Event-Related Alpha Oscillatory Response in Early Stage of Facial Expression Processing in Social Anxiety: Influence of Language Context

doi:10.21203/rs.3.rs-1887631/v2

Download PDF

Research Article

Event-Related Alpha Oscillatory Response in Early Stage of Facial Expression Processing in Social Anxiety: Influence of Language Context

https://doi.org/10.21203/rs.3.rs-1887631/v2

This work is licensed under a CC BY 4.0 License

Version 2

posted

You are reading this latest preprint version

Background: Accurate interpretation of the emotional information conveyed by others’ facial expression is crucial for social interactions. The alpha oscillation response, which is an important event-related potential-based metric in studies of emotional information processing, to emotional facial processing in social anxiety is unclear.

Method: In this case-control study, participants viewed facial expressions (angry, happy, neutral) preceded by a contextual sentence conveying either positive or negative evaluations about the participant. Contextual influences were reflected in rating and occipital and frontal alpha oscillation measured by event-related potentials (ERP).

Results: With congruently emotional context and expression, occipital alpha oscillation power was significantly lower in social anxiety group than that in healthy control group. And in negative context, frontal alpha lateralization was significantly lower in social anxiety group than that in healthy control group.

Conclusion: Social anxiety individuals showed stronger attentional inhibition of face expression processing only when the language context was emotionally congruent with the facial expression. The effect of context on facial expression processing in social anxiety individuals was manifested in the early stage of face processing as a contextual consistency effect, and the effect of negative information may be more pronounced.

facial expression

social anxiety

context

alpha oscillation

frontal alpha lateralization

Facial expression is an important clue to convey individual emotional states. Accurate interpretation of the emotional information conveyed by others’ facial expression is crucial for building interpersonal relationships (Ekman & Oster, 1979). Individuals with social anxiety disorder (SAD) have an attentional bias in facial processing, as evidenced by preferential processing of information perceived as threatening (Wieser et al., 2009; Song et al., 2022). It has been shown that individuals’ facial expression processing includes not only processing of bottom-up facial constructions but is also influenced by top-down processing of emotional contextual information (Dieguez-Risco et al., 2015; Schwarz et al., 2013). For individuals with SAD characterized by attention bias in facial processing, contextual information during social interactions, which is perceived as top-down background information, may bias their perception of facial expression perception.

In real life, there is abundant contextual information associated with facial expression that affects the perception of facial expressions, including language, body posture, natural and social scenes (Hassin et al., 2013; Masuda et al., 2008; Song, Wu, & Feng, 2022; Song, Zhao, Gao, et al., 2022). For instance, the verbal description of the context in which facial expressions exist provides strong contextual clues that strongly influence the observer's subjective perception of facial expressions (Barrett et al., 2007). Emotional congruence effects between context and facial expressions have been demonstrated by using the event-related potential (ERP) technique, whereby neutral facial expressions in a positive context are rated as more positive, while neutral facial expressions in a negative context are rated as more negative. (Liu et al., 2019). The effect of emotional congruence persisted for groups with different levels of social anxiety, with high social anxiety (HSA) individuals evaluating neutral facial expression more negatively in negative context (Wieser & Moscovitch, 2015), which indicates that face processing in HSA individuals was more influenced by top-down processing of context relative to low social anxiety (LSA) individuals.

To date, most studies on the characteristics of facial expression cognitive processing adopting ERPs technique have focused on the analysis of time-domain information (Li et al., 2019; Zhang et al., 2018; Riwkes et al., 2015; Hinojosa et al., 2015; Kolassa et al., 2007; Muhlberger et al., 2009; Song, Zhao, Gao, et al., 2022), and the results related to high social anxiety mainly focus on the ERP components in early stage of static emotional facial processing, including P100 component of early visual processing (Taylor & Khan, 2000) and N170 component of facial feature processing (Ran & Chen, 2017). However, critical information about the corresponding time windows in the frequency domain (e.g., power distribution in different frequency bands) is often neglected. In recent years, the oscillation dynamics method, which reflects the characteristics of signal changes with frequency (e.g., power variation and phase-locked features), has been applied in studies of facial expression perception (Guntekin & Başar, 2014). It has been demonstrated that the most emotion-relevant frequency bands and cerebral regions were the alpha power in the occipital and parietal regions, the beta power in the central region, the gamma power in the left frontal and right temporal regions, which was based on the analysis of electroencephalography (EEG) recorded under positive and negative emotion conditions (Nie et al., 2011).

Previous studies have shown that anxiety level influenced alpha oscillation responses to emotional facial expressions (Knyazev et al., 2008), as measured by resting frontal alpha asymmetry (FAA) and occipital alpha oscillation (Balconi & Lucchiari, 2006; Knyazev et al., 2008; Coan & Allen, 2004; Moscovitch et al., 2011; Roh et al., 2020; Harrewijn et al., 2016; Schmidt, 1999; Miskovic et al., 2011; Coan & Allen, 2003; Davidson et al., 2000). However, the researchers have argued that the relationship between resting state FAA and social anxiety is complex and irrelevant (Coan & Allen, 2004) and few studies have explored the relationship between SAD and task-state FAA (Elgavish et al., 2003; SchὅNe et al., 2016). In general, it remains to be explored whether emotional context influence facial expression processing in SAD individuals and which aspects are reflected in alpha oscillations. In present study, the emotional language contexts (positive, negative) were manipulated to explore the effect on emotional facial expression processing in SA individuals, with occipital alpha oscillation and task-state FFA as indicators. We hypothesized that the desynchronization in occipital alpha power is more prominent in individuals with HSA in negative language contexts compared to positive language contexts. Furthermore, we conducted an exploratory analysis on task-state FFA in SA individuals within different emotionally language contexts.

