Functional Brain Networks Underlying Cyberbullying and the Moderating Role of Harm Aversion

Functional Brain Networks Underlying Cyberbullying and the Moderating Role of Harm Aversion | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 December 2025 V1 Latest version Share on Functional Brain Networks Underlying Cyberbullying and the Moderating Role of Harm Aversion Authors : Yu-Shan Cen 0000-0001-8103-6475 and Ling-Xiang Xia 0000-0003-4024-5312 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176639170.05929809/v1 146 views 82 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Understanding the brain mechanisms underlying cyberbullying is crucial, yet the neural correlates remain unclear. In this study, we recruited 234 Chinese college students and performed functional network connectivity analysis to address this gap. The results revealed that connectivity between the language network (LN) and sensorimotor network, as well as between the LN and dorsal attention network, is positively associated with cyberbullying. In contrast, LN–visual network (VN) connectivity is negatively linked to cyberbullying. Moreover, harm aversion moderates the relationship between LN–VN connectivity and cyberbullying. This suggests that harm aversion functions to discount the brain’s facilitating effect, emphasizing the critical roles of the LN and harm-related moral factors such as harm aversion. This study also provides empirical evidence supporting the development of neuropsychological interventions to prevent cyberbullying. 1 Introduction Cyberbullying refers to intentional and repeated attacks, such as threats, flaming, harassment, and denigration, either by an individual or a group, on vulnerable victims via information and communication technologies (Guo & Xia, 2023; Kowalski et al., 2014; Smith et al., 2008). It leads to deleterious outcomes for both victims and perpetrators (Englander et al., 2017; John et al., 2018; Sun et al., 2024). Its prevalence has significantly increased, ranging from 4.8–18% and 27–73.5%, as reported in various studies (Gohal et al., 2023; Kee et al., 2024; X. M. Li et al., 2024; Li & Xia, 2024; Modecki et al., 2014). Understanding the formation and development mechanisms of cyberbullying is necessary to develop targeted prevention and intervention strategies. The brain is critical to understanding the mechanisms of negative social behavior (Klaus et al., 2024; Nelson & Trainor, 2007; Wong et al., 2014). Certain brain areas, such as the sensorimotor network (SMN), inferior frontal gyrus (IFG), medial prefrontal cortex (mPFC), and insula, have been linked to traditional bullying (Perino et al., 2019; Wen et al., 2023); however, the brain correlates of cyberbullying remain unknown. Cyberbullying (Smith et al., 2008; Waasdorp & Bradshaw, 2015) differs from traditional bullying in its form, environment, influencing factors, and perpetrators (Casas et al., 2013; Ding et al., 2020; Francisco et al., 2015), suggesting different mechanisms underlying these two bullying types. This study seeks to explore the brain mechanisms underlying cyberbullying. The brain is a complex structural network constituting highly interconnected regions (Bullmore & Sporns, 2009; van den Heuvel & Hulshoff Pol, 2010), and understanding the interactions between these regions can aid in the understanding of cognition, emotion, and behavior (Pessoa, 2014). Resting-state brain network analysis allows the identification of intrinsic brain networks (Raichle, 2015) with high reproducibility and reliability (Canario et al., 2021; Van Dijk et al., 2010). Given that bullying (Wen et al., 2023; Wen et al., 2022) and aggression (Dailey et al., 2018; Weathersby et al., 2019; Werhahn et al., 2021) recruit large-scale intrinsic networks, this study aims to investigate the functional brain network underlying cyberbullying. 1.1 Potential Brain Networks Underlying Cyberbullying Research on the neural correlates of cyberbullying is limited; however, studying its psychological components and related behaviors can offer valuable insights into potential brain networks or regions critical to cyberbullying. Specifically, verbal aggression (Mason, 2008; Shaikh et al., 2020) and the use of social media and the Internet (Belsey, 2005; Francisco et al., 2015; Juvonen & Gross, 2008) are the core components of cyberbullying, which typically involves language-based interactions conducted via social media or the Internet. Moreover, as a form of negative behavior, cyberbullying is conceptually linked to bullying (Smith et al., 2008; Waasdorp & Bradshaw, 2015) and conduct disorder (Schultze-Krumbholz & Scheithauer, 2014). Structural imaging studies have indicated that verbal aggression (Poling et al., 2019; Progovac & Benitez-Burraco, 2019), particularly in individuals with schizophrenia spectrum disorders, is positively correlated with gray matter volume in the IFG (Schoretsanitis et al., 2019). Additionally, a task-based functional magnetic resonance imaging (fMRI) study by Klaas et al. (2015) revealed that aggressive vocal expressions are associated with functional connectivity between the bilateral motor cortex and IFG, as well as between the motor cortex and superior temporal gyrus (STG). These findings suggest that brain regions representing verbal aggression may include regions within the language network (LN) (such as the IFG and STG) and the SMN (such as the bilateral motor cortex), and verbal aggression may involve functional connectivity between these two networks. Several studies related to the resting-state brain network have identified key brain networks associated with social media and Internet use (Áfra et al., 2024; Meri et al., 2023). Specifically, prolonged screen time has been linked to reduced connectivity between the dorsal attention