Fast periodic visual stimulation to study the processing of health- related images in the human brain

Fast periodic visual stimulation to study the processing of health- related images in the human brain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Fast periodic visual stimulation to study the processing of health- related images in the human brain Paola Castellano, Chiara Leu, Michela Mazzetti, Giulia Liberati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6989317/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Health anxiety is often linked to lower pain thresholds and heightened sensitivity to health-related stimuli, yet the relationship between these psychological and physiological traits remains complex. In this study, we relied on the use of the brain’s ability to discriminate fast and periodically presented stimuli (i.e. oddballs) of a given image category within a stream of unrelated images, to investigate whether neural responses to health-related visual stimuli are associated with individual differences in pain sensitivity and psychological traits such as anxiety and depression. We hypothesized that, if the periodically presented health-related oddball elicits a periodic neural response, this image category might lead to a stronger response in individuals with health anxiety and psychological malaise. This is the first evidence that periodically presented health-related images elicit a neural response which can be clearly differentiated from unrelated images. Additionally, this neural response shared a relationship with depressive traits, which was in turn moderated by the pain threshold. While these results offer insight into the interaction between psychological traits, pain threshold and the processing of health-related images, future studies will have to confirm the specificity of the obtained relationships. Biological sciences/Neuroscience/Cognitive neuroscience Biological sciences/Neuroscience/Neuronal physiology FPVS EEG health anxiety frequency-tagging pain threshold Figures Figure 1 Figure 2 Figure 3 1. Introduction The human brain continuously processes external stimuli, often below the level of conscious awareness 1 . Among these, health-related stimuli are of particular interest due to their potential relevance for survival and well-being. Understanding how the brain responds to such stimuli, even when they are not consciously perceived, can shed light on interacting mechanisms of conceptual and visual processing. The present study aims to investigate whether the brain shows an implicit processing of health-related images and whether this response is modulated by individual psychological traits such as anxiety, emotion regulation, impulsivity, and stress or the individuals’ pain threshold. Health anxiety, a construct characterized by excessive worry and preoccupation with one's health 2 , provides a compelling framework for examining selective and heightened sensitivity to health-related stimuli. Numerous studies have shown that individuals with high levels of health anxiety tend to exhibit selective attention to illness-related information 3 , 4 , and this attentional bias is often accompanied by stronger emotional and physiological responses when confronted with such stimuli. These findings suggest that health-related stimuli may trigger stronger responses in the brain, especially in individuals with elevated anxiety levels. However, the neural mechanisms underlying these responses remain underexplored, and understanding the neural underpinnings of these responses is crucial for developing effective interventions and therapies. To address this, our study seeks to determine whether we can capture neural responses elicited by health-related stimuli. To this end, we employed the Fast Periodic Visual Stimulation (FPVS) paradigm. This technique is based on the well-established principle that a periodic stimulation elicits a periodic modulation of the EEG signal at the frequency of the stimulation and its harmonics 5 . Thus, when combined with electroencephalography (EEG), this paradigm allows to “tag” the neural response to a periodic oddball stimulus within a stream of unrelated images at the frequency at which both the oddballs and unrelated images were presented. This paradigm has been widely applied in vision research 6 , with a particular success in the studies of facial discrimination (see Rossion, et al. 7 for a comprehensive review). Additionally, it has been successfully implemented in object categorization 8 , the recognition of letters and words 9 , 10 , as well as visual recognition of semantic categories 11 , 12 . To our knowledge, this is the first application of FPVS to differentiate between health-related and neutral visual stimuli, adding a novel dimension to the growing body of research using the FPVS paradigm. In addition to assessing the neural response to these stimuli, we also explored the potential role of individual psychological traits in modulating this response. Psychological factors such as anxiety, emotion regulation, impulsivity, coping strategies and stress have been linked to attentional biases and heightened emotional reactivity 13 – 16 . Specifically, trait anxiety, particularly health anxiety, has been associated with an increased focus on threat-related or illness-related information, as demonstrated in numerous behavioral and neuroimaging studies 17 , 18 . Moreover, the interplay between health anxiety and individual traits such as impulsivity and emotional regulation is an area ripe for exploration. Impulsivity may drive individuals to react quickly and sometimes maladaptively to health-related stimuli, while effective emotional regulation can help mitigate anxiety responses. By examining these traits in conjunction with neural responses to health-related images, we aimed to elucidate the complex relationships that govern how individuals with varying levels of health anxiety engage with health-related information. We hypothesized that individuals with higher levels of anxiety and related traits may exhibit a stronger unconscious neural response to health-related stimuli, as measured by EEG during the FPVS task. Furthermore, we investigated the potential contribution of physical traits, such as the individual pain threshold, to the neural processing of health-related stimuli. Pain is inherently linked to health and survival and engages in both psychological and physiological processes. Previous research reports that individuals with high levels of anxiety, including health anxiety, tend to experience lower pain thresholds 19 , 20 and higher pain sensitivity 21 , 22 . These findings are consistent with the well-established understanding that pain perception is influenced not only by physical factors, but also by affective and cognitive processes - particularly anxiety. Moreover, recent research suggests that individual differences in pain perception are closely tied to neural responses to threat and health-related cues 23 – 25 . In this study, we used thermal cutaneous stimulation to measure pain thresholds and assess whether individuals with lower pain thresholds exhibit heightened neural responses to health-related stimuli, aiming to understand how both psychological and physical traits may jointly influence the brain’s reaction to health-related information. Psychological constructs were measured using validated questionnaires, each selected for its established link with attention to threat, emotional reactivity, or health-related cognitive biases. Based on previous research, the underlying hypothesis is that the periodic brain response to health-related images may be associated with higher features of psychological distress. In summary, we investigated whether the brain is able to distinguish periodically presented health-related images from a stream of random images and explored whether this response is associated with individual differences in psychological traits and pain thresholds. 2. Results Participants completed a series of questionnaires assessing their psychological traits, as well as a measurement of their individual pain threshold. Then, participants had to focus on fast periodic visual stimuli depicting health-related images, which were embedded in a periodic stream of unrelated images, while their neural response to the visual stimuli was measured using EEG. 2.1. Behavioral response To ensure that participants were paying attention to the presented visual stimuli, they had to count the number of times the dot in the middle of the presented visual stimuli changed to the color red within each trial. Accuracy rates in this behavioral task were similar in both conditions (Original: 33.88% ± 12.28%; Scrambled: 36.72% ± 11.44%) and no significant difference was found between conditions in accuracy rates (F(1,40) = 1.327, p = 0.256, η p 2 = 0.032). 2.2. Neural response In the scrambled condition, only few and nonconsecutive oddball harmonics were statistically different from zero. Conversely in the original condition, all oddball harmonics up to the 5th harmonics (i.e. ~7.29 Hz) showed a significant periodic response to the health-related visual stimuli. Thus, for the statistical analysis, the response at the oddball frequency was summed up with the first 5 of its harmonics (excluding the 4th harmonic at ~ 6 Hz, which overlaps with the base presentation rate). To be consistent, the same number of oddball harmonics was summed up in the scrambled condition. Following the same rationale, the baseline response and its first harmonics were aggregated in both conditions. Congruent to other investigations using the FPVS paradigm, the largest response to the original images was found at electrode PO8, which was subsequently chosen as electrode of interest for the base response. In the scrambled condition, responses were mainly found at the base frequency at electrode Oz. Figure 1 a illustrates the frequency spectra for these EEG responses at electrodes PO8 (original images) and Oz (scrambled images) in both conditions. For the correlation with pain thresholds and personality traits, the summed-up oddball response was used (0.47 ± 0.34 µV, range: -0.08-1.92 µV). The three-way ANOVA (condition: original / scrambled, frequency: oddball / base, electrode: PO8/ Oz) revealed significant main effects of condition (F(1,40) = 45.163, p < 0.001, η p 2 = 0.530), frequency (F(40,1) = 161.020, p < 0.001, η p 2 = 0.801) and electrode (F(1,40) = 5.761, p = 0.021, η p 2 = 0.126), as well as significant interactions between condition and frequency (F(40,1) = 46.333, p < 0.001, η p 2 = 0.537), and condition and electrode (F(40,1) = 24.949, p < 0.001, η p 2 = 0.384). Post-hoc pairwise comparisons demonstrated a significant difference between conditions at the oddball frequency (t(81) = 13.245, p < 0.001) but not at the base presentation frequency (t(81) = 0.907, p = 0.367) (Fig. 1 b). The multi-sensor cluster-based t-test between the original and scrambled condition demonstrated multiple clusters of electrodes with larger activity during the original condition. Precisely, a fronto-central clusters of interest was formed (F1, Fz, FC3, FC1, FCz, FC2, C1, Cz) as well as a occipital-temporal cluster (the latter corresponding to frequently observed distribution of responses 7 consisting of a left (PO7, PO3, P7, P9), middle (Oz, O2, O1, Iz) and right (PO8, PO10, P8, P6, P4) temporal-occipital electrodes (Fig. 1 c). 2.3. Pain Threshold Pain thresholds were rated on average at 46.66 ± 2.01°C (mean ± std dev), which corresponds to normative values for this age group 26 . Of the total sample, 20 participants had an above-average pain threshold. 2.4. Psychological traits The psychological individual characteristics of the sample are presented in Table 2 . Table 2 Participants’ baseline characteristics (mean ± standard deviation) for all the psychological variables investigated. VARIABLE M ± SD (range min-max) Depression 8.39 ± 6.44 (0–25) Anxiety 44.15 ± 7.07 (29–58) Perceived stress 17.98 ± 5.68 (6–34) Nonacceptance of emotional response (DERS) 13.10 ± 5.57 (6–29) Difficulties in adopting goal-directed behaviors (DERS) 14.78 ± 4.73 (5–24) Difficulties in controlling impulsive behaviors (DERS) 12.63 ± 4.60 (6–25) Lack of emotional awareness (DERS) 15.68 ± 4.63 (7–30) Limited access to emotion regulation strategies (DERS) 18.22 ± 6.79 (6–35) Lack of emotional identification or clarity (DERS) 10.85 ± 3.45 (5–18) General Difficulties in emotional regulation 85.27 ± 21.45 (42–150) Planning difficulties (BIS-11) 23.17 ± 4.67 (11–36) Motor Impulsivity (BIS-11) 18.20 ± 3.32 (11–24) Cognitive Impulsivity (BIS-11) 17.12 ± 3.99 (9–29) General Impulsivity 58.49 ± 9.41 (37–84) Behavioral Inhibition (BIS/BAS) 21.17 ± 3.93 (10–28) Reward Responsiveness (BIS/BAS) 17.02 ± 2.29 (9–20) Drive (BIS/BAS) 9.41 ± 2.29 (5–15) Fun Seeking (BIS/BAS) 12.00 ± 2.04 (7–16) Active Coping (Brief-COPE) 4.51 ± 1.56 (2–8) Planning (Brief-COPE) 5.34 ± 1.27 (3–8) Instrumental Support (Brief-COPE) 5.00 ± 1.32 (2–7) Problem-focused Coping (Brief-COE) 14.85 ± 2.94 (8–21) Acceptance (Brief-COPE) 5.61 ± 1.11 (4–8) Emotional Support (Brief-COPE) 4.22 ± 1.33 (2–7) Humor (Brief-COPE) 4.24 ± 0.96 (2–6) Positive Reframing (Brief-COPE) 5.83 ± 1.37 (3–9) Religion (Brief-COPE) 2.98 ± 1.32 (2–7) Emotion-Focused Coping (Brief-COPE) 22.88 ± 3.31 (17–32) Behavioral Disengagement (Brief-COPE) 3.73 ± 1.04 (2–7) Denial (Brief-COPE) 4.10 ± 1.20 (2–7) Self-Blame (Brief-COPE) 5.63 ± 1.22 (3–8) Self-Distraction (Brief-COPE) 5.24 ± 1.3 (3–8) Substance Use (Brief-COPE) 3.93 ± 0.72 (2–6) Venting (Brief-COPE) 4.12 ± 1.16 (2–6) Dysfunctional Coping (Brief-COPE) 26.76 ± 4.21 (18–37) DERS = Difficulties in Emotion Regulation Scale; BIS-11 = Barratt Impulsiveness Scale; BIS/BAS = Behavioral Inhibition and Behavioral Activation Scales; Brief-COPE = Brief Coping Orientation to Problems Experienced Inventory. 