An electroencephalographic examination of the autonomous sensory meridian response (ASMR)
Introduction
Autonomous sensory meridian response (ASMR) is a sensory-emotional phenomenon in which specific auditory, audiovisual, or tactile stimuli, known as “ASMR triggers,” elicit tingling sensations on the scalp, neck, and arms (Barratt & Davis, 2015). The sensorimotor experiences associated with ASMR are often accompanied by a sense of calm that many individuals find helpful in reducing feelings of stress. Previous research has demonstrated that ASMR is commonly elicited by low-frequency, complex sounds and detail-focused, slow-paced visual stimuli (Barratt, Spence, & Davis, 2017). Indeed, multiple survey studies of individuals with ASMR consistently found that whispering, close-up attention, and slow movements such as hair brushing elicited tingles (Barratt and Davis, 2015, Fredborg et al., 2017, Fredborg et al., 2018). ASMR has also been linked with repetitive sounds (e.g., finger tapping, gum chewing), suggesting that auditory stimuli can trigger tingles in the absence of visual stimulation (Barratt et al., 2017, Fredborg et al., 2017). However, surprisingly little is known about the neural structures that cause these auditory and audiovisual stimuli to elicit sensory-emotional responses. In the current research, we use electroencephalography (EEG) to measure changes in neural activity when ASMR tingles are elicited by auditory and audiovisual stimuli.
Previous neuroimaging investigations of ASMR have used functional magnetic resonance imaging (fMRI), a technique that provides detailed, three-dimensional depictions of neural activity (e.g., Smith et al., 2017, Smith et al., 2019a, Smith et al., 2019b, Smith et al., 2020). These studies include both resting-state fMRI, which measures correlated fluctuations in neural activity when the participant is not performing a task (Biswal et al., 1995, Raichle et al., 2001; see Raichle, 2015, for a review), and task-based fMRI, which measures neural activity during the performance of a cognitive or motoric task. Resting-state fMRI studies have shown that ASMR is linked with reduced functional connectivity in several of the brain’s resting-state networks (Smith et al., 2017, Smith et al., 2019a). These studies also indicate that ASMR is associated with a “blending” of networks in which brain areas not typically associated with a given resting-state network show correlated firing with that network. Task-based studies have shown ASMR-dependent activity in many of these same brain areas. For example, one task-based fMRI study measured the neural responses of 17 individuals with ASMR and 17 control participants during the viewing of ASMR-relevant and non-ASMR videos (Smith et al., 2019b). Participants who reported experiencing ASMR showed increased activity in medial prefrontal regions, bilateral precentral gyri, the right superior prefrontal cortex, the left superior temporal cortex, and midline occipito-parietal structures (precuneus and cuneus) during the viewing of ASMR-relevant videos. Controls participants showed only a decrease in activity in the cuneus. These data demonstrate that the self-reported sensory and emotional changes during ASMR are associated with measurable changes in neural activity. However, although these experiments highlight unique characteristics of ASMR-related brain activity, they do not provide information about the specific changes that occur within an individual during an ASMR tingling experience.
To date, one neuroimaging study has examined the changes in neural activity that occur during ASMR relative to an individual’s baseline. An fMRI study by Lochte, Guillory, Richard, and Kelley (2018) measured changes in neural activity occurring over the course of an ASMR video. Ten participants who reported experiencing ASMR viewed five ASMR-eliciting videos (hereafter, “ASMR videos”) and indicated whether they were experiencing baseline responses (i.e., no tingles or changes in emotional state), a feeling of relaxation, or ASMR tingles. When the feeling of relaxation was compared to baseline responses to the video, increased activity was observed in the medial prefrontal cortex. This region was also active during ASMR tingles; however, these sensations—which made up 6% of the scan time in the study—were also associated with activity in a number of other areas, including the bilateral nucleus accumbens, insula, and supplementary motor area, as well in the left secondary somatosensory cortex. Given that these structures are associated with reward responses (e.g., Schultz, 2000), sensitivity to interoceptive feedback (e.g., Craig, 2009), and emotional arousal (e.g., Mazzola et al., 2013, Olivieri et al., 2003), the results are consistent with the reported phenomenology of ASMR (e.g., Barratt & Davis, 2015). However, although informative, the study had some limitations, including the lack of a control group of individuals who are not sensitive to ASMR stimuli.