network (DAN) and salience network (SN) (Meri et al., 2023). Internet-related behavioral addiction is associated with abnormal activity within the LN, visual network (VN), and fronto-parietal network (FPN) (Áfra et al., 2024). Individuals with longer screen time exhibit reduced connectivity within the default mode network (DMN), executive control network (ECN), and DAN, as well as reduced connectivity between the SMN and DMN or DAN, and increased connectivity within the SN and between the VN and basal ganglia (Xue et al., 2024). Furthermore, the usage of social networking sites is linked to stronger connectivity within the DAN and increased connectivity between the DMN and VN (Wadsley & Ihssen, 2023). Therefore, social media and Internet use may involve activity and connectivity within or between the LN, VN, DAN, DMN, ECN, SMN, FPN, and SN. Resting-state fMRI studies have revealed that traditional bullying is associated with disruptions in brain network connectivity, particularly between the attention network and SMN (Wen et al., 2023). Compared with non-perpetrators, bullying perpetrators exhibit significantly reduced functional connectivity within the DMN and a lower anti-correlation between the DMN and DAN (Wen et al., 2022). Additionally, structural imaging results have revealed that bullying perpetrators exhibit reduced bilateral cortical volume in the superior frontal gyrus (SFG) and precentral gyrus but greater cortical volume in the lateral occipitotemporal cortex (LOTC) and posterior cingulate cortex (PCC) compared to preadolescents in both suicide attempters and healthy control groups (Wen et al., 2022). Furthermore, Swartz et al. (2019), using an emotional face matching task in a task-based fMRI study, discovered that higher amygdala activity in response to angry faces and lower amygdala activity in response to fearful faces were associated with more self-reported bullying. In contrast, heightened rostral anterior cingulate cortex (ACC) activity in response to fearful faces was linked to less bullying. Another study revealed that bullying is associated with greater activation of the IFG, ventral striatum, amygdala, mPFC, and insula in social exclusion compared with social inclusion (Perino et al., 2019). These findings suggest that the DAN, SMN (including the precentral gyrus), and DMN (including the SFG, mPFC, and PCC) may be pivotal for understanding bullying. Additionally, the brain regions that represent bullying include the SN (the ACC, amygdala, and insula) and LN (the IFG and LOTC). Finally, some resting-state fMRI studies have revealed that conduct disorders (such as general behavioral problems, externalizing problems, and social dysfunction) are associated with reduced connectivity within the SMN (Lu et al., 2015), VN (Lu et al., 2015; Wee et al., 2018), and DMN (Lu et al., 2015). Moreover, behavioral problems are linked to internetwork connectivity involving the DAN (such as DAN–ventral attention network (VAN), DAN–cerebellar network (CN), DAN–frontal network; Wee et al., 2018), SMN (such as SMN–VAN, SMN–VN, LN–SMN; Wee et al., 2018; H. Zhang et al., 2021), and LN (such as LN–VN, LN–SMN; H. Zhang et al., 2021). A structural imaging study linked behavioral problems to a smaller amygdala, thinner cortex in the inferior parietal lobule (IPL), supramarginal, and postcentral gyrus, and reduced gyrification in a large cluster encompassing the right precentral, postcentral, frontal, occipital, and parietal regions (Thijssen et al., 2015). Therefore, conduct disorders may involve brain networks such as the SMN (including the precentral and postcentral gyrus), VN (including the occipital cortex), DMN, DAN (including the IPL), and LN. In summary, existing literature suggests that LN and SMN are common brain networks underlying verbal aggression, social media use, traditional bullying, and conduct disorders. Additionally, the SN, DAN, and DMN play significant roles in social media use, traditional bullying, and conduct disorders. Consequently, the connectivity within or between these brain networks can aid in understanding the neural mechanisms of cyberbullying. 1.2 Moderating Role of Harm Aversion Previous studies have indicated that the effects of neural bases on certain behavioral and psychological variables (e.g., aggression) may be moderated by other factors (Chat et al., 2021; Wang et al., 2023; Zhou et al., 2023). For example, self-control moderates the relationship between the orbitofrontal cortex and aggression (Wang et al., 2023), while critical thinking moderates the relationship between gray matter in the PCC and positive risk-taking (Liu et al., 2023). To further uncover the neural mechanisms of cyberbullying, we sought to examine the potential moderators of the effect of neural bases on cyberbullying. We propose that harm aversion may serve as this moderator, buffering the relationship between functional brain network indicators and cyberbullying. Harm aversion refers to the emotional response or tendency to feel distress when witnessing, considering, or engaging in harmful actions or outcomes (Crockett et al., 2010; Cushman, 2013; Hou et al., 2024). Our hypothesis rests on the view that harm aversion does not directly suppress aggression. Rather, its inhibitory effect on aggression stems from a discounting function, which reduces the influence of motivators that drive aggression. First, some indirect evidence suggests that harm aversion may not directly inhibit aggression. For example, although harm aversion is believed to decrease aggression (Crockett et al., 2010; Crockett et al., 2015; Martinez et al., 2024; Perera et al., 2016), strong empirical evidence is lacking. Findings on the relationship between harm aversion and aggression are mixed and sometimes contradictory. Reported