Brain Activity During Defensive Reactions to Virtual Threats

Brain Activity During Defensive Reactions to Virtual Threats | 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 Brain Activity During Defensive Reactions to Virtual Threats Carolina Lopes, Jaime Godinho, César Teixeira, Lorena Petrella This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4139730/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 The study of brain activity under the appearance of an unexpected visual threat can give some insights into how the brain reacts to potential dangers, and how the consequent defensive response is originated. In this study, a virtual reality (VR) scene is used to present an unexpected threat aiming to invoke a defensive reaction, as well as non-threatening stimuli as control. The brain activity is measured along the pre and post stimuli conditions using electroencephalography (EEG). The goal is to identify how the information propagates between cortical regions once the threatening situation is presented. The functional connectivity study evidenced a flux of information from the left middle temporal gyrus to the premotor cortex, evidencing a defensive response induced by the sound involved in the stimulus. Additional connections involving diverse cortical areas as the left inferior frontal gyrus, the primary motor cortex, the prefrontal cortex, beside the premotor cortex may represent part of the information flux involved in action planning. Other activated cortical areas were the supplementary motor cortex, the right temporal gyrus, the associative visual cortex, and primary somatosensory cortex. Concluding, the immersive scenario provided by VR allowed to induce more natural defensive response, and consequently the identification of relevant brain activity. Biological sciences/Neuroscience Biological sciences/Computational biology and bioinformatics/Data processing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The study of brain activity under the appearance of an unexpected visual threat can give some insights into how the brain reacts to potential dangers and how the defensive response originates. In humans, the visual system plays an important role in protecting the body from threats once it is the primary sense used to evaluate and respond to the surroundings and potential sources of danger. It has been proved that threat stimuli evoke defensive responses 1 , which consist of survival mechanisms that help to protect and prepare the body from dangers, and include avoidance responses, flight, freezing, and risk assessment. An avoidance behavior consists of fast actions directed to avoid or minimize the negative consequences of the perceived threat 2 , 3 . Also, the perception of the motion of the threatening stimulus is needed to adapt the behavior to the situation 4 – 7 . Indeed, past research has shown that the direction of the threat, that is, if it is coming towards or away from the person, can alter brain activity and psychological responses 8 . In humans, some studies found diverse brain regions that activate under threatening stimuli namely the premotor cortex, the pre-supplementary motor area, and the inferior frontal gyrus 1 , 8 , 9 . Threat-related studies have been conducted in humans, monkeys, and rodents. To identify the activated brain areas, electroencephalogram (EEG) 4 , 5 , 10 and functional magnetic resonance imaging (fMRI) 1 , 6 , 8 , 9 have been used. fMRI has a good spatial resolution, but a poor temporal resolution, which limits the study of fast responses. On the other hand, EEG presents good temporal resolution, but more limited spatial resolution compared to fMRI 11 . Regarding the threatening stimulus used in human studies, most of the studies resort to images, videos played on a monitor, and sounds, representing a certain threat (mostly animals considered threatening, such as spiders and sharks, or sudden objects and sounds). However, in these previous studies, emotional responses are inherent to visual stimuli recognition and interpretation. Moreover, observing an image or a scene projected on a monitor, reduces the person’s feeling of being in a threatening situation. In this sense, virtual reality (VR) videos present as a promising solution to induce a more natural response. Virtual reality (VR) technology uses interactive computer-generated graphics to create a simulated environment and give the user an immersive feel of a virtual world. It gives the possibility to present complex stimuli in a controlled manner and has attracted the attention of researchers and psychotherapists in the last few years 12 , 13 . Also, it has been proved that VR can activate the threat-processing circuitry in the brain, and it is already used in the treatment of phobias and post-traumatic stress disorder (PTSD), once it allows the creation of highly realistic and immersive simulations of potentially threatening scenarios in a controlled