An additional limitation, relevant to both the Lochte et al., 2018, Smith et al., 2019b studies, was that brain activity was measured using fMRI. Although this technique provides excellent spatial resolution, it can only measure activity across the entire brain every 2–3 s. This temporal resolution is not sensitive enough to track fluctuations in neural activity that occur at faster speeds. A second weakness of fMRI is that it is very loud; given that ASMR-relevant stimuli often involve whispering (Barratt et al., 2017), a quieter brain-imaging tool may be more ecologically valid for the study of ASMR. To address these issues, the current study used electroencephalography (EEG) to measure rapid (<1 s) changes in brain activity during the onset of ASMR tingles. This neuroimaging method allows for the presentation of low-volume stimuli such as whispering while brain activity is measured.
EEG is an electrophysiological measurement of neural activity. Sensors placed on the scalp are used to measure changes in the ionic activity of groups of neurons. This ionic activity occurs as a result of ions entering and exiting the ion channels of the neuronal membrane; this activity is associated with action potentials (i.e., a neuron “firing”). When enough similarly charged ions exit a group of neurons at the same time, they form an ion current, a process known as volume conduction. When this current of ions reaches the scalp, it interacts with electrons on the EEG sensors. So, a spike in activity detected by EEG would indicate that the neuronal activity near the EEG sensor has increased (see Jackson and Bolger, 2014, Nunez and Srinivasan, 2006, Olejniczak, 2006).
It is important to note that EEG is a continuous measurement; neurons are constantly firing. However, the rates of firing do change. These changes can occur in response to internal thoughts or external stimuli; they can also occur when an individual enters a different conscious state, as takes place in the different stages of sleep. Previous research has noted that specific frequencies of neural activity are often associated with different phenomenological experiences. Slow delta waves (<4 Hz), for instance, are linked with deep sleep. Alpha waves (8–12 Hz) are often associated with a related state whereas beta waves (16–31 Hz) occur during a more cognitively active state. Gamma waves (>32 Hz) typically reflect somatosensory and sensorimotor responses. Therefore, by examining the prevalence of different EEG waves at specific points in time, it is possible to gain a greater understanding of the types of processes that are occurring at that moment. This is the strategy used in the current examination of ASMR.
Importantly, EEG has been used to measure brain responses in phenomena similar to ASMR. The experience of ASMR has been compared to meditation-like flow states (Barratt & Davis, 2015), although ASMR involves more of a passive response to a stimulus than an active attempt to change conscious states. As a result of this similarity, EEG studies of meditation may provide some insight into the changes in brain activity that occur during the induction of ASMR. Meditation typically involves actively focusing attention on the present moment; many forms of meditation are initiated by focusing on a specific internal or external stimulus such as a body part or a spoken mantra (Lutz, Brefczynski-Lewis, Johnstone, & Davidson, 2008). It also involves focused attention on an external stimulus (e.g., whispering). The link between ASMR and the attentional components of mindfulness was further demonstrated in previous survey studies of 290 individuals with ASMR. ASMR participants demonstrated significantly higher levels of openness-to-experience (according to the Big Five Inventory), as well as higher levels of mindful attention (according to the Mindful Attention and Awareness scale) and a greater tendency to engage in decentering (according to the Toronto Mindfulness Scale) than controls (Fredborg et al., 2017, Fredborg et al., 2018). Due to the similarities between experiencing ASMR and focused-attention meditation, as well as the higher levels of several facets of mindfulness reported by ASMR participants, the results of EEG studies of meditative states informed the hypotheses of the current studies. Numerous studies comparing EEG activity in meditative and control (non-meditative) states have reported increases in alpha wave (8–12 Hz) activity (e.g., Aftanas and Golocheikine, 2001, Dunn et al., 1999; see Cahn & Polich, 2006 for a review). Literature reviews specific to mindfulness meditation have also noted increases in theta waves (4–7 Hz; see Lomas, Ivtzan, & Fu, 2015, for a review), although the degree to which ASMR is related to this specific form of meditation is uncertain (Roberts, Beath, & Boag, 2019). Given the phenomenological similarities between meditation and ASMR, we would expect that individuals sensitive to ASMR stimuli will show increased alpha power during the perception of these stimuli, particularly at electrode sites near the brain areas identified in earlier fMRI studies, such as the medial prefrontal cortex and precuneus (i.e., Lochte et al., 2018, Smith et al., 2019b). Control participants, on the other hand, would not experience tingling sensations in response to ASMR-related stimuli; therefore, there should be little difference in their responses to ASMR and non-ASMR stimuli.
