Rearrangement of anti-synchronous activities in the brain functional network plays a crucial role in behavioral contagion

Rearrangement of anti-synchronous activities in the brain functional network plays a crucial role in behavioral contagion | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Rearrangement of anti-synchronous activities in the brain functional network plays a crucial role in behavioral contagion Mohsen Mobasseri, Abdol-Hossein Vahabie, Gholamreza Jafari, Javad Hatami, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4524070/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Behavioral contagion has been defined as the tendency of individuals to imitate the behavior of others after observing them. Despite the important role that behavioral contagion plays in societies, its mechanism in the brain is still not fully understood. In this study, we hypothesized that the brain tends to go to a more stable state after updating behavior by observation of the others’ behaviors. Therefore, the stability of the brain network before and after observing others’ preferences was assessed using structural balance theory (SBT) on the fMRI data. To this end, we developed a version of the Dictator Game as the task, and recorded participants' brain responses using fMRI (before and after observing others' preferences). A threshold for changes in participants' preferences was considered to be the occurrence of behavioral contagion. With regard to this threshold, the participants were classified into two groups, the Contagion and No Contagion. The changes in SBT parameters of the brain network were calculated for both groups. A distinct pattern of changes in SBT parameters was observed for each group. The results of the Contagion group suggested that behavioral contagion is accompanied with a rearrangement of links in the network to transform imbalanced triads into balanced triads. This process lowers the balance energy of the brain network and pushes the network to a more stable state. We hope that these findings on the restructuring of the functional brain network could pave the way to a better understanding of behavioral contagion. Biological sciences/Neuroscience Biological sciences/Psychology Physical sciences/Physics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Behavioral contagion is a type of social influence defined as the modification of people's behavior through observation or knowledge of others’ behavior. This term was first used by Gustave Le Bon in 1895 1 . This type of contagion has a significant impact on the behavior of individuals and groups. Considerable evidence suggests that behavior contagion can change the preferences of individuals, and in this way, behaviors and beliefs can be affected 2–4 . Some researchers have defined the behavioral contagion as "spontaneous, unsolicited and uncritical imitation of another's behavior" 5 . Various researchers and authors from different fields of psychology and social sciences have provided some explanations for this contagion, such as social learning 6 , social conformity 7 , and imitation of behavior 8 . Some studies have suggested that individuals' decisions to conform to a group change, probably because they feel social pressure to impose social norms 9 or because they tend to be similar to others 10 . Asch's (1950s) conformity experiments were conducted to examine behavioral contagion and the tendency of individuals to conform to the preferences of others. In these experiments, participants were shown lines of different lengths and asked to match these lines to a set standard line. These experiments showed that participants were willing to accept incorrect answers when surrounded by peers with incorrect answers 11 . Using fMRI, Suzuki et al. (2016) showed how behavioral contagion during learning the risk preferences of others influences the neural representation of risky decisions in brain regions such as the caudate nucleus 4 . Despite the important role that behavioral contagion plays in decision-making, knowledge about brain mechanisms is sparse. In this study, we aimed to investigate the brain mechanisms of behavioral contagion to provide a new understanding of the contagion effect on individuals' decision-making and social behavior. Since we believe that decision making takes place in a stable brain functional network, we thought that behavioral contagion must put the brain network in a more stable state. In this study, we investigated our hypothesis by comparing the balance energy level and stability of the brain functional network before and after behavioral contagion. To this end, we used structural balance theory (SBT) to measure the balance energy level, and the stability of the network. SBT was originally proposed by Heider in social psychology to describe the dynamics of interpersonal relationships 12 . This theory focuses on triads to analyze relationships and the tendency of networks to reach balanced states. In recent years, SBT has been further developed and improved to apply to various complex systems in different fields such as genetics 13 , social networks 14 and psychology 15 . It is a new perspective on the dynamics of brain networks. SBT uniquely considers the signed links between brain regions and offers a different perspective on understanding the function and structure of the brain network. It explains how the interaction between signed links and triads forms the activity change in the brain network and behaviors. SBT has been applied to model higher-order cognitive processes involving multiple brain regions to study phenomena such as cognitive dissonance, decision making, and brain health disorders 3,13,14 . In neuroscience, Moradimanesh et al. used SBT to study the brain network in autism spectrum disorders (ASD) during development 16 . In another study, Saberi and colleagues used SBT to investigate the stability of the brain network in the resting state. They showed that negative TMH (tendency to make hub) and topology put the brain network in a more stable state 17 . In another SBT-based study, Talesh et al. investigated the stability of brain network in the resting state and the role of the topology of the functional links in reducing the balance energy of the brain network in OCDs compared to a healthy control group 18 . In this study, using neuroimaging approaches and SBT-based modeling, we sought to develop a new conceptual framework for behavioral contagion and to investigate the resulting stability in the brain network. We aimed to show that after observing others’ preferences, the brain reorganizes its pair-wise regional synchronies in a way to go to a more stable state. As the outcome of this change in the brain state, behavior contagion as the associated response may be observed. Methods Participants 31 young healthy adults aged 18–40 years (mean: 28.22 years), with at least a bachelor’s degree, right-handed, consisting of 15 men and 16 women, participated in this study. All steps of the study were approved by the ethical review committee of the Institute with the IRB number of IR.UT.IRICSS.REC.1400.034. All methods complied with standard guidelines, protocols and regulations in accordance with the Declaration of Helsinki. In addition, participants signed an informed consent form and were free to leave the study whenever they wished. Experimental Procedure Before entering the scanner, participants were informed about the task and the procedure through verbal and written explanations. Our task consisted of 3 sessions and each session consisted of 66 trials. The time limit for each session was 15 minutes, but we did not set a time limit for responding to each trial. The task did not include any jittering in its steps. There was a two-minute break between sessions. After data acquisition, the fMRI data were preprocessed and analyzed with CONN 19,20 . The adjacency matrices were extracted and in the next step the signed matrices were created with these matrices. These types of matrices were actually the primary data for extracting the SBT parameters(Fig. 1 ). These parameters were then used for statistical analysis, extraction of the results, and discussion. Experimental design Imaging Acquisition The functional and structural MRI data were acquired using a 3 Tesla Siemens Prisma scanner equipped with a 32-channel head coil. Functional images were obtained with the following parameters: the voxel size was set at 3x3x3 mm³, indicating isotropic dimensions. A repetition time (TR) of 2000 ms was chosen to determine the time interval between successive image acquisitions. The echo time (TE) was 32 ms, which indicates the time span in which the peak echo signal was measured after each excitation pulse. The field of view (FOV) was set to 240 mm, defining the spatial coverage of each imaging volume. Parallel imaging with a GRAPPA algorithm (GeneRalized Autocalibrating Partial Parallel Acquisition) was utilized to reduce acquisition time. The scanner had high-performance gradient coils that facilitate rapid spatial encoding for improved image quality. The flip angle was 80 degrees. The number of slices was chosen to be 35, with a slice thickness of 3.00 mm. Experimental Stimuli We developed a task based on the Dictator Game (DG) to investigate the occurrence of behavioral contagion (ref). The DG is among the experimental paradigms used in behavioral economics studies. In the DG, the participant (as dictator) decides how to divide an endowed amount of money between himself (herself) and an unknown person. The important question for the dictator is: 'How much for me and how much for the other person?'