Humans, as social creatures, tend to mirror the emotions of others they see, whether in person or digitally. Witnessing a person walk across a tightrope might induce most viewers to feel anxious and stressed, while a cute video of a cat might be heartwarming (Uhrig et al., 2016). Recent studies have quantified the neural correlates of mirroring or “sync up” with others by calculating inter-subject correlation (ISC) of neuroimaging and electrophysiological data analyzed on naturalistic stimuli such as films and movie clips. In essence, ISC is a useful neural marker in answering the question “How alike is one subject to the rest of the group?”. The first major study in this field measured blood-oxygen-level-dependent (BOLD) activity as subjects viewed sections of a movie. High levels of correlated brain activity between subjects were observed in areas beyond basic sensory processing regions, including the limbic system and superior temporal cortex (Hasson et al., 2004). This suggested that there are common, temporally similar patterns of activity in higher order associative processing regions. Follow up studies (Jääskeläinen et al., 2008; Nummenmaa et al., 2012; Di and Biswal, 2020), using functional magnetic resonance imaging (fMRI) or functional near infrared spectroscopy (fNIRS), have corroborated Hasson’s earlier findings, showing ISC in a number of brain regions beyond basic sensory cortex. Additional studies have shown this effect is likely related to emotional processing or engagement and not strictly external stimulus dependent, as ISC can be elicited with narrative content in either written or audio form (Regev et al., 2013; Wang and He, 2014).

The BOLD signal measured in most of the studies noted above is a proxy for actual neural activity (frequency band power) and is expensive to measure. Electroencephalography (EEG), which is a low-cost method that directly reflects physiological activity of neural populations, has also been used to measure ISC (Dmochowski et al., 2012, 2014). Such studies have corroborated those noted above, demonstrating that ISC in EEG signals is greater during periods of high engagement and arousal (Dmochowski et al., 2012; Cohen et al., 2017, 2018). For example, subjects hearing the same song in a repetitive manner showed lower ISC compared to a remix of the same song that had less predictable attributes, which would cause subjects to be more attentive (Dauer et al., 2021). Similarly, when subjects were presented an audiovisual stimulus, lower ISC was observed when they were distracted with mental math (Ki et al., 2016). One additional benefit of EEG is the high temporal resolution which allows frequency band filtering of specific brain activity. Theta band activity is known to increase in response to facial stimuli that occur during naturalistic viewing (Busch et al., 2009; Dravida et al., 2019). Beta oscillations have been associated with attentiveness (Posada-Quintero et al., 2019), while in other contexts, alpha and beta oscillations have been related to anxiety and stress (Knyazev et al., 2004; Price and Budzynshi, 2009; Abhang et al., 2016). In fact, a study has already noted that ISC during emotional states does show distinct differences across frequency bands, though low frequency (delta) and high frequency gamma bands were most prominent (Maffei, 2020). As such, band-pass filtering to specific frequency bands is necessary to capture this oscillatory power (Ki et al., 2016). Altogether EEG-ISC analyses can offer complementary information to that observed from fMRI. The previous studies mentioned all used dense electrode systems, either 64 or 128 channels. Although using higher density EEG systems will provide more spatial resolution, a low-density system (14 channel EEG) was also shown sufficient to capture and measure ISC (Poulsen et al., 2017).

Thus, we can use ISC not only to assess how alike subjects are to each other, but also their engagement to the emotional stimuli being presented to them (Poulsen et al., 2017). Several studies have also begun to explore whether ISC differs in individuals with anxiety and depression. For example, ISC is lower in individuals with social anxiety (Morrison et al., 2016), and major depressive disorders (Guo et al., 2015). Electrodermal activity measures (EDA) observed decreased ISC values in dysphoric individuals (Li et al., 2021). Thus, prior work has generally shown that natural videos can be used as a way of identifying “common” patterns of activity that occur across subjects, and individuals with various levels of anxiety and depression may show greater differences from this common pattern of visual-evoked activity, resulting in lower than expected ISC values.

In this study, we wanted to contribute to the above literature in two ways. First, it is increasingly recognized that a larger sample size is required for robust estimates of neurocognitive phenomena (Szucs and Ioannidis, 2020; Feng et al., 2022). As such, we wanted to estimate the degree to which ISC occurs using a large sample of subjects. Second, we were interested in observing, in this large sample, whether ISC might be inversely related to either anxiety or depressive symptoms. To accomplish this, we performed an EEG-based ISC analysis with data parsed into physiologically relevant theta (4–8 Hz), alpha (8–12 Hz), and beta (15–30 Hz) frequency bands as subjects watched an emotionally evocative short movie clip. Additionally, we analyzed the correlation between each subject’s average ISC and mental health symptoms of anxiety and depression. We also use sparse Bayesian source localization to identify brain regions that contribute to the EEG-based ISC.