environment 13 . In this study, a VR video is used to provide an immersive scenario where an unexpected visual threat is presented to invoke a defensive reaction, while brain activity is measured using EEG. To invoke a defensive reaction, a neutral stimulus was used, overcoming some bias of previous studies, where the threatening stimuli involved an emotional perception. On the other hand, the immersive scene provided by VR is more efficient in generating a defensive response under the threatening situation. With this approach is expected to depict the flux of information involved in an escape response. Methods The general experimental setup comprises the following steps: the setup of the VR headset, the development of the VR video, the acquisition of EEG signals along video visualization, and data analysis. The main steps involved in data analysis are presented in Fig. 1 and detailed in the next sections. Visual Stimulus The VR video was created recurring to the cross-platform game engine Unity (Unity Software Inc., San Francisco) and consists of a calm scenario of a forest, at dusk light, containing typical elements such as trees, flowers, and grass (Fig. 2 .a), light breeze movements, and soft background forest sounds. The goal was to create a calm scenario with few distracting elements to reduce strong modifications in brain activity, besides the ones related to the threat stimulus. The scene was presented as if the observer were walking straight ahead along the forest path. The visual stimulus was projected using a VR headset (Reverb G2, HP, Palo Alto, California) comprising lenses and headphones. The video lasts 5 m 35 s, and at 2 m 3 s the threatening stimulus is presented (Figs. 2 .b and 2.c). It consists of an unexpected rock coming towards the viewer that ends up obscuring the scene and then disappears. The rock is in the scene for about 1 s and is also accompanied by a crashing sound. To identify whether the participant's reaction to the threat stimulus and the associated brain activity relate to defensive mechanisms or just to the sensory inputs, two additional stimuli were introduced. To segregate the effect of the crashing sound accompanying the threat stimulus, at 3 m 14 s, the same sound was reproduced but without the visual stimulus (without the rock). Also, at 4 m 45 s, a small bird crosses the scene (Fig. 2 .d), representing a neutral visual input. EEG Data Acquisition To register the participant's brain activity, an electrode cap (Electro-Cap International, Eaton, Ohio, United States) following the international 10–20 electrode placement system was used. Activity was registered from 19 channels including: Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2. Signals were acquired at a sampling frequency of 512 Hz and stored on a computer for posterior analysis. Both, VR video and EEG records were synchronized to identify the stimuli’ times. Participants and Protocol The study was approved by the Ethics Committee of the Faculty of Medicine of the University of Coimbra under the protocol CE-017/2023, and all procedures were performed in accordance with the relevant guidelines and regulations. Participants were informed about the study protocols, except for the content of the video. Each participant who agreed to enroll signed an informed consent. The inclusion criteria consisted of young adults between 18 and 40 years old. The exclusion criteria were the presence of visual deficits, neuropathological conditions, cardiac pathologies, and the use of neuropharmaceuticals. No further gender, social, or educational conditions were considered. The study included 26 eligible volunteers, and data was anonymized with a numeric code to preserve participants' identity. The involvement of each participant contemplated only one session, where the participant was connected to the EEG system to record brain activity while simultaneously viewing the VR video through the VR headset (Fig. 3 ). The whole procedure (including questionnaires, participant preparation, and video projection) took about one hour, and no posterior follow-up was required. During the procedure, participants were comfortably seated in a chair and were instructed to stay as still as possible, not talk, and stay calm during the video visualization. At the end, they were asked about their personal experiences while watching the video, specifically, if the threat stimulus was observed and if they felt threatened by it. Signal Processing and Feature Extraction All processing and subsequent analysis of the acquired EEG data was implemented using MATLAB programming language. The EEG frequency range of interest was considered between 0.5 Hz and 80 Hz. An infinite impulse response (IIR) notch digital filter of second order was applied to exclude the frequency component of 50 Hz related to the power-line interference. Additionally, a second IIR notch filter of second order was applied at 37 Hz since this frequency