The correlation coefficients between the BOLD signal in the left AI seed and that in the dACC (for detail, see Method: Psychophysiological interaction). (C) The BOLD signal extracted from the dACC in (A). Error bars indicate the variance of the correlation coefficents (±SEM). * p<0.05; (*) 0.05<p<0.07.
Figure 5
Figure 5. The structure and parameters of the model with the best fit (Model 1).
The black lines illustrated the intrinsic connectivities between brain regions. Intrinsic connectivity refers to the connectivity between regions across the whole scanning session, irrespective of stimulus and task. In color are the modulations of stimulus/task on the intrinsic connectivities. The numbers are the strength of connectivity (Hz). * p<0.05, corrected for multiple comparison with Bonferroni's procedure.

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