. This game is used to explore socio-economic behavior, altruism, egalitarianism and considerations of fairness or unfairness in decision making. Self-Selection-1 trials Prediction-Observation trials Self-Selection-2 trials) Our task consisted of 3 sessions, and each session had 66 trials. In each trial, participant was shown two different patterns of money allocation between the participant (as self) and the other (Fig. 2 ). The duration of a trial is the required time to show each pair of patterns and participant’s response. In the first session (entitled as “Self-Selection-1” trials), participant (as dictator) was asked to repeatedly choose one of the pairs of patterns in trials. In session 2 (called Observation-Prediction trials), the preferences of a non-real person (entitled as "other" ) were generated based on the participant’s preferences from session 1 and using the Fehr-Schmidt model that describes socioeconomic behaviors under uncertainty. In this step, the parameters of the Fehr-Schmidt model were extracted on the basis of the participant’s answers. These parameters had to be modified to reflect the preferences for unknown person. With these modified parameters, the non-real person’s preferences to be used in session 2 were generated for each participant. In session 2, each participant was told that the generated preferences were the choices of an unnamed real person. In this session, each participant was shown the patterns of each trial and asked to guess the unknown person's preferences (66 trials). If the participant's guess was correct, a blue square would appear around the choice on each trial, otherwise the red square would appear. In session 2, the goal was for participants to pay attention to the generated preferences. Indirectly, participants became familiar with the preferences of others in this session. Session 3 (Self-Selections-2 trials) was similar to session 1 and the participant had to choose one of the two patterns in each trial. In this session, if contagion had happened, we would have seen changes in people's preferences. In all sessions, while performing the task, the participants were scanned by the fMRI scanner. We implemented our task using MATLAB R2021a ( http://www.mathworks.com/products/matlab/ ) and Psychtoolbox-3(Clavien & Klein,2010) which is a set of MATLAB functions developed for neuroscience studies that provide a platform for evaluating stimuli and responses in experimental paradigms. Fehr and Schmidt Model The Fehr-Schmidt model provides a framework for explaining how individuals’ preferences regarding fairness and inequality influence economic behaviors. In the context of this model, individuals are classified into two main types based on their socioeconomic behaviors: "fair types" and "selfish types". But most people are of the intermediate types. In this model, the utility function is able to quantify and model individuals' preferences to different levels of fairness and unfairness. This allows researchers to analyze and predict preferences and behaviors influenced by some parameters. The general form of utility function can be described as: Ui = Mi – \(\varvec{\alpha }\varvec{i}\) max [(Mj- Mi), 0]- \(\varvec{\beta }\varvec{i}\) max [(Mi - Mj),0] i \(\ne\) j ( 1 ) In the above equation, Ui represents the utility function for individual i. Mi denotes the monetary payoff or income of individual i and Mj shows the monetary payoff of the other. \(\alpha\) and \(\beta\) denote the weights assigned to the positive and negative difference between the monetary payoffs of individual i ( Mi ) and the other ( Mj ) respectively. They indicate how much the individual likes (or dislikes) having a higher (or lower) payoff compared to others 21–24 . Data analysis Behavioral Analysis After collecting the fMRI data, the first step was to analyze the behavioral data. In this way, the number of matches between choices in sessions 1 and 2 and the number of matches between the choices in sessions 2 and 3 were calculated. The behavioral contagion rate (BCR) was defined as the difference between the numbers of these matches. BCR = N s2& s3 - Ns 1&s2 ( 2 ) Where N presents the number of matched trials and s denotes the session number by 1,2, and 3. The threshold of BCR was set at 4 for the occurrence of contagion (larger than 5% of 66 trials). Using this threshold, the participants were categorized into two groups: Contagion and No Contagion. Neuroimaging Analysis After data acquisition and behavioral analysis, CONN (version 22a) was used to analyze the fMRI data. The standard CONN pipeline was used to preprocess the data. The steps of this preprocessing are: import of functional and structural data, realignment, coregistration, segmentation, normalization, smoothing, and artifact detection and correction 19 . The standard atlas in CONN was converted to the Schaefer-400 atlas for our analysis. The Schaefer-400 divides the cerebral cortex into 400 different regions. Compared to many other atlases, the Schaefer-400 provides a finer view to obtain more accurate data. The results of CONN were adjacency matrices of the participants. Structural Balance Theory (SBT) SBT uses two types of triads to study and analyze systems. The balanced triads, whose product of the signs of the three links is positive, are balanced triads (Fig. 3 ). Triads in which the product of the signs of their links is negative are called imbalanced triads. Two types of balanced triads are defined as strong (T3) and weak (T1). Strong balanced triads have three positive links [+++], but in weak triads there is one positive and two negative links [+--]. In imbalanced triads there are also two types: strong (T2) and weak (T0). The strong imbalanced type has one negative and two positive links [++-], while the weak type has three negative links [---]. The terms "strong" and "weak" in imbalanced triads refer to the degree of frustration that a triad can cause in a network. The relationship between imbalanced triads and frustration is the basic tenet of structural balance theory. Imbalanced triads refer to the presence of inconsistencies in the relationships between three nodes in a network. Tension and frustration in the networks are the main consequences of imbalanced triads and lead to changes in behavior. The balanced triads have a lower balance energy than the imbalanced triads, which are in a critical state due to their higher balance energy. Normally, the brain network is active in both resting and non-resting states, so there are a number of balanced and imbalanced triads in both states. In critical states and transitional periods, the imbalanced triads tend to be frustrated and change their links to reach more balanced states. In SBT, balance energy was defined as the minus of the sum of the total products of the connections of triads in the network. The negative sign in front of the product represents compliance with the physical principle of minimum balance energy. Less balance energy means more stability in a system. In the context of SBT, the increase in balanced triads leads to lower balance energy and more stable systems. $$\text{U}=-\frac{1}{\left(\genfrac{}{}{0pt}{}{n}{3}\right)}{\sum }_{i,j,k}{S}_{ij}{S}_{ik}{S}_{jk}$$ 3 In the above equation, U denotes the total balance energy ,n is the number of nodes in the network, and \({S}_{ij}\) represents the connection between node i and node j, which can be 0 or 1. The term \(\left(\genfrac{}{}{0pt}{}{n}{3}\right)\) is the 3-combination of n, the number of triads that can be formed in a network with n nodes. TMH (Tendency to Make Hub) is a global hubness measure to evaluate the tendency of a network's connections to form hubs. Based on the signs of connections, two types of negative and positive TMH are known. It is mathematically defined as: Negative TMH= \(\frac{{\sum }_{i=1}^{n}{{NegD}_{i}}^{2}}{\sum _{i=1}^{n}{NegD}_{i}}\) ( 4 ), Positive TMH= \(\frac{{\sum }_{i=1}^{n}{{PosD}_{i}}^{2}}{\sum _{i=1}^{n}{PosD}_{i}}\) ( 5 ) Where positive degree ( PosD ) and negative degree ( NegD ) are the numbers of positive and negative links of node i, respectively, and n represents the number of nodes. Calculation of the SBT parameters Once preprocessing was complete, both first and second level analyses were performed. The adjacency matrices are mathematical representations of the brain network. Each element of these matrices represents the presence or absence of a link between two nodes. These matrices were used to create the signed matrices. The signed values in these matrices represent the directionality of the connections between the nodes. Positive signs represent excitatory links, while negative signs represent inhibitory links (or anti-correlations). The signed matrices were then used to calculate the 11 parameters of the balance theory in both session 1 and session 3 for each participant .The parameters were the number of positive links, the number of negative links, T0, T1, T2, T3, positive TMH, negative TMH, the number of balanced triads, the number of imbalanced triads, and balance energy. Statistical Analysis Various statistical methods were used to assess the differences, relationships and quality of the variables. First, the normality of the calculated parameters had to be checked using the Shapiro-Wilk test. If the SBT parameters of 2 sessions were normally distributed (p-value > 0.05), in the next step they could be measured with a paired t-test, and if not and at least one of each matched pair was not normally distributed, the measurement had to be performed with the Wilcoxon signed-rank test, a non-parametric alternative to the paired t-test. Both tests determined whether there were significant differences between the SBT parameters in the sessions (before and after observing others' preferences). Another statistical method that we used in our analysis was the independent t-test. The independent t-test is a parametric test that can be used for normally distributed data from two independent groups. If the assumptions of normality for two groups are not met, the Mann-Whitney U-test serves as a suitable alternative to the independent t-test. The Mann-Whitney U-test determines whether the medians of two independent groups are different or not. In our analysis, the independent t-test and the Mann-Whitney U-test were used to compare the SBT parameters of the first sessions of the Contagion and No Contagion groups. Results In this study, we applied SBT to understand the dynamics of the brain network. The main goal was to investigate the asynchrony of the brain's functional network during behavioral contagion. To this end, participants were scanned while performing the task. In the behavioral analysis conducted using the BCR equation for contagion based on the preferences of 31 participants, 17 individuals were identified with BCRs greater than 3 as the threshold for the occurrence of contagion. These 17 individuals were labeled as the Contagion group. 