2.1 Participants

Sixty-two healthy university students from Shandong Province in China with normal or corrected-to-normal vision took part in this study. The data of all subjects in this study were obtained from the previous study (Song, Zhao, Gao, et al., 2022). Based on the score in the Chinese version of the Liebowitz Social Anxiety Scale (LSAS) (He & Zhang, 2004) and the Beck Depression Inventory-II (BDI-II), participants were divided into two groups, social anxiety group (SA, LSAS > 50 points) and healthy control group (HC, SA, LSAS < 38 points). None of the participants had severe depressive symptoms (all participants scored BDI-II <= 8 points) and all underwent structured interviews that clarified their grouping. A different preprocessing method was used for the EEG data in this study compared to previous studies (Song, Zhao, Gao, et al., 2022, see EEG recording and processing section for details). Therefore, data from 2 participants in this study were excluded from the final analysis due to excessive electroencephalographic (EEG) artifacts (>40%). In the end, 31 participants in SA group and 29 in HC group included in the analysis of this study (Table 1). All participants signed the informed consent before the experiment and got a cash reward after the experiment. The study was approved by the Ethics Committee of the School of Education and Psychology, the University of Jinan.

(Insert Table 1 here)

2.2 Stimuli

Face materials were selected from Chinese Affective Picture System (CAPS) (Bai et al., 2005), and 3 different types of emotional facial pictures (angry, happy, neutral) were selected as experimental materials. The use of the Chinese Affective Picture System was authorized and all subjects who provided face images gave informed consent for the publication of the identified images in an online open publication. Examples of face pictures are shown in Figure 1. There were 40 pictures of each type (20 males and 20 females faces), with a total of 120 pictures. Face pictures retained facial internal features without hair and other modifiers. All pictures were kept consistent in size, clarity, and contrast.

Sentences with positive or negative valance were designed with reference to prior research (Wieser et al., 2014; Wieser & Brosch, 2012; Wieser & Moscovitch, 2015). All sentences were self-relevant (Tsuji & Shimada, 2018) and the grammatical structure is consistent. Based on the evaluation of contextual valence and arousal level by additional 20 college students in nine-point rating scale, a total of 20 sentences were selected (ten positive and ten negative) with each has two versions of a male or female tone (e.g., “he/she thinks your comment is negative”). There was no significant difference in arousal level between positive and negative valenced sentences [6.39 ± 0.91 vs. 6.51 ± 1.11, t(19)=1.39, p=0.182], but significant difference in valence level [7.01 ± 0.75 vs. 3.05 ± 0.90, t(19)=11.30, p<0.001].

2.3 Procedure

Experimental paradigm (Fig.1) was adopted from previous studies (Kim et al., 2004; Schwarz et al., 2013) with a mixed experimental design of 2 (groups: HC, SA) × 2 (contextual valence: positive, negative) × 3 (expression type: angry, happy, neutral). In each trial, participants passively viewed sentences (2s) and facial expressions (0.5s), with a 0.5s blank before and after the facial expression. Then, participants were asked to rate the faces in terms of valence (1 = very negative to 9 = very positive) and arousal (1 = no emotional arousal at all to 9 = very strong emotional arousal). After a trial, a " + " fixation point was presented in the center of the screen and presented randomly for 2~3 seconds. Participants were asked to fixate consistently on the center of the screen.

(Insert Fig.1 here)

Each sentence (positive, negative) was shown 12 times, 6 times with a male personal pronoun and 6 with a female personal pronoun. Each facial picture was presented 2 times and combined with different valenced sentences. The whole experiment, presented by E-prime software (version 2.0), consisted of 240 trials performed in 4 blocks and lasted 40 min.

2.4 EEG recording and processing

EEG was continuously recorded from 64-conductor Ag/AgCl electrode caps manufactured by NeuroScan with the electrodes arranged according to the International 10-20 extension system. Electrode impedances were kept below 5 kΩ. Electrical signals were band-pass filtered from 0.01 to 100 Hz and digitized at 1000 Hz. Furthermore, vertical and horizontal eye movements were recorded with two electrodes attached above and below the left eye (VEOG) and two electrodes attached to the right and left outer canthi (HEOG). An electrode positioned at Cz served as the reference during recording.

EEG data were preprocessed using Curry software (version 7). EEG signals were band-pass filtered at 0.01-30 Hz for statistical analysis and time locked to facial pictures and rereferenced to the average of all channels. Data were segmented into −300 to +1000 ms epochs and baseline corrected from -300 to 0 ms. Trials containing artifacts (e.g., blinks, eye movements, muscle noise) were detected and rejected semiautomatically using independent component analysis (ICA) and peak-to-peak amplitude thresholds with starting values of 100 μV.

2.5 Data analysis

Event-related oscillation analysis was based on the MATLAB platform of Letswave 7.0 (https://letswave.cn). The time-frequency analysis was performed on a single-trial basis via complex Morlet wavelets prior to averaging. Single trial spectral amplitudes were then averaged across trials, segmented into −300 to +1000 ms epochs, and mean baseline amplitude (−300 to 0 ms) was subtracted from each individual time point for each frequency separately. The time window, frequency window, and electrode locations for analysis of alpha power were chosen based on a priori hypotheses (Song, Zhao, Gao, et al., 2022; Knyazev et al., 2008; Mennella et al., 2017). Since the attentional processing bias of social anxiety to facial expressions has accumulated consistent results in time-domain characteristics related to ERP components in the early stage (Li et al., 2019), we analyzed the alpha oscillation in 0-200 ms after facial pictures presentation. The alpha frequency range of analysis was 8-13 Hz, and electrodes selected for statistical analysis located at occipital (P7, P8, PO3, PO4, PO7, PO8, O1, O2, Knyazev et al., 2008) and frontal (F3, F4, Mennella et al., 2017) regions. ANOVA with the within-subject factors contextual valence (positive, negative), facial expressions (angry, happy, neutral) and the between-subject factor group (HC, SA) was used to test occipital alpha oscillation. The alpha oscillation in the occipital lobe is calculated from the average of the electrode clusters. FAA index is computed by subtracting the natural log of left hemisphere alpha power from the natural log of right hemisphere alpha power (ln [right alpha] − ln [left alpha], Coan & Allen, 2004).