component, related to external sources, was also evidenced in the signal spectrum. To eliminate the lower ( 80 Hz) frequency components, the data was bandpass filtered, applying a finite impulse response (FIR) filter of minimum-order (i.e., the order is automatically computed to be the minimum the FIR filter must have to meet the specifications). To remove the remaining artifacts (including eye blinking, eye movements, muscular activity, and cardiac artifacts) it was used independent component analysis (ICA). The number of computed independent components was the same as the number of EEG channels (i.e., 19). The identified components related to artifacts were eliminated, and the EEG was again reconstructed. The following step consisted of extracting features from the filtered data. The relative power spectral density (rPSD) values were obtained for each EEG channel and for each frequency band: delta (0.5 to 4 Hz), theta (4 to 7 Hz), alpha (8 to 12 Hz), beta (13 to 30 Hz) and gamma (30 to 80 Hz). To compute the PSD values, the signals were first segmented in 1 s windows with an overlap of 50%, using a Hamming window. Then, for each segment, the PSD value of each frequency band was computed by adding all the PSD values within each frequency band. Finally, the rPSD value for each band resulted from dividing the corresponding PSD values by the sum of all the PDS values. Therefore, 95 features were obtained, corresponding to each frequency band (5 bands) and each EEG channel (19 channels). Identification of Relevant Brain Areas The next step concerns the study of the activated brain areas during a threatening situation. To discard activations associated exclusively with sensorial perception, the activated brain areas for the two control stimuli (crashing sound and bird stimuli) described in the Visual Stimulus section were also considered. Being so, the neutral state (pre-stimulus) and each of the stimulus states (post-stimulus) were analyzed. Related to the time window for the neutral state, it was used the average of the rPSD values between 20 s and 100 s (before the appearance of the first stimulus), aiming to reduce variability in the neutral condition. Regarding the time window for each stimulus, several consecutive post-stimulus windows were considered to account for the total duration of the stimuli, as well as for possible delays in reaching the different brain areas. For the threat stimulus, the time windows considered were 122.5 s to 123.5 s, 123 s to 124 s, and 123.5 s to 124.5 s. Meanwhile, for the crashing sound stimulus, the intervals were 193.5 s to 194.5 s, 194 s to 195 s, and 194.5 s to 195.5 s. Lastly, in the case of the bird stimulus, the time windows included were 285.5 s to 286.5 s, 286 s to 287 s, 286.5 s to 287.5 s, and 287 s to 288 s (the additional window accounting for the longer duration of this stimulus). Firstly, the One-sample Kolmogorov-Smirnov test (with a significance level of 0.05) was applied to the rPSD values of each frequency band and EEG channel of all the participants, to depict if the data followed a normal distribution. The subsequent step consisted of identifying the relevant EEG channels and frequency bands. A channel is considered relevant if the associated rPSD values exhibit significant differences between the neutral state (time interval without stimuli) and the stimuli states (time intervals starting at the beginning of each applied stimulus). For this, the Kruskal-Wallis nonparametric multiple comparison test was applied, with a significance level of 0.05. For each of the three stimuli separately (threat, crashing sound, and bird), the Kruskal-Wallis test was conducted among the rPSD data concerning the neutral condition and each of the consecutive post-stimulus time windows, as shown in Fig. 4 . After the multiple comparison tests, a multiple comparison correction using the Bonferroni method was applied, with α set to 0.05. Of the 26 participants, 6 did not react or feel threatened by the stimulus and were excluded from the analyses. However, the data from these 6 participants were posteriorly used for comparison, following the same statistical analysis first performed for the 20 participants who felt threatened. Functional Connectivity Between Relevant Brain Areas The next step was to find the existence and strength of connections between the activated brain areas (i.e., the areas identified in the previous step as being involved in the defensive reaction). In this sense, five methods of functional connectivity were implemented: coherence (COH) 14 , 15 , imaginary part of coherence (iCOH) 14 , 16 , weighted phase lag index (wPLI) 17 , 18 , mean phase coherence (MPC) 19 , and direct transfer function (DTF) 20 . The connectivity methods iCOH and wPLIi overcome the problems of volume conduction and signal-to-noise ratio (SNR). The volume conduction problem refers to the electrical activity of a source being present in more than one