Table 1 shows subject demographic and mental health (anxiety and depression) symptom report data that was available for 162 of 163 human subject participants. The sample had about a 2:1 ratio of females to males and a Caucasian majority. Subjects with anxiety/depression scores > 5 are considered to have mild symptoms; there were 28.75 and 22.01% subjects with mild anxiety and mild depression symptoms, respectively.

Figure 1 shows brief screenshots of the short film where each screenshot is meant to capture the scene of each particular 23 s time window. Below it are heatmaps of the ISC values across the film’s 10 time windows, electrodes, and frequency bands. Repeated measures ANOVA resulted in F(9,1.2E5) = 32.9, p < 1.3E-36 after Greenhouse–Geisser correlation for sphericity. Post-hoc Tukey’s test revealed that peak window 8 was significantly different than all other windows (p < 0.0002). Table 2 provides a scene description by time window, and the potential emotion that may be evoked when watching the scene.

From the ISC heatmaps, we observed that highest average inter-subject correlation across electrodes was found during time window 8 (highlighted in red in Figure 1 scene panel), which coincided with the permanent onset of emotional relief. Almost all electrodes showed significant correlation across time, p < 0.05 FDR-corrected. FDR-correction was performed across the 24 electrodes and 10 time windows, i.e., correcting for 240 statistical tests, separately for each frequency band. For all further analyses, we focused on the peak ISC time window 8 which had the highest relief rating by all authors across the 10 scenes.

Figure 2 shows the scalp topography of the peak time window 8 ISC, showing clear focality in the midline centro-parietal electrodes in all theta, alpha and beta frequency bands. The B panel shows the corresponding average neural activity during the same time window, which interestingly showed lateral but not central maxima, suggesting ISC magnitude was not a simple function of activity magnitude. The theta frequency band appeared by eye to have the highest magnitude ISC values compared to the other frequency bands; hence, we statistically analyzed this. The bar graphs in the right panel quantify and compare the magnitude of ISC (A) and activity (B) across frequency bands in the peak (Cz, CPz, Pz, and POz) electrodes (within-subjects rm-ANOVA across frequency bands, ISC: F(2, 46) = 45.42, p = 1.95E-7, average activity: F(2, 46) = 22.52, p = 4.1E-5). ISC was largest for the theta frequency band compared to both other frequencies, and alpha showed higher ISC than beta frequency bands.

Figure 3 shows the neurobehavioral correlations between the peak ISC (within peak time window eight and peak midline centro-parietal channels) and subjects’ mental health scores. All correlations were FDR-corrected for multiple comparisons. Neurobehavioral correlations with anxiety in the alpha and beta band showed a significant inverse relationship [alpha band rho (ρ) = –0.244, p = 0.012; beta band ρ = –0.221, p = 0.015]; correlation with anxiety in the theta band was not significant (theta band ρ = –0.115, p = 0.179). Neurobehavioral correlations were anxiety specific and did not achieve FDR-corrected significance with depression scores in our healthy sample (p > 0.05). Age and gender were not significant covariates of these ISC-anxiety neurobehavioral relationships (Spearman partial correlations, alpha ISC vs. age ρ = –0.073, p = 0.361; vs. gender ρ = –0.123, p = 0.123; beta ISC vs. age ρ = 0.017, p = 0.831; vs. gender ρ = –0.129, p = 0.105; although age and anxiety were significantly inversely related in these partial correlations: ρ = –0.33, p < 0.0001, anxiety and gender were not: ρ = 0.08, p = 0.3).

Finally, Figure 4 shows the EEG cortical source reconstruction of the peak window ISC data, mapped onto the 68 ROI brain regions as per the Desikan-Killiany atlas. Similar to Figure 3, theta band ISC had larger magnitude values than alpha and beta band with greatest intensity in the right superior parietal cortex across all bands.

Inter-subject correlation of physiological data can provide useful proof of how similarly humans process stimulus information (Hasson et al., 2004). Several neuroimaging studies using fMRI during video watching have shown evidence of ISC in sensory, multisensory and limbic brain regions (Jääskeläinen et al., 2008; Nummenmaa et al., 2012; Di and Biswal, 2020). Recent studies have utilized EEG for generating ISC during classroom video watching (Poulsen et al., 2017) and to detect stress/anxiety states from EEG features during video watching (Giannakakis et al., 2015). Here, we extend this literature to ask whether ISC during naturalistic video watching especially in a large subject sample relates to mental health. We find that ISC, as a physiological marker of inter-subject neural processing similarity during passive viewing, has a significant negative relationship with anxiety, showing stronger correlation in subjects without anxiety than with anxiety.