The results were corrected with the Greenhouse-Geisser method if the sphericity requirement was not met (Geisser & Greenhouse, 1958). Significant effects in ANOVA were further analyzed by simple effects ANOVA and the least significant difference (Bonferroni) test (Girden, 1992). Main effects and interactions were considered significant if p < 0.05.

3.1 Behavioral results

Arousal score. Figure 2A shows that participants' arousal rating of facial pictures is affected by context [F(1, 58)= 14.798, p < 0.001, η_p²= 0.203] and expression type [F(2, 116) = 72.106, p < 0.001, η_p²= 0.554]. The arousal scores of negative contexts [5.47 ± 0.14 (M ± SD)] were higher than that of positive context (5.33 ± 0.13, p < .001). Participants rated higher arousal level of angry (6.07 ± 0.14) and happy expressions (5.48 ± 0.16) than neutral expressions (4.64 ± 0.16, p < 0.001), and the arousal level of angry expressions was rated higher than that of happy expressions (p < 0.001). There was no significant difference between the two groups [SA: 5.37 ± 0.19, HC: 5.43 ± 0.19, F(1, 58) = 0.041, p = 0.840, η_p²= 0.001].

Valence score. The valence score of facial pictures was also influenced by context and expression types, as shown in Figure 2B [context: F(1, 58) = 41.134, p < 0.001, η_p² = 0.415, negativity: 4.05 ± 0.13, positivity: 4.99 ± 0.11; expression types: F(2, 116) = 67.088, p < 0.001, η_p² = 0.536, neutral expression: 4.36 ± 0.10, angry expression: 3.63 ± 0.16, happy expression: 5.59 ± 0.15].

In addition, a significant interaction of context and expression types showed that angry and neutral expressions in negative contexts were evaluated as more negative [F(2, 116) = 29.928, p < 0.001, η_p² = 0.340; angry expressions in negative context: 3.36 ± 0.17, positive context: 3.90 ± 0.18; neutral expressions in negative context: 3.89 ± 0.12, positive context: 4.78 ± 0.13, ps < 0.001]. While happy expressions in positive context was considered more positive (negative context: 4.90 ± 0.20, positive context: 6.28 ± 0.15, p < 0.001). No other main effect and interaction results were significant.

(Insert Fig.2 here)

3.2 Analysis of occipital Alpha Oscillation

The triple interaction Group × Context × Facial expression yielded a significant result [F(2,114)=4.185, p=0.024, η_p²=0.068]. For angry expressions in negative contexts, the power values of HC (2.758 ± 0.257) were significantly higher than that of SA (1.723 ± 0.253, p=0.006), however, for angry expressions in positive contexts, there was no significant difference between the power values of HC and SA (HC: 2.307 ± 0.221, SA: 1.924 ± 0.217, p=0.221). Similarly, for happy expressions in positive contexts, the energy values of HC (2.790 ± 0.266) were significantly higher than that of SA (1.972 ± 0.261, p=0.032), however, for happy expressions in negative contexts, there was no significant difference in energy values between HC and SA (HC: 2.994 ± 0.283, SA: 2.177 ± 0.289, p=0.058). In other words, when the language context and facial expressions were emotionally congruent, the power values of SA were lower than that of HC. This suggests that, in contrast to HC, SA imposes inhibition on the processing of emotional congruence between the context and facial expressions. Besides, for neutral expressions, the power values of HC were significantly higher than that of SA for both negative and positive contexts (neutral expression in negative context, HC: 2.577 ± 0.266, SA: 1.934 ± 0.222, p=0.047; neutral expression in positive context, HC: 3.088 ± 0.299, SA: 2.150 ± 0.294, p=0.029)

(Insert Fig.3 here)

As shown in Figure 4, the main effect of expression types was significant [F(2,114)=5.783, p=0.011, η_p²=0.092]. Power value of angry facial expression (2.178 ± 0.156) was significantly lower than that of neutral (2.437 ± 0.168, p=0.001) and happy facial expression (2.471 ± 0.185, p=0.007). In addition, the main effect of group was significant [F(1,57)= 5.64, p<0.05, η_p²=0.090]. Power value are higher in HC group (2.744 ± 0.229) than SA group [1.980 ± 0.021, F(1,57)=5.64, p<0.05, η_p²=0.090].

(Insert Fig.4 here)

3.3 Analysis of FAA

The interaction of language context × group significantly affected frontal alpha asymmetry [F(1, 57)=5.257, p=0.026, η_p²=0.084]. In the negative language context, HC group frontal asymmetry (0.149 ± 0.110) significantly higher than SA group (-0.168 ± 0.108, p=0.044). While there was no significant difference of frontal asymmetry between HC (-0.205 ± 0.092) and SA group (-1.24 ± 0.090) in the positive language context (p=.527), see Figure 5. The remaining main effect and interactions were not significant.

(Insert Fig.5 here)

It is difficult to truly understand the neural basis of emotion perception in real life because most of the previous studies presented faces in isolation, leading to the lack of ecological validity. Therefore, researchers have pointed out that facial structure information is necessary but not sufficient for emotion perception, and context provides top-down constraints in emotion perception, which provides information beyond the emotional significance of facial structure itself (Barrett et al., 2007; Barrett et al., 2011). The purpose of this study was to investigate the effect of self-relevant language contextual information on facial expression perception of social anxiety, using alpha oscillatory response as an indicator.

We found that language context influences the early stage of facial expression processing in social anxiety individuals. Occipital alpha power was significantly lower in the SA group than the HC group when the language context and facial expressions were emotionally congruent. In addition, FAA in SA group significantly lower than HC group in negative language context. Results indicated that language context influences the perception of facial expressions in early stage, especially the subjects with social anxiety.