2.5. Relationship between neural, behavioral and psychological factors Pearson correlation analyses were conducted to explore the relationships between the recorded EEG signal amplitude at the presentation frequency of the oddball stimuli and other psychological and pain-related variables. Results indicated no significant correlation between amplitude and pain threshold (r = − 0.133 p = 0.408), or most psychological indices. However, a significant positive correlation was observed between amplitude and depression (r = 0.327, p = 0.037). This suggests a potential relationship between higher EEG amplitude and levels of depression. No other significant correlations were found between amplitude and measures of psychological distress (e.g., anxiety, perceived stress, emotion dysregulation) or coping strategies. Significant negative correlations were observed between pain threshold and several subscales of emotional dysregulation (i.e., “Nonacceptance of emotional response”, “Difficulties in adopting goal-directed behaviors”, “Difficulties in controlling impulsive behaviors”, “Limited access to emotion regulation strategies”, “Lack of emotional identification or clarity”) (p < 0.05), as well as overall emotional dysregulation (p < 0.01), indicating that people who tend to better regulate their emotions have also a higher pain threshold. Additionally, a significant negative correlation was observed between pain threshold and the BIS-11 subscale “Motor Impulsivity” (r=-0.38, p = 0.014), as well as with the Brief-COPE subscale “Positive Reframing” (r = − 0.331, p = 0.035), suggesting that individuals with higher motor impulsivity and using this coping strategy had lower pain thresholds. A linear regression analysis also revealed the predictive role of emotional dysregulation on pain threshold (B = − 0.051, SE = 0.013, β=−0.541, p < 0.001). As shown in Fig. 2 a, the effect of depression on EEG amplitude was stronger at higher levels of pain threshold. Thus, at lower (1SD – mean) pain thresholds the effect of depression on EEG amplitude was not significant (B = -0.005, p = 0.951), while the effect of depression on EEG amplitude was positive and significant both at medium pain threshold (B = 0.23, p = 0.003) and at higher (1SD + mean) pain threshold (B = 0.47, p = 0.0001). Based on this information, to better investigate the relationship between depression and brain signal, the moderating role of pain threshold was examined, using depression as the independent variable and EEG amplitude as the dependent one. The overall model was significant (F(3, 37) = 6.79, p = 0.001, with an R² = 0.355), as well as the interaction of depression and pain threshold (B = 0.12, p = 0.001), indicating that the latter significantly moderate the way depression affect brain response to health images (Fig. 2 b). Although the bivariate correlation between depression scores and EEG amplitude was positive, the regression analysis controlling for the moderating effect of pain threshold revealed a negative conditional effect of depression on amplitude (see Fig. 2 b). This shift in direction likely reflects the influence of the moderator, emphasizing that the association between depressive symptoms and neural response is not uniform across different levels of pain sensitivity. These results suggest that pain threshold moderates the relationship between depression and EEG amplitude. At higher pain threshold levels, depression is more strongly associated with increased EEG amplitude, while at lower pain threshold levels, the effect of depression on EEG amplitude is not significant. Figure 2 3. Discussion Notably, this study is the first to demonstrate that also the periodic presentation of health-related images can evoke distinguishable neural responses, even when these stimuli are more abstract and do not belong to a naturalistic category such as faces or tools. Specifically, the periodically presented health-related images (i.e. oddball) elicited a periodic neural response at the presentation frequency of this image category, which could be clearly differentiated from the response to images with random content. These findings support the hypothesis that FPVS can be used to "tag" neural responses to conceptual categories, broadening the applicability of this paradigm beyond its traditional domains. Additionally, we found that psychophysiological features, such as depression levels and individual pain threshold, are significantly related to the neural response to those oddball stimuli. Generally, periodically presented stimuli (such as human faces) have the ability to elicit a periodic neural response, which can be easily assessed in the EEG frequency domain 6–8,27−30 . As in previous studies using the FPVS paradigm, a consistent periodic response was found at the base image presentation frequency (~ 6Hz), for both the presentation of original as well as scrambled images. Scalp topographies and channels with the largest activity at this frequency also matched these previous investigations, highlighting that the basic assumptions of the paradigm have been met. More importantly though, a periodic neural response was also found at the presentation frequency of the health-related oddball images (~ 1.2 Hz and harmonics), with neural activity being distributed similarly over the scalp as in the base response. Presenting scrambled images led to a much smaller, but nevertheless statistically significant periodic response at some oddball harmonics (but not its frequency of presentation). Thus, this demonstrates that an abstract image category as oddball stimulus can lead to a periodic neural response in the FPVS paradigm, even considering the relatively low rate of correct answers in the behavioral task. Previously, it had already been demonstrated that the FPVS paradigm elicits neural responses not only related to faces or tools but also related to semantic processing. This was achieved by presenting images which could be categorized into semantic categories of different specificity (e.g. natural vs non-natural, animal vs non-animal images) 12 and natural vs man-made images 11 . While the present results extend these findings, as the health-related images represent a more abstract image category than the semantic subgroups and support the use of FPVS to study conceptual processing beyond traditional domains, caution is needed in interpreting the specificity of response. Indeed, we cannot attribute this response specifically to the "health" content of the images, as we did not include a control image category (e.g., tools or neutral objects) matched in terms of affective properties. It is therefore possible that the neural responses observed here reflect general processing of emotionally salient or negatively valanced images, rather than a conceptual response specific to health-related content. One possible explanation for the small but nevertheless present response during the scrambled oddball images would be that it was not only the recognition of the image category itself which led to the periodic response at the oddball frequency, but also some low-level qualities in this image category. Basic features such as contrast, and luminance were controlled by adjusting these characteristics across the entire image sample (including both health-related and non-health-related images) pre-experiment. Yet, other features such as animacy 31 or spatial frequency and orientation 11 (commonly assessed qualities in machine learning algorithms for visual features) could also elicit a neural response. Nevertheless, as demonstrated by our cluster-based analysis, the responses to the intact oddball images were not only considerably larger than the ones elicited by the oddball using scrambled images, but also demonstrated a different distribution of the neural activity across the scalp, with a marked frontal activity which was not present in the scrambled condition (similar to Stothart, et al. 12 ). As any of these low-level characteristics should be present in both the original as well as scrambled images, they are unlikely to be the main contributor to the observed response to the health-related images. An exploratory objective of the study was to examine whether individual differences in psychological traits, such as anxiety, depression, emotional regulation, impulsivity or coping strategies, modulate neural responses to health-related images. Although in previous studies emotional dysregulation has been associated with attentional biases (e.g., Ciccarelli, et al. 32 ;Harrison, et al. 33 ), and health anxiety has been linked to stronger responses to illness-related stimuli 4 , 34 , in the current investigation a significant correlation was only observed between the neural responses to the oddball stimuli and one of the major psychological traits, namely depression. These findings should be interpreted with caution, as they represent exploratory and secondary analyses. Nevertheless, they are consistent with the hypothesis of an attentional bias toward emotionally salient stimuli in depressed individuals (see Suslow, et al. 35 for a comprehensive review), and with evidence indicating amplified processing of signals of threat or vulnerability to illness 36 , 37 in those individuals. However, a particularly novel element of this study concerns the role of pain threshold in moderating this relationship. Indeed, moderation analyses showed that the association between depression and neural response to health stimuli was significant only in participants with medium or high pain thresholds, whereas it was absent in subjects with low thresholds. Additionally, pain threshold was negatively predicted by emotional dysregulation trait. This finding not only align with prior research linking emotional dysregulation to heightened pain sensitivity 38 , 39 , but suggests that pain threshold, potentially indicative of broader affective and physiological functioning 40 , may influence, in specific psychological conditions, how an individual processes this kind of periodic information. This analysis on pain threshold shed light on an interesting and potentially counterintuitive aspect. Indeed, emotional dysregulation represents a transdiagnostic factor commonly associated with depression 41 , 42 and is in turn correlated to an increased experience of pain 43 . Nevertheless, our data show that the psychophysiological relationship between depression and neural response to health-related stimuli is only significant in participants with higher pain threshold (i.e., with an improved emotional regulation). This apparent inconsistency can be interpreted in light of neurocognitive models that consider the role of residual affective-sensory resources in modulating brain responses 44 . It might be that the individual’s affective system needs to be sufficiently responsive, in order to allow depression to increase neural activity in response to emotionally relevant stimuli, such as the health-related images. Individuals with augmented levels of depression and with higher pain threshold (i.e., potentially more emotionally regulated) may have enough neural resources to actively process the stimulus and have an increased EEG response to that. On the contrary, individuals with augmented levels of depression but lower pain threshold (i.e., potentially less emotionally regulated) may have a blunted neural response as a response of combined affective vulnerability and sensory overload. These findings have several implications for the understanding of neural processing of health-related stimuli. First, they demonstrate the feasibility of using FPVS to explore neural responses to abstract and potentially emotionally salient categories. Second, while preliminary, the psychophysiological correlates suggest that individual factors might modulate this implicit neural response to emotionally meaningful visual cues. Yet, one of the main limitations of the present study is the inability to conclusively determine whether the observed neural activation is specifically driven by the health-related content of the stimuli. Further research is needed to corroborate these hypotheses and to establish the specificity of neural responses to health-related stimuli, for example by directly contrasting them with other categories of stimuli in future studies. In summary, this study expands the utility of the FPVS paradigm to abstract categories such as health-related stimuli and highlights the need to consider psychological well-being when investigating individual differences in the processing of health-related cues such as illness representations or medical imagery. Future research should continue to refine the methodological approaches used to investigate the complex interplay between neural, psychological, and physiological processes in health-related contexts. 