We observed that peak ISC was obtained in EEG signals when passively viewing a particular scene segment (161–184 s) in a video clip of Charlie Chaplin’s (CC) film “Lion Cage” that was 230 s in total duration. This 8th time window is observed to coincide with the possible onset of strongest emotional relief in the film when CC is let out of the dangerous lion’s cage. This period contrasts to the rest of the film up to this point, potentially transitioning viewers to a more relaxed state of mind. As previous research has suggested, we would expect to see high ISC with low variation across subjects as they all go through the same emotional response (Jääskeläinen et al., 2008). Interestingly, the magnitude of peak ISC in the alpha/beta frequency bands at the centro-parietal electrodes was inversely related to subjects’ self-reported anxiety, i.e., more anxious individuals reporting greater anxiety had lower ISC scores. Greater activity in the alpha and beta bands have been related to anxiety and stress (Knyazev et al., 2004; Price and Budzynshi, 2009; Abhang et al., 2016). More pertinently, it is possible that individuals with higher levels of anxiety are less distinct during this epoch when, in general, there is a sense of “relief.” Higher theta activity is also expected with the presentation of visual stimuli as well as high facial perception, which is also observed in our analysis (Busch et al., 2009; Dravida et al., 2019). We further verified that these neurobehavioral correlations were not affected by age or gender. We also confirmed that this peak ISC localized to the superior parietal brain region, which is a well-known component of the attentional network (Corbetta and Shulman, 2002; Dosenbach et al., 2007). Meta-analyses have identified this region as important for attention and emotion, coding for emotional dysregulation especially in mood and anxiety disorders (Compton et al., 2003; Picó-Pérez et al., 2017). ISC-anxiety correlations being most significant in the alpha and beta bands suggests local region-specific modulation selective for attention and emotion processing (Ray and Cole, 1985; Klimesch et al., 1998; Schutter et al., 2001; Güntekin and Basar, 2007; Güntekin and Başar, 2010; Peylo et al., 2021). Alpha activity in parietal cortex has been postulated to select visual information for attentive processing (Peylo et al., 2021), and the parietal alpha ISC-anxiety correlations may suggest that individuals with greater anxiety may not process relief, the positive emotion in the peak time window, as readily as those with lower anxiety scores.

Limitations of this research include use of a moderate density electrode system, which may lead to more approximate source localization; these results could be verified by high density EEG mapping or other neuroimaging in future research. Furthermore, the lack of having multiple videos (and/or scrambled videos) as a control stimulus, may complicate the interpretation of our results. With regards to neural signal processing, we used a bandpass filtering approach to extract frequency band information. However, as noted in Widmann et al. (2015), separate low and high pass filtering may be better for attenuating the signals below the cutoff frequency particularly for the high-pass filters. Thus, it is possible that our data within theta, alpha and beta frequencies have some degree of contamination from outside the filter band. This contamination effect may to some extent explain why we observe similar findings in the alpha and beta frequencies, in particular, similar correlations with anxiety. Importantly, though, the theta and alpha frequency bands are both narrower and closer together and thus more likely to show cross-band contamination, and we found that, while alpha-frequency electrodes significantly correlated with symptoms such as for depression (r = –0.177), theta frequency band signals did not (r = 0.003), suggesting some frequency band specificity. An additional limitation of this study is that our symptom correlation analysis relied purely on subjective reporting on symptoms scales of anxiety and depression. We did not have a systematic way to interrogate or otherwise clinically verify whether these scores were an accurate depiction of an individual’s actual level of anxiety or depression, although notably the prevalence of symptoms in this study reflects the prevalence of anxiety/depression in a mixed community and college sample (Kessler et al., 2012; Li et al., 2022). That symptom scales may have been misreported, or there may simply have been some misunderstanding as individuals were responding, is a small possibility. However, we believe this type of misreporting/error would have been more likely to reduce rather than increase any neurobehavioral correlations observed, and is a general challenge with many neurobehavioral correlation studies. As such, results also need to be extended to individuals with clinically diagnosed anxiety to investigate if these relationships hold true in more anxious individuals in a clinical population.

In conclusion, the neurophysiological ISC measures may parallel the mirrored human emotions elicited during video watching. Being able to properly understand and feel similar emotions as others around us, i.e., empathy, is an important trait possessed by healthy humans. Yet, research has shown that individuals with anxiety, especially social anxiety, have difficulty understanding positive emotions of others as readily as healthy controls (Morrison et al., 2016). Other research suggests that empathy may be intact in social anxiety but that there may be deficits in prosocial action (Auyeung and Alden, 2016, 2020), and that links between anxiety and empathy are not fully clear (Pittelkow et al., 2021). Our physiological analysis suggests that aspects of parietal attention, especially during a period of positive emotion, i.e., relief from prior stress during emotional video viewing, may not occur as readily in more anxious individuals. Furthermore, we show that these neurobehavioral relationships hold true for continuous data that is not event-locked to specific stimuli.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by the University of California San Diego IRB. The patients/participants provided their written informed consent to participate in this study.

PB, DR, and JM contributed to the conception and design of the study. JN performed the statistical analysis and wrote the first draft of the manuscript. All authors contributed to the manuscript revision, read, and approved the submitted version.