correlations between harm aversion and aggression are generally weak (Cen et al., 2025; Hou et al., 2024; Patil, 2015). For example, harm action/outcome aversion is not related to reactive aggression (Hou et al., 2024); harm action aversion is not associated with verbal aggression or aggressive behavior (Cen et al., 2025); and harm outcome aversion cannot predict decisions in impersonal and personal moral dilemmas involving harmful behaviors (Patil, 2015). Some research even suggests a positive relationship between harm aversion and aggression (Hou et al., 2024; Sarkar & Wrangham, 2023). Importantly, harm aversion has been found to buffer the effect of anger rumination on aggression (Hou et al., 2024). Second, based on the concept of moral cost (Abbink & Herrmann, 2011; Attanasi et al., 2019; Crockett et al., 2014; Eriksson et al., 2017; Keum & Meier, 2024; Levitt & List, 2007; Rothenhausler et al., 2015; Thielmann & Hilbig, 2019), harm aversion can be regarded as a form of moral cost (Qu et al., 2022). Costs can devalue or discount the subjective appeal of certain behaviors or choices (Escobar et al., 2023; Mitchell, 2003; Mitchell, 2017). Accordingly, harm aversion may reduce the subjective value of aggressive motivators and choices. In other words, harm aversion could discount (or buffer) the drive (or effect) of motivators on aggression. Given that neural mechanisms can act as motivators for aggression (Hashikawa et al., 2017; Nelson & Trainor, 2007; Yang et al., 2023), harm aversion, as a moral cost, may moderate the relationship between such neural correlates and cyberbullying. Specifically, higher harm aversion would more strongly discount the effect of neural mechanisms on cyberbullying compared with lower harm aversion. 1.3 The Present Study Understanding the brain mechanisms underlying cyberbullying is critical for designing effective interventions. Although previous studies have explored the neural correlates of traditional bullying (Longobardi et al., 2022; McLoughlin et al., 2020) and highlighted the differences between traditional bullying and cyberbullying (Casas et al., 2013; Ding et al., 2020; Francisco et al., 2015), the brain mechanisms underlying cyberbullying remain unexplored. As functional network connectivity (FNC) is a better indicator of the brain correlates of negative behavior (Chen et al., 2021; Dugré & Potvin, 2021; Werhahn et al., 2021), this study utilizes FNC to explore the brain networks involved in cyberbullying. Furthermore, the potentially moderating role of harm aversion is tested to better elucidate the brain mechanisms underlying cyberbullying. The hypotheses of this study are as follows: (1) cyberbullying may be associated with connectivity within or between the SMN, LN, SN, DAN, and DMN, and (2) harm aversion may inhibit the effect of these neural correlates on cyberbullying. 2 Method 2.1 Participants In total, 260 college students aged between 17.33 and 25.83 years (mean age = 20.111 ± 1.706 years; 149 females) were recruited via online advertisements from our University in China. Twenty-six participants were excluded from further analyses owing to excessive head movement (> 2 mm in translation or > 2.0° in rotation) or images corrupted by artifacts, resulting in a final sample of 234 participants (129 females; mean age = 20.122 ± 1.722). All the participants were right-handed, had normal or corrected-to-normal vision, and reported no history of psychiatric or neurological illnesses or contraindications for MRI. All participants provided written informed consent before the experiment and received monetary compensation for their participation. The study protocol was approved by the Institutional Review Board of the Faculty of Psychology at our University and was conducted in accordance with the Declaration of Helsinki. 2.2 Measures 2.2.1 Cyberbullying Cyberbullying behavior was assessed using the Cyberbullying Inventory for College Students (Francisco et al., 2015), which measures the frequency of cyberbullying behaviors, including sending insulting, slanderous, or harassing information through platforms such as email, online chat rooms, QQ, SMS, WeChat, Weibo, blogs, and forums, within the past year. The inventory comprises nine items rated on a 5-point Likert scale ranging from 1 (never) to 5 (many times a week). The average score for all items (for example, “spreading rumors about one’s life”) in the inventory was used. Higher scores indicate a higher frequency of cyberbullying. Previous studies have demonstrated that this inventory has high reliability and validity in Chinese samples (Guo & Xia, 2023; Tang et al., 2022). In this study, the inventory exhibited high internal consistency, α = 0.858. 2.2.2 Harm action/outcome aversion Harm aversion was assessed using the Harm Action /Outcome Aversion Questionnaire (Miller et al., 2014) , which consists of 23 items. Participants rated their level of unease (uncomfortable or upset) for each situation on a 7-point Likert scale ranging from “not at all” (1) to “very strong” (7). This scale includes two relatively independent subscales. The harm action subscale contains nine items, which describe harm actions without harm outcomes (for example, “stab a fellow actor in the neck during a play using a stage knife with a retractable blade”). The harm outcome subscale includes 14 items, which describe harm outcomes without harm behaviors (for example, “see someone step barefoot on broken shards of glass”). Higher values indicate a stronger aversion to harmful behavior toward others or harmful outcomes toward others. Its Chinese version has demonstrated robust reliability and validity (Hou et al., 2024). Cronbach’s α for the harm action aversion subscale and harm outcome aversion subscale were 0.813 and 0.922, respectively. 