12 individuals with BCRs lower than 4 were named as the No Contagion group. Because of quality issues, the data of 2 participants was excluded from the final analysis. We then analyzed the fMRI data of the "Contagion" and "No Contagion" groups with CONN and extracted their adjacency matrices. For all participants in both groups, the signed networks were calculated using their adjacency matrices. Then, 11 parameters of SBT (as described in Calculation of the SBT parameters section) were calculated for session 1 and session 3 of the participants in the groups. First, all parameters (session 1 and session 3) were tested for normality using the Shapiro-Wilk test. In the Contagion group, all parameters related to session 1 were normally distributed (p-value > 0.05), but in session 3 of this group, the parameters T1, T3, the negative link count and the positive link count were not normally distributed. In the No Contagion group, in session 1, all calculated SBT parameters of the participants were normally distributed, except for balance energy, the number of balanced triads and the number of imbalanced triads. In session 3 of this group, all parameters were normally distributed. In the next step, the paired t-test was performed for the Contagion group participants to compare the corresponding parameters in two sessions, with the exception of the parameters T1, T3, the negative link counts and the positive link counts, whose differences between the two sessions were measured using the Wilcoxon signed-rank test. For the Contagion group, the comparisons of the 11 matched pairs (session 1 and session 3) showed that there were significant differences between the matched pairs of the number of balanced triads, the number of imbalanced triads and the balance energy (p-value < 0.05). No significant difference was found for other matched pairs (See Fig. 4 ). For the No Contagion group participants, the paired t-test was applied to compare the SBT parameters of two sessions except for the balanced energy, the number of balanced triads and the number of imbalanced triads. These 3 parameters were measured in 2 sessions using the Wilcoxon signed-rank test. The comparison of 11 matched pairs revealed that there were significant differences between all SBT parameters in two sessions (p-value < 0.05) except for the number of balanced triads, the number of imbalanced triads and the balance energy (See supplementary information, Figures S1and S2 ). The SBT parameters of two groups that have been significantly changed between session1 and session 3 are listed separately in Table 1 . Table 1 SBT parameters with significant differences between session1 and 3 for the Contagion and No Contagion groups. Group name SBT parameters which had significant differences between sessions 1 and 3 Contagion Balance Energy, Balanced Triads, Imbalanced Triads No Contagion Positive links, Negative links, T0, T1, T2, T3, Positive TMH, Negative TMH See supplementary materials (S1-S2) In the next step of the statistical analysis, we examined whether there were fundamental differences between the SBT parameters of the Contagion and No Contagion groups in session 1 (before observation) that might be the reason behind the contagion effect. After testing for normality, statistical comparisons between the sessions 1 of both groups were performed using the independent t-test and the Mann-Whitney U-test. As shown in Table 2, there were significant differences in 7 SBT parameters, including the numbers of positive and negative links, the numbers of T0, T1, and T3 triads, and the positive and negative tendency to make hubs (TMH)(p-value 0.05), which denotes that although the brain functional network stability was at the same level in two groups before observation, the numbers of negative and positive links and their tendency to make hub (TMH) could influence the rearrangement of the network, not allowing it to change the stability in the No Contagion group(See supplementary information, Table S1 ). Table.2 Comparing the SBT parameters of sessions 1 of Contagion and No Contagion groups. SBT parameter Negative Links Positive Links T0 T1 T2 T3 Negative TMH Positive TMH Balanced Triads Imbalanced Triads Balance Energy P -values 0.0008 0.0008 0.0004 0.02 0.11 0.0009 0.0004 0.0016 0.16 0.16 0.16 Discussion There is no doubt that observing the behavior of others influences our preferences. In this study, we sought to understand how contagion affects the stability of the brain functional network and causes the brain to respond like the observed behaviors. Therefore, we used a modified version of the DG to investigate how observing others’ preferences influences participants’ preferences. Based on participants' responses before and after observing others’ preferences, and the BCR threshold (5 percent of 66 trials) the participants were divided into the Contagion and No Contagion groups .Structural balance theory was then used to examine the stability of brain functional network extracted from participants' fMRI data that had been obtained while performing the task. Statistical analysis of SBT parameters between sessions 1 (before) and session 3 (after) showed distinct patterns for the two groups (presented in Table 1 ). For participants in the Contagion group, there were significant differences between two sessions in balance energy level, numbers of balanced and imbalanced triads. We believe that the occurrence of contagion reorganizes the brain functional network by leading to new synchronization and asynchronization between the brain regions. This rearrangement in the brain network in response to cognitive demand raised from observation of others’ behaviors and change pushes the network to a more balanced state and influences the participant's preferences accordingly. Such a change in stability (decrease in balance energy level after observation) is introduced by an enhanced number of balanced triads and a decreased number of imbalanced triads. As mentioned in the methods section, T0 and T2 denote the imbalanced triads, and T1 and T3 denote the balanced triads. Based on the results presented in Figs. 4 and 5 , we believe that after observation of others’ preferences, the rearrangement process in the network takes place by an increase in the number of balanced triads that could occur by replacing a negative link from imbalanced triads (T0 or T2) with a positive link and transforming them into balanced triads (T1 or T3). If the change occurred in T0 triads, T0s would turn into T1 balanced triads. Another possible change may occur in the negative links of T2 and replace them with positive links that transfer the T2s to T3s triads. If the changes in the numbers of balanced and imbalanced triads had more to do with the transformation from T0 to T1, the number of T0s in session 3 compared to session 1 would decrease and the number of T1s in session 3 compared to session 1 would increase. But if there were more transformations from T2 to T3, the number of T2s in session 3 would decrease compared to session 1 and the number of T3s in session 3 would increase. By comparing the changes in the numbers of T0s and T1s and also the changes in the number of T2s and T3s in both sessions, we concluded that most of the changes in the number of balanced triads occurred through the transformation of strong imbalanced triads (T2s) into strong balanced triads (T3s) (Fig. 6 ). Therefore, the calculation of the balance energy level of the network before and after observation of others’ preferences showed that the balance energy level after the observation decreased compared to before. This decrease in balance energy level was expected due to the increase in the number of balanced triads and the decrease in the number of imbalanced triads and could indicate the achievement of a higher level of stability in the network, which could force changes in the preferences. It is worthy to mention that such a change in the stability of the functional brain network is not observed in the No Contagion group, which can emphasize the role of balance energy level on the change in participants’ preferences. In short, what has been said about dependent and non-dependent metrics shows that topology (the arrangement of links) plays the main role in the brain network. The changes, rearrangements of this topological framework, rather than changes in the quantity of links, are responsible for the change from one state to a more stable state in behavioral contagion. Limitation and suggestion for future work There were some limitations in our study. The first