An additional characteristic of ASMR that can be investigated with EEG is the tingling sensations themselves. These experiences are correlated with physiological arousal (Poerio, Blakey, Hostler, & Veltri, 2018) and may be linked with atypical functional connectivity of the sensorimotor resting-state network (Smith et al., 2019a). Previous research has indicated that gamma waves (30–100 Hz) are associated with several sensory and motor processes around the precentral and postcentral gyri (e.g., Cheyne et al., 2008, Muthukumaraswamy, 2010). Additionally, neural activity in the 12.5–15 Hz range has been detected over sensorimotor regions during the performance of tasks involving biofeedback; this waveform is known as the sensorimotor rhythm (Arroyo et al., 1993, Cheng et al., 2015, Tansey, 1984). Given that tingling sensations are a key feature of ASMR, it was predicted that ASMR tingles would also be associated with increased gamma wave and sensorimotor rhythm power in these sensorimotor regions.
In the current study, alpha wave, sensorimotor rhythm, and gamma wave activity were examined over the course of the ASMR experience, with an emphasis on the precise moment that ASMR tingles commenced. Over two test sessions, individuals with ASMR and matched control participants viewed two ASMR videos and two non-ASMR videos while EEG measurements were taken. Participants were also presented with four auditory stimuli, two designed to elicit ASMR and two that served as control stimuli. During all eight stimulus presentations (four per test session), participants pressed a response key to indicate the instant that ASMR tingles had begun (if applicable). It was predicted that ASMR participants would show larger changes than control participants in alpha, sensorimotor rhythm, and gamma wave activity in the regions described above during the perception of ASMR-related stimuli. We specifically predicted that increased alpha power would be detected in electrode sites identified to be related to the medial prefrontal cortex and the precuneus, because these regions were associated with ASMR in fMRI studies (e.g., Lochte et al., 2018, Smith et al., 2019b). Increased gamma wave and sensorimotor rhythm would be expected in electrodes near the somatosensory and motor cortices. A second prediction was that ASMR participants would show increases in these three EEG wave bands during the perception of ASMR-related stimuli but not during control stimuli (i.e., non-ASMR stimuli). Finally, we predicted similar patterns of data when comparing neural activity in ASMR participants prior to and during ASMR tingles. This design, combined with the use of EEG, allowed us to measure activity related to the onset of ASMR at a more precise timescale than previous studies. The results of these contrasts will provide data that complement previous fMRI studies of ASMR (Lochte et al., 2018, Smith et al., 2019b) while also generating novel information about neural responses to different types of ASMR triggers.
Section snippets
Participants
Data from 14 individuals with ASMR (4 males; Mage = 24; SDage = 4.85; age range: 19–37) were analyzed in this study. All ASMR participants had been recruited as part of an earlier investigation of this phenomenon at the University of Winnipeg in Winnipeg, Canada (Smith et al., 2017) and were invited to participate in this study after obtaining their consent to be contacted for additional research opportunities. Fourteen age- and gender-matched control participants (i.e., individuals who do not
Stimuli
ASMR audiovisual stimuli. The ASMR-relevant audiovisual stimuli consisted of two popular ASMR videos from YouTube.com.
The first video was entitled “Gently Playing with Hair (soft spoken ASMR)” (https://www.youtube.com/watch?v=yA2HcNRTdFY) and featured a female narrator brushing another woman’s hair while narrating her actions to the viewer in a whispering tone. This narration was dubbed over top of the video stimulus, such that the narration was not happening simultaneously, but rather after
Measures
The ASMR Checklist. Participants in the ASMR condition completed the ASMR Checklist (Fredborg et al., 2017), which is a measure of both average tingle intensity experienced as well as the average duration the stimuli needs to be before tingles are experienced in response to 14 popular ASMR triggers. All ASMR participants reported that they experienced ASMR reliably. Due to the small sample size and relative homogeneity of the responses, the data from the ASMR Checklist did not contribute
Data acquisition and preprocessing
Data were acquired using a 32-channel Actichamp II system, with Ag/AgCl electrodes positioned in the standard international 10–20 arrangement using ActiCaps (BrainVision, LLC, Morrisville, NC). Data were recorded at a sampling rate of 500 Hz and digitally referenced online to Fz using BrainVision PyCorder. Impedances were kept below 20 kΩ. Horizontal electrooculogram electrodes were placed at the temples, and vertical electrooculogram electrodes were placed above the middle of the eyebrow and
EEG Time-Frequency analysis
Pre-processed epochs were then decomposed to time–frequency domain by complex Morlet wavelet convolution (Cohen, 2014). A total of 30 wavelets were used, ranging logarithmically from 2 Hz to 40 Hz. The number of wavelet cycles also ranged logarithmically from 3 cycles (at 2 Hz) to 10 cycles (at 40 Hz). The resulting time–frequency power data were decibel-normalized to the average baseline power of the period from 1000 ms to 500 ms prior to ASMR onset. Decibel-normalized data were then exported
Data analysis
As described above, participants with ASMR were instructed to press a button on a response pad when they initially felt tingling sensations for a given stimulus trial. To minimize movement-related artifacts, we computed the average power for each frequency for the period from 500 ms to 1000 ms following ASMR onset. These values were then used for subsequent analyses described below. Where appropriate, these were compared against the average of the baseline period (1000 ms to 500 ms prior to
Results
We will first describe the results of our a priori hypotheses which focus on specific groups of EEG electrodes. We will then describe the results of the ANOVAs that include all of the EEG electrodes; these analyses are exploratory in nature. Additionally, given the relatively small sample size in this study, some contrasts were not statistically significant but did yield a moderate (i.e., noteworthy) effect size. The descriptions of the results will therefore include both information about
Between- and Within-Subjects contrasts during ASMR Tingles.