4. Methods 4.1. Participants A sample size of 30 participants was estimated using the software G*Power to be necessary for a repeated- measures ANOVA with two within-factors (condition: ordinary/scrambled; electrode: PO8/OZ) to reach a power of 0.90 with an effect size of 0.25 and with the statistical significance set to 0.05. A total of 44 participants were recruited for this experimental study. For reasons of lack of compliancy with the research directions (n = 2) and technical problems (n = 1), only 41 participants were considered for statistical analysis (age (mean ± std. dev.): 25.17 ± 6.3, 32 females). Recruitment was carried out on an established website through the distribution of a flyer, which described the study as an investigation into how individual psychophysiological characteristics influence brain signals. The flyer specified that participation in the study would require approximately 1 hour and 45 minutes, and participants would be compensated 20 euros for their participation. After scheduling and signing an informed consent form, participants were screened for inclusion criteria (aged 18–65, in good health). Those meeting the criteria were contacted by the experimenter to be enrolled in the study. 4.2. Procedure Prior to their laboratory session, participants received a link from the experimenter, inviting them to read and sign the informed consent. Then they were invited to complete a series of questions, including assessments of sociodemographic information (e.g., gender, age, occupation) and a set of validated psychological questionnaires for individual characteristics (depression, anxiety, stress, impulsivity trait, emotion regulation strategies, coping strategies). Upon arrival at the laboratory on the scheduled day and time, participants underwent two experimental procedures. First, individual pain thresholds were measured using thermal cutaneous stimulations. Next, participants completed the FPVS task, during which EEG data were collected. The local Research Ethics Committee approved all experimental procedures (Commisision d'Ethique hospitalo-facultaire Saint-Luc UCLouvain, 2024/14FEV/074 – HSP) and all procedures were conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent before participating and were debriefed after the completion of their laboratory session. 4.3. Questionnaires for the assessment of psychological characteristics A battery of validated questionnaires was employed to assess specific individual characteristics potentially related to the participant’s pain threshold and neural response to the FPVS task. The following questionnaires were included in the battery: i) Beck Depression Inventory (BDI 45 ): a self-report questionnaire widely used to assess levels of depressive traits in the general population, composed by 21 questions and each question is scored on a scale value of 0 to 3; ii) State-Trait Anxiety Inventory – Y2 (STAI-Y2 46 ;): a commonly used self-report scale to assess trait anxiety levels, composed by 20 items to answer using a 4-point Likert scale; iii) Difficulties in Emotion Regulation Scale (DERS 41 ): a self-report scale designed to assess difficulties in regulating emotions, not only about the control of emotional arousal but also the awareness, comprehension, and acceptance of emotions. It is composed of 36 items and divided into 6 subscales (non-acceptance of emotional response, difficulties in adopting goal-directed behaviors, difficulties in controlling impulsive behaviors, lack of emotional awareness, limited access to emotion regulation strategies, lack of emotional identification or clarity). Answers are provided on a 5-point Likert scale; iv) Barratt Impulsiveness Scale (BIS-11 47 ): a 30-item self-report scale assessing three types of impulsivities (attentional, motor, non-planning) and a general score of impulsivity trait. Answers are provided using a 4-point Likert scale. v) Behavioral Inhibition System and Behavioral Activation System Scales (BIS/BAS 48 ): two self-report scales assessing individual differences in the sensitivity of motivational systems, one related to avoidant behaviors and one related to approaching behaviors, composed by a totality of 20 items answered by the use of a 4-point Likert scale; vi) Brief Coping Orientation to Problem Experiences Inventory (Brief-COPE 49 ): the brief version of a self-report questionnaire measuring effective and ineffective ways to cope with a stressful life situation (active coping, planning, using instrumental support, using emotional support, venting, behavioral disengagement, self-distraction, self-blame, positive reframing, humor, denial, acceptance, religion, substance use), composed of 28 items and answers are provided by using a 4-point Likert Scale; vii) Perceived Stress Scale (PSS 50 ): a 10-items self-report questionnaire widely used to assess individual’s perception of stress in daily life with a 5-point Likert scale used to provide answers. Being the sample of the present study composed by French speakers, validated French-language versions of the questionnaires described were administered (BDI 51 ; STAI 52 ; DERS 53 ; BIS-11 54 ; BIS/BAS 55 ; Brief—COPE 56 ). 4.4. Pain threshold assessment The individual pain threshold was measured using thermal stimulation and the method of limits 26 , 57 , 58 . Thermal stimuli were delivered to the volar forearm of the subject before the start of the visual task using a contact-heat thermode made of 15 micro-Peltier elements (stimulation surface: 1.2 cm 2 ) (TCS, QST.Lab, Strasbourg, France). For each subject, the stimulation temperature was set to skin temperature (~ 32°C) and subsequently the temperature was raised by 1°C/s until the subject perceived that the stimulation started to be painful and pressed a response button. This procedure was repeated 3 times (each time displacing the thermode to avoid habituation / sensitization), and the average of those trials was considered as the subject’s pain threshold. For safety reasons, the stimulation would automatically stop at 50°C, no matter whether the button was pressed or not. 50°C would then be considered as the pain threshold for that trial. To ensure that each participant understood when to press the button, standardized instructions were used, defining “pain” as a burning or pricking sensation. 4.5. Fast Periodic Visual Stimulation task FPVS is typically employed to capture periodic EEG responses elicited by fast visual stimuli delivered at a specific frequency 7 , 8 , 30 , 59 , 60 . The paradigm consists of rapidly presenting "neutral" images at a frequency F, with every n th image (frequency = F/n) representing the "salient" image (i.e., the oddball) which is different from the neutral images and adhere to an image category in line with the study's hypothesis (e.g. face individuation). This technique underlies the hypothesis that a periodic stimulation elicits a periodic neural response at the frequency of the stimulation (and its harmonics), allowing the precise “tagging” of the neural response in the frequency domain 5 . Hence, neural responses to the neutral and oddball images can easily be differentiated 7 . In the current investigation, the oddballs were images representing health-related objects and features (e.g., syringes, hospital settings, pills, masks, white coats), while the neutral images could be any object (e.g. house, flower, cooking utensil, furniture, ….) The selected health-related stimuli were rated in their arousal by an independent sample of 31 participants, with the aim to select health-related images that are similarly arousing. Participants were presented with a series of images related to the medical and health domain and were instructed to evaluate each image based on its arousing quality, defined as the degree to which the image elicited a feeling of activation, regardless of its emotional valence. Responses were given using a visual analog scale (VAS) ranging from 1 (not at all) to 10 (totally). At the end, 75 health-related and 302 non-health-related images were selected. The images were then equalized for size (200 x 200 pixels), color (gray scale), contrast and pixel luminance across neutral and oddball images, to avoid confounding the results with differences in low-level image features. The MATLAB toolbox “SHINEtoolbox” 61 was used for this process. Additionally, both oddball and neutral images were phase-scrambled, to create the images for the control condition. All images used in the FPVS task are publicly available in the OSF repository associated with this investigation ( https://osf.io/9423j/ ). FPVS was performed using a custom MATLAB script (MATLAB 7, The MathWorks Inc, Natick, MA) and the images were shown to the participant on a Philips LCSD 190S6 monitor (1280 x 1024 pixels, panel size: 19"/48 cm). The refresh rate was set to ~ 50 Hz and a resolution of 1024 x 768 was used. Participants were seated ~ 58 cm away from the screen. The images were presented against a uniform gray background, from which it emerged with increasing contrast after a 2–5 sec presentation of a fixation point (Fig. 3 ). Then, images were presented for about 1 min at a rate of 6.075 Hz with every 5th image being a health-related image (rate of presentation 1.215 Hz) introduced every fifth stimulus (i.e., ~ 6 Hz/5 = 1.2 Hz). Within a condition (i.e. neutral / oddball), the sequence of the images was randomized for each participant. In total, 36 trials (6 blocks of 6 trials each) were applied. Each trial was composed of a stimulation which lasts 60 s and was flanked by 2 s of fade-in and fade-out at the beginning and the end of the sequence, respectively. During the fade-in, the contrast modulation depth of the periodic stimulation progressively increased from 0–100% (full contrast), while the opposite manipulation was applied during the fadeout. This fading is aimed at reducing blinks and abrupt eye movements due to the sudden appearance or disappearance of flickering stimuli. Full contrast is reached at 85 ms and then decreased at the same rate. A rate of ~ 6 Hz was used because this frequency leads to a large response over occipitotemporal regions and falls in an area of the EEG spectrum (theta band) where the noise level is low (i.e., above the EEG delta band but below the alpha band of 8–12 Hz) 59 . See Fig. 3 for a graphic representation of the task. During the presentation of the images, participants were asked to focus their attention on a colored dot in the middle of the screen, and to count the number of times it would change to the color red within one trial (i.e. ~1 min of image presentation) [similar to other studies, e.g. De Keyser, et al. 8 ; Rossion, et al. 59 ] This was done to ensure the participants’ focus on the presented images. Participants reported the number of counted color changes verbally to the experimenter after seeing a prompt being displayed on the screen. The time in between trials was self-paced by the participants and depended on the time they took to answer the task-related question. 4.6. EEG recording The neural response to the FPVS paradigm was recorded through scalp EEG, using an elastic electrode cap with 64 active, pre-amplified Ag-AgCl electrodes (BioSemi, Netherlands), arranged according to the international 10–10 system. The direct-current offset was kept below 30 µV during the electrode placement and subsequent experimental session. One participant’s data was acquired at 256 Hz instead of 1024 Hz and was subsequently removed from the analysis. 4.7. EEG preprocessing As the individual trials had slightly different durations due to the randomization of the fade-in and fade-out of the images, a custom MATLAB script was used to trim each trial to the exact duration of image presentation (cropped to 57344 bins / trial, equals ~ 56 seconds). The same script was used to label the randomized trials for the conditions (scrambled / original). The data were then imported into Letswave7, an open-source MATLAB toolbox ( www.letswave.cn ). The EEG signal was filtered using a 4th order Butterworth filter (0.1–100 Hz). Then, a 50 Hz notch filter (width: 2 Hz) was applied and the data was re-referenced to the average of all electrodes. For each condition, the trials were averaged, and a discrete Fourier Transform (FFT) 62 was applied to transform the data into the frequency domain (spectrum ranging from 0 to 512 Hz, frequency resolution: ~ 0.018 Hz). Then, the signal was baseline corrected to remove residual noise, by subtracting at each electrode and at each frequency bin the average amplitude of the signal measured at the 10 neighboring frequency bins. Finally, the baseline-subtracted group- and channel-averaged signal was z-scored by calculating the difference between the amplitude at a given frequency bin and the mean amplitude of the 20 surrounding (i.e. ± 10 ) bins divided by the standard deviation of these 20 surrounding bins 59 . This pooled data was then used to define the number of harmonics that are statistically different from zero (i.e. harmonics with z > 1.64, one-sided, signal > noise) in consecutive order (Table 1 ), which were then aggregated from the baseline-subtracted data 63 . The same steps were applied to aggregate responses at the base response frequency and its harmonics. Table 1 Group- and channel-averaged z-scores at the frequency of the oddball (O) and its harmonics (overlapping with the base presentation frequency (B) and its harmonics). Bold font indicates z-scores with a significance level of p < 0.001(i.e. z < 2.32, one-sided), while cursive font indicates p < 0.05 (i.e. z < 