limitation concerned the relatively small sample size (only 31 participants). This limitation could lead to some shortcomings and limitations in the statistical analysis. In addition, we only collected data from right-handed participants, so the results may not be directly transferable to left-handed participants. In addition, future works could investigate the reasons for the differences between people in the "Contagion" and "No Contagion" groups. The next studies could also investigate how to bring more stability to the brain network while observing the behavior of others. Conclusion We investigated the stability changes in the brain network in two sessions before and after the occurrence of contagion, using fMRI and SBT. Based on a threshold for BCR, participants were divided into the Contagion and No Contagion groups. Our analysis showed that there were two distinct patterns of SBT parameters for these groups. In the Contagion group, we found a significant difference only in the numbers of balanced and imbalanced triads and balance energy levels between the two sessions. There was no significant difference in the other parameters, including the numbers of positive and negative links, signed TMHs, T0, T0, T1, T2, and T3 triads. These results indicate that behavioral contagion is a rearrangement process in the brain functional network. A decrease in the number of imbalanced triads and an increase in the number of balanced triads in session 3 compared to session 1 lead to a decrease in balance energy level. The changes in the number of balanced and imbalanced triads were more related to the transformation from T2 (in the first session) to T3 (in session 3). In summary, this study shows that observing the behavior of others during behavioral contagion causes a reorganization that shifts the brain network to a more stable state. Declarations Data Availability The codes, supporting information ,and datasets generated and analyzed in this study are available at https://github.com/M-Mobasseri/-Behavioral-contagion . If you require further information, please contact the corresponding authors. Author Contributions RK designed the study, supervised the data gathering and analysis, validated the results, and edited the manuscript. AV designed the experiment and supervised the behavioral analysis. MM helped with experimental design, performed data acquisition and data analysis, and wrote the first draft and revised the manuscript. GJ and JH also helped in the validation of the results. Acknowledgements The authors would like to thank all participants in this study for their cooperation and also Dr. Malihe Milani for her valuable support. Competing Interests The authors declare no competing interests. References Le Bon, G. The Crowd. A Study of the Popular Mind. Science, 5 (123), 734–735(1897). Campbell-Meiklejohn DK, Bach DR, Roepstorff A, Dolan RJ, Frith, CD,. How the opinion of others affects our valuation of objects. Curr Biol 20(13):1165–1170(2010). 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Inequality aversion, efficiency, and maximin preferences in simple distribution experiments: Comment. American Economic Review, 96 (5), 1912–1917(2006). Rohde, K. I. A preference foundation for Fehr and Schmidt’s model of inequity aversion. Social Choice and Welfare, 34 , 537–547(2010). Sporns, O. Networks of the Brain, MIT Press , Cambridge (2010). Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev.Neurosci. 10, 186–198(2009). Saberi, M., Khosrowabadi, R., Khatibi, A., Misic, B. & Jafari, G. Requirement to change of functional brain network across the lifespan. PLoS ONE 16, e0260091 (2021). Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage. 52, 1059–1069 (2010). Vicente,U. Ara, A. & Josep Marco–Pallarés, O. Intra– and inter–brain synchrony oscillations underlying social adjustment. NeuroImage. 52, 1059–1069 (2010). Belaza, A. M. et al. Statistical physics of balance theory. PLoS ONE 12, e0183696 (2017). Doreian, P. & Mrvar, A. Structural balance and signed international relations. J. Soc. Struct. 16, 1–49(2019). Saiz, H. et al. Evidence of structural balance in spatial ecological networks. Ecography 40, 733–741(2017). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4524070","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":318572530,"identity":"dac518ce-e546-407e-88f4-49b314033f27","order_by":0,"name":"Mohsen Mobasseri","email":"","orcid":"","institution":"Institute for Cognitive Science Studies","correspondingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Mobasseri","suffix":""},{"id":318572531,"identity":"d0af16cb-4fad-4c73-a62a-42ebbcb3dffe","order_by":1,"name":"Abdol-Hossein Vahabie","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Abdol-Hossein","middleName":"","lastName":"Vahabie","suffix":""},{"id":318572532,"identity":"609c911e-5875-4c88-bf57-b8acdee38a4c","order_by":2,"name":"Gholamreza Jafari","email":"","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":false,"prefix":"","firstName":"Gholamreza","middleName":"","lastName":"Jafari","suffix":""},{"id":318572533,"identity":"3d30342e-604c-4bc7-b49a-ddf99a23fc96","order_by":3,"name":"Javad Hatami","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Javad","middleName":"","lastName":"Hatami","suffix":""},{"id":318572534,"identity":"b3e2e10c-655b-426e-8735-466e55409e79","order_by":4,"name":"Reza Khosrowabadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYFACNjYILQFECRUMCRCeAdFazpCshbENpgUPMG9gS3vw4c89Of7ZzQ9vPJxXl2dwgPnhB4aCezi1yBxgO244s63YWOLOMWOLxG2Hiw0OsBlLMBgU49QiwcDeJs3bkJDYcCPBTCJx24HEDQcYzIB+we1AsBaePwmJ82+kf5NInFMH1ML+jYAWtmPSPGwJiRtu5ABtaWAGauEhZAtbmuTMtgRjwztnii0Sjh0uljzMUyyRgF+LmcSHPwlycrfbN978UVOXx3e8feMHoAhOLQzyD9BFmIEYj4ZRMApGwSgYBUQAAHiNT+uY08d4AAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":true,"prefix":"","firstName":"Reza","middleName":"","lastName":"Khosrowabadi","suffix":""}],"badges":[],"createdAt":"2024-06-03 21:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4524070/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4524070/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59436380,"identity":"9adc5961-a059-484f-a555-e598658acdab","added_by":"auto","created_at":"2024-07-01 19:14:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63988,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental procedure\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4524070/v1/182a83cf8f5072a6420ad15c.png"},{"id":59436379,"identity":"fa6f260f-a42c-404e-9e46-f4d8ef4e6206","added_by":"auto","created_at":"2024-07-01 19:14:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe experimental task consisted of three sessions. In the first session (Self-Selections-1 trials), participant chose their preferences. In the second session (Observe-Predict trials), the participant had to predict the preferences of an unknown person. If the participant's guess was correct, a blue square would appear around the choice on each trial; otherwise, the red square would appear. The aim of this session was to get to know other people's preferences by paying attention. The third session (Self-Selections-2 trials) was similar to the first session, and the participant had to choose their own preferences. If contagion had occurred in this session, we would have been able to see the changes in the participant's preferences. The duration of a trial was the required time to show each pair of patterns and participant’s response.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4524070/v1/5836364851adc117fed1724d.png"},{"id":59436382,"identity":"6c4110fa-7031-413f-bc68-59770c71e542","added_by":"auto","created_at":"2024-07-01 19:14:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56562,"visible":true,"origin":"","legend":"\u003cp\u003eT3 and T1 represent strong and weak forms of balanced triads, respectively. Likewise, T2 and T0 are strong and weak variants of imbalanced triads.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4524070/v1/a229b851283e6ca0538ed7a9.png"},{"id":59436385,"identity":"7d18c642-1d71-4ba9-bf20-2bea0dc47def","added_by":"auto","created_at":"2024-07-01 19:14:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42032,"visible":true,"origin":"","legend":"\u003cp\u003eFor the Contagion group, the SBT parameters in sessions 1 and 3. There were significant differences only for 3 of them consisted of number of balanced triads, number of imbalanced triads, and balance energy.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4524070/v1/3d39a4e79f87aacfc88b7a02.png"},{"id":59436381,"identity":"5f5c8011-739e-4b6f-b2ce-ff2e39b46a02","added_by":"auto","created_at":"2024-07-01 19:14:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72953,"visible":true,"origin":"","legend":"\u003cp\u003eFor the Contagion group, the SBT parameters of T0, T1, T2, and T3 in sessions 1 and 3.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4524070/v1/e08687a148c2327d61a8a20f.png"},{"id":59436384,"identity":"87fbd218-1475-4773-9ad5-772e858a8a3d","added_by":"auto","created_at":"2024-07-01 19:14:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":47089,"visible":true,"origin":"","legend":"\u003cp\u003eIn the rearrangement, a negative link is replaced by a positive link, and in two different possible ways, either T0 becomes T1 or T2 becomes T3. We assessed the change in the number of T0, T1, T2, and T3 between the two sessions and concluded that the main pathway was the conversion of T2 to T3.