There were no significant increases found in gamma power in sensorimotor electrodes (FC1, FC2, C3, Cz, C4, CP1, and CP2) between participant groups for either ASMR audio or ASMR video trials (ps > 0.05, gs ≤ 0.39). When comparing ASMR audio trials to control audio trials for ASMR participants, however, we did identify a large and significant increase in gamma power (t11 = 2.39, p = 0.018, g = 0.76; see Fig. 5) that was not seen in the same comparison for control participants (t11 = 0.01, p
Between- and Within-Subjects contrasts during ASMR Tingles.
When examining sensorimotor rhythm over sensorimotor regions (C3, Cz, C4), we found a significant increase in power in ASMR participants relative to control participants after ASMR onset during ASMR audio trials (t11 = 2.90, p = 0.0047, g = 1.14; see Fig. 6) and a nonsignificant increase during ASMR video trials (t11 = 1.31, p = 0.10, g = 0.52). A similar, though nonsignificant, increase was found for ASMR participants during ASMR audio trials relative to control audio trials (t11 = 1.51, p
Exploratory analyses
As noted earlier, exploratory analyses were performed to detect any significant effects that would not have been predicted from earlier fMRI studies of ASMR (Lochte et al., 2018, Smith et al., 2019b). For these exploratory analyses, we used ANOVAs to determine whether any noteworthy effects existed for either audio or video trials on alpha, theta, or gamma frequency bands.
For alpha power during audio trials we found a significant main effect of group (i.e., ASMR or control participants; F1,22
Discussion
The purpose of the current research was to examine changes in neural activity associated with the onset of ASMR. The use of EEG allowed us to examine these changes in conscious state at a much more precise temporal resolution than was possible in fMRI experiments. In the current study, we tested individuals with self-reported ASMR and matched control participants in order to address three specific questions. First, how does brain activity during ASMR “tingles” differ from time-locked brain
Limitations
Although the current study provides novel information about the ASMR experience, there are some elements that could be improved. The current study recruited a relatively small sample; a larger sample size would increase the power of these analyses, allowing more accurate estimations of the effects seen. A larger sample size would also have allowed us to test whether differences in self-reported ASMR intensity influenced the changes in brain activity; our current sample size precluded such an
Conclusions
The purpose of the current study was to expand on previous neuroimaging assessments of ASMR and, in so doing, delineate the precise changes in neural activity associated with this phenomenon. Previous survey studies indicated that ASMR involves a relaxed, flow-like state as well as sensorimotor tingling sensations. The current results are consistent with these reports, with changes in frontal-lobe alpha activity being linked with the cognitive/attentional element of ASMR and the sensorimotor
Author contributions
SDS, BKF, and AD designed the experiment. BKF conducted the experiment. KCJ analyzed the data. All authors contributed to the writing of the manuscript.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors would like to thank Megan Sokoropud-Jones and Catherine Nadeau for their assistance in EEG data collection, as well as Todd Girard for statistical consultation. This research was funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada and a University of Winnipeg Major Research Grant. The EEG laboratory was also supported by grants from the Canada Foundation for Innovation (CFI), Leaders Opportunity Fund, and by matching funds from the Manitoba Research
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These authors made equal contributions to this manuscript.