Toward reanimating the laughter-involved large-scale brain networks to alleviate affective symptoms

Abstract

Introduction: The practicality of the idea whether the laughter-involved large-scale brain networks can be stimulated to remediate affective symptoms, namely depression, has remained elusive.

Methods: In this study, 25 healthy individuals were tested through 21-channel quantitative electroencephalography (qEEG) setup upon resting state and while submitted to standardized funny video clips (corated by two behavioral neuroscientists and a verified expert comedian, into neutral and mildly to highly funny). We evaluated the individuals' facial expressions against the valence and intensity of each stimulus through the Nuldos face analysis software. The study also employed an eye-tracking setup to examine fixations, gaze, and saccadic movements upon each task. In addition, changes in polygraphic parameters were monitored upon resting state and exposure to clips using the 4-channel Nexus polygraphy setup.

Results: The happy facial expression analysis, as a function of rated funny clips, showed a significant difference against neutral videos (p < 0.001). In terms of the polygraphic changes, heart rate variability and the trapezius muscle surface electromyography measures were significantly higher upon exposure to funny vs. neutral videos (p < 0.5). The average pupil size and fixation drifts were significantly higher and lower, respectively, upon exposure to funny videos (p < 0.01). The qEEG data revealed the highest current source density (CSD) for the alpha frequency band localized in the left frontotemporal network (FTN) upon exposure to funny clips. Additionally, left FTN acquired the highest value for theta coherence z-score, while the beta CSD predominantly fell upon the salience network (SN).

Conclusions: These preliminary data support the notion that left FTN may be targeted as a cortical hub for noninvasive neuromodulation as a single or adjunct therapy in remediating affective disorders in the clinical setting. Further studies are needed to test the hypotheses derived from the present report.

Keywords: eye-tracker; face analysis; laughter network; polygraphy; qEEG.

FIGURE 1

Upper panel: Study protocol, timeline of presented stimulus and active recording devices throughout the experiment. Prior to the experiment, calibration procedure was performed using a 12‐point calibration. As the initial part of the experiment, a neutral video content was displayed on a monitor, placed 60 centimeters from the examinee. This session was followed by the presentation of the funny content, which lasted for 4 min, after a short 2‐min break. During these two initial states, data from eye‐tracker, qEEG, and Polygraph were recorded synchronously. Then, the experiment proceeds by another 2‐min rest session, which ended by the presentation of another funny content and neutral one, while they lasted for 4 and 2 min, respectively. Meanwhile, from the beginning of second funny clip, the experiment was conducted by recording the participant's facial emotion reaction, utilizing face reader, and qEEG and Polygraphy data. Lower Panel: Data acquisition setup. (a) the experimental platform including simultaneous qEEG, polygraphy, face reader, eye‐tracking, and synchronized task submission setup. (b) the task‐concurrent qEEG acquisition setup including the EEG device, electrocap, 2 PCs, a mouse, and the WinEEG software. (c) (left to right) the Nexus‐10 polygraphic device, eye‐tracking setup, polygraphy recording, sensor placement, and trapezius muscle surface electromyography (EMG) electrodes’ position.

FIGURE 2

(a) Using a polygraph device, changes in polygraph parameters, including changes in heart rate and heart rate variability (HRV), blood volume pulse (BVP), respiration rate, electromyography (EMG), and skin conductance (SC) while watching neutral videos versus funny clips were reviewed. A notable comparative increase in heart rate, EMG (surface EMG over the trapezius muscles as shown in Figure 1), SC, and respiratory rate as well as a decreased BVP when subjects were submitted to funny clips (the act of genuine laughter as verified by face reading) suggest the sympathetic overdrive. Vertical bars indicate standard deviation of the means. *p < 0.05. (b) Face reader software results. The calibrated facial expression output is illustrated in the graph [happy facial expression (***p‐value: 0.0003); neutral facial expression (**p‐value: 0.001); other facial expression (p‐value = 0.11)]. Vertical bars indicate standard deviation of the means.

FIGURE 3

(a) The average pupil size while watching funny and neutral clips. (b) The average number of fixation points per person while watching funny and neutral clips. Vertical bars indicate standard deviation of the means. ***p‐value: 0.0001 **p‐value: 0.002.

FIGURE 4

Left frontotemporal theta coherence network during funny clips. Raw‐Score coherence: 84–99 (100X). The right frontotemporal network demonstrated the highest value for alpha current source density (CSD) gain (Brodmann area: 22, Value: 4.482e‐06). The salience network acquired the highest beta center values in the right Caudate (Brodmann area: 25, Value: 8.58922e‐07).

FIGURE 5

Absolute power paired t‐Test (p‐Value). Paired t‐test was used to evaluate any significant differences in the absolute power gain upon participants’ submission of the funny and neutral video clips.