4.1 Occipital alpha Oscillation in Context effect on Expression Perception

Previous research has suggested that providing subjects with language options when judging an isolated face has the potential to provide a context for shaping perceptual processing (Wang et al., 2015). The self-relevant contextual information provided prior to the presentation of the facial stimuli in the present study also provided a context for the subjects' processing of facial expressions.

Our study found that occipital alpha power was significantly lower in the SA group compared to the HC group only when the language context and facial expressions were emotionally congruent rather than incongruent. It is known from previous studies that alpha power values are negatively correlated with neural arousal, as demonstrated by an increase in alpha power values when brain regions are less involved in sensory processing (Jensen et al., 2012; VanRullen & Dubois, 2011). In other words, individuals with high social anxiety invest more cognitive resources in the early stage of attention and visual processing of facial expressions when the language context and facial expressions are emotionally congruent. As a result, alpha power is reduced. The results showed that compared with HC group, SA individuals were more influenced by context information from the top-down (Schwarz et al., 2013; Wieser et al., 2014). Emotional involvement and experience are higher in social anxiety individuals than healthy individuals (Herbert et al., 2011; McKendrick et al., 2016). We further extended previous findings in the time-domain (Peschard et al., 2013; Song, Zhao, Gao, et al., 2022; Wieser et al., 2009; Wieser & Moscovitch, 2015). In our previous study, we only found a tendency for the SA group to induce greater P1 than the HC group in negative contexts (the interaction effect between group and context was only trending, see a detailed discussion in Song, Zhao, Gao, et al., 2022), which was interpreted as more enhanced attention hypervigilance for SA under a negative context. The analysis in the time-frequency domain allowed us to provide robust evidence for the existence of an early-stage contextual influence on expression processing in social anxiety individuals. And this influence was found to manifest as an emotional congruency effect across language context and expression types.

Context clearly plays an even more important role when a facial expression contains ambiguous affective information, such as a surprised expression (Kim et al., 2004; Neta et al., 2011) or no affective information available, such as a neutral expression (Schwarz et al., 2013). The evaluation of ambiguous faces is based on valence and dominance when there is no affective information available at all (Todorov, 2011). In present study, we found lower occipital alpha power in high social anxiety groups than healthy groups only in neutral facial expression, regardless of whether context was positive or negative. This may be because the top-down information provided by the context exceeds the emotional significance of the facial structure itself (Barrett et al., 2007; Barrett et al., 2011), conveyed threatening information regardless of contextual valence, and further influenced the perception of the meaning of neutral facial expressions by social anxiety individuals. The results in both positive and negative contextual conditions support that context is critical to ambiguous expression perception or causes a thorough change in the meaning of expressions (Aviezer et al., 2017).

In addition, results showed that occipital power was significantly lower in angry faces than happy and neutral faces, suggesting that individuals have enhanced attention and processing to angry stimuli, consistent with individuals' attentional bias toward threatening stimuli (relative to neutral stimuli) (Wieser et al., 2009), with important survival value for individuals.

4.2 Frontal alpha lateralization of context on expression perception

The results showed that FAA was significantly lower in the SA group than in the HC group in the negative context, whereas there was no difference in the positive context, and this effect appeared within the first 100 ms of stimulus presentation and lasted at least until 200 ms. In other words, the degree of left lateralization was higher in the HC group, but lower in the SA group. The valence model of emotional brain unilaterally holds that the right brain is exclusively responsible for negative emotional perceptions while the left is responsible for positive emotions, and EEG studies of the frontal regions similarly showed elevated left-brain activity in positive emotional responses and increased right-brain activity in negative emotional responses (Davidson & Psychopathology, 1994; Fox, 1991; Coan & Allen, 2004). According to the model, stronger right frontal activity in social anxiety individuals indicates that social anxiety individuals are more sensitive to the valence provided by negative contexts and show a particularly negative emotional experience to the threatening information contained in them, regardless of the type of expression. Therefore, high social anxiety individuals increased expressions processing and attentional resources investment in negative contexts.

Finally, we observed a contextual effect in subjects' affective ratings of expressions, in which subjects perceived angry and neutral expressions in the negative context as more unpleasant than expressions in the positive context, while happy expressions in the positive context as more pleasant than expressions in the negative context. This result, which captures the affective congruence effect, is consistent with our previous results on the valuation of positive and neutral faces in different contexts (Song et al., 2020) and with our valence rating of negative expressions that have not been reported. But the subjective rating results did not find the interaction between context, expression type, and groups, which suggested that compared with behavioral data, neurophysiological indexes might be a more sensitive and objective method to reveal the characteristics of facial expression processing in high and low social anxiety individuals. In addition, researchers have indicated that the ecological validity of static emotional faces is inferior to that of dynamic faces (Campagnoli et al., 2019; Ran et al., 2020), few relevant findings on the neural mechanisms underlying emotional face processing in high social anxiety have addressed dynamic emotional faces. Future neuroscience researh needs to examine more ecologically dynamic stimuli as well as expand the sample of subjects with a view to more fully examining the mechanisms of emotional face processing in people with high social anxiety.

Social anxiety individuals showed stronger attentional inhibition of face expression processing only when the language context was emotionally congruent with the facial expression. The effect of context on facial expression processing in social anxiety individuals was manifested in the early stage of face processing as a contextual consistency effect, and the effect of negative information may be more pronounced. The present study enriches the understanding of the characteristics of the early stage of facial expression processing in individuals with social anxiety at the level of neural oscillations.

Al-Ezzi, A., Kamel, N., Faye, I., Gunaseli, E., 2020. Review of EEG, ERP, and Brain Connectivity Estimators as Predictive Biomarkers of Social Anxiety Disorder. Front. Psychol. 11, 730. https://doi.org/10.3389/fpsyg.2020.00730

Aviezer, H., Ensenberg, N., Hassin, R.R., 2017. The inherently contextualized nature of facial emotion perception. Curr. Opin. Psychol. 17, 47–54. https://doi.org/10.1016/j.copsyc.2017.06.006

Bai, L., Ma, H., Huang, Y., Luo, Y., 2005. The Development of Native Chinese Affective Picture System—A pretest in 46 College Students. Chin. Ment. Health J. 19, 719–722.