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4524070/v1/7632f79549e0f2a20b559eed.png"},{"id":60495991,"identity":"632b7b08-3446-410a-bcdc-734340692fd9","added_by":"auto","created_at":"2024-07-17 11:42:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":845324,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4524070/v1/312df103-06c2-426e-8dd9-30747de84ed8.pdf"},{"id":59436383,"identity":"92ccaf96-c693-4ec6-82cf-cc246606f8db","added_by":"auto","created_at":"2024-07-01 19:14:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":191317,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4524070/v1/69f03c55a514bfb4a63d0e08.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rearrangement of anti-synchronous activities in the brain functional network plays a crucial role in behavioral contagion","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBehavioral contagion is a type of social influence defined as the modification of people's behavior through observation or knowledge of others\u0026rsquo; behavior. This term was first used by Gustave Le Bon in 1895\u003csup\u003e1\u003c/sup\u003e. This type of contagion has a significant impact on the behavior of individuals and groups. Considerable evidence suggests that behavior contagion can change the preferences of individuals, and in this way, behaviors and beliefs can be affected\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e. Some researchers have defined the behavioral contagion as \"spontaneous, unsolicited and uncritical imitation of another's behavior\"\u003csup\u003e5\u003c/sup\u003e. Various researchers and authors from different fields of psychology and social sciences have provided some explanations for this contagion, such as social learning\u003csup\u003e6\u003c/sup\u003e, social conformity\u003csup\u003e7\u003c/sup\u003e, and imitation of behavior\u003csup\u003e8\u003c/sup\u003e. Some studies have suggested that individuals' decisions to conform to a group change, probably because they feel social pressure to impose social norms\u003csup\u003e9\u003c/sup\u003e or because they tend to be similar to others\u003csup\u003e10\u003c/sup\u003e. Asch's (1950s) conformity experiments were conducted to examine behavioral contagion and the tendency of individuals to conform to the preferences of others. In these experiments, participants were shown lines of different lengths and asked to match these lines to a set standard line. These experiments showed that participants were willing to accept incorrect answers when surrounded by peers with incorrect answers\u003csup\u003e11\u003c/sup\u003e. Using fMRI, Suzuki et al. (2016) showed how behavioral contagion during learning the risk preferences of others influences the neural representation of risky decisions in brain regions such as the caudate nucleus\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the important role that behavioral contagion plays in decision-making, knowledge about brain mechanisms is sparse. In this study, we aimed to investigate the brain mechanisms of behavioral contagion to provide a new understanding of the contagion effect on individuals' decision-making and social behavior.\u003c/p\u003e \u003cp\u003eSince we believe that decision making takes place in a stable brain functional network, we thought that behavioral contagion must put the brain network in a more stable state. In this study, we investigated our hypothesis by comparing the balance energy level and stability of the brain functional network before and after behavioral contagion. To this end, we used structural balance theory (SBT) to measure the balance energy level, and the stability of the network.\u003c/p\u003e \u003cp\u003eSBT was originally proposed by Heider in social psychology to describe the dynamics of interpersonal relationships\u003csup\u003e12\u003c/sup\u003e. This theory focuses on triads to analyze relationships and the tendency of networks to reach balanced states. In recent years, SBT has been further developed and improved to apply to various complex systems in different fields such as genetics\u003csup\u003e13\u003c/sup\u003e, social networks\u003csup\u003e14\u003c/sup\u003e and psychology\u003csup\u003e15\u003c/sup\u003e. It is a new perspective on the dynamics of brain networks.\u003c/p\u003e \u003cp\u003eSBT uniquely considers the signed links between brain regions and offers a different perspective on understanding the function and structure of the brain network. It explains how the interaction between signed links and triads forms the activity change in the brain network and behaviors. SBT has been applied to model higher-order cognitive processes involving multiple brain regions to study phenomena such as cognitive dissonance, decision making, and brain health disorders\u003csup\u003e3,13,14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn neuroscience, Moradimanesh et al. used SBT to study the brain network in autism spectrum disorders (ASD) during development\u003csup\u003e16\u003c/sup\u003e. In another study, Saberi and colleagues used SBT to investigate the stability of the brain network in the resting state. They showed that negative TMH (tendency to make hub) and topology put the brain network in a more stable state\u003csup\u003e17\u003c/sup\u003e. In another SBT-based study, Talesh et al. investigated the stability of brain network in the resting state and the role of the topology of the functional links in reducing the balance energy of the brain network in OCDs compared to a healthy control group\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, using neuroimaging approaches and SBT-based modeling, we sought to develop a new conceptual framework for behavioral contagion and to investigate the resulting stability in the brain network. We aimed to show that after observing others\u0026rsquo; preferences, the brain reorganizes its pair-wise regional synchronies in a way to go to a more stable state. As the outcome of this change in the brain state, behavior contagion as the associated response may be observed.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e31 young healthy adults aged 18\u0026ndash;40 years (mean: 28.22 years), with at least a bachelor\u0026rsquo;s degree, right-handed, consisting of 15 men and 16 women, participated in this study. All steps of the study were approved by the ethical review committee of the Institute with the IRB number of IR.UT.IRICSS.REC.1400.034. All methods complied with standard guidelines, protocols and regulations in accordance with the Declaration of Helsinki. In addition, participants signed an informed consent form and were free to leave the study whenever they wished.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Procedure\u003c/h2\u003e \u003cp\u003eBefore entering the scanner, participants were informed about the task and the procedure through verbal and written explanations.\u003c/p\u003e \u003cp\u003eOur task consisted of 3 sessions and each session consisted of 66 trials. The time limit for each session was 15 minutes, but we did not set a time limit for responding to each trial. The task did not include any jittering in its steps. There was a two-minute break between sessions. After data acquisition, the fMRI data were preprocessed and analyzed with CONN\u003csup\u003e19,20\u003c/sup\u003e. The adjacency matrices were extracted and in the next step the signed matrices were created with these matrices.\u003c/p\u003e \u003cp\u003eThese types of matrices were actually the primary data for extracting the SBT parameters(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These parameters were then used for statistical analysis, extraction of the results, and discussion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eImaging Acquisition\u003c/h2\u003e \u003cp\u003eThe functional and structural MRI data were acquired using a 3 Tesla Siemens Prisma scanner equipped with a 32-channel head coil. Functional images were obtained with the following parameters: the voxel size was set at 3x3x3 mm\u0026sup3;, indicating isotropic dimensions. A repetition time (TR) of 2000 ms was chosen to determine the time interval between successive image acquisitions. The echo time (TE) was 32 ms, which indicates the time span in which the peak echo signal was measured after each excitation pulse. The field of view (FOV) was set to 240 mm, defining the spatial coverage of each imaging volume. Parallel imaging with a GRAPPA algorithm (GeneRalized Autocalibrating Partial Parallel Acquisition) was utilized to reduce acquisition time. The scanner had high-performance gradient coils that facilitate rapid spatial encoding for improved image quality. The flip angle was 80 degrees. The number of slices was chosen to be 35, with a slice thickness of 3.00 mm.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Stimuli\u003c/h2\u003e \u003cp\u003eWe developed a task based on the Dictator Game (DG) to investigate the occurrence of behavioral contagion (ref). The DG is among the experimental paradigms used in behavioral economics studies. In the DG, the participant (as dictator) decides how to divide an endowed amount of money between himself (herself) and an unknown person. The important question for the dictator is: 'How much for me and how much for the other person?'. This game is used to explore socio-economic behavior, altruism, egalitarianism and considerations of fairness or unfairness in decision making.