2.3 Image Acquisition and Preprocessing 2.3.1 Data acquisition Scans were acquired using a 3 Tesla Trio scanner (Siemens Medical, Erlangen, Germany) at the Brain Imaging Center of our University. Structural MRI data were collected for co-registration of the functional data using a T1-weighted magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) sequence: repetition time (TR) = 2,530 ms, echo time (TE) = 2.98 ms, slices = 192, flip angle = 7°, resolution matrix = 256 × 256, field of view (FOV) = 256 × 256 mm², voxel size = 0.5 × 0.5 × 1 mm 3 , thickness = 1.0 mm with no gap). Functional T2*-weighted echo-planar images (EPIs) were acquired during an 8-min scan using resting-state fMRI in a sagittal orientation (TR = 2,000 ms, TE = 30 ms, slices = 62, slice thickness = 2 mm, resolution matrix = 112 × 112, flip angle = 90°, FOV = 224 × 224 mm 2 , voxel size = 2 × 2 × 2 mm³, 240 volumes). During scanning, the participants were instructed to close their eyes, stay awake, and relax without engaging in specific thoughts. Foam cushions and earplugs were used to minimize head movements and reduce scanner noise. 2.3.2 Data preprocessing The data were processed using CONN v22.a (https://www.nitrc.org/projects/conn; Nieto-Castanon & Whitfield-Gabrieli, 2022), which employs SPM12 (http://www.fil.ion.ucl.ac.uk/spm; Friston et al., 1994). Preprocessing followed the default pipeline: raw functional images were realigned (motion-corrected), slice-time-corrected, and co-registered to the participants’ anatomical images. Following co-registration, structural images were segmented and normalized to the Montreal Neurological Institute (MNI) template at a resolution of 1 x 1 x 1 mm. Functional images were spatially realigned after co-registration and normalized to the MNI template with a 2 x 2 x 2 mm resolution using direct spatial segmentation and normalization. Functional images were smoothed to a 6 mm full-width at half-maximum Gaussian kernel. For functional outlier detection, we used an artifact detection tool to identify outlier scans set at the 97th percentile (https://www.nitrc.org/projects/artifact_detect). Outlier scans were scrubbed with a threshold for global signal deviation above z = 5 and head motion above 0.90 mm. Preprocessing was followed by denoising steps, including removing physiological noise using the aCompCor method, which enhances the quality of the functional connectivity results (Behzadi et al., 2007). Temporal band-pass filtering was performed to isolate signals within the 0.008–0.09 Hz range to improve the signal-to-noise ratio (Biswal et al., 1995; He et al., 2014). 2.4 Statistical Analysis 2.4.1 Region of interest defined The regions of interest (ROIs) were obtained from the CONN toolbox as defined by independent component analysis of the Human Connectome Project (HCP–ICA) dataset (n = 497) (https://www.nitrc.org/projects/conn/; Whitfield-Gabrieli & Nieto-Castanon, 2012). The HCP–ICA atlas comprises 32 ROIs grouped into eight networks: the DMN, four ROIs; SMN, three ROIs; VN, four ROIs; SN, seven ROIs; DAN, four ROIs; FPN, four ROIs; LN, four ROIs; and CN, two ROIs. Figure S1 and Table S1 present the seeds from each resting-state network and the peak MNI coordinates. 2.4.2 Functional network connectivity (FNC) analyses FNC analyses were performed to assess the correlation between cyberbullying and network connectivity using CONN v22.a. For each participant, the average BOLD time series for all voxels in each seed ROI was extracted and Fisher’s z-transformed. A symmetrical 32 × 32 correlation matrix (ROI-to-ROI connectivity matrix) was constructed using bivariate correlation in the ROI-based functional connectivity mode during the first-level analysis. At the second level, multiple regression analyses were performed to correlate the connectivity coefficients with the cyberbullying scores. Statistical significance was determined using a voxel-wise threshold of p < 0.05 (uncorrected) and a cluster-level threshold of p < 0.05, corrected for false discovery rate (FDR) (two-sided) (Whitfield-Gabrieli & Nieto-Castanon, 2012). Age and sex were included as covariates (Buchwitz et al., 2023; Huggins et al., 2018). Mean connectivity strength values (z-scores) within and between the eight networks were correlated with cyberbullying at the group level. Figure 1 presents the within- and between-network correlation matrices. Figure 1. Within-network and between-network correlation matrices. 2.4.3 Prediction analyses This study utilized the LIBSVM toolbox 3.32 (available for download at http://www.csie.ntu.edu.tw/~cjlin/libsvm; Chang & Lin, 2011) to implement the linear support vector regression (SVR) algorithm to examine the relationship between cyberbullying and resting-state networks. SVR with ten-fold cross-validation provides insights at the individual level rather than at the group level (Xiao et al., 2023; Yarkoni & Westfall, 2017) and produces more stable estimates of predictive accuracy (Varoquaux et al., 2017). The dataset was randomly divided into ten subsets, with nine folds used for training and the tenth fold for testing. FNCs, represented by the mean network connectivity values, served as independent variables, whereas cyberbullying scores served as dependent variables in the SVR algorithm. The procedure was repeated 10 times, resulting in the computation of r (predicted,observed) based on the predicted and actual values, revealing the predictive capabilities of FNCs for cyberbullying. Subsequently, a permutation test was conducted to determine whether the correlation between FNCs and cyberbullying exceeded chance levels. This involved randomly shuffling the labels between the observed cyberbullying scores and FNCs during each iteration of the prediction procedure. Finally, this process generated 1,000 correlations between predicted and actual cyberbullying scores. To calculate the p -value for the correlation, the number of permutations that yielded a correlation higher than the actual correlation was divided by the total number of permutations (1,000). 