Balconi, M., Lucchiari, C., 2006. EEG correlates (event-related desynchronization) of emotional face elaboration: A temporal analysis. Neurosci. Lett. 392, 118–123. https://doi.org/10.1016/j.neulet.2005.09.004

Balconi, M., Mazza, G., 2009. Brain oscillations and BIS/BAS (behavioral inhibition/activation system) effects on processing masked emotional cues. Int. J. Psychophysiol. 74, 158–165. https://doi.org/10.1016/j.ijpsycho.2009.08.006

Barrett, L.F., Lindquist, K.A., Gendron, M., 2007. Language as context for the perception of emotion. Trends Cogn. Sci. 11, 327–332. https://doi.org/10.1016/j.tics.2007.06.003

Barrett, L.F., Mesquita, B., Gendron, M., 2011. Context in Emotion Perception. Curr. Dir. Psychol. Sci. 20, 286–290. https://doi.org/10.1177/0963721411422522

Blackhart, G.C., Minnix, J.A., Kline, J.P., 2006. Can EEG asymmetry patterns predict future development of anxiety and depression? Biol. Psychol. 72, 46–50. https://doi.org/10.1016/j.biopsycho.2005.06.010

Bollimunta, A., Mo, J., Schroeder, C.E., Ding, M., 2011. Neuronal Mechanisms and Attentional Modulation of Corticothalamic Alpha Oscillations. J. Neurosci. 31, 4935–4943. https://doi.org/10.1523/JNEUROSCI.5580-10.2011

Campagnoli, R.R., Wieser, M.J., Gruss, L.F., Boylan, M.R., McTeague, L.M., Keil, A., 2019. How the visual brain detects emotional changes in facial expressions: Evidence from driven and intrinsic brain oscillations. Cortex 111, 35–50. https://doi.org/10.1016/j.cortex.2018.10.006

Coan, J.A., Allen, J.J.B., 2003. Frontal EEG asymmetry and the behavioral activation and inhibition systems. Psychophysiology 40, 106–114. https://doi.org/10.1111/1469-8986.00011

Coan, J.A., Allen, J.J.B., 2004. Frontal EEG asymmetry as a moderator and mediator of emotion. Biol. Psychol. 67, 7–50. https://doi.org/10.1016/j.biopsycho.2004.03.002

Davidson, R., 1979. Frontal versus perietal EEG asymmetry during positive and negative affect. Psychophysiology 16, 202–203.

Davidson, R.J., 1988. EEG measures of cerebral asymmetry: conceptual and methodological issues. Int. J. Neurosci. 39, 71–89.

Davidson, R.J., 1994. Asymmetric brain function, affective style, and psychopathology: The role of early experience and plasticity. Dev. Psychopathol. 6, 741–758.

Davidson, R.J., 1996. Cerebral Asymmetry, Emotion, and Affective Style. Brain Asymmetry 361.

Davidson, R.J., 1998. Anterior electrophysiological asymmetries, emotion, and depression: Conceptual and methodological conundrums. Psychophysiology 35, 607–614.

Davidson, R.J., Marshall, J.R., Tomarken, A.J., Henriques, J.B., 2000. While a phobic waits: Regional brain electrical and autonomic activity in social phobics during anticipation of public speaking. Biol. Psychiatry 47, 85–95.

Davidson, R.J., Saron, C.D., Senulis, J.A., Ekman, P., Friesen, W.V., 1990. Approach-Withdrawal and Cerebral Asymmetry: Emotional Expression and Brain Physiology: I. J. Pers. Soc. Psychol. 58, 330–341.

Davis, F.C., Johnstone, T., Mazzulla, E.C., Oler, J.A., Whalen, P.J., 2010. Regional Response Differences Across the Human Amygdaloid Complex during Social Conditioning. Cereb. Cortex 20, 612–621. https://doi.org/10.1093/cercor/bhp126

De Cesarei, A., Codispoti, M., 2011. Affective modulation of the LPP and α-ERD during picture viewing: Affective modulation of ERPs and EEG oscillations. Psychophysiology 48, 1397–1404. https://doi.org/10.1111/j.1469-8986.2011.01204.x

Diéguez-Risco, T., Aguado, L., Albert, J., Hinojosa, J.A., 2015. Judging emotional congruency: Explicit attention to situational context modulates processing of facial expressions of emotion. Biol. Psychol. 112, 27–38. https://doi.org/10.1016/j.biopsycho.2015.09.012

Ekman, P., Oster, H., 1979. Facial expressions of emotion. Annu. Rev. Psychol. 30, 527–554.

Fox, N.A., 1991. If it’s not left, it’s right: Electroencephalograph asymmetry and the development of emotion. Am. Psychol. 46, 863–872.

Geisser, S., Greenhouse, S.W., 1958. An extension of box’s results on the use of the $ F $ distribution in multivariate analysis. Ann. Math. Stat. 29, 885–891.

Girden, E.R., 1992. ANOVA: Repeated measures. sage.