EEG channel due to bone and other scalp structures conduction. The SNR quantifies the relative noise content in the signal 14 , 16 . The COH, iCOH, wPLI, and MPC methods were used to find the relevant connections while the DTF method was used to find the direction of these connections. For each connectivity method, the connectivity values for each channel pair were evaluated under two conditions: neutral state (pre-stimulus) and threat stimulus state (post-stimulus). Once again, to reduce variance within the neutral state, the connectivity was calculated for the time series data corresponding to each second of the interval comprehended between 20 s to 100 s (corresponding to a neutral part of the video), and then the resultant connectivity values were averaged. Regarding the threat stimulus state, the associated connectivity values were obtained for the time series data corresponding to the instant of the threat appearance on the video, i.e. the 122.8 s − 123.8 s time window. It is to be noted that this connectivity is independent of frequency bands, encompassing the entire spectrum of the signals (0.5 Hz to 80 Hz). For each connectivity method, the connectivity values of each channel pair were stored in three matrices containing the average of these values across participants: one for the neutral state (pre-stimulus), one for the threat stimulus state (post-stimulus), and one for the difference between the connectivity values of the stimulus and the neutral states (post-pre). Using the post-pre matrices, for each connectivity method, the pairs of channels whose post-pre values were greater than a threshold corresponding to the 0.65-quantile (that is, the value above which 35% of the post-pre values are) were obtained. These correspond to the pairs of channels with a relevant connection. To obtain more reliable results, only the relevant channel connections that were present in at least three of the four connectivity methods were considered relevant. The next step consisted of depicting the direction of the identified connections, that is, the direction of the information flow. In this sense, the DTF method was applied to the post-stimulus data to discover the direction of each of the previously found significant connections. The post-stimulus data was the same used for the other connectivity methods (corresponding to the 122.8 s − 123.8 s time window), and a DTF matrix containing the DTF values of each channel pair averaged over all the participants was obtained. Results Relevant Brain Areas Because the One-sample Kolmogorov-Smirnov test indicated that the data did not follow a normal distribution, the Kruskal-Wallis nonparametric multiple comparison test was applied in the following steps. The features exhibiting significant differences between the neutral state and the stimulus state, for each of the three stimuli (threat, crashing sound, and bird), are presented in Table 1. Significant differences were observed in almost all the consecutive post-stimulus time windows compared to the neutral window. Table 1. EEG channels and frequency bands presenting significant differences between the neutral state and each of the three stimuli, using a significance level of 0.05. The results were corrected for multiple comparison effects. Threat Sound (crashing sound) Visual (bird) T5- beta, alpha, delta T4- theta T4- theta T4- beta, delta Cz- beta F8- beta Cz- beta F8- beta, delta C3- gamma, beta, delta T3- gamma, delta F8- gamma, beta, delta Fz- beta F7- gamma, delta Fp2- gamma Fp1- beta, delta Some brain areas were activated with both, the threat stimulus, and the sound stimulus, including Cz-beta, F8-beta, and F8-delta. Comparing the threat stimulus with the visual stimulus results, only F8-beta activation was common to both situations. As such, the activation of F8-beta and F8-delta will not be deemed to play a role in an escape reaction. However, Cz activation is possibly related to the motor activity resulting from the unexpected appearance of threatening or sound stimuli, so the Cz-beta will be kept. This way, Figure 5 represents the relevant channels and frequency bands activated under the threat stimulus, mentioned above. It is possible to observe that most discriminative brain regions between the neutral and threat states locate in the front half of the brain and slightly more concentrated in the left hemisphere. Also, regarding the threatening stimulus, it is possible to observe the predominance of the delta, beta, and gamma bands in several channels. Functional Connectivity Between the Relevant Brain Areas Table 2 shows the connections that presented significant post-pre connectivity values (higher than the 0.65-quantile), for each of the connectivity methods. It includes the channel pairs and the corresponding connectivity values for each method, as well as the connections identified as significant in at least three of the methods (last column). Table 2. Significant