1.64, one-sided). harmonic z-score n Frequency [Hz] Normal scrambled O1 1.215 5.5089 -0.7943 O2 2.43 5.6869 2.0674 O3 3.645 5.5847 1.6004 O4 4.86 4.6971 0.8815 B1 6.075 6.7112 6.6535 O6 7.29 2.0538 -1.121 O7 8.505 1.4592 -0.9617 O8 9.72 3.2953 2.1319 O9 10.935 1.1486 1.1009 B2 12.15 6.6139 6.5788 O11 13.365 0.9394 0.4077 O12 14.58 3.3322 0.9857 O13 15.795 -0.8435 0.6585 O14 17.01 -0.7856 1.8834 B3 18.225 0.1438 -0.5894 O16 19.44 0.3342 0.2595 O17 20.655 0.5741 2.3385 O18 21.87 -2.4336 1.8454 O19 23.085 1.764 0.4827 B4 24.3 0.619 0.359 4.8. Statistical analysis To detect electrode activity clusters in response to the health-related visual stimuli and compare the distribution of the neural activity of the aggregated responses between the original and scrambled condition, a multi-sensor cluster-based analysis 12 employing a paired t-test (set to an alpha of 0.0001 with 2000 permutations, the sensor connection threshold was set so each electrode has four neighbors on average) was used. The remaining statistical analyses were conducted using IBM SPSS Statistics 28 and R Studio (v4.3.1, R Core Team (2023)). To assess differences in the aggregated neural responses to oddball and base frequency stimuli in the different conditions (original vs scrambled images) and across different electrodes (PO8 vs Oz), a repeated-measured ANOVA was used. A log-transform was applied to all EEG data to correct right-skewedness and conform them to the assumption of normality 64 . Significant results were further assessed post-hoc using pairwise t-test- The relationship between pain thresholds, neural responses and psychological traits was assessed using correlational analyses, regression analyses and moderation analyses (PROCESS analysis algorithm 65 ). Behavioral differences between the conditions were examined through a repeated-measure ANOVA. For all statistical analysis, the significance threshold was set at p < 0.05. Declarations Acknowledgments This study was supported by a Mandat d'Impulsion Scientifique (MIS) - F.N.R.S grant awarded to GL. CL was supported by a FRIA doctoral grant of the Belgian Fund for Scientific Research (F.R.S.–FNRS, grant 5118622). PC was supported by a doctoral fellowship of the National Operation Program (PON) Research and Innovation 2014-2020 (CCI. 2014IT16M2OP005), ESF REACT-EU resources, Action IV.4 "Doctorates and research contracts on themes of innovation" and Action IV.5 "Doctorates on Green themes." CUP code J35F21003350006. We would like to thank the CATL team and especially Fanny Fievez for their help in setting up the experiment and Valéry Goffaux for valuable advice. Additionally, we would like to thank the NOCIONS lab for the valuable discussions. Conflict of interest The authors declare no competing interests. Data availability The raw data (EEG), and individual data for pain thresholds and trait characteristics as well as supporting material (images used for the FPVS paradigm) are publicly available on OSF (https://osf.io/9423j/ ). Author contributions Conceptualization: PC, GL Methodology: PC, CL, GL Investigation : PC Validation: CL Formal analysis: CL (EEG), PC (psychological traits) Data Curation: CL Visualization: PC, CL Supervision: MM, GL Funding acquisition: GL Writing - Original Draft: PC, CL Writing - Review & Editing: PC, CL, MM, GL References Kouider, S. & Dehaene, S. Levels of processing during non-conscious perception: a critical review of visual masking. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6989317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488192430,"identity":"3bcb5d74-e648-4d84-8d8b-7db7eb1d52f7","order_by":0,"name":"Paola Castellano","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Paola","middleName":"","lastName":"Castellano","suffix":""},{"id":488192431,"identity":"1e6e1f42-bc11-4db9-903c-cf725287eebf","order_by":1,"name":"Chiara Leu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACCcYGECXDwMB8gIGhAMQGikAE8WvhYWBgS2BgMCBKC4QCauExgGqB6MIJ+Gc3N36uqGHg4ZfI+Sbxw8AmccPxw20PGHfY4LbkzsFmyTPHGHgkZ+Ruk+wxSEvccCax3YDxTBpOLQYSiQ2SDWxAV93I3SbNYHA4cWZDYpsEY9thfFqafzb8Y+Cxv5HzDKKl/yFIy398WtokG9uAtkjksIG19EuAbTmA2y83EtssG/skeCTOPDO2BPrFuF8CaEvimWScWvhnpD++2fDNRo6/PfnhjR8VNrJt/OnPJD7usMOpBWYZGj+BkIZRMApGwSgYBXgBAL6lUOItjaYZAAAAAElFTkSuQmCC","orcid":"","institution":"Université Catholique de Louvain","correspondingAuthor":true,"prefix":"","firstName":"Chiara","middleName":"","lastName":"Leu","suffix":""},{"id":488192432,"identity":"db72de5f-0752-4158-bdd9-a2d43c102b07","order_by":2,"name":"Michela Mazzetti","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Michela","middleName":"","lastName":"Mazzetti","suffix":""},{"id":488192433,"identity":"b1439006-2f9c-41bd-a236-fdfe9cfdffe4","order_by":3,"name":"Giulia Liberati","email":"","orcid":"","institution":"Université Catholique de Louvain","correspondingAuthor":false,"prefix":"","firstName":"Giulia","middleName":"","lastName":"Liberati","suffix":""}],"badges":[],"createdAt":"2025-06-27 08:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6989317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6989317/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-29515-z","type":"published","date":"2025-11-28T15:58:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87796435,"identity":"4d9d14f9-7262-4535-8bb9-602869860e7a","added_by":"auto","created_at":"2025-07-29 07:03:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82915,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea:\u003c/strong\u003e \u003cstrong\u003eFrequency spectrum representation of the EEG response at the electrodes of interest.\u003c/strong\u003e \u0026nbsp;Neural response to the presentation of original (electrode PO8) vs. scrambled (electrode Oz) images (mean (bold) ± 95% confidence interval (dashed)). Topographic plots show global channel activity at 1.2 Hz (frequency of the oddball) and 6 Hz (base image presentation frequency).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb:\u003c/strong\u003e \u003cstrong\u003eComparison of aggregated responses at the oddball and base presentation frequency.\u003c/strong\u003e Sum of the neural responses at the oddball and base frequency and its first 4 harmonics (summed up separately into the “frequency of interest” for each condition and electrode). Differences between the magnitude of the response were assessed using paired t-tests (****p\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec:\u003c/strong\u003e \u003cstrong\u003eTopographic illustration of the difference between the aggregated oddball response for intact and scrambled images. \u003c/strong\u003eAggregated responses consisted of the sum of the 1.215 Hz and its harmonics at 2.43 Hz, 3.645 Hz, 4.86 Hz and 7.29 Hz for each condition.\u003cstrong\u003e \u003c/strong\u003eDifferences between conditions were assessed using a cluster-based multi-sensor analysis employing a paired t-test (p\u0026lt; 0.0001, right-tailed).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6989317/v1/29cbb8a21414d9f9a28f52e1.jpg"},{"id":87796091,"identity":"9b11e339-d96b-4a17-931e-776e8f53dc3c","added_by":"auto","created_at":"2025-07-29 06:55:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModeration models between depression, aggregated EEG amplitude and pain threshold\u003c/strong\u003e. \u0026nbsp;\u003cstrong\u003ea:\u003c/strong\u003e Graphic illustration of the model and the relationship between the variables. \u003cstrong\u003eb: \u003c/strong\u003eGraphs show slopes of pain threshold predicting aggregated oddball EEG amplitudes at low, mean, and high levels of depression. When plotting the graphs, data automatically generated by the PROCESS analysis algorithm are used to classify low, mean, and high levels of continuous variables.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6989317/v1/a373e18d94f78f66a63ac566.jpg"},{"id":87796436,"identity":"e45e24d0-fd12-4422-b2fa-a1d6e122150a","added_by":"auto","created_at":"2025-07-29 07:03:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustration of the FPVS paradigm.\u003c/strong\u003e Health-related images were presented at a frequency of ~ 1.2 Hz in a stream of unrelated images (~6 Hz). Participants were asked to track the color changes of a dot presented in the middle of each image and reported this number at the end of each trial.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6989317/v1/dfa17bcd0251d448d91c5ffd.jpg"},{"id":97179410,"identity":"32a49e18-4eaf-4528-86e7-698c18faaa10","added_by":"auto","created_at":"2025-12-01 16:15:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1376951,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6989317/v1/d68db9aa-6b2e-440a-93e3-f311a26173bc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fast periodic visual stimulation to study the processing of health- related images in the human brain","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe human brain continuously processes external stimuli, often below the level of conscious awareness \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Among these, health-related stimuli are of particular interest due to their potential relevance for survival and well-being. Understanding how the brain responds to such stimuli, even when they are not consciously perceived, can shed light on interacting mechanisms of conceptual and visual processing. The present study aims to investigate whether the brain shows an implicit processing of health-related images and whether this response is modulated by individual psychological traits such as anxiety, emotion regulation, impulsivity, and stress or the individuals\u0026rsquo; pain threshold.\u003c/p\u003e \u003cp\u003eHealth anxiety, a construct characterized by excessive worry and preoccupation with one's health \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, provides a compelling framework for examining selective and heightened sensitivity to health-related stimuli. Numerous studies have shown that individuals with high levels of health anxiety tend to exhibit selective attention to illness-related information \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, and this attentional bias is often accompanied by stronger emotional and physiological responses when confronted with such stimuli. These findings suggest that health-related stimuli may trigger stronger responses in the brain, especially in individuals with elevated anxiety levels. However, the neural mechanisms underlying these responses remain underexplored, and understanding the neural underpinnings of these responses is crucial for developing effective interventions and therapies.\u003c/p\u003e \u003cp\u003eTo address this, our study seeks to determine whether we can capture neural responses elicited by health-related stimuli. To this end, we employed the Fast Periodic Visual Stimulation (FPVS) paradigm. This technique is based on the well-established principle that a periodic stimulation elicits a periodic modulation of the EEG signal at the frequency of the stimulation and its harmonics \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Thus, when combined with electroencephalography (EEG), this paradigm allows to \u0026ldquo;tag\u0026rdquo; the neural response to a periodic oddball stimulus within a stream of unrelated images at the frequency at which both the oddballs and unrelated images were presented. This paradigm has been widely applied in vision research \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, with a particular success in the studies of facial discrimination (see Rossion, et al. \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e for a comprehensive review). Additionally, it has been successfully implemented in object categorization \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, the recognition of letters and words \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, as well as visual recognition of semantic categories \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. To our knowledge, this is the first application of FPVS to differentiate between health-related and neutral visual stimuli, adding a novel dimension to the growing body of research using the FPVS paradigm.\u003c/p\u003e \u003cp\u003eIn addition to assessing the neural response to these stimuli, we also explored the potential role of individual psychological traits in modulating this response. Psychological factors such as anxiety, emotion regulation, impulsivity, coping strategies and stress have been linked to attentional biases and heightened emotional reactivity \u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Specifically, trait anxiety, particularly health anxiety, has been associated with an increased focus on threat-related or illness-related information, as demonstrated in numerous behavioral and neuroimaging studies \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Moreover, the interplay between health anxiety and individual traits such as impulsivity and emotional regulation is an area ripe for exploration. Impulsivity may drive individuals to react quickly and sometimes maladaptively to health-related stimuli, while effective emotional regulation can help mitigate anxiety responses. By examining these traits in conjunction with neural responses to health-related images, we aimed to elucidate the complex relationships that govern how individuals with varying levels of health anxiety engage with health-related information. We hypothesized that individuals with higher levels of anxiety and related traits may exhibit a stronger unconscious neural response to health-related stimuli, as measured by EEG during the FPVS task.\u003c/p\u003e \u003cp\u003eFurthermore, we investigated the potential contribution of physical traits, such as the individual pain threshold, to the neural processing of health-related stimuli. Pain is inherently linked to health and survival and engages in both psychological and physiological processes. Previous research reports that individuals with high levels of anxiety, including health anxiety, tend to experience lower pain thresholds \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and higher pain sensitivity \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. These findings are consistent with the well-established understanding that pain perception is influenced not only by physical factors, but also by affective and cognitive processes - particularly anxiety.\u003c/p\u003e \u003cp\u003eMoreover, recent research suggests that individual differences in pain perception are closely tied to neural responses to threat and health-related cues \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In this study, we used thermal cutaneous stimulation to measure pain thresholds and assess whether individuals with lower pain thresholds exhibit heightened neural responses to health-related stimuli, aiming to understand how both psychological and physical traits may jointly influence the brain\u0026rsquo;s reaction to health-related information. Psychological constructs were measured using validated questionnaires, each selected for its established link with attention to threat, emotional reactivity, or health-related cognitive biases. Based on previous research, the underlying hypothesis is that the periodic brain response to health-related images may be associated with higher features of psychological distress.\u003c/p\u003e \u003cp\u003eIn summary, we investigated whether the brain is able to distinguish periodically presented health-related images from a stream of random images and explored whether this response is associated with individual differences in psychological traits and pain thresholds.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eParticipants completed a series of questionnaires assessing their psychological traits, as well as a measurement of their individual pain threshold. Then, participants had to focus on fast periodic visual stimuli depicting health-related images, which were embedded in a periodic stream of unrelated images, while their neural response to the visual stimuli was measured using EEG.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Behavioral response\u003c/h2\u003e \u003cp\u003e To ensure that participants were paying attention to the presented visual stimuli, they had to count the number of times the dot in the middle of the presented visual stimuli changed to the color red within each trial. Accuracy rates in this behavioral task were similar in both conditions (Original: 33.88% \u0026plusmn; 12.28%; Scrambled: 36.72% \u0026plusmn; 11.44%) and no significant difference was found between conditions in accuracy rates (F(1,40)\u0026thinsp;=\u0026thinsp;1.327, p\u0026thinsp;=\u0026thinsp;0.256, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.032).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Neural response\u003c/h2\u003e \u003cp\u003eIn the scrambled condition, only few and nonconsecutive oddball harmonics were statistically different from zero. Conversely in the original condition, all oddball harmonics up to the 5th harmonics (i.e. ~7.29 Hz) showed a significant periodic response to the health-related visual stimuli. Thus, for the statistical analysis, the response at the oddball frequency was summed up with the first 5 of its harmonics (excluding the 4th harmonic at ~\u0026thinsp;6 Hz, which overlaps with the base presentation rate). To be consistent, the same number of oddball harmonics was summed up in the scrambled condition. Following the same rationale, the baseline response and its first harmonics were aggregated in both conditions.\u003c/p\u003e \u003cp\u003eCongruent to other investigations using the FPVS paradigm, the largest response to the original images was found at electrode PO8, which was subsequently chosen as electrode of interest for the base response. In the scrambled condition, responses were mainly found at the base frequency at electrode Oz. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea illustrates the frequency spectra for these EEG responses at electrodes PO8 (original images) and Oz (scrambled images) in both conditions. For the correlation with pain thresholds and personality traits, the summed-up oddball response was used (0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 \u0026micro;V, range: -0.08-1.92 \u0026micro;V).\u003c/p\u003e \u003cp\u003eThe three-way ANOVA (condition: original / scrambled, frequency: oddball / base, electrode: PO8/ Oz) revealed significant main effects of \u003cem\u003econdition\u003c/em\u003e (F(1,40)\u0026thinsp;=\u0026thinsp;45.163, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.530), \u003cem\u003efrequency\u003c/em\u003e (F(40,1)\u0026thinsp;=\u0026thinsp;161.020, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csub\u003ep\u003c/sub\u003e \u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.801) and \u003cem\u003eelectrode\u003c/em\u003e (F(1,40)\u0026thinsp;=\u0026thinsp;5.761, p\u0026thinsp;=\u0026thinsp;0.021, η\u003csub\u003ep\u003c/sub\u003e \u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.126), as well as significant interactions between \u003cem\u003econdition\u003c/em\u003e and \u003cem\u003efrequency\u003c/em\u003e (F(40,1)\u0026thinsp;=\u0026thinsp;46.333, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csub\u003ep\u003c/sub\u003e \u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.537), and \u003cem\u003econdition\u003c/em\u003e and \u003cem\u003eelectrode\u003c/em\u003e (F(40,1)\u0026thinsp;=\u0026thinsp;24.949, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csub\u003ep\u003c/sub\u003e \u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.384). Post-hoc pairwise comparisons demonstrated a significant difference between conditions at the oddball frequency (t(81)\u0026thinsp;=\u0026thinsp;13.245, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not at the base presentation frequency (t(81)\u0026thinsp;=\u0026thinsp;0.907, p\u0026thinsp;=\u0026thinsp;0.367) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe multi-sensor cluster-based t-test between the original and scrambled condition demonstrated multiple clusters of electrodes with larger activity during the original condition. Precisely, a fronto-central clusters of interest was formed (F1, Fz, FC3, FC1, FCz, FC2, C1, Cz) as well as a occipital-temporal cluster (the latter corresponding to frequently observed distribution of responses \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e consisting of a left (PO7, PO3, P7, P9), middle (Oz, O2, O1, Iz) and right (PO8, PO10, P8, P6, P4) temporal-occipital electrodes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Pain Threshold\u003c/h2\u003e \u003cp\u003ePain thresholds were rated on average at 46.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u0026deg;C (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;std dev), which corresponds to normative values for this age group \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Of the total sample, 20 participants had an above-average pain threshold.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Psychological traits\u003c/h2\u003e \u003cp\u003eThe psychological individual characteristics of the sample are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants\u0026rsquo; baseline characteristics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) for all the psychological variables investigated.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (range min-max)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.39\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44 (0\u0026ndash;25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.15\u0026thinsp;\u0026plusmn;\u0026thinsp;7.07 (29\u0026ndash;58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68 (6\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonacceptance of emotional response (DERS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57 (6\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulties in adopting goal-directed behaviors (DERS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73 (5\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficulties in controlling impulsive behaviors (DERS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.60 (6\u0026ndash;25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of emotional awareness (DERS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.63 (7\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimited access to emotion regulation strategies (DERS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.22\u0026thinsp;\u0026plusmn;\u0026thinsp;6.79 (6\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of emotional identification or clarity (DERS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45 (5\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Difficulties in emotional regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.27\u0026thinsp;\u0026plusmn;\u0026thinsp;21.45 (42\u0026ndash;150)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanning difficulties (BIS-11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.17\u0026thinsp;\u0026plusmn;\u0026thinsp;4.67 (11\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotor Impulsivity (BIS-11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32 (11\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Impulsivity (BIS-11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.99 (9\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Impulsivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.49\u0026thinsp;\u0026plusmn;\u0026thinsp;9.41 (37\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Inhibition (BIS/BAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.93 (10\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReward Responsiveness (BIS/BAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29 (9\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrive (BIS/BAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29 (5\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFun Seeking (BIS/BAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04 (7\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive Coping (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56 (2\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanning (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27 (3\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstrumental Support (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32 (2\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProblem-focused Coping (Brief-COE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94 (8\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceptance (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11 (4\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional Support (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33 (2\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumor (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96 (2\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive Reframing (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37 (3\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32 (2\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotion-Focused Coping (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31 (17\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Disengagement (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04 (2\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenial (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20 (2\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Blame (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22 (3\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Distraction (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (3\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubstance Use (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72 (2\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenting (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16 (2\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysfunctional Coping (Brief-COPE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.21 (18\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDERS\u0026thinsp;=\u0026thinsp;Difficulties in Emotion Regulation Scale; BIS-11\u0026thinsp;=\u0026thinsp;Barratt Impulsiveness Scale; BIS/BAS\u0026thinsp;=\u0026thinsp;Behavioral Inhibition and Behavioral Activation Scales; Brief-COPE\u0026thinsp;=\u0026thinsp;Brief Coping Orientation to Problems Experienced Inventory.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Relationship between neural, behavioral and psychological factors\u003c/h2\u003e \u003cp\u003ePearson correlation analyses were conducted to explore the relationships between the recorded EEG signal amplitude at the presentation frequency of the oddball stimuli and other psychological and pain-related variables. Results indicated no significant correlation between amplitude and pain threshold (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.133 p\u0026thinsp;=\u0026thinsp;0.408), or most psychological indices. However, a significant positive correlation was observed between amplitude and depression (r\u0026thinsp;=\u0026thinsp;0.327, p\u0026thinsp;=\u0026thinsp;0.037). This suggests a potential relationship between higher EEG amplitude and levels of depression. No other significant correlations were found between amplitude and measures of psychological distress (e.g., anxiety, perceived stress, emotion dysregulation) or coping strategies.\u003c/p\u003e \u003cp\u003eSignificant negative correlations were observed between pain threshold and several subscales of emotional dysregulation (i.e., \u0026ldquo;Nonacceptance of emotional response\u0026rdquo;, \u0026ldquo;Difficulties in adopting goal-directed behaviors\u0026rdquo;, \u0026ldquo;Difficulties in controlling impulsive behaviors\u0026rdquo;, \u0026ldquo;Limited access to emotion regulation strategies\u0026rdquo;, \u0026ldquo;Lack of emotional identification or clarity\u0026rdquo;) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as well as overall emotional dysregulation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that people who tend to better regulate their emotions have also a higher pain threshold. Additionally, a significant negative correlation was observed between pain threshold and the BIS-11 subscale \u0026ldquo;Motor Impulsivity\u0026rdquo; (r=-0.38, p\u0026thinsp;=\u0026thinsp;0.014), as well as with the Brief-COPE subscale \u0026ldquo;Positive Reframing\u0026rdquo; (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.331, p\u0026thinsp;=\u0026thinsp;0.035), suggesting that individuals with higher motor impulsivity and using this coping strategy had lower pain thresholds.\u003c/p\u003e \u003cp\u003eA linear regression analysis also revealed the predictive role of emotional dysregulation on pain threshold (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.051, SE\u0026thinsp;=\u0026thinsp;0.013, β=\u0026minus;0.541, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, the effect of depression on EEG amplitude was stronger at higher levels of pain threshold. Thus, at lower (1SD \u0026ndash; mean) pain thresholds the effect of depression on EEG amplitude was not significant (B = -0.005, p\u0026thinsp;=\u0026thinsp;0.951), while the effect of depression on EEG amplitude was positive and significant both at medium pain threshold (B\u0026thinsp;=\u0026thinsp;0.23, p\u0026thinsp;=\u0026thinsp;0.003) and at higher (1SD\u0026thinsp;+\u0026thinsp;mean) pain threshold (B\u0026thinsp;=\u0026thinsp;0.47, p\u0026thinsp;=\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on this information, to better investigate the relationship between depression and brain signal, the moderating role of pain threshold was examined, using depression as the independent variable and EEG amplitude as the dependent one. The overall model was significant (F(3, 37)\u0026thinsp;=\u0026thinsp;6.79, p\u0026thinsp;=\u0026thinsp;0.001, with an R\u0026sup2; = 0.355), as well as the interaction of depression and pain threshold (B\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;=\u0026thinsp;0.001), indicating that the latter significantly moderate the way depression affect brain response to health images (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eAlthough the bivariate correlation between depression scores and EEG amplitude was positive, the regression analysis controlling for the moderating effect of pain threshold revealed a negative conditional effect of depression on amplitude (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). This shift in direction likely reflects the influence of the moderator, emphasizing that the association between depressive symptoms and neural response is not uniform across different levels of pain sensitivity. These results suggest that pain threshold moderates the relationship between depression and EEG amplitude. At higher pain threshold levels, depression is more strongly associated with increased EEG amplitude, while at lower pain threshold levels, the effect of depression on EEG amplitude is not significant.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eNotably, this study is the first to demonstrate that also the periodic presentation of health-related images can evoke distinguishable neural responses, even when these stimuli are more abstract and do not belong to a naturalistic category such as faces or tools. Specifically, the periodically presented health-related images (i.e. oddball) elicited a periodic neural response at the presentation frequency of this image category, which could be clearly differentiated from the response to images with random content. These findings support the hypothesis that FPVS can be used to \"tag\" neural responses to conceptual categories, broadening the applicability of this paradigm beyond its traditional domains. Additionally, we found that psychophysiological features, such as depression levels and individual pain threshold, are significantly related to the neural response to those oddball stimuli.\u003c/p\u003e \u003cp\u003eGenerally, periodically presented stimuli (such as human faces) have the ability to elicit a periodic neural response, which can be easily assessed in the EEG frequency domain \u003csup\u003e6\u0026ndash;8,27\u0026minus;30\u003c/sup\u003e. As in previous studies using the FPVS paradigm, a consistent periodic response was found at the base image presentation frequency (~\u0026thinsp;6Hz), for both the presentation of original as well as scrambled images. Scalp topographies and channels with the largest activity at this frequency also matched these previous investigations, highlighting that the basic assumptions of the paradigm have been met. More importantly though, a periodic neural response was also found at the presentation frequency of the health-related oddball images (~\u0026thinsp;1.2 Hz and harmonics), with neural activity being distributed similarly over the scalp as in the base response. Presenting scrambled images led to a much smaller, but nevertheless statistically significant periodic response at some oddball harmonics (but not its frequency of presentation). Thus, this demonstrates that an abstract image category as oddball stimulus can lead to a periodic neural response in the FPVS paradigm, even considering the relatively low rate of correct answers in the behavioral task. Previously, it had already been demonstrated that the FPVS paradigm elicits neural responses not only related to faces or tools but also related to semantic processing. This was achieved by presenting images which could be categorized into semantic categories of different specificity (e.g. natural vs non-natural, animal vs non-animal images) \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and natural vs man-made images \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. While the present results extend these findings, as the health-related images represent a more abstract image category than the semantic subgroups and support the use of FPVS to study conceptual processing beyond traditional domains, caution is needed in interpreting the specificity of response. Indeed, we cannot attribute this response specifically to the \"health\" content of the images, as we did not include a control image category (e.g., tools or neutral objects) matched in terms of affective properties. It is therefore possible that the neural responses observed here reflect general processing of emotionally salient or negatively valanced images, rather than a conceptual response specific to health-related content.\u003c/p\u003e \u003cp\u003eOne possible explanation for the small but nevertheless present response during the scrambled oddball images would be that it was not only the recognition of the image category itself which led to the periodic response at the oddball frequency, but also some low-level qualities in this image category. Basic features such as contrast, and luminance were controlled by adjusting these characteristics across the entire image sample (including both health-related and non-health-related images) pre-experiment. Yet, other features such as animacy \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e or spatial frequency and orientation \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e (commonly assessed qualities in machine learning algorithms for visual features) could also elicit a neural response. Nevertheless, as demonstrated by our cluster-based analysis, the responses to the intact oddball images were not only considerably larger than the ones elicited by the oddball using scrambled images, but also demonstrated a different distribution of the neural activity across the scalp, with a marked frontal activity which was not present in the scrambled condition (similar to Stothart, et al. \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e). As any of these low-level characteristics should be present in both the original as well as scrambled images, they are unlikely to be the main contributor to the observed response to the health-related images.\u003c/p\u003e \u003cp\u003eAn exploratory objective of the study was to examine whether individual differences in psychological traits, such as anxiety, depression, emotional regulation, impulsivity or coping strategies, modulate neural responses to health-related images. Although in previous studies emotional dysregulation has been associated with attentional biases (e.g., Ciccarelli, et al. \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e;Harrison, et al. \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e ), and health anxiety has been linked to stronger responses to illness-related stimuli \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, in the current investigation a significant correlation was only observed between the neural responses to the oddball stimuli and one of the major psychological traits, namely depression. These findings should be interpreted with caution, as they represent exploratory and secondary analyses. Nevertheless, they are consistent with the hypothesis of an attentional bias toward emotionally salient stimuli in depressed individuals (see Suslow, et al. \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e for a comprehensive review), and with evidence indicating amplified processing of signals of threat or vulnerability to illness \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e in those individuals. However, a particularly novel element of this study concerns the role of pain threshold in moderating this relationship. Indeed, moderation analyses showed that the association between depression and neural response to health stimuli was significant only in participants with medium or high pain thresholds, whereas it was absent in subjects with low thresholds. Additionally, pain threshold was negatively predicted by emotional dysregulation trait. This finding not only align with prior research linking emotional dysregulation to heightened pain sensitivity \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, but suggests that pain threshold, potentially indicative of broader affective and physiological functioning \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, may influence, in specific psychological conditions, how an individual processes this kind of periodic information.\u003c/p\u003e \u003cp\u003eThis analysis on pain threshold shed light on an interesting and potentially counterintuitive aspect. Indeed, emotional dysregulation represents a transdiagnostic factor commonly associated with depression\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e and is in turn correlated to an increased experience of pain\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Nevertheless, our data show that the psychophysiological relationship between depression and neural response to health-related stimuli is only significant in participants with higher pain threshold (i.e., with an improved emotional regulation). This apparent inconsistency can be interpreted in light of neurocognitive models that consider the role of residual affective-sensory resources in modulating brain responses\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. It might be that the individual\u0026rsquo;s affective system needs to be sufficiently responsive, in order to allow depression to increase neural activity in response to emotionally relevant stimuli, such as the health-related images. Individuals with augmented levels of depression and with higher pain threshold (i.e., potentially more emotionally regulated) may have enough neural resources to actively process the stimulus and have an increased EEG response to that. On the contrary, individuals with augmented levels of depression but lower pain threshold (i.e., potentially less emotionally regulated) may have a blunted neural response as a response of combined affective vulnerability and sensory overload.\u003c/p\u003e \u003cp\u003eThese findings have several implications for the understanding of neural processing of health-related stimuli. First, they demonstrate the feasibility of using FPVS to explore neural responses to abstract and potentially emotionally salient categories. Second, while preliminary, the psychophysiological correlates suggest that individual factors might modulate this implicit neural response to emotionally meaningful visual cues. Yet, one of the main limitations of the present study is the inability to conclusively determine whether the observed neural activation is specifically driven by the health-related content of the stimuli. Further research is needed to corroborate these hypotheses and to establish the specificity of neural responses to health-related stimuli, for example by directly contrasting them with other categories of stimuli in future studies.\u003c/p\u003e \u003cp\u003eIn summary, this study expands the utility of the FPVS paradigm to abstract categories such as health-related stimuli and highlights the need to consider psychological well-being when investigating individual differences in the processing of health-related cues such as illness representations or medical imagery. Future research should continue to refine the methodological approaches used to investigate the complex interplay between neural, psychological, and physiological processes in health-related contexts.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Participants\u003c/h2\u003e \u003cp\u003eA sample size of 30 participants was estimated using the software G*Power to be necessary for a repeated- measures ANOVA with two within-factors (condition: ordinary/scrambled; electrode: PO8/OZ) to reach a power of 0.90 with an effect size of 0.25 and with the statistical significance set to 0.05. A total of 44 participants were recruited for this experimental study. For reasons of lack of compliancy with the research directions (n\u0026thinsp;=\u0026thinsp;2) and technical problems (n\u0026thinsp;=\u0026thinsp;1), only 41 participants were considered for statistical analysis (age (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;std. dev.): 25.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3, 32 females).\u003c/p\u003e \u003cp\u003eRecruitment was carried out on an established website through the distribution of a flyer, which described the study as an investigation into how individual psychophysiological characteristics influence brain signals. The flyer specified that participation in the study would require approximately 1 hour and 45 minutes, and participants would be compensated 20 euros for their participation.\u003c/p\u003e \u003cp\u003e After scheduling and signing an informed consent form, participants were screened for inclusion criteria (aged 18\u0026ndash;65, in good health). Those meeting the criteria were contacted by the experimenter to be enrolled in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Procedure\u003c/h2\u003e \u003cp\u003e Prior to their laboratory session, participants received a link from the experimenter, inviting them to read and sign the informed consent. Then they were invited to complete a series of questions, including assessments of sociodemographic information (e.g., gender, age, occupation) and a set of validated psychological questionnaires for individual characteristics (depression, anxiety, stress, impulsivity trait, emotion regulation strategies, coping strategies).\u003c/p\u003e \u003cp\u003eUpon arrival at the laboratory on the scheduled day and time, participants underwent two experimental procedures. First, individual pain thresholds were measured using thermal cutaneous stimulations. Next, participants completed the FPVS task, during which EEG data were collected.\u003c/p\u003e \u003cp\u003e The local Research Ethics Committee approved all experimental procedures (Commisision d'Ethique hospitalo-facultaire Saint-Luc UCLouvain, 2024/14FEV/074 \u0026ndash; HSP) and all procedures were conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent before participating and were debriefed after the completion of their laboratory session.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Questionnaires for the assessment of psychological characteristics\u003c/h2\u003e \u003cp\u003eA battery of validated questionnaires was employed to assess specific individual characteristics potentially related to the participant\u0026rsquo;s pain threshold and neural response to the FPVS task. The following questionnaires were included in the battery: i) Beck Depression Inventory (BDI\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e): a self-report questionnaire widely used to assess levels of depressive traits in the general population, composed by 21 questions and each question is scored on a scale value of 0 to 3; ii) State-Trait Anxiety Inventory \u0026ndash; Y2 (STAI-Y2\u003csup\u003e46\u003c/sup\u003e;): a commonly used self-report scale to assess trait anxiety levels, composed by 20 items to answer using a 4-point Likert scale; iii) Difficulties in Emotion Regulation Scale (DERS\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e): a self-report scale designed to assess difficulties in regulating emotions, not only about the control of emotional arousal but also the awareness, comprehension, and acceptance of emotions. It is composed of 36 items and divided into 6 subscales (non-acceptance of emotional response, difficulties in adopting goal-directed behaviors, difficulties in controlling impulsive behaviors, lack of emotional awareness, limited access to emotion regulation strategies, lack of emotional identification or clarity). Answers are provided on a 5-point Likert scale; iv) Barratt Impulsiveness Scale (BIS-11\u003csup\u003e47\u003c/sup\u003e): a 30-item self-report scale assessing three types of impulsivities (attentional, motor, non-planning) and a general score of impulsivity trait. Answers are provided using a 4-point Likert scale. v) Behavioral Inhibition System and Behavioral Activation System Scales (BIS/BAS\u003csup\u003e48\u003c/sup\u003e): two self-report scales assessing individual differences in the sensitivity of motivational systems, one related to avoidant behaviors and one related to approaching behaviors, composed by a totality of 20 items answered by the use of a 4-point Likert scale; vi) Brief Coping Orientation to Problem Experiences Inventory (Brief-COPE\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e): the brief version of a self-report questionnaire measuring effective and ineffective ways to cope with a stressful life situation (active coping, planning, using instrumental support, using emotional support, venting, behavioral disengagement, self-distraction, self-blame, positive reframing, humor, denial, acceptance, religion, substance use), composed of 28 items and answers are provided by using a 4-point Likert Scale; vii) Perceived Stress Scale (PSS\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e): a 10-items self-report questionnaire widely used to assess individual\u0026rsquo;s perception of stress in daily life with a 5-point Likert scale used to provide answers.\u003c/p\u003e \u003cp\u003eBeing the sample of the present study composed by French speakers, validated French-language versions of the questionnaires described were administered (BDI\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e; STAI\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e; DERS\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e; BIS-11\u003csup\u003e54\u003c/sup\u003e; BIS/BAS\u003csup\u003e55\u003c/sup\u003e; Brief\u0026mdash;COPE\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Pain threshold assessment\u003c/h2\u003e \u003cp\u003eThe individual pain threshold was measured using thermal stimulation and the method of limits \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Thermal stimuli were delivered to the volar forearm of the subject before the start of the visual task using a contact-heat thermode made of 15 micro-Peltier elements (stimulation surface: 1.2 cm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) (TCS, QST.Lab, Strasbourg, France). For each subject, the stimulation temperature was set to skin temperature (~\u0026thinsp;32\u0026deg;C) and subsequently the temperature was raised by 1\u0026deg;C/s until the subject perceived that the stimulation started to be painful and pressed a response button. This procedure was repeated 3 times (each time displacing the thermode to avoid habituation / sensitization), and the average of those trials was considered as the subject\u0026rsquo;s pain threshold. For safety reasons, the stimulation would automatically stop at 50\u0026deg;C, no matter whether the button was pressed or not. 50\u0026deg;C would then be considered as the pain threshold for that trial. To ensure that each participant understood when to press the button, standardized instructions were used, defining \u0026ldquo;pain\u0026rdquo; as a burning or pricking sensation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Fast Periodic Visual Stimulation task\u003c/h2\u003e \u003cp\u003eFPVS is typically employed to capture periodic EEG responses elicited by fast visual stimuli delivered at a specific frequency \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. The paradigm consists of rapidly presenting \"neutral\" images at a frequency F, with every n\u003csup\u003eth\u003c/sup\u003e image (frequency\u0026thinsp;=\u0026thinsp;F/n) representing the \"salient\" image (i.e., the oddball) which is different from the neutral images and adhere to an image category in line with the study's hypothesis (e.g. face individuation). This technique underlies the hypothesis that a periodic stimulation elicits a periodic neural response at the frequency of the stimulation (and its harmonics), allowing the precise \u0026ldquo;tagging\u0026rdquo; of the neural response in the frequency domain \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Hence, neural responses to the neutral and oddball images can easily be differentiated \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the current investigation, the oddballs were images representing health-related objects and features (e.g., syringes, hospital settings, pills, masks, white coats), while the neutral images could be any object (e.g. house, flower, cooking utensil, furniture, \u0026hellip;.) The selected health-related stimuli were rated in their arousal by an independent sample of 31 participants, with the aim to select health-related images that are similarly arousing. Participants were presented with a series of images related to the medical and health domain and were instructed to evaluate each image based on its arousing quality, defined as the degree to which the image elicited a feeling of activation, regardless of its emotional valence. Responses were given using a visual analog scale (VAS) ranging from 1 (not at all) to 10 (totally). At the end, 75 health-related and 302 non-health-related images were selected.\u003c/p\u003e \u003cp\u003eThe images were then equalized for size (200 x 200 pixels), color (gray scale), contrast and pixel luminance across neutral and oddball images, to avoid confounding the results with differences in low-level image features. The MATLAB toolbox \u0026ldquo;SHINEtoolbox\u0026rdquo; \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e was used for this process. Additionally, both oddball and neutral images were phase-scrambled, to create the images for the control condition. All images used in the FPVS task are publicly available in the OSF repository associated with this investigation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/9423j/\u003c/span\u003e\u003cspan address=\"https://osf.io/9423j/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFPVS was performed using a custom MATLAB script (MATLAB 7, The MathWorks Inc, Natick, MA) and the images were shown to the participant on a Philips LCSD 190S6 monitor (1280 x 1024 pixels, panel size: 19\"/48 cm). The refresh rate was set to ~\u0026thinsp;50 Hz and a resolution of 1024 x 768 was used. Participants were seated\u0026thinsp;~\u0026thinsp;58 cm away from the screen. The images were presented against a uniform gray background, from which it emerged with increasing contrast after a 2\u0026ndash;5 sec presentation of a fixation point (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Then, images were presented for about 1 min at a rate of 6.075 Hz with every 5th image being a health-related image (rate of presentation 1.215 Hz) introduced every fifth stimulus (i.e., ~\u0026thinsp;6 Hz/5\u0026thinsp;=\u0026thinsp;1.2 Hz). Within a condition (i.e. neutral / oddball), the sequence of the images was randomized for each participant. In total, 36 trials (6 blocks of 6 trials each) were applied. Each trial was composed of a stimulation which lasts 60 s and was flanked by 2 s of fade-in and fade-out at the beginning and the end of the sequence, respectively. During the fade-in, the contrast modulation depth of the periodic stimulation progressively increased from 0\u0026ndash;100% (full contrast), while the opposite manipulation was applied during the fadeout. This fading is aimed at reducing blinks and abrupt eye movements due to the sudden appearance or disappearance of flickering stimuli. Full contrast is reached at 85 ms and then decreased at the same rate. A rate of ~\u0026thinsp;6 Hz was used because this frequency leads to a large response over occipitotemporal regions and falls in an area of the EEG spectrum (theta band) where the noise level is low (i.e., above the EEG delta band but below the alpha band of 8\u0026ndash;12 Hz) \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for a graphic representation of the task.\u003c/p\u003e \u003cp\u003eDuring the presentation of the images, participants were asked to focus their attention on a colored dot in the middle of the screen, and to count the number of times it would change to the color red within one trial (i.e. ~1 min of image presentation) [similar to other studies, e.g. De Keyser, et al. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e; Rossion, et al. \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e] This was done to ensure the participants\u0026rsquo; focus on the presented images. Participants reported the number of counted color changes verbally to the experimenter after seeing a prompt being displayed on the screen. The time in between trials was self-paced by the participants and depended on the time they took to answer the task-related question.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.6. EEG recording\u003c/h2\u003e \u003cp\u003eThe neural response to the FPVS paradigm was recorded through scalp EEG, using an elastic electrode cap with 64 active, pre-amplified Ag-AgCl electrodes (BioSemi, Netherlands), arranged according to the international 10\u0026ndash;10 system. The direct-current offset was kept below 30 \u0026micro;V during the electrode placement and subsequent experimental session. One participant\u0026rsquo;s data was acquired at 256 Hz instead of 1024 Hz and was subsequently removed from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.7. EEG preprocessing\u003c/h2\u003e \u003cp\u003eAs the individual trials had slightly different durations due to the randomization of the fade-in and fade-out of the images, a custom MATLAB script was used to trim each trial to the exact duration of image presentation (cropped to 57344 bins / trial, equals\u0026thinsp;~\u0026thinsp;56 seconds). The same script was used to label the randomized trials for the conditions (scrambled / original). The data were then imported into Letswave7, an open-source MATLAB toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://osf.io/9423j/\" target=\"_blank\"\u003ewww.letswave.cn\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.letswave.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The EEG signal was filtered using a 4th order Butterworth filter (0.1\u0026ndash;100 Hz). Then, a 50 Hz notch filter (width: 2 Hz) was applied and the data was re-referenced to the average of all electrodes. For each condition, the trials were averaged, and a discrete Fourier Transform (FFT)\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e was applied to transform the data into the frequency domain (spectrum ranging from 0 to 512 Hz, frequency resolution: ~ 0.018 Hz). Then, the signal was baseline corrected to remove residual noise, by subtracting at each electrode and at each frequency bin the average amplitude of the signal measured at the 10 neighboring frequency bins.\u003c/p\u003e \u003cp\u003eFinally, the baseline-subtracted group- and channel-averaged signal was z-scored by calculating the difference between the amplitude at a given frequency bin and the mean amplitude of the 20 surrounding (i.e. \u0026plusmn; 10 ) bins divided by the standard deviation of these 20 surrounding bins \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. This pooled data was then used to define the number of harmonics that are statistically different from zero (i.e. harmonics with z\u0026thinsp;\u0026gt;\u0026thinsp;1.64, one-sided, signal\u0026thinsp;\u0026gt;\u0026thinsp;noise) in consecutive order (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which were then aggregated from the baseline-subtracted data \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. The same steps were applied to aggregate responses at the base response frequency and its harmonics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroup- and channel-averaged z-scores at the frequency of the oddball (O) and its harmonics (overlapping with the base presentation frequency (B) and its harmonics). Bold font indicates z-scores with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.001(i.e. z\u0026thinsp;\u0026lt;\u0026thinsp;2.32, one-sided), while cursive font indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (i.e. z\u0026thinsp;\u0026lt;\u0026thinsp;1.64, one-sided).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eharmonic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ez-score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency [Hz]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003escrambled\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.5089\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.7943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.6869\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.0674\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.5847\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.6971\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6.7112\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.6535\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2.0538\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.9617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.2953\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.1319\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6.6139\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.5788\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.3322\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.8435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.7856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.8834\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.5894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.3385\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.4336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.8454\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.764\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.8. Statistical analysis\u003c/h2\u003e \u003cp\u003eTo detect electrode activity clusters in response to the health-related visual stimuli and compare the distribution of the neural activity of the aggregated responses between the original and scrambled condition, a multi-sensor cluster-based analysis \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e employing a paired t-test (set to an alpha of 0.0001 with 2000 permutations, the sensor connection threshold was set so each electrode has four neighbors on average) was used.\u003c/p\u003e \u003cp\u003eThe remaining statistical analyses were conducted using IBM SPSS Statistics 28 and R Studio (v4.3.1, R Core Team (2023)). To assess differences in the aggregated neural responses to oddball and base frequency stimuli in the different conditions (original vs scrambled images) and across different electrodes (PO8 vs Oz), a repeated-measured ANOVA was used. A log-transform was applied to all EEG data to correct right-skewedness and conform them to the assumption of normality \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Significant results were further assessed post-hoc using pairwise t-test- The relationship between pain thresholds, neural responses and psychological traits was assessed using correlational analyses, regression analyses and moderation analyses (PROCESS analysis algorithm\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e). Behavioral differences between the conditions were examined through a repeated-measure ANOVA. For all statistical analysis, the significance threshold was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a Mandat d\u0026apos;Impulsion Scientifique (MIS) - F.N.R.S grant awarded to GL. CL was supported by a FRIA doctoral grant of the Belgian Fund for Scientific Research (F.R.S.\u0026ndash;FNRS, grant 5118622). PC was supported by a doctoral fellowship of the National Operation Program (PON) Research and Innovation 2014-2020 (CCI. 2014IT16M2OP005), ESF REACT-EU resources, Action IV.4 \u0026quot;Doctorates and research contracts on themes of innovation\u0026quot; and Action IV.5 \u0026quot;Doctorates on Green themes.\u0026quot; CUP code J35F21003350006.\u003c/p\u003e\n\u003cp\u003eWe would like to thank the CATL team and especially Fanny Fievez for their help in setting up the experiment and Val\u0026eacute;ry Goffaux for valuable advice. Additionally, we would like to thank the NOCIONS lab for the valuable discussions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data (EEG), and individual data for pain thresholds and trait characteristics as well as supporting material (images used for the FPVS paradigm) are publicly available on OSF (https://osf.io/9423j/ ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: \u003cstrong\u003ePC, GL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethodology:\u003cstrong\u003e\u0026nbsp;PC, CL, GL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInvestigation : \u003cstrong\u003ePC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValidation:\u003cstrong\u003e\u0026nbsp;CL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormal analysis: \u003cstrong\u003eCL\u0026nbsp;\u003c/strong\u003e(EEG), \u003cstrong\u003ePC\u0026nbsp;\u003c/strong\u003e(psychological traits)\u003c/p\u003e\n\u003cp\u003eData Curation: \u003cstrong\u003eCL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisualization: \u003cstrong\u003ePC, CL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupervision: \u003cstrong\u003eMM, GL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding acquisition: \u003cstrong\u003eGL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWriting - Original Draft: \u003cstrong\u003ePC, CL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWriting - Review \u0026amp; Editing:\u003cstrong\u003e\u0026nbsp;PC, CL, MM, GL\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKouider, S. \u0026amp; Dehaene, S. 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(Guilford publications, 2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"FPVS, EEG, health anxiety, frequency-tagging, pain threshold","lastPublishedDoi":"10.21203/rs.3.rs-6989317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6989317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHealth anxiety is often linked to lower pain thresholds and heightened sensitivity to health-related stimuli, yet the relationship between these psychological and physiological traits remains complex. In this study, we relied on the use of the brain\u0026rsquo;s ability to discriminate fast and periodically presented stimuli (i.e. oddballs) of a given image category within a stream of unrelated images, to investigate whether neural responses to health-related visual stimuli are associated with individual differences in pain sensitivity and psychological traits such as anxiety and depression. We hypothesized that, if the periodically presented health-related oddball elicits a periodic neural response, this image category might lead to a stronger response in individuals with health anxiety and psychological malaise. This is the first evidence that periodically presented health-related images elicit a neural response which can be clearly differentiated from unrelated images. Additionally, this neural response shared a relationship with depressive traits, which was in turn moderated by the pain threshold. While these results offer insight into the interaction between psychological traits, pain threshold and the processing of health-related images, future studies will have to confirm the specificity of the obtained relationships.\u003c/p\u003e","manuscriptTitle":"Fast periodic visual stimulation to study the processing of health- related images in the human brain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 06:55:14","doi":"10.21203/rs.3.rs-6989317/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-04T07:04:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T08:09:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311009581185229562003732258826178826845","date":"2025-07-21T00:56:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T12:48:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61769770711580517620769329691970103964","date":"2025-07-05T14:29:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-03T08:28:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-03T08:22:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-30T12:08:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-27T10:40:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-27T07:52:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b3d66f81-c504-494c-bb0b-019b7bb3c185","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51818702,"name":"Biological sciences/Neuroscience/Cognitive neuroscience"},{"id":51818703,"name":"Biological sciences/Neuroscience/Neuronal physiology"}],"tags":[],"updatedAt":"2025-12-01T16:10:39+00:00","versionOfRecord":{"articleIdentity":"rs-6989317","link":"https://doi.org/10.1038/s41598-025-29515-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-28 15:58:08","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-07-29 06:55:14","video":"","vorDoi":"10.1038/s41598-025-29515-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-29515-z","workflowStages":[]},"version":"v1","identity":"rs-6989317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6989317","identity":"rs-6989317","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}