2.4.4 Moderation analyses We utilized a series of simple moderation models to examine whether harm action/outcome aversion moderated the relationship between FNCs and cyberbullying using the PROCESS macro (Model 1) in SPSS (Hayes, 2013). Age and sex were included as covariates. Cyberbullying was treated as the outcome variable, with within-network and between-network FNC values as predictor variables and harm action aversion and harm outcome aversion as moderators in separate moderation models. Before the analysis, all variables were standardized except for sex, which was encoded as a dummy variable (female = 0, male = 1). The significance of the moderating effect was assessed using the bootstrapping method with 5,000 iterations. A moderating effect was deemed statistically significant if the 95% confidence interval (CI) excluded zero and p < 0.05. 2.4.5 Exploratory analyses Associations between FNCs and cyberbullying in males: Additional FNC analysis was conducted for males using the whole brain, including the eight previously defined networks ( N = 105, mean age = 20.101 years). Age was included as a covariate of no interest, and statistical thresholds were consistent with those in the primary FNC analysis. Associations between FNCs and cyberbullying in females: The same analysis procedure, covariates, and statistical thresholds were applied to females ( N = 129, mean age = 20.140 years). 2.4.6 Sensitivity analyses Sensitivity analyses were conducted using an alternative atlas of resting-state networks to assess the robustness of the networks. The ROIs used in the present study encompassed the entire brain network. Thus, 236 nodes were selected from Power’s 264 MNI coordinates (Power et al., 2011), excluding 28 nodes with unclear attributions in the brain network. The atlas defined 12 networks: SMN (35 ROIs), Cingulo-opercular network (CON,14 ROIs), auditory network (AN, 13 ROIs), DMN (58 ROIs), memory retrieval network (MRN, 5 ROIs), VN (31 ROIs), FPN (25 ROIs), SN (18 ROIs), subcortical network (SCN, 5 ROIs), ventral attention network (VAN, 9 ROIs), DAN (11 ROIs), and CN (4 ROIs). The analyses conducted to examine FNC in relation to cyberbullying scores followed the procedures outlined in the FNC analysis section. 3 Results 3.1 Participant Characteristics Table 1 presents the descriptive statistics and correlations between age and behavioral variables. No significant sex differences were observed in cyberbullying ( t (232) = -0.396, p = 0.692) and harm action aversion ( t (232) = 1.698, p = 0.091). A significant difference in harm outcome aversion was observed between males and females ( t (232) = 2.269, p = 0.024), with females reporting higher levels of harm outcome aversion ( M = 5.221, SD = 0.911) than males ( M = 4.934, SD = 1.021). Cyberbullying scores decreased with age ( r = -0.135, p = 0.039), and positive correlations were identified between harm action aversion and harm outcome aversion ( r = 0.527, p < 0.001). However, harm action aversion and harm outcome aversion were not significantly correlated with age or cyberbullying. Table 1. Descriptive statistics and correlations for variables ( N = 234) Variables M SD 1 2 3 4 1. Age 20.122 1.722 - 2. Cyberbully 1.232 0.389 -0.135 * - 3. Harm action aversion 5.242 0.991 0.054 -0.096 - 4. Harm outcome aversion 5.092 0.970 0.033 -0.015 0.527 *** - Notes : N = number; M = mean; SD = standard deviation. * p < 0.05; ** p < 0.01; *** p < 0.001. 3.2 FNC Analyses After controlling for sex and age, we conducted whole-brain ROI-to-ROI functional connectivity analyses to investigate neural network connectivity associated with cyberbullying. Correlations between cyberbullying and within-network functional connectivity of the eight networks did not survive FDR correction. Cyberbullying was positively associated with functional connectivity of the LN–SMN and LN–DAN but negatively correlated with the LN–VN (detailed statistics are reported in Table 2, Figure 2, and Figure S2). Specifically, higher cyberbullying scores were correlated with stronger connectivity between LN (ROI: left inferior frontal gyrus, IFG_L; and right inferior frontal gyrus, IFG_R) and SMN (ROI: superior sensorimotor cortex, superior), as well as between LN (ROI: IFG_R) and DAN (ROI: right intraparietal sulcus, IPS_R; and left frontal eye field, FEF_L). Moreover, higher cyberbullying scores were associated with lower connectivity between LN (ROI: IFG_L and IFG_R) and VN (ROI: occipital visual cortex and occipital cortex). Table 2. Functional connectivity among the LN, DAN, and VN Functional network connectivity t -values p- FDR Connectivity values Between LN and SMN LN (IFG_L)–SMN (Superior) 3.97 <0.001 -0.120 (±0.180) LN (IFG_R)–SMN (Superior) 2.83 0.015 -0.140 (±0.177) Between LN and DAN LN (IFG_R)–DAN (IPS_R) 2.64 0.018 -0.086 (±0.217) LN (IFG_R)–DAN (FEF_L) 2.34 0.024 -0.065 (±0.150) Between LN and VN LN (IFG_L)–VN (Occipital) –2.41 0.024 0.034 (±0.161) LN (IFG_R)–VN (Occipital) –2.14 0.034 0.028 (±0.156) Notes : Only significant results are reported. Statistical significance was set at p- FDR < 0.05. Connectivity values are M (Mean) ± SD ( standard deviation ) . Abbreviations: LN, language network; SMN, sensorimotor network; DAN, right executive control network; VN, visual network; IFG, inferior frontal gyrus; Superior, superior sensorimotor cortex; IPS, intraparietal sulcus; FEF, frontal eye field; Occipital, occipital visual cortex; L, left; R, right. Figure 2. Resting-state FNC of cyberbullying. (a) The circle plot shows significant connectivity patterns related to cyberbullying. (b) The connectome view displays network connectivity associated with cyberbullying scores ( p -FDR < 0.05, two-sided). (c) Depiction of the ROI-to-ROI connectivity matrix. Only significant ROI pairs are shown in the correlation matrix. Abbreviations: LN, language network; SMN, sensorimotor network; DAN, right executive control network; VN, visual network; IFG, inferior frontal gyrus; Superior, superior sensorimotor cortex; IPS, intraparietal sulcus; FEF, frontal eye field; Occipital, occipital visual cortex; L, left; R, right. 3.3 Prediction Analysis The SVR analysis results aligned with those of the FNC analysis, supporting the robustness of our findings. The predicted cyberbullying exhibited a significant correlation with observed cyberbullying ( r (predicted, observed) = 0.314, p < 0.001; Figure S3). This suggests a robust association between FNCs (LN–SMN, LN–DAN, LN–VN) and cyberbullying. 3.4 Moderation Models Six moderation models were developed to explore whether harm action/outcome aversion moderated the relationships between cyberbullying-related FNC (specifically, the between-network FNC of LN–SMN, LN–DAN, LN–VN) and cyberbullying. No interaction effects were observed between harm action/outcome aversion and LN–SMN, LN–DAN on cyberbullying scores. After controlling for sex and age, harm action aversion moderated the relationship between the FNC of the LN–VN and cyberbullying ( interaction effect = 0.191, 95% CI = [0.066, 0.316], p = 0.003). Subsequently, we examined the conditional effect of LN–VN on cyberbullying at low (one standard deviation below the mean), medium (mean), and high (one standard deviation above the mean) levels of harm aversion. The FNC of LN–VN had a negative effect on cyberbullying when harm action aversion was low ( simple slope = -0.361, 95% CI = [-0.541, -0.181], p = 0.001) and medium ( simple slope = -0.183, 95% CI = [-0.311, -0.055], p = 0.005), but the effect was not significant at a high level of harm action aversion ( simple slope = -0.005, 95% CI = [-0.171, 0.161], p = 0.952) (Figure 3a). Moreover, the interaction between the FNC of LN–VN and harm outcome aversion was significant ( interaction effect = 0.211, 95% CI = [0.079, 0.343], p = 0.018 ). The negative relationship between LN–VN and cyberbullying was significant with low ( simple slope = -0.359, 95% CI = [-0.536, -0.181], p = 0.001) and medium ( simple slope = -0.172, 95% CI = [-0.300, -0.044], p = 0.009) levels of harm outcome aversion, but it was not significant with high levels of harm outcome aversion ( simple slope = -0.015, 95% CI = [-0.153, 0.183], p = 0.863) (Figure 3b). Figure 3. Moderation analyses. (a) Moderation effect of harm action aversion between LN–VN and cyberbullying; (b) Moderation effect of harm outcome aversion between LN–VN and cyberbullying. 3.5 Exploratory Analyses Associations between FNCs and cyberbullying in males: Age was considered a covariate, and the statistical threshold was set at p < .05 with FDR correction. Cyberbullying was positively associated with LN–SMN, LN–DAN, intra-CN, intra-VN, and intra-FPN connectivity and inversely associated with LN–VN connectivity in males ( p- FDR < 0.05) (Table S3 and Figure S4). Associations between the FNCs and cyberbullying in females: The results for females mirrored those for males. Cyberbullying was associated with increased connectivity of the LN–SMN, LN–DAN, intra-CN, intra-VN, and intra-FPN and decreased connectivity of the LN–VN ( p- FDR < 0.05) (Table S4 and Figure S5). 3.6 Sensitivity Analyses Figure S6 presents the results of the sensitivity analysis. Cyberbullying was associated with DMN–SMN and DMN–FPN connectivity when using an alternative atlas (236 nodes from Power’s atlas) ( p- FDR < 0.05). Specifically, the DMN involved eight brain areas (left middle temporal gyrus, left and right temporal pole, middle temporal gyrus, left and right medial superior frontal gyrus, left dorsolateral superior frontal gyrus, right cerebellum_crus1), the SMN involved three brain areas (left inferior parietal, left and right postcentral gyrus), and the FPN involved two brain areas (right inferior temporal gyrus and right precentral gyrus). 4 Discussion To investigate the overlooked issue of the neural mechanisms underlying cyberbullying, this study utilized FNC analyses to explore the neural basis and examine the moderating role of harm aversion in the relationship between neural correlates and cyberbullying. The results revealed that connectivity between the LN and other neural networks (the FNC of LN–SMN, LN–DAN, and LN–VN) is correlated with cyberbullying. Ten-fold cross-validation supported the robustness of these network effects. Furthermore, this study revealed that higher harm aversion weakens the negative association between LN–VN connectivity and cyberbullying. 4.1 Functional Brain Networks Underlying Cyberbullying This study demonstrated that connectivity between the LN and other brain networks (SMN, DAN, and VN) is linked to cyberbullying. This suggests that the LN plays a central role in predicting cyberbullying behavior. Indirect evidence supports this hypothesis. First, the LN and its functional connectivity are primarily involved in language production (Fedorenko & Thompson-Schill, 2014; Friederici & Gierhan, 2013; Hertrich et al., 2020), a core component of verbal aggression (Fedorenko & Thompson-Schill, 2014; Friederici & Gierhan, 2013; Hertrich et al., 2020; Poling et al., 2019; Progovac & Benitez-Burraco, 2019). For example, LN–SMN connectivity, identified in this study as relevant to cyberbullying, has also been linked to language production (Friederici & Gierhan, 2013). Second, the LN plays a crucial role in Internet and social media use, which are integral components of cyberbullying. A resting-state fMRI study revealed that problematic smartphone and social media use correlate with reduced intra-LN connectivity (Áfra et al., 2024). Individuals with Internet use disorder exhibit altered functional connectivity between the LN and Broca’s area during silent word-generation tasks (Darnai et al., 2022). Third, the LN plays a pivotal role in bullying and behavioral problems. Increased activity in the IFG and cortical volume in the LOTC (two brain regions of the LN) are associated with traditional bullying (Perino et al., 2019; Wen et al., 2022). Intra-LN connectivity and LN connections with other networks (SMN, VN) are associated with behavioral problems (Áfra et al., 2024; Darnai et al., 2022; M. Zhang et al., 2021). Fourth, the LN is involved in social information processing (such as hostile attribution bias), a key factor in cyberbullying (Fang et al., 2023; C. Li et al., 2024; Runions et al., 2013; Wei et al., 2024). For example, the IFG, a core LN region, has been found to play a role in social information processing (Norris et al., 2004; Rudie et al., 2012). The finding that LN–SMN connectivity positively correlates with cyberbullying supports the research hypothesis and aligns with previous findings on the neural underpinnings of cyberbullying (Friederici & Gierhan, 2013; H. Zhang et al., 2021). The connectivity between the IFG and motor cortex is vital for language production (involved in verbal aggression) (Friederici & Gierhan, 2013; Greenlee et al., 2004; Simonyan & Fuertinger, 2015). For example, connections between the IFG and supplementary motor area have been observed during speech production tasks (Simonyan & Fuertinger, 2015). Additionally, LN–SMN connectivity has been associated with conduct disorders such as cyberbullying, aggression, and antisocial behavior (H. Zhang et al., 2021). This study identified a positive correlation between LN–DAN connectivity and cyberbullying, consistent with our hypothesis. The connectivity between the LN and DAN might be related to language production (Wang et al., 2022). Furthermore, DAN activity is pivotal in cyberbullying behaviors. For example, DAN activity has been linked to social media use (Meri et al., 2023), traditional bullying (Wen et al., 2022), conduct disorders (Wee et al., 2018), and social information biases (Li et al., 2023; Yu et al., 2019). The study also revealed a negative correlation between LN–VN connectivity and cyberbullying, supporting our hypothesis and partially aligning with prior findings (Áfra et al., 2024; Rolheiser et al., 2011). For example, smartphone and social media addictions have been linked to reduced functional connectivity within the LN and VN (Áfra et al., 2024). Additionally, the inferior fronto-occipital fasciculus (IFOF) is involved in semantic processing (Rolheiser et al., 2011), an important factor in verbal aggression. 4.2 Moderating Role of Harm Aversion This study revealed that the negative relationship between LN–VN connectivity and cyberbullying was significant only among individuals with low harm action/outcome aversion. This suggests that harm aversion may moderate the promoting effect of neural correlates on cyberbullying. These results are consistent with previous findings (Hou et al., 2024; Li et al., 2018; Yu et al., 2020), which suggest that moral traits (such as guilt and empathy) can moderate the effects of brain correlates on social psychology and behavior (Li et al., 2018; Yu et al., 2014; Yu et al., 2020). For example, activity in the anterior middle cingulate cortex and bilateral anterior insula predicts compensatory behavior only when individuals experience high levels of guilt, such as when they are solely responsible for their partner’s suffering (Yu et al., 2014; Yu et al., 2020). Similarly, empathy has been found to moderate the relationship between gray matter volume in the precuneus and self-enhancing humor styles (Li et al., 2018). Furthermore, prior research revealed that morality-related inhibitory factors (e.g., empathy, negative aggression outcome expectancy) can modulate the effects of influencing factors (moral disengagement, anger rumination) on aggression (Wang et al., 2017; Wei & Xia, 2023). Moral emotions can also moderate the relationship between brain network indicators and social behavior (Li et al., 2018; Yu et al., 2014; Yu et al., 2020). In addition, certain psychological variables (e.g., neuroticism, self-control, executive control) can modulate the effects of neural associations on aggression (Chester et al., 2014; Li & Jeong, 2020; Wang et al., 2023). Notably, we found no significant correlation between harm action/outcome aversion and cyberbullying. This aligns with previous research reporting no significant association between some moral inhibition factors (e.g., harm aversion, guilt, shame, anticipated social costs, negative aggression outcome expectancy) and aggression (Avnaim et al., 2022; Cen et al., 2025; Hou et al., 2024; Jansma et al., 2018; Marks et al., 2012; Nickoletti & Taussig, 2006; Patil, 2015; Wei & Xia, 2023). The above findings support our perspective that harm aversion does not directly suppress aggression but instead functions as a moral cost (Qu et al., 2020), exerting a discounting effect (Mitchell, 2003; Mitchell, 2017) on the approach motivations underlying aggression. Specifically, robust findings demonstrating that harm aversion could directly inhibit aggression are lacking, and several studies have reported nonsignificant correlations between harm aversion and certain types of aggression (Cen et al., 2025; Hou et al., 2024; Patil, 2015). This aligns with the perspective of moral cost (Abbink & Herrmann, 2011; Attanasi et al., 2019; Crockett et al., 2014; Eriksson et al., 2017; Keum & Meier, 2024; Levitt & List, 2007; Rothenhausler