connections (higher than the 0.65-quantile) related to the threat stimulus; the values correspond to the difference between the connectivity values under threat and neutral states. Results for each of the implemented methods are presented in each column, and connections considered significant for at least three of the methods are identified in the last column. Connections COH iCOH wPLI MPC Common Significant Connections Fp1-T4 0.100 0.147 0.158 x Fp1-Cz 0.084 Fp1-F7 0.076 0.053 0.178 x Fp1-F8 0.046 0.146 Fp1-C3 0.044 Fp1-T5 0.164 Fp2-Cz 0.070 Fp2-C3 0.068 0.049 0.170 x Fp2-F7 0.058 0.184 0.140 x Fp2-F8 0.051 Fp2-T3 0.147 Fp2-T4 0.090 0.054 F7-Cz 0.095 0.048 0.179 x F7-T4 0.084 0.155 F7-Fz 0.082 0.061 F7-C3 0.074 0.157 F7-T3 0.194 F7-F8 0.163 F7-T5 0.169 Cz-T4 0.089 0.057 Cz-T5 0.078 0.184 Fz-T4 0.085 Fz-T3 0.076 0.057 0.189 x Fz-Cz 0.069 0.053 0.160 x Fz-C3 0.043 Fz-T5 0.144 C3-Cz 0.059 C3-T4 0.160 F8-T4 0.051 F8-C3 0.200 F8-Cz 0.195 F8-T5 0.179 F8-T3 0.138 T3-T4 0.191 T3-Cz 0.175 0.150 T3-C3 0.158 T3-T5 0.160 T4-T5 0.181 The most relevant channel connections (Table 2, last column) related to the threat situation are represented in Figure 6, as well as their directions obtained by analyzing the DTF(i,j) and DTF(j,i) values of the corresponding channel pairs. From them, stand out the connections Fp1-T4, Fp2-F7, Fz-T3, and F7-Cz which also presented the highest connectivity values for each method. Discussion In this study it was observed that the cortical areas and frequency bands involved in threat evoked reaction correspond to T5-beta, T5-alpha, T5-delta, T4-beta, T4-delta, Cz-beta, C3-gamma, C3-beta, C3-delta, T3-gamma, T3-delta, F8-gamma, Fz-beta, F7-gamma, F7-delta, Fp2-gamma, Fp1-beta and Fp1-delta. Although brain activity involves highly complex mechanisms that cannot be completely explored by EEG analysis, some insights are presented below. The T5 channel locates over the left associative visual cortex, and is responsible for complex processing of visual information concerning spatial orientation, depth perception, object recognition, location and movement of objects in space 21 – 24 ; and the object moving towards the observer in this study can cause its activation. The T3 and T4 channels captures activity from the left and right temporal gyrus, respectively, both involved in sound processing and recognition, and their activation may relate to the crash sound involved in the threatening stimulus. However, T3 was not activated when only the sound stimulus was presented, and T4 was also activated when only the visual stimulus was presented. So that, the role of these areas under a threatening situation should be further explored 21 , 22 , 25 , 26 . The Cz and Fz channels locate over the motor cortex, with Cz near the primary motor cortex and Fz near the premotor and supplementary motor cortex. While the primary motor cortex and the supplementary motor cortex are more strongly related to the control of voluntary movement, the premotor cortex plays a key role in autonomic reactions and motor responses in defensive behaviors 9 , 21 , 22 , 25 , 27 – 30 . Overall, the activation of these regions may relate to a short movement (escape reaction) observed in the participants when the threat stimulus appears. Cz was also activated only with the sound stimulus, and it evidence that an unexpected sound can induce a defensive reaction by itself. The C3 channel is over the left primary somatosensory cortex, responsible for receiving and processing tactile information (touch, pressure, temperature, and pain), and although there is no evident relation with the threatening stimulus, past studies have also observed its activation in response to fear stimuli not consciously perceived 21 , 31 – 33 . The F7 and F8 channels are located near the left and right inferior frontal gyrus 21 , involved in attentional processing 34 , 35 , fear processing 36 , anxiety regulation during threat exposure 38 , and auditory processing 35 . On the other hand, it has been reported that the left frontal gyrus is involved in action preparation, and plays a role in threat processing, specifically where the threat is directed towards the person, which represents a direct relation with this study 1 , 8 , 21 , 22 , 25 . Fp2 and Fp1 channels are located over the right and left anterior prefrontal cortex, which are associative areas involved in complex functions like reasoning, planning, execution, decision-making, vigilance, and processing emotional stimuli (fear control). Given the complexity of the prefrontal cortex functions, there is a wide range of mechanisms that can lead to its activation under threat stimuli 8 , 21 , 22 , 25 , 39 , 40 . Interestingly, regarding the statistical analysis performed with the six participants who did not feel threatened by the stimulus, no significant differences were obtained between the neutral and threat situations for any of the brain areas. Regarding the connectivity analysis (Fig. 6 ), the prefrontal areas, with complex functions like reasoning and planning 22 , 25 , communicates with the inferior frontal gyrus involved in actions preparation (F7), and it connect to the primary motor cortex. Together it unveils part of the information flux involved in action planning in threatening situations. It is observed that the area involved in sound processing and recognition (left middle temporal gyrus, T3) acts over the premotor cortex (Fz), identified as a key cortical area in defensive response, since here it is monitored the peri personal space favoring a quick reaction. So that, sound recognition seems to play a main role in human autonomic reaction. Indeed, along the development of the threatening stimulus, it was observed that the visual stimulus alone (without sound), induced few or non-reaction. Also, the role of sound in triggering a defensive action has been reported in previous studies 22 , 26 . A connection from the premotor cortex to the primary motor cortex (Cz) was also evidenced, and despite no considered significant in this analysis, a connection between the associative visual cortex to the primary motor cortex was depicted by two connectivity methods. Intriguingly, it was also evidenced a connection from the prefrontal regions to the somatosensory cortex (C3). Despite this observation should be further explored, we hypothesize that it may relate to some mechanism for alerting potential body injuries. Conclusion The goal of this study was to explore the brain areas involved in defensive reactions to a threatening stimulus, as well as the functional connectivity between them, using a combined VR and EEG approach. The brain areas activated by the applied threat stimulus consisted of the inferior frontal gyrus, premotor cortex, supplementary motor cortex, prefrontal cortex, temporal gyrus, associative visual cortex, primary motor cortex, and primary somatosensory cortex. Regarding the connectivity analysis, our results suggest that the threat stimulus triggers a defensive reaction mediated by sound recognition (connectivity from T3 to Fz) which acts over the premotor cortex, and that action planning involves a complex brain network including the Cz, F7, FP1 and FP2 related areas. These results contribute to the understanding of defensive reactions in a threatening situation and may, in the future, support the development of treatments for certain psychopathological conditions. Future work consists of finding the temporal sequence of activation of the brain areas involved and incorporating techniques able to study the activity of subcortical structures. Declarations Data Availability The datasets generated during the current study are available from the corresponding author upon reasonable request. Acknowledgments This work is founded by FCT – Foundation for Science and Technology, I.P., within the scope of the projects: CISUC – UID/CEC/00326/2020 with founds from the European Social Fund, through the Regional Operational Program Centro 2020; and project RECoD – PTDC/EEI-EEE/5788/2020 financed with national funds (PIDDAC) via the Portuguese State Budget. Author contributions statement Conceptualization: C.L., L.P., and C.T.; experiment design: C.L. and L.P.; VR video development: C.L.; instrumentation setup and data acquisition: C.L., J.G. and C.T.; data analysis and interpretation: C.L., L.P., and C.T.; manuscript revision: C.L., L.P., and C.T. Additional information The authors declare no competing interests. References Pichon, S., de Gelder, B. & Grèzes, J. Threat prompts defensive brain responses independently of attentional control. Cereb. Cortex N. Y. N 1991 22 , 274–285 (2012). Litvin, Y., Blanchard, D. C. & Blanchard, R. J. 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The role of the right premotor cortex and temporo-parietal junction in defensive responses to visual threats. Cortex 120 , 532–538 (2019). Liddell, B. J. et al. A direct brainstem-amygdala-cortical ‘alarm’ system for subliminal signals of fear. NeuroImage 24 , 235–243 (2005). Primary Somatosensory Cortex - an overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/medicine-and-dentistry/primary-somatosensory-cortex. Kropf, E., Syan, S. K., Minuzzi, L. & Frey, B. N. From anatomy to function: the role of the somatosensory cortex in emotional regulation. Rev. Bras. Psiquiatr. 41 , 261–269 (2018). Walkup, J. T., Friedland, S. J., Peris, T. S. & Strawn, J. R. Dysregulation, Catastrophic Reactions, and the Anxiety Disorders. Child Adolesc. Psychiatr. Clin. N. Am. 30 , 431–444 (2021). Cazzoli, D. et al. Anterior insula and inferior frontal gyrus: where ventral and dorsal visual attention systems meet. Brain Commun. 3 , fcaa220 (2021). Tao, D., He, Z., Lin, Y., Liu, C. & Tao, Q. Where does fear originate in the brain? A coordinate-based meta-analysis of explicit and implicit fear processing. NeuroImage 227 , 117686 (2021). Herrmann, M. J., Beier, J. S., Simons, B. & