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BMC Med
BMC Med
BMC Medicine
1741-7015
BioMed Central London

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10.1186/s12916-024-03610-w
Research
Between faces: childhood adversity is associated with reduced threat-safety discrimination during facial expression processing in adolescence
Samaey Celine celine.samaey@kuleuven.be

12
Van der Donck Stephanie 23
Lecei Aleksandra 12
Vettori Sofie 234
Qiao Zhiling 12
van Winkel Ruud 125
Boets Bart 23
1 https://ror.org/05f950310 grid.5596.f 0000 0001 0668 7884 Center for Clinical Psychiatry, Research Group Psychiatry, Department of Neurosciences, KU Leuven, Louvain, Belgium
2 https://ror.org/05f950310 grid.5596.f 0000 0001 0668 7884 Leuven Brain Institute (LBI), KU Leuven, Louvain, Belgium
3 https://ror.org/05f950310 grid.5596.f 0000 0001 0668 7884 Center for Developmental Psychiatry, Research Group Psychiatry, Department of Neurosciences, KU Leuven, Louvain, Belgium
4 grid.4444.0 0000 0001 2112 9282 Institute for Cognitive Sciences Marc Jeannerod, Centre National de La Recherche Scientifique, Lyon, France
5 https://ror.org/05f950310 grid.5596.f 0000 0001 0668 7884 University Psychiatric Center (UPC), KU Leuven, Louvain, Belgium
11 9 2024
11 9 2024
2024
22 3822 5 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Childhood adversity has been associated with alterations in threat-related information processing, including heightened perceptual sensitivity and attention bias towards threatening facial expressions, as well as hostile attributions of neutral faces, although there is a large degree of variability and inconsistency in reported findings.

Methods

Here, we aimed to implicitly measure neural facial expression processing in 120 adolescents between 12 and 16 years old with and without exposure to childhood adversity. Participants were excluded if they had any major medical or neurological disorder or intellectual disability, were pregnant, used psychotropic medication or reported acute suicidality or an ongoing abusive situation. We combined fast periodic visual stimulation with electroencephalography in two separate paradigms to assess the neural sensitivity and responsivity towards neutral and expressive, i.e. happy and angry, faces. Linear mixed effects models were used to assess the impact of childhood adversity on facial expression processing.

Results

Sixty-six girls, 53 boys and one adolescent who identified as ‘other’, between 12 and 16 years old (M = 13.93), participated in the current study. Of those, 64 participants were exposed to childhood adversity. In contrast to our hypotheses, adolescents exposed to adversity show lower expression-discrimination responses for angry faces presented in between neutral faces and higher expression-discrimination responses for happy faces presented in between neutral faces than unexposed controls. Moreover, adolescents exposed to adversity, but not unexposed controls, showed lower neural responsivity to both angry and neutral faces that were simultaneously presented.

Conclusions

We therefore conclude that childhood adversity is associated with a hostile attribution of neutral faces, thereby reducing the dissimilarity between neutral and angry faces. This reduced threat-safety discrimination may increase risk for psychopathology in individuals exposed to childhood adversity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12916-024-03610-w.

Keywords

Childhood adversity
Facial expression processing
Electroencephalogram
EEG
Adolescence
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcBackground

From the moment of birth, humans are drawn to faces and face-like patterns, hinting at the importance of face processing in our daily life functioning [1–3]. Indeed, adequate face processing is key for successful social interactions [4]. On the other hand, social interactions in turn may shape face processing throughout the lifespan [5]. In particular, extreme environmental factors, such as experiencing childhood adversity (CA), may alter facial expression processing to promote survival within a dangerous environment [6–8]. Although various operationalizations of CA exist, it is typically defined as ‘environmental circumstances that are either serious (i.e., severe) or ongoing over time (i.e., chronic); are likely to require significant adaptation by an average child; and represent a deviation from the expectable environment’ [9]. Based on this definition, in the current study, CA was defined as exposure to childhood maltreatment, peer and sibling victimization, sexual victimization, family violence and abuse, and physical and emotional neglect.

Research has suggested that CA is associated with altered threat-related information processing, including heightened perceptual sensitivity and attention bias towards threatening facial expressions, as well as hostile attributions of neutral faces [6, 9–11]. Nonetheless, to date, findings often do not converge, with individual studies applying a wide variety of methods in widely varying populations, using various definitions of CA, thus making interpretation and replicability of findings challenging [12–15].

Generally, meta-analyses underscore the evidence for altered facial expression processing following CA in children, adolescents and adults [12–14]. Behaviourally, children exposed to adversity categorize faces as angry starting from a lower emotional intensity [15] and are faster but less accurate in categorizing and recognizing both angry and fearful faces [13, 16]. Healthy adults exposed to adversity more often interpret neutral faces as threatening [17, 18], a bias that is even more pronounced in adults with concurrent psychopathology [19]. Findings on attention biases for facial expressions have been mixed, as recent research reports either no attention bias at all, an attention bias towards angry faces, as well as an attention bias away from angry faces [15]. Attention allocation may possibly fluctuate over time, as children exposed to war show an initial 100 ms avoidance of angry and happy faces, followed by sustained attention towards angry faces [20].

At a neural level, CA has been associated with hyperactivity of the amygdala and superior temporal gyrus during facial expression processing, which may signal enhanced early detection of emotion [12–14, 21]. Furthermore, increased functional connectivity of the amygdala to the hippocampus and prefrontal cortex has been shown in adults exposed to CA [22]. In line with fMRI research, children exposed to adversity show higher event-related potentials (ERPs) to angry faces compared to healthy controls [15, 23–25], even at the age of 15 months [25]. However, this hyperresponsivity may not be limited to negative expressions, as [26] reported increased ERPs irrespective of facial valence. Interestingly, healthy adults who experienced adversity failed to implicitly differentiate between threatening and non-threatening social information [27]. Whereas unexposed controls showed distinct peak N170 amplitudes indicating face-specific (emotional) processing for both consciously and non-consciously presented happy and angry faces, the adults experiencing CA could only distinguish the consciously presented facial expressions. While these findings do not support a heightened early response to angry faces, they suggest that the altered facial expression processing observed in CA may stem from an early and implicit failure to differentiate between threatening and safe social cues.

Conflicting findings on altered facial expression processing may in part be attributed to the different types of experienced CA, which may exert a differential impact on facial expression processing. Recently, McLaughlin and colleagues [28, 29] have proposed a distinction between childhood threat and childhood deprivation to account for different neurodevelopmental outcomes reported within the literature. Experiences of threat, i.e. the presence of potential harm, are hypothesized to alter neurodevelopment to facilitate the rapid identification of, and response to, potentially harmful environments, whereas experiences of deprivation, i.e. the absence of age-appropriate experiences, are hypothesized to disrupt neurodevelopment due to a lack of developmentally appropriate stimulation [28, 29]. More specifically, childhood threat may be associated with enhanced processing of facial expressions conveying threat as well as a hostile interpretation of ambiguous (including neutral) facial expressions. Childhood deprivation on the other hand might more generally disrupt normative facial expression processing, as it deprives children from the stimulation required for maturation of the face processing system [2, 30].

In support of this distinct impact of deprivation, young adults exposed to emotional neglect respond slower, albeit not less accurate, while rating the valence of positive and negative facial expressions [31]. Threat and deprivation have also been associated with distinct neural signatures of facial expression processing: whereas exposure to threat was associated with higher amygdala activation to angry faces, emotional and physical neglect were associated with lower ventral striatum activity to happy faces [32]. Moreover, Romanian children placed in institutional care between 5 and 31 months of age have been shown to present smaller and slower P100 (early visual processing), N170 (face-specific processing) and P400 (higher-level visual and semantic processing) ERPs when watching expressive faces, both positive and negative, compared to family-reared children [30, 33]. Nonetheless, an attentional bias towards processing angry faces has been reported for both childhood threat and deprivation, including institutionalization, emotional and physical neglect [34].

While facial expression processing is likely impacted by both childhood threat and deprivation [24], empirical findings may have been obscured by studies merging both dimensions of adversity or, contrarily, by failing to assess either one of them. Against this background, the current study assessed both childhood threat and deprivation in order to investigate their distinct impact on facial expression processing. Moreover, the current study included childhood adversity as a categorical as well as a continuous predictor, as taking the number of adverse childhood experiences and their frequency into account may provide a more sensitive measure of the potentially subtle neural consequences of childhood adversity.

In addition, much of the conflicting results in the literature may be attributed to the myriad of different methods applied, including many methods that have not been proven to be sensitive or reliable at the individual subject-level, including but not limited to passive viewing, gender discrimination, go/no-go, attention shifting, emotional face matching, target identification and emotion recognition tasks [8, 14, 15]. In view of this, here, we applied a highly robust and sensitive neuroimaging approach, i.e. we combined fast periodic visual stimulation (FPVS) with frequency-tagging EEG to objectively quantify the implicit neural sensitivity to brief changes in facial expressions, as well as neural saliency for expressive compared to neutral faces [35]. FPVS EEG is based on the principle that the frequency of the electrophysiological response on the human scalp corresponds exactly to the periodicity of the visual stimulation [36]. As such, neural responses to this visual stimulation can be objectively measured at this predefined stimulation frequency in only a few minutes of recording, resulting in a high signal-to-noise ratio. It is therefore ideally suited for sensitive populations, including minors and patients. Furthermore, FPVS EEG does not require any explicit behavioural tasks that may allow for compensatory strategies to take place, thereby resulting in a behaviour-free neural signature of facial expression processing [8].

In the current study, we applied two FPVS paradigms in 120 adolescents between 12 and 16 years of age with variable exposures to childhood threat and deprivation. First, an oddball paradigm, in which an expressive face was periodically interleaved in a rapidly presented stream of neutral faces, was administered to pinpoint subtle differences in facial expression discrimination, in particular for angry and happy faces versus neutral faces (similar to [37, 38]). Second, a multi-input paradigm where neutral and expressive faces were presented side-by-side, flickering at different stimulation frequencies, was applied to measure the neural saliency of expressive (i.e. angry or happy) faces compared to neutral faces (similar to [39]). To investigate explicit attentional biases as well as implicit neural responses, concurrent eye-tracking recording was included in a subsample of participants during this multi-input paradigm. Based on the potentially increased neural salience of threatening social cues, we hypothesized that exposure to childhood adversity would be associated with increased neural discrimination of angry faces presented in between neutral faces in the oddball paradigm, as well as with heightened neural sensitivity towards angry faces during the multi-input paradigm. We further expected a stronger association between CA and facial expression processing when taking CA frequency into account, yet we did not expect to find any strong effects of either childhood threat or deprivation in and by themselves.

Methods

Study design

One hundred twenty adolescents between 12 and 16 years of age were recruited to participate in the EMBRACE study investigating the impact of childhood adversity on adolescent development. Participants were recruited from the general population through posters and flyers distributed in schools, leisure centres, medical practices and health centres, as well as from specialized centres providing specialized care for children and adolescents exposed to adversity. Participants and their parents/caregivers provided written informed consent prior to participation and received a monetary compensation of €10/h, as well as a reimbursement of any travel expenses. The study received ethical approval from the UZ/KU Leuven Medical Ethics Committee (S62124). Data collection was completed between August 2020 and July 2022.

Inclusion and exclusion criteria

As we wanted to examine the impact of childhood adversity during adolescence, boys and girls between 12 and 16 years old were eligible to participate in the current study. Exclusion criteria included:Major medical or neurological disorders

Claustrophobia

Intellectual disability

Pregnancy

Use of psychotropic medication

Acute suicidality, as measured during a semi-structured interview

Currently ongoing abusive situation

Outcome measures

Childhood adversity

An adapted version of the abbreviated Juvenile Victimization Questionnaire—2nd Revision (JVQ-R2) [40] and 5 questions of the Emotional Neglect subscale of the Childhood Trauma Questionnaire (CTQ) [41] were used to assess exposure to CA. A total of 33 screener questions inquired about exposure to child maltreatment, peer and sibling victimization, sexual victimization, family violence and abuse, physical neglect and emotional neglect (Additional file 1: Table S1). Both JVQ-R2 and CTQ have shown good construct validity and acceptable to substantial test–retest reliability in adolescent samples [40, 42]. All screener questions were translated to Dutch and could be answered with ‘yes’ or ‘no’. An affirmative answer led to follow-up questions considering the age-of-onset, frequency, duration, and past and current impact of the adverse event. For the categorical group analysis, participants were included in the CA group if they experienced any physical, emotional or sexual abuse or physical neglect ‘at least once’ in their lives, or peer victimization or emotional neglect ‘at least sometimes’, or in the healthy control (HC) group otherwise. Moreover, a composite score incorporating both the number of adverse events and their frequency was calculated as follows: for every endorsed screener question, the frequency of the adversity was assessed on a 5-point Likert scale, ranging from 1 = ‘once’ to 5 = ‘very often’. If the participant did not endorse the screener questionnaire, a frequency of 0 was entered. For every adversity category, we then calculated the mean frequency of adverse events, after which we summed these means across all adversity categories, with higher scores indicating more exposure to CA. Finally, childhood threat was operationalized as exposure to childhood maltreatment, peer and sibling victimization, sexual victimization and family violence and abuse, whereas childhood deprivation constituted physical and emotional neglect. The composite score showed good internal consistency (α = 0.83), while childhood threat and childhood deprivation showed acceptable internal consistency (α = 0.75 and α = 0.78, respectively) in the current sample.

EEG recording

Procedure

Participants completed two FPVS EEG tasks in a dimly lit room at 60 cm viewing distance of a 24-inch LCD computer screen. The procedure and design were similar to previous studies [37, 39], and the order of the tasks was counterbalanced between participants. For both tasks, participants viewed full frontal images of 26 Caucasian adults, of which 13 males and 13 females, displaying neutral, happy and angry facial expressions at a size of 300 × 300 pixels. All stimuli were retrieved from the Radboud Faces Database [43] and equalized for mean pixel luminance and contrast.b) Oddball task

Neutral faces were presented sequentially through sinusoidal contrast modulation (0–100%) at a 6 Hz base frequency, while expressive oddball faces (i.e. happy or angry, in separate sequences) were periodically inserted into this sequence at every fifth image (6 Hz/5 = 1.2 Hz oddball frequency) (Fig. 1A). If participants can discriminate between the neutral base faces and the expressive oddball faces, an expression-discrimination response will be present at this 1.2 Hz oddball frequency. Every sequence commenced with a blank screen that was shown for a variable duration of 2–5 s, after which the faces gradually faded in (0–100%) during 2 s to avoid abrupt eye movements and blinks. The images were presented for 60 s, followed by a gradual fade out (100–0%) for 2 s. The task consisted of four separate sequences for every emotional expression (i.e. happy or angry), of which two with male faces and two with female faces, resulting in a total of eight sequences presented in randomized order. To ensure continued attention, participants completed an orthogonal task during which a fixation cross, presented on the nasion of the face, briefly (300 ms) changed from black to red 12 times within every sequence. Upon this colour change, participants had to press the spacebar as soon and accurately as possible.c) Multi-input task

Fig. 1 A Oddball paradigm: neutral faces are presented sequentially at a 6 Hz base rate, periodically interleaved with a happy or angry face every fifth image (1.2 Hz oddball rate). The identity of the faces changes every image. B Multi-input paradigm: two streams of images containing neutral and either happy or angry faces are presented simultaneously at different stimulation frequencies, i.e. 5 and 6 Hz. Presentation side and frequency are counterbalanced across trials, and the identity of the faces changes every image

Two streams of images containing neutral and expressive faces (i.e. either happy or angry, in separate sequences) were presented simultaneously through sinusoidal contrast modulation (0–100%) at different stimulation frequencies, i.e. 5 and 6 Hz (Fig. 1B), thereby allowing for a specific neural tag of neutral versus expressive faces. The flickering faces were presented side-by-side with 1.5 cm distance between the outer borders of the streams. Participants viewed a total of 16 sequences, of which eight containing happy and eight containing angry faces, divided equally over male and female faces. A blank screen was shown for a variable duration of 2–5 s at the start of every sequence, followed by a gradual fade-in (0–100%) during 2 s. The sequences lasted 20 s, followed by a gradual fade-out (100–0%) for 2 s. The stimulation frequency (5 vs. 6 Hz) and the position of the expressive vs. neutral faces (left vs. right) were counterbalanced across the sequences. Participants were instructed to look freely at the images and to press the spacebar whenever a red rectangular outline surrounding the faces appeared briefly (300 ms) on the screen. This rectangular outline appeared at random four times during every sequence to ensure attention to the screen.d) EEG acquisition

EEG activity was recorded at a sampling rate of 512 Hz using the BioSemi Active-Two amplifier system with 64 Ag/AgCl electrodes and a Common Mode Sense active electrode and Driven Right Leg passive electrode as reference and ground electrode, respectively. Vertical and horizontal eye movements were recorded through four electrodes: one above and below the right eye and two at the outer canthi of the eyes.e) Eye-tracking recording

For a subsample of 70 participants, of whom 34 HC and 36 CA, the multi-input paradigm was combined with concurrent eye-tracking. Eye-tracking data was collected using a Tobii X3-120 screen-based eye tracker with a sampling rate of 120 Hz and the Tobii Pro Lab software. Following the standard calibration procedure, whereby participants had to follow a red dot moving across the screen, participants completed an additional calibration paradigm in order to obtain a subject-specific quantitative measure of data quality [39]. In this additional calibration, participants fixated on 10 consecutive fixation crosses on different target locations on the screen. A personalized quantitative measure of error angle (i.e. mean and variance) and accuracy was calculated based on the difference between the centre of the displayed fixation cross and the actual gaze point. The accuracy was subsequently used in the analysis to attribute gaze points more precisely to particular areas of interest.

Statistical analysis

EEG pre-processing

All EEG pre-processing was performed using Letswave 6 and Matlab R2023a. The EEG data was cropped into segments of 70 s and 30 s (2 s before and 8 s after each sequence) for the oddball and multi-input task, respectively. A fourth-order Butterworth band-pass filter (0.1–100 Hz) was applied, and the data was resampled to 256 Hz. All electrodes were visually inspected, and noisy electrodes with deflections exceeding 150 µV were linearly interpolated from the three spatially nearest electrodes. A maximum of 5% of the electrodes, i.e. 3 electrodes, were interpolated, and on average less than one electrode was interpolated across all participants. All data segments were re-referenced to a common average reference. Three participants (oddball: 1 CA and 2 HC; multi-input: 2 CA and 1 HC) blinked on average more than 2SD above the mean (oddball: M = 0.15 blinks per s, SD = 0.11; multi-input: M = 0.19 blinks per s, SD = 0.16). Consequently, blink correction using independent component analysis was applied for these participants.

Frequency domain analysis

Oddball paradigm

The pre-processed segments were further cropped to contain an integer number of 1.2 Hz cycles starting immediately after fade-in until 59.31 s (15,183 time bins). The resulting segments were then averaged per condition (i.e. happy or angry oddball face) and per participant in the time domain, after which these averaged waveforms were transformed into the frequency domain using a fast Fourier transform (FFT). The amplitude spectrum was computed with a high spectral resolution (0.017 Hz, 1/59.31 s), resulting in a very high signal-to-noise ratio (SNR). The recorded EEG contains signals at frequencies that are integer multiples (harmonics) of the base and oddball frequencies [44]. The amplitudes at the base frequency and its harmonics (i.e. 6 Hz, 12 Hz, 18 Hz,…) are considered base responses, indexing general visual processing, while the amplitudes at the oddball frequency and its harmonics (i.e. 1.2 Hz, 2.4 Hz, 3.6 Hz,…) are considered as an index of facial expression discrimination. We applied two measures to quantify these responses in relation to the noise level: (a) SNR, which is the amplitude of a specific frequency bin divided by the amplitude of the 20 surrounding frequency bins [45], and (b) baseline-corrected amplitudes, computed by subtracting the average amplitude of the 20 neighbouring frequency bins from the amplitude of the frequency bin of interest [46]. For both measures, the 20 neighbouring frequency bins were selected from 12 bins on each side excluding the two bins directly adjacent to the bin of interest, as well as the two bins with the most extreme values. As the neural response is typically distributed over multiple harmonics representing the periodic response [44], we combined the response amplitudes across all harmonics whose response amplitude was significantly higher than the amplitude of the surrounding noise bins. To define the harmonics that were significantly above noise level, we assessed the significance of every harmonic response by calculating Z-scores using the mean and SD of the 20 frequency bins surrounding the bin of interest on the FFT grand-averaged data across the relevant regions of interest (ROIs). Harmonics were included if Z-scores were significant (Z > 1.64 or p < 0.05) for two consecutive harmonics across groups and expressions. Consequently, the oddball response was quantified as the sum of the responses of five harmonics (i.e. 1.2 Hz, 2.4 Hz, 3.6 Hz, 4.8 Hz and 7.2 Hz, excluding 6 Hz), and the base response as the summed responses of three harmonics (i.e. 6 Hz, 12 Hz and 18 Hz). Visual inspection of the topographical maps (Fig. 2A) and identification of the most responsive regions for oddball and base stimulation led to three ROIs in the left (LOT) and right (ROT) occipito-temporal and medial-occipital (MO) cortex. In line with previous studies applying an identical FPVS oddball paradigm [37, 38, 47, 48], LOT and ROT were defined by averaging the three channels with the highest summed baseline-corrected amplitudes for each hemisphere and expression (i.e. channels P7, P9 and PO7 for LOT and P8, P10 and PO8 for ROT), whereas MO was defined by averaging the four channels with the largest common response at 6 Hz (i.e. channels O1, O2, Iz and Oz). Oddball responses showed an excellent internal consistency (α = 0.91).b) Multi-input paradigm

Fig. 2 A Scalp distribution of the expression-discrimination responses to angry and happy faces in the CA and HC group. B Summed baseline-subtracted amplitudes in μV for the first five oddball harmonics (until 7.2 Hz, excluding 6 Hz) displaying the expression-discrimination responses. The main effects of expression and ROI indicate significantly higher responses for happy compared to angry facial expressions and in MO (channels O1, O2, Iz and Oz) and ROT (channels P8, P10 and PO8) compared to LOT (channels P7, P9 and PO7). HC, healthy control; CA, childhood adversity

The pre-processed segments from the multi-input task were cropped to contain an integer number of 1 Hz cycles (i.e. the greatest common divisor of 5 and 6 Hz), beginning after fade-in until 19.05 s (4878 time bins). To reduce sequence-specific noise, the resulting segments were averaged per condition (i.e. expression and presentation rate) and participant in the time domain, after which these averaged waveforms were transformed into the frequency domain using FFT. The amplitude spectrum was computed with a high spectral resolution (0.0525 Hz, 1/19.05 s), resulting in a high SNR. The recorded EEG contains signals at frequencies that are integer multiples (harmonics) of the frequencies at which the faces were presented (i.e. 5 Hz, 10 Hz, 15 Hz,… and 6 Hz, 12 Hz, 18 Hz,…). We again applied two measures to quantify the response in relation to the noise level: SNR for data visualization and baseline-corrected amplitudes to quantify and analyse the response. We combined the response amplitudes across all harmonics whose response amplitude was significantly higher than the amplitude of the surrounding noise bins [44]. To define the harmonics that were significantly above noise level, we assessed the significance of every harmonic response by calculating Z-scores using the mean and SD of the 20 frequency bins surrounding the bin of interest on the FFT grand-averaged data across the relevant ROIs. For both 5 and 6 Hz, Z-scores were significant (Z > 1.64 or p < 0.05) until the fourth harmonic (i.e. 20 and 24 Hz). Visual inspection of the topographical maps (Fig. 3A) and identification of the most responsive regions led to one medial-occipital (MO) ROI, defined by averaging channels Iz, Oz, O1, O2, PO7 and PO8. Multi-input responses showed a good to excellent internal consistency for neutral and expressive faces (α = 0.86 and α = 0.90, respectively).Fig. 3 A Scalp distribution of the responses to angry versus neutral and happy versus neutral faces in the CA and HC group. B Summed baseline-subtracted amplitudes in μV for the first four harmonics (until 20 and 24 Hz, respectively). For trials contrasting angry and neutral facial expressions, statistical analyses showed a main effect of group: HC show significantly higher EEG responses than CA. C Average looking times to angry and neutral and happy and neutral faces, respectively. HC, healthy control; CA, childhood adversity

Eye-tracking analysis

Similar to [39], the eye-tracking data were analysed through a set of custom-built Matlab scripts (The Mathworks, see https://github.com/TimVanWesemael/Fuzzy-AOI-EyeTracking). First, the raw data was filtered using the I2MC algorithm [49]. Next, the left and right AOIs were defined as the rectangular areas where the faces were shown. Additionally, an ‘outside AOI’ was defined to account for all fixation points that could not be attributed to either the left or right AOI. Fixations were assigned to these AOIs through probability weighting, taking the subject-specific data quality obtained during the additional calibration procedure into account. More specifically, for every gaze point, a proportional score representing the probability that the corresponding AOI includes the recorded gaze point is attributed to each of the three AOIs. The proportional looking scores are defined based on a two-dimensional bell curve around the gaze point with a standard deviation equal to the RMS recorded during calibration. As such, better data quality during calibration results in more precise sample points around the gaze point and vice versa. For each AOI, proportional looking times were calculated from the relative duration of all fixation points averaged across four (i.e. neutral faces on the left or right side) trials. The eye-tracking responses showed an excellent internal consistency (α = 0.91).

Statistical analysis

For statistical analysis of the baseline-corrected amplitudes, linear mixed effects models were run separately for either the base or the oddball responses as dependent variables in the oddball paradigm. Expression (angry or happy) and ROI (LOT, ROT and MO) were included as fixed within-subject independent variables and exposure to CA as a fixed between-subject independent variable. Age and gender, coded as ‘0’ for boys, ‘1’ for girls and ‘2’ for other, were included as covariates in all models. To account for repeated testing, we included a random intercept per participant, as well as a random slope for expression for the oddball responses and a random slope for ROI for the base responses, as these random effects provided the most optimal data fit. In addition to analysing the categorical effect of CA (i.e. comparing the CA and the HC group), we also investigated whether dimensional individual differences in CA exposure would impact facial expression processing. Therefore, the previous models were repeated with the dimensional CA composite score as a continuous predictor.

For statistical analysis of the baseline-corrected amplitudes and proportional looking times in the multi-input paradigm, linear mixed effects models were run separately for the happy and angry condition. Expression (neutral or expressive) was included as fixed within-subject independent variables and exposure to CA as a fixed between-subject independent variable. Age and gender were included as covariates and a random intercept per participant was added to account for repeated testing.

Finally, for both the oddball responses and the multi-input paradigm, we included an exploratory analysis to examine the potentially differential effect of threat versus deprivation on facial expression processing by separately including the composite measure of exposure to childhood threat and the composite measure of childhood deprivation as fixed between-subject independent variables within the same linear mixed effects model. Outlying values for baseline-corrected amplitudes (oddball: ampl. ≥  − 0.83 and ≤ 1.57 (n = 36/920 trials); multi-input: angry condition ampl. ≥ 1.48 and ≤ 4.20 (n = 33/936 trials), happy condition ampl. ≥ 1.46 and ≤ 4.25 (n = 82/936 trials)) and the CA composites (CA composite score < 6.23 (n = 6)) were determined using the median absolute deviation and were removed from the models prior to analysis. Degrees of freedom were calculated using the Kenward-Roger method. Post hoc contrasts were tested for significance using a Bonferroni correction for multiple comparisons, which was applied on a median split for interactions with the continuous CA composite score. All analyses were performed using RStudio [50]. For a complete overview of model selection and model fit, we refer the reader to Additional file 2.

Results

Descriptive statistics

One hundred twenty adolescents, of which 66 girls, 53 boys and one adolescent who identified as ‘other’, between 12 and 16 years old (mean = 13.93, SD = 1.36) participated in the current study. Sixty-four participants were included in the adversity group, while the control group consisted of 56 participants. Descriptive statistics of all included variables are shown in Table 1. Pairwise correlations between all included variables are depicted in Additional file 1: Table S2. Adolescents within the adversity group were not different from the control group regarding age (t(113) =  − 1.53, p = 0.13), yet there were proportionally more girls within the adversity group (χ2(2) = 7.62, p = 0.0221). Table 1 Descriptive statistics for all variables included in the current study (N = 120)

Variable	HC	CA	Observed range	t	χ2	p	
N	56	64					
Age, mean (SD)	13.73 (1.30)	14.11 (1.39)	12–16	 − 1.53		.13	
Gender					7.62	.0221	
 Boys	32	21					
 Girls	24	42					
 Other	0	1					
Childhood adversitya, mean (SD)	.10 (.17)	2.29 (2.64)	0–6.23	 − 5.97		 < .001	
Childhood threatb, mean (SD)	.01 (.03)	.38 (.40)	0–2.21	 − 6.80		 < .001	
 Child maltreatmentc, mean (SD)	.02 (.06)	.35 (.54)	0–2.50	 − 4.32		 < .001	
 Peer victimizationc, mean (SD)	.04 (.09)	.71 (.73)	0–4.29	 − 7.03		 < .001	
 Sexual victimizationc, mean (SD)	0 (0)	.16 (.36)	0–2.14	 − 3.42		 < .001	
 Indirect victimizationc, mean (SD)	0 (.03)	.29 (.46)	0–3.50	 − 3.52		 < .001	
Childhood deprivationb, mean (SD)	.01 (.04)	.22 (.51)	0–2.70	 − 3.06		.0028	
 Physical neglectc, mean (SD)	0 (0)	.12 (.44)	0–3.00	 − 2.06		.0416	
 Emotional neglectc, mean (SD)	.02 (.08)	.31 (.76)	0–3.20	 − 2.92		.0042	
Bold values are statistically significant (p < .05)

HC healthy control, CA childhood adversity

aFor every adversity category, we calculated the mean frequency of adverse events, after which we summed these means across all adversity categories. Higher scores indicate more exposure to childhood adversity

bChildhood threat was operationalized as the mean exposure to childhood maltreatment, peer and sibling victimization, sexual victimization and family violence and abuse, whereas childhood deprivation constituted the mean reported physical and emotional neglect

cMean frequency of all endorsed items within an adversity category

Due to technical failure (n = 2) and participant drop-out (n = 3 and n = 1 for the oddball and multi-input paradigm, respectively), n = 115 (61 CA and 54 HC) and n = 117 (62 HC and 55 HC) out of 120 participants were included in the analysis of the oddball and multi-input paradigm. No participants were excluded based on orthogonal task performance to measure attention. Considering the proportional looking times during the multi-input paradigm, n = 19 participants were excluded from analyses due to technical failure. N = 14 additional participants were excluded from analyses due to poor data quality (i.e. a calibration accuracy below 50%), resulting in a total sample of 37 participants (20 CA and 17 HC). With the exception of a difference in age between included (n = 117) and excluded (n = 3) participants for the multi-input paradigm, excluded participants did not differ from included participants on age, gender or CA exposure (Additional file 1: Table S3). A total of 46 potential participants were excluded from participation based on the predefined exclusion criteria.

Orthogonal task performance

Both groups performed equally well on the orthogonal colour change detection task during both the oddball (accuracy: t(73) = 1.19, p = 0.24; response times: t(101) =  − 0.01, p = 0.99) and multi-input EEG paradigm (accuracy: t(114) = 0.87, p = 0.39; response times: t(100) = 1.35, p = 0.18). Healthy controls and adolescents exposed to adversity thus showed a similar level of attention to the screen during both experiments, with similar high levels of accuracy (oddball: MHC = 94.89%, SDHC = 3.53%, MCA = 92.69%, SDCA = 6.35%; multi-input: MHC = 97.66%, SDHC = 4.93%, MCA = 97.19%, SDCA = 5.58%) and similar response times (oddball: MHC = 0.50 s, SDHC = 0.04 s, MCA = 0.50 s, SDCA = 0.17 s; multi-input: MHC = 0.45 s, SDHC = 0.14 s, MCA = 0.43 s, SDCA = 0.04 s).

Oddball paradigm

General visual base rate responses

Base rate responses were significantly different for ROI (F(2, 110) = 57.28, p < 0.001), with highest responses in the MO region, followed by ROT and LOT (t(108)LOT-MO =  − 15.01, p < 0.001, d =  − 4.58; t(113)LOT-ROT =  − 5.11, p < 0.001, d =  − 1.25; t(109)MO-ROT = 10.21, p < 0.001, d = 3.32). There was no significant effect of CA on the base rate amplitudes (p = 0.40), indicative of a similar neural synchronization to the flickering stimuli independent of CA history. Similarly, the composite score of CA was not significantly associated with base rate amplitudes (p = 0.21).

Expression-discrimination responses

Both happy and angry facial expressions elicited robust expression-discrimination responses at the oddball rate and its harmonics (Fig. 4). The linear mixed model analysis revealed a main effect of expression (F(1, 110) = 17.82, p < 0.001) and ROI (F(2, 425) = 11.99, p < 0.001), with significantly higher expression-discrimination responses for happy compared to angry faces (t(110)ANGRY-HAPPY =  − 0.28, p < 0.001, d =  − 1.01) and for MO and ROT regions (t(426)LOT-MO =  − 4.01, p < 0.001, d =  − 0.39; t(428)LOT-ROT =  − 5.18, p < 0.001, d =  − 0.50) as compared to LOT (Fig. 2B). When entering CA as a categorical predictor, the interaction effect between adversity and expression (F(1, 110) = 3.62, p = 0.06) was not significant. Entering CA as a continuous predictor revealed a significant adversity by expression interaction (F(1, 104) = 4.09, p = 0.0458, d =  − 1.06), indicating that higher levels of CA were associated with higher expression-discrimination responses for happy faces and lower expression-discrimination responses for angry faces (Fig. 5). The exploratory model disentangling the impact of childhood threat versus deprivation revealed no main effect nor any interaction effect of either dimension (all p > 0.18).Fig. 4 SNR spectra visualizing the expression-discrimination responses averaged over LOT (channels P7, P9 and PO7), MO (channels O1, O2, Iz and Oz) and ROT (channels P8, P10 and PO8) for angry and happy faces. The dashed line at 6 Hz indicates the 6 Hz base response. HC, healthy control; CA, childhood adversity

Fig. 5 The linear mixed effects model with CA as a continuous predictor shows a significant interaction effect between CA and expression. Higher levels of CA were associated with higher expression-discrimination responses for happy faces and lower expression-discrimination responses for angry faces. CA, childhood adversity

Multi-input paradigm

EEG response

We observed robust frequency-tagged responses in MO for both the expressive and neutral faces in the angry and happy condition (Fig. 6). In the angry condition, there was a significant main effect of CA group on baseline-corrected amplitudes (F(1, 111) = 12.41, p < 0.001), with HC displaying higher neural responses to the flickering faces than adolescents exposed to adversity (t(111)HC-CA = 0.37, p < 0.001, d = 0.46) (Fig. 3B). There was no main effect of expression, nor was there an interaction effect between CA and expression (all p > 0.35). Considering CA as a continuous predictor, the main effect of CA was not significant (F(1, 106) = 3.84, p = 0.0527). There was no main effect of expression, nor any interaction effect between CA and expression (all p > 0.42). The exploratory model disentangling childhood threat versus deprivation similarly did not show any main or interaction effects (all p > 0.13).Fig. 6 SNR spectra visualizing the MO (channels Iz, Oz, O1, O2, PO7 and PO8) responses to angry and neutral (upper panels) and happy and neutral (lower panels) faces. Full circles denote responses to expressive faces, while open circles denote responses to the neutral faces. HC, healthy control; CA, childhood adversity

In the happy condition, there was no significant main effect of expression (p = 0.07), nor was there a significant main or interaction effect of CA group (all p > 0.44). Similarly, neither the continuous adversity variable nor the separate threat or deprivation dimensions showed any main or interaction effects (all p > 0.29).

Eye-tracking

In the angry condition, there was a significant main effect of CA (F(1, 33) = 6.18, p = 0.0181) and expression (F(1, 257) = 12.44, p < 0.001), as well as a significant interaction effect between CA and expression (F(1, 257) = 23.40, p < 0.001) on looking times: adolescents exposed to CA looked significantly more to angry faces compared to neutral faces, whereas the reverse was true for HC. Moreover, HC looked significantly more to neutral faces compared to those exposed to adversity (Fig. 3C). Similarly, considering CA as a continuous predictor, the interaction effect between CA and expression was significant (F(1, 243) =  − 3.26, p = 0.0013). The exploratory model disentangling childhood threat and deprivation did not show any main or interaction effects (all p > 0.32).

In the happy condition, there was a significant main effect of CA group on looking time (F(1, 33) = 7.01, p = 0.0123): adolescents exposed to adversity showed significantly lower looking times compared to HC (Fig. 3C). Moreover, considering CA as a continuous predictor, there was a significant interaction effect between CA and expression (F(1, 243) = 11.53, p < 0.001): higher exposure to CA was associated with decreased looking times towards happy and, to a lesser extent, neutral faces. Similarly, there was a significant interaction effect between childhood threat and expression (F(1, 249) = 7.78, p = 0.0057) and childhood deprivation and expression (F(1, 249) = 5.75, p = 0.0172): while childhood threat was associated with lower looking times to happy faces, childhood deprivation was associated with reduced looking times to neutral faces.

Over both groups, individual differences in sensitivity to expressive faces (i.e. the response to expressive faces minus the response to neutral faces divided by the sum of both responses) show low correlations when measured by EEG and eye-tracking (r = 0.01, p = 0.96 and r =  − 0.03, p = 0.80 for the angry and happy condition, respectively).

Discussion

In this paper, we aimed to quantify how CA influences neural facial expression processing during adolescence using two robust and implicit FPVS EEG paradigms. Based on the existing literature [15, 21], we hypothesized that CA would be associated with increased neural discrimination of angry faces presented in between neutral faces in the oddball paradigm, as well as with heightened neural sensitivity towards angry faces during the multi-input paradigm. Our results however did not support these hypotheses, but instead provided support for reduced threat-safety discrimination during facial expression processing [51].

Although adolescents with and without CA exposure show similar neural synchronization to the base stimulation during the oddball paradigm, two intriguing differences did appear. First, during the oddball paradigm, CA was associated with lower expression-discrimination responses for angry faces in between neutral faces and vice versa for happy faces. Thus, adolescents exposed to adversity likely perceive angry faces as less distinct from neutral faces than unexposed controls, while they likely perceive happy faces as more distinct from neutral faces. This finding is in line with the reported hostile interpretation of ambiguous social information, including neutral faces, in individuals exposed to adversity [17, 18, 51]. Second, during the multi-input paradigm, we found a main effect of CA only when angry and neutral faces were presented simultaneously: adolescents exposed to adversity displayed significantly lower baseline-subtracted amplitudes to both angry and neutral faces compared to healthy controls. Simultaneously recorded eye-tracking data in a subsample of 37 participants however showed that adolescents exposed to adversity spent longer looking at the angry compared to the neutral faces, whereas the reverse was true for healthy controls. CA is thus associated with altered neural sensitivity to threatening stimuli, and less distinction is made between threatening (i.e. angry) and safe (i.e. neutral) faces within a threatening context, although explicitly, more attention may be given to angry faces. There were no differences in neural responses between CA and HC while viewing happy and neutral faces presented side-by-side. Nonetheless, CA was associated with lower looking times towards happy and neutral faces overall. Interestingly, this decreased threat-safety discrimination is strikingly similar to those obtained in a parallel study in late adolescents and young adults between 16 and 25 years old with concurrent symptoms of anxiety, depression and psychosis, thus underscoring the robustness of these effects beyond specific populations and the presence of symptoms [52]. Moreover, a decreased threat-safety discrimination in children, adolescents and adults has been reported across experimental paradigms. Previous research has shown that CA is associated with reduced behavioural and psychophysiological discrimination responses between threat and safety cues during fear conditioning [53], a finding that is robust across fear conditioning studies [54]. In the same adolescent sample as reported here, CA was indeed associated with significantly reduced threat-safety discrimination, apparent by both a blunted behavioural response to the threat cue, as well as an elevated response to the safety cue, during fear conditioning [55]. As such, the convergence across paradigms and modalities underscores the robustness of the reduced threat-safety discrimination in those exposed to adversity.

Exploratory analyses, in which we separately evaluated the effect of childhood threat and childhood deprivation, revealed specific effects of both adversity dimensions upon looking times in the happy condition: while childhood threat was associated with lower looking times to happy faces, childhood deprivation was associated with reduced looking times to neutral faces. However, neural alterations in expression-discrimination responses in the oddball paradigm and in sensitivity in the multi-input paradigm were not specific to either childhood threat or deprivation. This is in contrast with the dimensional model of childhood adversity, in which McLaughlin and colleagues [28, 29] hypothesize that childhood threat is associated with a hostile interpretation of ambiguous (including neutral) facial expressions. Nonetheless, in the current study, childhood deprivation consisted only of physical and emotional neglect, whereas previous studies have also considered institutionalization and severe poverty. Moreover, participants reported low levels of deprivation compared to previous studies [30]. As a consequence, the current study may not have had sufficient power to uncover any specific effect of childhood deprivation on facial expression processing [56]. Future studies should consider additional types of childhood deprivation, including institutionalization, parental absence and severe poverty [54], and aim to disentangle the effects of emotional and physical deprivation.

Both a categorical and continuous measure of CA were included in the current study, as previous research has shown a dose–response relationship between CA and psychopathology [57]. As a consequence, we expected the continuous measure of CA to be more sensitive to subtle individual differences compared to the categorical measure. While the interaction between CA and expression in the oddball paradigm was significant for the continuous, but not the categorical, measure of CA, the reverse was true for the main effect of CA in the multi-input paradigm. Moreover, the eye-tracking results in the angry condition reveal a significant main effect for categorical CA and a significant CA × expression interaction effect for both categorical and continuously measured CA. In the happy condition, there was again a significant main effect of categorical CA, while the interaction between CA and expression was significant for continuously measured CA. Our results thus do not support the hypothesis that continuously measured CA necessarily captures more subtle individual differences compared to a categorical distinction. The differences between both measures may be due to differences in the task demands: the oddball paradigm was designed to capture subtle differences in facial expression discrimination, while the multi-input paradigm was applied to capture the (neural) saliency of faces. Furthermore, the continuous measure of CA, although providing valuable information, may have reduced our power to uncover strong effects.

The current study consisted of a general population sample with on average mild to moderate exposure to CA, yet we did find a robust impact of CA on implicitly measured facial expression processing. CA exposure, even mild levels, thus exerts subtle yet potentially impactful alterations in threat-related information processing, which may in turn increase the risk for lifetime psychopathology. Difficulties differentiating safe from threatening social cues may result in enduring feelings of threat, lack of safety, social isolation and problems with maintaining relationships [4, 58–60]. Indeed, a sensitivity analysis including symptoms of anxiety, depression, psychosis and post-traumatic stress disorder underscored that the reduced threat-safety discrimination reported here can be ascribed to CA exposure rather than psychopathology (Additional file 1: Tables S4–S6). Future research should evaluate interventions that target this reduced threat-safety discrimination and hostile interpretation bias in daily life [10]. Longitudinal research comparing children with varying exposure to childhood deprivation (i.e. children in institutional rearing, children in foster care following institutionalization and family-reared children) reported intermediary ERPs during facial expression processing in children that were placed in foster care following institutionalization compared to children still in institutionalized rearing and family-reared children [30, 31]. Face processing is characterized by continued plasticity throughout adolescence and, to a lesser extent, adulthood [1], and as such, normative daily life experiences may aid in reducing the hostile interpretation bias following CA. Moreover, to further elucidate the impact of facial expression processing in the association between CA and psychopathology, future research should aim to causally examine these relationships.

The results reported above should be interpreted in light of the current study’s limitations. First, we only included angry and happy faces, whereas previous studies also reported effects of CA on processing of sad and fearful faces [13, 21]. Including additional negative facial expressions within an implicit facial expression processing paradigm may further elucidate the specificity versus generalizability of the reported findings. Moreover, our results suggest that neutral faces are perceived as more threatening in those exposed to CA, thereby introducing a potential bias in the base stimulation during the oddball paradigm. Future research should carefully consider which stimuli to include as a baseline, depending on the specific research questions. Second, exposure to childhood threat and deprivation was based solely upon self-report. Retrospective reports of childhood adversity may be susceptible to recall bias [11, 61] and typically show low correspondence to objective reports. Nonetheless, recent research underscored the importance of subjective rather than objective reports of CA for the development of psychopathology [62, 63]. Clinically, it is thus most sensible to investigate childhood adversity subjectively. Third, we did not take age of exposure to CA into account in the current study. Previous research has highlighted the importance of sensitive periods, i.e. certain time frames during which adverse events is especially potent in impacting neural development. Nonetheless, CA is associated with psychopathology regardless of age of exposure, thereby underscoring an ongoing impact of adversity throughout childhood [64]. Fourth, adolescents in the current study experienced mild to moderate adversity, which may have confined strong empirical effects. Even at these mild to moderate levels of adversity however, subtle alterations in facial expression processing were present. As CA is both common and a major transdiagnostic risk factor for psychopathology across the lifespan [65], studying the impact of CA within community samples has important merit. In the future, longitudinal research is required to evaluate the impact of facial expression processing alterations following CA on mental health outcomes.

Conclusions

Using two implicit FPVS EEG paradigms, the current study was able to uncover subtle differences in facial expression processing in adolescents exposed to CA. In an oddball paradigm, CA was associated with both reduced expression-discrimination responses to angry faces presented in between neutral faces and enhanced expression-discrimination responses to happy faces presented in between neutral faces, indicative of a hostile interpretation of neutral faces. Moreover, results from a multi-input paradigm suggest that, in a threatening context (i.e. while viewing angry and neutral faces presented side-by-side), adolescents exposed to adversity display lower neural sensitivity to both angry and neutral faces. CA is thus associated with altered facial expression processing, even at mild to moderate levels of exposure, which may in turn increase risk for social difficulties and the development of psychopathology.

Supplementary Information

Additional file 1: Table S1. Screener questions to assess exposure to childhood adversity. Table S2. Pearson’s pairwise correlation coefficients. Table S3. Comparison between included and excluded participants. Table S4. Oddball results including psychopathology. Table S5. Multi-input results including psychopathology. Table S6. Eye-tracking results including psychopathology.

Additional file 2: Details on statistical analysis, including model selection and model fit.

Abbreviations

CA Childhood adversity

HC Healthy control

EEG Electroencephalography

fMRI Functional magnetic resonance imaging

Acknowledgements

The authors would like to thank all professionals working with youth for their help in distributing the recruitment material for the EMBRACE study, as well as Maarten Jackers, Nele Mattelaer, Emma Lambeets and Hanne Creten for their help with data collection.

Authors’ contributions

CS: Conceptualization, Methodology, Software, Investigation, Data curation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Project administration; SVDD: Conceptualization, Methodology, Software, Formal analysis, Writing – review & editing; AL: Conceptualization, Investigation, Writing – review & editing, Project administration; SV: Conceptualization, Methodology, Software, Writing – review & editing; ZQ: Formal analysis, Writing – review & editing; RVW: Conceptualization, Methodology, Writing – review & editing, Supervision, Funding acquisition; BB: Conceptualization, Methodology, Writing – review & editing, Supervision, Funding acquisition. All authors reviewed and approved of the manuscript.

Funding

This work was supported by a Research Foundation Flanders (FWO) grant awarded to RVW and BB (FWO G063518N) supporting CS and a KU Leuven Small Research Equipment grant (KA/20/080). SVDD is supported by an FWO Postdoctoral Fellowship (12C9723N). AL was awarded an FWO PhD Fellowship (FWO 1104219N). ZQ was awarded a China Scholarship Council (CSC) with grant number 202009110102. RVW is supported by a Research Foundation Flanders Senior Clinical Fellowship (FWO 1803616N) and by the Funds Julie Renson, Queen Fabiola and King Baudoin Foundation (Chair for Transition Psychiatry). BB is supported by an Excellence of Science (EOS) grant with number G0E8718N/HUMVISCAT.

Availability of data and materials

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

Declarations

Ethics approval and consent to participate

The study received ethical approval from the UZ/KU Leuven Medical Ethics Committee (S62124) and was performed according to the principles of the Declaration of Helsinki. Written informed assent was obtained from all participants in this study, as well as written informed consent from their parent or legal guardian.

Consent for publication

All participants within this study, as well as their parents or legal guardians, provided written consent for the publication of the collected data.

Competing interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ruud van Winkel and Bart Boets shared last authors.
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