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSelf-Selection-1 trials Prediction-Observation trials Self-Selection-2 trials)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur task consisted of 3 sessions, and each session had 66 trials. In each trial, participant was shown two different patterns of money allocation between the participant (as self) and the other (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The duration of a trial is the required time to show each pair of patterns and participant\u0026rsquo;s response. In the first session (entitled as \u003cem\u003e\u0026ldquo;Self-Selection-1\u0026rdquo;\u003c/em\u003e trials), participant (as dictator) was asked to repeatedly choose one of the pairs of patterns in trials. In session 2 (called Observation-Prediction trials), the preferences of a non-real person (entitled as \u003cem\u003e\"other\"\u003c/em\u003e) were generated based on the participant\u0026rsquo;s preferences from session 1 and using the Fehr-Schmidt model that describes socioeconomic behaviors under uncertainty. In this step, the parameters of the Fehr-Schmidt model were extracted on the basis of the participant\u0026rsquo;s answers. These parameters had to be modified to reflect the preferences for unknown person.\u003c/p\u003e \u003cp\u003eWith these modified parameters, the non-real person\u0026rsquo;s preferences to be used in session 2 were generated for each participant. In session 2, each participant was told that the generated preferences were the choices of an unnamed real person. In this session, each participant was shown the patterns of each trial and asked to guess the unknown person's preferences (66 trials). If the participant's guess was correct, a blue square would appear around the choice on each trial, otherwise the red square would appear. In session 2, the goal was for participants to pay attention to the generated preferences. Indirectly, participants became familiar with the preferences of others in this session. Session 3 (Self-Selections-2 trials) was similar to session 1 and the participant had to choose one of the two patterns in each trial. In this session, if contagion had happened, we would have seen changes in people's preferences. In all sessions, while performing the task, the participants were scanned by the fMRI scanner. We implemented our task using MATLAB R2021a (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mathworks.com/products/matlab/\u003c/span\u003e\u003cspan address=\"http://www.mathworks.com/products/matlab/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Psychtoolbox-3(Clavien \u0026amp; Klein,2010) which is a set of MATLAB functions developed for neuroscience studies that provide a platform for evaluating stimuli and responses in experimental paradigms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFehr and Schmidt Model\u003c/h2\u003e \u003cp\u003eThe Fehr-Schmidt model provides a framework for explaining how individuals\u0026rsquo; preferences regarding fairness and inequality influence economic behaviors. In the context of this model, individuals are classified into two main types based on their socioeconomic behaviors: \"fair types\" and \"selfish types\". But most people are of the intermediate types. In this model, the utility function is able to quantify and model individuals' preferences to different levels of fairness and unfairness. This allows researchers to analyze and predict preferences and behaviors influenced by some parameters. The general form of utility function can be described as:\u003c/p\u003e \u003cp\u003e \u003cb\u003eUi\u0026thinsp;=\u0026thinsp;Mi \u0026ndash;\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{\\alpha }\\varvec{i}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003emax [(Mj- Mi), 0]-\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{\\beta }\\varvec{i}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003emax [(Mi - Mj),0] i\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ne\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003ej\u003c/b\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn the above equation, \u003cem\u003eUi\u003c/em\u003e represents the utility function for individual i. \u003cem\u003eMi\u003c/em\u003e denotes the monetary payoff or income of individual i and \u003cem\u003eMj\u003c/em\u003e shows the monetary payoff of the other. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e denote the weights assigned to the positive and negative difference between the monetary payoffs of individual i (\u003cem\u003eMi\u003c/em\u003e) and the other (\u003cem\u003eMj\u003c/em\u003e) respectively. They indicate how much the individual likes (or dislikes) having a higher (or lower) payoff compared to others\u003csup\u003e21\u0026ndash;24\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eBehavioral Analysis\u003c/h2\u003e \u003cp\u003eAfter collecting the fMRI data, the first step was to analyze the behavioral data. In this way, the number of matches between choices in sessions 1 and 2 and the number of matches between the choices in sessions 2 and 3 were calculated. The behavioral contagion rate (BCR) was defined as the difference between the numbers of these matches.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBCR\u0026thinsp;=\u0026thinsp;N\u003c/b\u003e \u003csub\u003e \u003cb\u003es2\u0026amp; s3\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e- Ns\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u0026amp;s2\u003c/b\u003e\u003c/sub\u003e (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhere N presents the number of matched trials and s denotes the session number by 1,2, and 3.\u003c/p\u003e \u003cp\u003eThe threshold of BCR was set at 4 for the occurrence of contagion (larger than 5% of 66 trials). Using this threshold, the participants were categorized into two groups: Contagion and No Contagion.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNeuroimaging Analysis\u003c/h2\u003e \u003cp\u003eAfter data acquisition and behavioral analysis, CONN (version 22a) was used to analyze the fMRI data. The standard CONN pipeline was used to preprocess the data. The steps of this preprocessing are: import of functional and structural data, realignment, coregistration, segmentation, normalization, smoothing, and artifact detection and correction\u003csup\u003e19\u003c/sup\u003e. The standard atlas in CONN was converted to the Schaefer-400 atlas for our analysis. The Schaefer-400 divides the cerebral cortex into 400 different regions. Compared to many other atlases, the Schaefer-400 provides a finer view to obtain more accurate data. The results of CONN were adjacency matrices of the participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStructural Balance Theory (SBT)\u003c/h2\u003e \u003cp\u003eSBT uses two types of triads to study and analyze systems. The balanced triads, whose product of the signs of the three links is positive, are balanced triads (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Triads in which the product of the signs of their links is negative are called imbalanced triads. Two types of balanced triads are defined as strong (T3) and weak (T1). Strong balanced triads have three positive links [+++], but in weak triads there is one positive and two negative links [+--]. In imbalanced triads there are also two types: strong (T2) and weak (T0). The strong imbalanced type has one negative and two positive links [++-], while the weak type has three negative links [---]. The terms \"strong\" and \"weak\" in imbalanced triads refer to the degree of frustration that a triad can cause in a network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe relationship between imbalanced triads and frustration is the basic tenet of structural balance theory. Imbalanced triads refer to the presence of inconsistencies in the relationships between three nodes in a network. Tension and frustration in the networks are the main consequences of imbalanced triads and lead to changes in behavior.\u003c/p\u003e \u003cp\u003eThe balanced triads have a lower balance energy than the imbalanced triads, which are in a critical state due to their higher balance energy. Normally, the brain network is active in both resting and non-resting states, so there are a number of balanced and imbalanced triads in both states. In critical states and transitional periods, the imbalanced triads tend to be frustrated and change their links to reach more balanced states.\u003c/p\u003e \u003cp\u003eIn SBT, balance energy was defined as the minus of the sum of the total products of the connections of triads in the network. The negative sign in front of the product represents compliance with the physical principle of minimum balance energy. Less balance energy means more stability in a system. In the context of SBT, the increase in balanced triads leads to lower balance energy and more stable systems.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{U}=-\\frac{1}{\\left(\\genfrac{}{}{0pt}{}{n}{3}\\right)}{\\sum }_{i,j,k}{S}_{ij}{S}_{ik}{S}_{jk}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the above equation, U denotes the total balance energy ,n is the number of nodes in the network, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{ij}\\)\u003c/span\u003e\u003c/span\u003e represents the connection between node i and node j, which can be 0 or 1. The term\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\genfrac{}{}{0pt}{}{n}{3}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the 3-combination of n, the number of triads that can be formed in a network with n nodes.