et al., 2015; Thielmann & Hilbig, 2019), which posits that costs can discount behavior (Escobar et al., 2023; Mitchell, 2003, 2017). Consequently, discounting factors may devalue the influence of promoting factors (such as specific neural mechanisms and trait aggressiveness) on antisocial behaviors such as cyberbullying and aggression. More specifically, individuals with high harm aversion may possess a stronger capacity to discount the facilitating effect of disrupted LN–VN connectivity on cyberbullying, compared with those with low harm aversion. These findings advance our understanding of the neural mechanisms underlying cyberbullying and the potential discounting role of harm aversion as a moral cost. The focus on LN–VN connectivity may be explained by its dual role. Evidence suggests that LN–VN connectivity can facilitate both immoral (Comes-Fayos et al., 2018; Hoppenbrouwers et al., 2013) and moral responses (Sundram et al., 2012; M. Zhang et al., 2021). For example, decreased fractional anisotropy values in the IFOF have been observed in psychopathic offenders prone to antisocial behavior (Hare & Neumann, 2008) compared to controls (Hoppenbrouwers et al., 2013). Similarly, diffusion tensor imaging studies have linked altered IFOF mean diffusivity to antisocial behaviors (Dotterer et al., 2019) and personality disorders (Sundram et al., 2012). A resting-state fMRI study also associated LN–VN connectivity with greater social problems (H. Zhang et al., 2021). Conversely, emotional empathy (a moral emotion) has been positively correlated with white matter integrity in the IFOF (Comes-Fayos et al., 2018; Parkinson & Wheatley, 2014), and disrupted IFOF integrity has been linked to personal distress and the fantasy aspect of empathy (Fujino et al., 2014). Whether LN–VN connectivity triggers moral or immoral responses may depend on an individual’s moral level. At low moral levels, such as diminished harm aversion, LN–VN connectivity tends to promote immoral rather than moral responses. Conversely, higher moral levels may suppress the effect of LN–VN connectivity on immoral responses and instead facilitate moral responses. Hence, the effect of LN–VN connectivity on cyberbullying likely depends on harm-related moral levels such as harm aversion. Specifically, under conditions of low harm aversion, LN–VN connectivity may promote cyberbullying. Conversely, this effect would be discounted or devalued when harm aversion is high. 4.3 Limitations This study has several limitations. First, it used a cross-sectional design and focused only on resting-state FNC analyses among college students. Future research should utilize longitudinal designs, structural fMRI and task-based fMRI studies, and multiple neural indicators to further examine the neural basis of cyberbullying across diverse populations. Second, the study did not calculate the absolute weighted values of connections, making the relative importance of FNCs unclear. Future studies should calculate absolute weighted values to quantify the relative importance of each connection (Schrouff et al., 2013; Wu et al., 2019). Finally, cyberbullying was measured through self-reporting owing to its undetectable nature (Slonje & Smith, 2008). Future research should apply other methods, such as web data scraping and experimental tasks, to achieve higher validity in assessing cyberbullying. 4.4 Contributions This study makes several contributions: First, it is the first to explore the neural correlates of cyberbullying, providing empirical evidence on brain networks underlying cyberbullying. Specifically, it highlights the central role of the LN and its connectivity (the FNC of LN–SMN, LN–DAN, and LN–VN), which are critical to understanding cyberbullying. Second, the study reveals that harm aversion moderates the relationship between LN–VN connectivity and cyberbullying. It further argues that LN–VN connectivity has a dual function (triggering either moral or immoral responses) and that an individual’s moral level (such as harm aversion) may impact the LN–VN connectivity function. This finding deepens our understanding of the neural mechanisms of cyberbullying by emphasizing the discounting role of moral inhibition factors. Third, the identified brain network connectivity could help identify individuals susceptible to engaging in cyberbullying, potentially allowing interventions such as transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS) to target potential cyberbullying perpetrators. 5 Conclusion This study revealed that the connectivity between LN–SMN, LN–DAN, and LN–VN is related to cyberbullying and that harm aversion modulates the relationship between LN–VN connectivity and cyberbullying. These findings uncover the neural mechanisms of cyberbullying, suggesting that the LN is pivotal for presenting cyberbullying, and harm aversion may exert a discounting function in the facilitating effect of brain correlates on cyberbullying. These results provide empirical evidence for developing future neuropsychological interventions to prevent cyberbullying. 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Authors Affiliations Yu-Shan Cen 0000-0001-8103-6475 Southwest University Faculty of Psychology View all articles by this author Ling-Xiang Xia 0000-0003-4024-5312 [email protected] Southwest University Faculty of Psychology View all articles by this author Metrics & Citations Metrics Article Usage 146 views 82 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yu-Shan Cen, Ling-Xiang Xia. Functional Brain Networks Underlying Cyberbullying and the Moderating Role of Harm Aversion. Authorea . 22 December 2025. DOI: https://doi.org/10.22541/au.176639170.05929809/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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