Güntekin, B., Basar, E., 2007. Emotional face expressions are differentiated with brain oscillations. Int. J. Psychophysiol. 64, 91–100. https://doi.org/10.1016/j.ijpsycho.2006.07.003

Güntekin, B., Başar, E., 2014. A review of brain oscillations in perception of faces and emotional pictures. Neuropsychologia 58, 33–51. https://doi.org/10.1016/j.neuropsychologia.2014.03.014

Hannesdóttir, D.Kr., Doxie, J., Bell, M.A., Ollendick, T.H., Wolfe, C.D., 2010. A longitudinal study of emotion regulation and anxiety in middle childhood: Associations with frontal EEG asymmetry in early childhood. Dev. Psychobiol. 52, 197–204. https://doi.org/10.1002/dev.20425

Harmon-Jones, E., Gable, P.A., Peterson, C.K., 2010. The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. Biol. Psychol. 84, 451–462. https://doi.org/10.1016/j.biopsycho.2009.08.010

Harrewijn, A., Van der Molen, M.J.W., Westenberg, P.M., 2016. Putative EEG measures of social anxiety: Comparing frontal alpha asymmetry and delta–beta cross-frequency correlation. Cogn. Affect. Behav. Neurosci. 16, 1086–1098. https://doi.org/10.3758/s13415-016-0455-y

Hassin, R.R., Aviezer, H., Bentin, S., 2013. Inherently Ambiguous: Facial Expressions of Emotions, in Context. Emot. Rev. 5, 60–65. https://doi.org/10.1177/1754073912451331

He, Y., Zhang, M., 2004. Psychometric investigation of Liebowitz Social Anxiety Scale. J. Diagn. Concepts Pract. 3, 89–93.

Herbert, C., Pauli, P., Herbert, B.M., 2011. Self-reference modulates the processing of emotional stimuli in the absence of explicit self-referential appraisal instructions. Soc. Cogn. Affect. Neurosci. 6, 653–661. https://doi.org/10.1093/scan/nsq082

Hinojosa, J.A., Mercado, F., Carretié, L., 2015. N170 sensitivity to facial expression: A meta-analysis. Neurosci. Biobehav. Rev. 55, 498–509. https://doi.org/10.1016/j.neubiorev.2015.06.002

Hu, L., Zhang, Z., 2019. EEG signal processing and feature extraction. Springer.

Jensen, O., Bonnefond, M., VanRullen, R., 2012. An oscillatory mechanism for prioritizing salient unattended stimuli. Trends Cogn. Sci. 16, 200–206. https://doi.org/10.1016/j.tics.2012.03.002

Keil, A., Mussweiler, T., Epstude, K., 2006. Alpha-band activity reflects reduction of mental effort in a comparison task: A source space analysis. Brain Res. 1121, 117–127. https://doi.org/10.1016/j.brainres.2006.08.118

Kim, H., Somerville, L.H., Johnstone, T., Polis, S., Alexander, A.L., Shin, L.M., Whalen, P.J., 2004. Contextual Modulation of Amygdala Responsivity to Surprised Faces. J. Cogn. Neurosci. 16, 1730–1745. https://doi.org/10.1162/0898929042947865

Knyazev, G.G., Bocharov, A.V., Levin, E.A., Savostyanov, A.N., Slobodskoj-Plusnin, J.Yu., 2008. Anxiety and oscillatory responses to emotional facial expressions. Brain Res. 1227, 174–188. https://doi.org/10.1016/j.brainres.2008.06.108

Kolassa, I.-T., Kolassa, S., Musial, F., Miltner, W.H., 2007. Event-related potentials to schematic faces in social phobia. Cogn. Emot. 21, 1721–1744.

Leleu, A., Demily, C., Franck, N., Durand, K., Schaal, B., Baudouin, J.-Y., 2015. The Odor Context Facilitates the Perception of Low-Intensity Facial Expressions of Emotion. PLOS ONE 10, e0138656. https://doi.org/10.1371/journal.pone.0138656

Li, S., Zhu, X., Ding, R., Ren, J., Luo, W., 2019. The effect of emotional and self-referential contexts on ERP responses towards surprised faces. Biol. Psychol. 146, 107728. https://doi.org/10.1016/j.biopsycho.2019.107728

Liu L., Zhou R., 2015. An Important Neural Indicator of Measuring Depression: The Asymmetry of Resting Frontal Activity. Adv. Psychol. Sci. 23, 1000–1008. https://doi.org/10.3724/SP.J.1042.2015.01000

Liu, S., Tan, Q., Han, S., Li, W., Wang, X., Gan, Y., Xu, Q., Zhang, X., Zhang, L., 2019. The language context effect in facial expressions processing and its mandatory characteristic. Sci. Rep. 9, 11045. https://doi.org/10.1038/s41598-019-47075-x

Lu, Y., Zhang, W.-N., Shen, D.-L., 2010. Subliminal affective priming effect by faces with different valence: An ERP study. Acta Psychol. Sin. 42, 929–938.

Lydiard, R.B., 2001. Social anxiety disorder: comorbidity and its implications. J. Clin. Psychiatry 62, 17–24.

Masuda, T., Ellsworth, P.C., Mesquita, B., Leu, J., Tanida, S., Van de Veerdonk, E., 2008. Placing the face in context: cultural differences in the perception of facial emotion. J. Pers. Soc. Psychol. 94, 365–381.

McKendrick, M., Butler, S.H., Grealy, M.A., 2016. The Effect of Self-Referential Expectation on Emotional Face Processing. PLOS ONE 11, e0155576. https://doi.org/10.1371/journal.pone.0155576

Meiers, G., Nooner, K., De Bellis, M.D., Debnath, R., Tang, A., 2020. Alpha EEG asymmetry, childhood maltreatment, and problem behaviors: A pilot home-based study. Child Abuse Negl. 101, 104358. https://doi.org/10.1016/j.chiabu.2020.104358

Mennella, R., Patron, E., Palomba, D., 2017. Frontal alpha asymmetry neurofeedback for the reduction of negative affect and anxiety. Behav. Res. Ther. 92, 32–40. https://doi.org/10.1016/j.brat.2017.02.002

Mikolajczak, M., Bodarwé, K., Laloyaux, O., Hansenne, M., Nelis, D., 2010. Association between frontal EEG asymmetries and emotional intelligence among adults. Personal. Individ. Differ. 48, 177–181. https://doi.org/10.1016/j.paid.2009.10.001

Mikolajczak, M., Nelis, D., Hansenne, M., Quoidbach, J., 2008. If you can regulate sadness, you can probably regulate shame: Associations between trait emotional intelligence, emotion regulation and coping efficiency across discrete emotions. Personal. Individ. Differ. 44, 1356–1368. https://doi.org/10.1016/j.paid.2007.12.004

Miskovic, V., Campbell, M.J., Santesso, D.L., Ameringen, M.V., Mancini, C.L., Schmidt, L.A., 2011. Frontal Brain Oscillatory Coupling in Children of Parents With Social Phobia: A Pilot Study. J Neuropsychiatry Clin Neurosci 23, 111–114.

Moscovitch, D.A., Santesso, D.L., Miskovic, V., McCabe, R.E., Antony, M.M., Schmidt, L.A., 2011. Frontal EEG asymmetry and symptom response to cognitive behavioral therapy in patients with social anxiety disorder. Biol. Psychol. 87, 379–385. https://doi.org/10.1016/j.biopsycho.2011.04.009

Mühlberger, A., Wieser, M.J., Herrmann, M.J., Weyers, P., Tröger, C., Pauli, P., 2009. Early cortical processing of natural and artificial emotional faces differs between lower and higher socially anxious persons. J. Neural Transm. 116, 735–746.

Neta, M., Davis, F.C., Whalen, P.J., 2011. Valence resolution of ambiguous facial expressions using an emotional oddball task. Emotion 11, 1425–1433.

Nie, D., Wang, X.-W., Shi, L.-C., Lu, B.-L., 2011. EEG-based emotion recognition during watching movies, in: 2011 5th International IEEE/EMBS Conference on Neural Engineering. IEEE, pp. 667–670.

Onoda, K., Okamoto, Y., Shishida, K., Hashizume, A., Ueda, K., Yamashita, H., Yamawaki, S., 2007. Anticipation of affective images and event-related desynchronization (ERD) of alpha activity: An MEG study. Brain Res. 1151, 134–141. https://doi.org/10.1016/j.brainres.2007.03.026

Papousek, I., Reiser, E.M., Weber, B., Freudenthaler, H.H., Schulter, G., 2012. Frontal brain asymmetry and affective flexibility in an emotional contagion paradigm: Frontal brain asymmetry and affective flexibility. Psychophysiology 49, 489–498. https://doi.org/10.1111/j.1469-8986.2011.01324.x

Papousek, I., Weiss, E.M., Schulter, G., Fink, A., Reiser, E.M., Lackner, H.K., 2014. Prefrontal EEG alpha asymmetry changes while observing disaster happening to other people: Cardiac correlates and prediction of emotional impact. Biol. Psychol. 103, 184–194. https://doi.org/10.1016/j.biopsycho.2014.09.001

Peschard, V., Philippot, P., Joassin, F., Rossignol, M., 2013. The impact of the stimulus features and task instructions on facial processing in social anxiety: An ERP investigation. Biol. Psychol. 93, 88–96. https://doi.org/10.1016/j.biopsycho.2013.01.009

Pfurtscheller, G., 1992. Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest. Electroencephalogr. Clin. Neurophysiol. 83, 62–69.

Ran, G., Chen, X., 2017. The Impact of Top-Down Prediction on Emotional Face Processing in Social Anxiety. Front. Psychol. 8, 1269. https://doi.org/10.3389/fpsyg.2017.01269

Ran G., Li R., Zhang Q., 2020. Neural mechanism underlying recognition of dynamic emotional faces in social anxiety. Adv. Psychol. Sci. 28, 1979–1988. https://doi.org/10.3724/SP.J.1042.2020.01979

Rapee, R.M., Spence, S.H., 2004. The etiology of social phobia: empirical evidence and an initial model. Clin. Psychol. Rev. 24, 737–767. https://doi.org/10.1016/j.cpr.2004.06.004

Riwkes, S., Goldstein, A., Gilboa-Schechtman, E., 2015. The temporal unfolding of face processing in social anxiety disorder — a MEG study. NeuroImage Clin. 7, 678–687. https://doi.org/10.1016/j.nicl.2014.11.002

Roh, S.-C., Kim, J.S., Kim, S., Kim, Y., Lee, S.-H., 2020. Frontal Alpha Asymmetry Moderated by Suicidal Ideation in Patients with Major Depressive Disorder: A Comparison with Healthy Individuals. Clin. Psychopharmacol. Neurosci. 18, 58–66. https://doi.org/10.9758/cpn.2020.18.1.58

Schmidt, L.A., 1999. Frontal brain electrical activity in shyness and sociability. Psychol. Sci. 10, 316–320.

Schneider, T.R., Hipp, J.F., Domnick, C., Carl, C., Büchel, C., Engel, A.K., 2018. Modulation of neuronal oscillatory activity in the beta- and gamma-band is associated with current individual anxiety levels. NeuroImage 178, 423–434. https://doi.org/10.1016/j.neuroimage.2018.05.059

Schöne, B., Schomberg, J., Gruber, T., Quirin, M., 2016. Event-related frontal alpha asymmetries: electrophysiological correlates of approach motivation. Exp. Brain Res. 234, 559–567.