\u003c/p\u003e \u003cp\u003eTMH (Tendency to Make Hub) is a global hubness measure to evaluate the tendency of a network's connections to form hubs. Based on the signs of connections, two types of negative and positive TMH are known. It is mathematically defined as:\u003c/p\u003e \u003cp\u003eNegative TMH=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{\\sum }_{i=1}^{n}{{NegD}_{i}}^{2}}{\\sum _{i=1}^{n}{NegD}_{i}}\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), Positive TMH=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{\\sum }_{i=1}^{n}{{PosD}_{i}}^{2}}{\\sum _{i=1}^{n}{PosD}_{i}}\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhere positive degree (\u003cem\u003ePosD\u003c/em\u003e) and negative degree (\u003cem\u003eNegD\u003c/em\u003e) are the numbers of positive and negative links of node i, respectively, and n represents the number of nodes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of the SBT parameters\u003c/h2\u003e \u003cp\u003eOnce preprocessing was complete, both first and second level analyses were performed. The adjacency matrices are mathematical representations of the brain network. Each element of these matrices represents the presence or absence of a link between two nodes. These matrices were used to create the signed matrices. The signed values in these matrices represent the directionality of the connections between the nodes. Positive signs represent excitatory links, while negative signs represent inhibitory links (or anti-correlations). The signed matrices were then used to calculate the 11 parameters of the balance theory in both session 1 and session 3 for each participant .The parameters were the number of positive links, the number of negative links, T0, T1, T2, T3, positive TMH, negative TMH, the number of balanced triads, the number of imbalanced triads, and balance energy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eVarious statistical methods were used to assess the differences, relationships and quality of the variables. First, the normality of the calculated parameters had to be checked using the Shapiro-Wilk test. If the SBT parameters of 2 sessions were normally distributed (p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), in the next step they could be measured with a paired t-test, and if not and at least one of each matched pair was not normally distributed, the measurement had to be performed with the Wilcoxon signed-rank test, a non-parametric alternative to the paired t-test. Both tests determined whether there were significant differences between the SBT parameters in the sessions (before and after observing others' preferences).\u003c/p\u003e \u003cp\u003eAnother statistical method that we used in our analysis was the independent t-test. The independent t-test is a parametric test that can be used for normally distributed data from two independent groups. If the assumptions of normality for two groups are not met, the Mann-Whitney U-test serves as a suitable alternative to the independent t-test. The Mann-Whitney U-test determines whether the medians of two independent groups are different or not. In our analysis, the independent t-test and the Mann-Whitney U-test were used to compare the SBT parameters of the first sessions of the Contagion and No Contagion groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn this study, we applied SBT to understand the dynamics of the brain network. The main goal was to investigate the asynchrony of the brain's functional network during behavioral contagion. To this end, participants were scanned while performing the task. In the behavioral analysis conducted using the BCR equation for contagion based on the preferences of 31 participants, 17 individuals were identified with BCRs greater than 3 as the threshold for the occurrence of contagion. These 17 individuals were labeled as the Contagion group. 12 individuals with BCRs lower than 4 were named as the No Contagion group. Because of quality issues, the data of 2 participants was excluded from the final analysis.\u003c/p\u003e \u003cp\u003eWe then analyzed the fMRI data of the \"Contagion\" and \"No Contagion\" groups with CONN and extracted their adjacency matrices. For all participants in both groups, the signed networks were calculated using their adjacency matrices. Then, 11 parameters of SBT (as described in \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003eCalculation of the SBT parameters\u003c/span\u003e section) were calculated for session 1 and session 3 of the participants in the groups. First, all parameters (session 1 and session 3) were tested for normality using the Shapiro-Wilk test. In the Contagion group, all parameters related to session 1 were normally distributed (p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but in session 3 of this group, the parameters T1, T3, the negative link count and the positive link count were not normally distributed. In the No Contagion group, in session 1, all calculated SBT parameters of the participants were normally distributed, except for balance energy, the number of balanced triads and the number of imbalanced triads. In session 3 of this group, all parameters were normally distributed.\u003c/p\u003e \u003cp\u003eIn the next step, the paired t-test was performed for the Contagion group participants to compare the corresponding parameters in two sessions, with the exception of the parameters T1, T3, the negative link counts and the positive link counts, whose differences between the two sessions were measured using the Wilcoxon signed-rank test.\u003c/p\u003e \u003cp\u003eFor the Contagion group, the comparisons of the 11 matched pairs (session 1 and session 3) showed that there were significant differences between the matched pairs of the number of balanced triads, the number of imbalanced triads and the balance energy (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant difference was found for other matched pairs (See Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the No Contagion group participants, the paired t-test was applied to compare the SBT parameters of two sessions except for the balanced energy, the number of balanced triads and the number of imbalanced triads. These 3 parameters were measured in 2 sessions using the Wilcoxon signed-rank test. The comparison of 11 matched pairs revealed that there were significant differences between all SBT parameters in two sessions (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) except for the number of balanced triads, the number of imbalanced triads and the balance energy (See supplementary information, Figures \u003cem\u003eS1and S2\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThe SBT parameters of two groups that have been significantly changed between session1 and session 3 are listed separately in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSBT parameters with significant differences between session1 and 3 for the Contagion and No Contagion groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBT parameters which had significant differences between sessions 1 and 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContagion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBalance Energy, Balanced Triads, Imbalanced Triads\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo Contagion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePositive links, Negative links, T0, T1, T2, T3, Positive TMH, Negative TMH\u003c/em\u003e\u003c/p\u003e \u003cp\u003eSee supplementary materials (S1-S2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the next step of the statistical analysis, we examined whether there were fundamental differences between the SBT parameters of the Contagion and No Contagion groups in session 1 (before observation) that might be the reason behind the contagion effect. After testing for normality, statistical comparisons between the sessions 1 of both groups were performed using the independent t-test and the Mann-Whitney U-test. As shown in Table\u0026nbsp;2, there were significant differences in 7 SBT parameters, including the numbers of positive and negative links, the numbers of T0, T1, and T3 triads, and the positive and negative tendency to make hubs (TMH)(p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).But no significant differences were observed in the balance energy level(p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which denotes that although the brain functional network stability was at the same level in two groups before observation, the numbers of negative and positive links and their tendency to make hub (TMH) could influence the rearrangement of the network, not allowing it to change the stability in the No Contagion group(See supplementary information, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eTable.2 Comparing the SBT parameters of sessions 1 of Contagion and No Contagion groups.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBT parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative Links\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive Links\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNegative TMH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePositive TMH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBalanced Triads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eImbalanced Triads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eBalance Energy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP -values\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere is no doubt that observing the behavior of others influences our preferences. In this study, we sought to understand how contagion affects the stability of the brain functional network and causes the brain to respond like the observed behaviors. Therefore, we used a modified version of the DG to investigate how observing others\u0026rsquo; preferences influences participants\u0026rsquo; preferences. Based on participants' responses before and after observing others\u0026rsquo; preferences, and the BCR threshold (5 percent of 66 trials) the participants were divided into the Contagion and No Contagion groups .Structural balance theory was then used to examine the stability of brain functional network extracted from participants' fMRI data that had been obtained while performing the task. Statistical analysis of SBT parameters between sessions 1 (before) and session 3 (after) showed distinct patterns for the two groups (presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For participants in the Contagion group, there were significant differences between two sessions in balance energy level, numbers of balanced and imbalanced triads.\u003c/p\u003e \u003cp\u003eWe believe that the occurrence of contagion reorganizes the brain functional network by leading to new synchronization and asynchronization between the brain regions. This rearrangement in the brain network in response to cognitive demand raised from observation of others\u0026rsquo; behaviors and change pushes the network to a more balanced state and influences the participant's preferences accordingly. Such a change in stability (decrease in balance energy level after observation) is introduced by an enhanced number of balanced triads and a decreased number of imbalanced triads. As mentioned in the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003emethods\u003c/span\u003e section, T0 and T2 denote the imbalanced triads, and T1 and T3 denote the balanced triads.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the results presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we believe that after observation of others\u0026rsquo; preferences, the rearrangement process in the network takes place by an increase in the number of balanced triads that could occur by replacing a negative link from imbalanced triads (T0 or T2) with a positive link and transforming them into balanced triads (T1 or T3). If the change occurred in T0 triads, T0s would turn into T1 balanced triads. Another possible change may occur in the negative links of T2 and replace them with positive links that transfer the T2s to T3s triads. If the changes in the numbers of balanced and imbalanced triads had more to do with the transformation from T0 to T1, the number of T0s in session 3 compared to session 1 would decrease and the number of T1s in session 3 compared to session 1 would increase. But if there were more transformations from T2 to T3, the number of T2s in session 3 would decrease compared to session 1 and the number of T3s in session 3 would increase. By comparing the changes in the numbers of T0s and T1s and also the changes in the number of T2s and T3s in both sessions, we concluded that most of the changes in the number of balanced triads occurred through the transformation of strong imbalanced triads (T2s) into strong balanced triads (T3s) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Therefore, the calculation of the balance energy level of the network before and after observation of others\u0026rsquo; preferences showed that the balance energy level after the observation decreased compared to before. This decrease in balance energy level was expected due to the increase in the number of balanced triads and the decrease in the number of imbalanced triads and could indicate the achievement of a higher level of stability in the network, which could force changes in the preferences. It is worthy to mention that such a change in the stability of the functional brain network is not observed in the No Contagion group, which can emphasize the role of balance energy level on the change in participants\u0026rsquo; preferences.\u003c/p\u003e \u003cp\u003eIn short, what has been said about dependent and non-dependent metrics shows that topology (the arrangement of links) plays the main role in the brain network. The changes, rearrangements of this topological framework, rather than changes in the quantity of links, are responsible for the change from one state to a more stable state in behavioral contagion.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitation and suggestion for future work\u003c/h2\u003e \u003cp\u003eThere were some limitations in our study. The first limitation concerned the relatively small sample size (only 31 participants). This limitation could lead to some shortcomings and limitations in the statistical analysis. In addition, we only collected data from right-handed participants, so the results may not be directly transferable to left-handed participants. In addition, future works could investigate the reasons for the differences between people in the \"Contagion\" and \"No Contagion\" groups. The next studies could also investigate how to bring more stability to the brain network while observing the behavior of others.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe investigated the stability changes in the brain network in two sessions before and after the occurrence of contagion, using fMRI and SBT. Based on a threshold for BCR, participants were divided into the Contagion and No Contagion groups. Our analysis showed that there were two distinct patterns of SBT parameters for these groups. In the Contagion group, we found a significant difference only in the numbers of balanced and imbalanced triads and balance energy levels between the two sessions. There was no significant difference in the other parameters, including the numbers of positive and negative links, signed TMHs, T0, T0, T1, T2, and T3 triads. These results indicate that behavioral contagion is a rearrangement process in the brain functional network. A decrease in the number of imbalanced triads and an increase in the number of balanced triads in session 3 compared to session 1 lead to a decrease in balance energy level. The changes in the number of balanced and imbalanced triads were more related to the transformation from T2 (in the first session) to T3 (in session 3). In summary, this study shows that observing the behavior of others during behavioral contagion causes a reorganization that shifts the brain network to a more stable state.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe codes, supporting information ,and datasets generated and analyzed in this study are available at https://github.com/M-Mobasseri/-Behavioral-contagion . If you require further information, please contact the corresponding authors. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRK designed the study, supervised the data gathering and analysis, validated the results, and edited the manuscript. AV designed the experiment and supervised the behavioral analysis. MM helped with experimental design, performed data acquisition and data analysis, and wrote the first draft and revised the manuscript. GJ and JH also helped in the validation of the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants in this study for their cooperation and also Dr. Malihe Milani for her valuable support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLe Bon, G. The Crowd. A Study of the Popular Mind. Science, \u003cem\u003e5\u003c/em\u003e(123), 734\u0026ndash;735(1897).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell-Meiklejohn DK, Bach DR, Roepstorff A, Dolan RJ, Frith, CD,. 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Struct. 16, 1\u0026ndash;49(2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaiz, H. et al. Evidence of structural balance in spatial ecological networks. Ecography 40, 733\u0026ndash;741(2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4524070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4524070/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBehavioral contagion has been defined as the tendency of individuals to imitate the behavior of others after observing them. Despite the important role that behavioral contagion plays in societies, its mechanism in the brain is still not fully understood. In this study, we hypothesized that the brain tends to go to a more stable state after updating behavior by observation of the others\u0026rsquo; behaviors. Therefore, the stability of the brain network before and after observing others\u0026rsquo; preferences was assessed using structural balance theory (SBT) on the fMRI data. To this end, we developed a version of the Dictator Game as the task, and recorded participants' brain responses using fMRI (before and after observing others' preferences). A threshold for changes in participants' preferences was considered to be the occurrence of behavioral contagion. With regard to this threshold, the participants were classified into two groups, the Contagion and No Contagion. The changes in SBT parameters of the brain network were calculated for both groups. A distinct pattern of changes in SBT parameters was observed for each group. The results of the Contagion group suggested that behavioral contagion is accompanied with a rearrangement of links in the network to transform imbalanced triads into balanced triads. This process lowers the balance energy of the brain network and pushes the network to a more stable state. We hope that these findings on the restructuring of the functional brain network could pave the way to a better understanding of behavioral contagion.\u003c/p\u003e","manuscriptTitle":"Rearrangement of anti-synchronous activities in the brain functional network plays a crucial role in behavioral contagion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-01 19:14:34","doi":"10.21203/rs.3.rs-4524070/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b2a0c99-d13b-4f0b-954b-9a9c0fde89a6","owner":[],"postedDate":"July 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33684997,"name":"Biological sciences/Neuroscience"},{"id":33684998,"name":"Biological sciences/Psychology"},{"id":33684999,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2024-07-17T11:42:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-01 19:14:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4524070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4524070","identity":"rs-4524070","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}