Schubring, D., Schupp, H.T., 2019. Affective picture processing: Alpha‐ and lower beta‐band desynchronization reflects emotional arousal. Psychophysiology 56, e13386. https://doi.org/10.1111/psyp.13386

Schwarz, K.A., Wieser, M.J., Gerdes, A.B.M., Mühlberger, A., Pauli, P., 2013. Why are you looking like that? How the context influences evaluation and processing of human faces. Soc. Cogn. Affect. Neurosci. 8, 438–445. https://doi.org/10.1093/scan/nss013

Smit, D.J.A., Posthuma, D., Boomsma, D.I., De Geus, E.J.C., 2007. The relation between frontal EEG asymmetry and the risk for anxiety and depression. Biol. Psychol. 74, 26–33. https://doi.org/10.1016/j.biopsycho.2006.06.002

Song, S., Wu, M., Feng, C., 2022a. Early Influence of Emotional Scenes on the Encoding of Fearful Expressions With Different Intensities: An Event-Related Potential Study. Front. Hum. Neurosci. 16, 866253. https://doi.org/10.3389/fnhum.2022.866253

Song, S., Zhao, S., Gao, Z., Lu, M., Zhang, M., Gao, S., Zheng, Y., 2022b. Influence of affective verbal context on emotional facial expression perception of social anxiety. Int. J. Psychophysiol. 181, 141–149. https://doi.org/10.1016/j.ijpsycho.2022.09.002

Song, S., Zhao, S., Jiang, T., Li, S., Zhang, M., Ren, W., Zheng, Y., Ge, R., 2022c. Positive attention bias in high socially anxious individuals: Evidence from an ERP study. J. Affect. Disord. 319, 300–308. https://doi.org/10.1016/j.jad.2022.09.087

Song, S.T., Jiang, M., Tong, Y.H., Quan, L.N., Wang, R., 2020. The Effect of Context on Positive Facial Expression Perception. Chin. J. Clin. Psychol. 28, 1111–1115.

Todorov, A., 2011. Evaluating faces on social dimensions. 54–76.

Tsuji, Y., Shimada, S., 2018. Socially Anxious Tendencies Affect Impressions of Others’ Positive and Negative Emotional Gazes. Front. Psychol. 9, 2111. https://doi.org/10.3389/fpsyg.2018.02111

VanRullen, R., Dubois, J., 2011. The Psychophysics of Brain Rhythms. Front. Psychol. 2, 203. https://doi.org/10.3389/fpsyg.2011.00203

Wang, R., Mao, W., Zhu, Y., 2015. The Effect of Emotional Context on Facial Emotion Perception. J. Psychol. Sci. 38, 80–84.

Wang, Z., Yuan, C.-M., Huang, J., Li, Z.-Z., Chen, J., Zhang, H.-Y., Fang, Y.-R., Xiao, Z.-P., 2011. Reliability and validity of the Chinese version of Beck Depression Inventory-II among depression patients. Chin. Ment. Health J. 25, 476–480.

Wieser, M.J., Brosch, T., 2012. Faces in Context: A Review and Systematization of Contextual Influences on Affective Face Processing. Front. Psychol. 3, 471. https://doi.org/10.3389/fpsyg.2012.00471

Wieser, M.J., Gerdes, A.B.M., Büngel, I., Schwarz, K.A., Mühlberger, A., Pauli, P., 2014. Not so harmless anymore: How context impacts the perception and electrocortical processing of neutral faces. NeuroImage 92, 74–82. https://doi.org/10.1016/j.neuroimage.2014.01.022

Wieser, M.J., Moscovitch, D.A., 2015. The Effect of Affective Context on Visuocortical Processing of Neutral Faces in Social Anxiety. Front. Psychol. 6, 1824. https://doi.org/10.3389/fpsyg.2015.01824

Wieser, M.J., Pauli, P., Weyers, P., Alpers, G.W., Mühlberger, A., 2009. Fear of negative evaluation and the hypervigilance-avoidance hypothesis: an eye-tracking study. J. Neural Transm. 116, 717–723.

Zhang, Q., Ran, G., Li, X., 2018. The Perception of Facial Emotional Change in Social Anxiety: An ERP Study. Front. Psychol. 9, 1737. https://doi.org/10.3389/fpsyg.2018.01737

Table 1 Basic information on subjects

	SA	HC	p
Gender (M/F)	4/27	14/15	< .01^a
Age	19.39±1.20	19.72±1.73	.388^b
LSAS	65.39±11.18	22.00±7.68	< .001^b
BDI-II	5.39±2.33	2.93±2.12	< .001^b

Note: LSAS =Liebowitz Social Anxiety Scale; BDI -II =Beck Depression Inventory-II (Second edition of Beck Depression Scale).

^a Indicates the chi-square test.

^b Indicates that a two-sample t-test was performed.

ERP

event-related potentials

social anxiety group

healthy group

LSAS

Liebowitz Social Anxiety Scale

BDI-II

the Beck depression inventory Second Edition

Ethics approval and consent to participate

Experiment was approved by the Institutional Review Board of the School of Education and Psychology at the University of Jinan. In accordance with the Declaration of Helsinki, written informed consent as approved by the Human Research Protections Program of University of Jinan was obtained from all subjects before their participation. And all subjects who provided face images gave informed consent for the publication of the identified images in an online open publication.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This research was financially supported by the Natural Science Foundation of Shandong Province, China (ZR2021MF043), and the Humanities and Social Sciences Foundation for the Youth Scholars of the Ministry of Education of China (18YJCZH149).

Authors' contributions

STS and AXL conceived and prepared the first draft of the manuscript and revised it according to the opinions of YJZ, RYG. All authors interpreted the data and revised the manuscript. MXZ, SMZ is responsible for data collection and storage. Data analysis was performed on STS, AXL, HQS, SHG and ZYG. STS and AXL take full responsibility for the accuracy of the data and analysis. All authors read and approved the final manuscript.

Acknowledgements

All authors of the article are Sutao Song, Aixin Liu, Haiqing Shang, Shihao Gao, Zeyuan Gao, Mingxian Zhang, Shimeng Zhao, Yuanjie Zheng, Ruiyang Ge. This article was submitted with the permission of all those mentioned in the acknowledgements section.

No competing interests reported.

Download PDF

Version 2

posted

You are reading this latest preprint version

Event-Related Alpha Oscillatory Response in Early Stage of Facial Expression Processing in Social Anxiety: Influence of Language Context

Status:

Version 2

Abstract

Figures

1. Introduction

2. Methods

3 Result

4. Discussion

5. Conclusions

References

Tables

Abbreviations

Declarations

Additional Declarations

Status:

Version 2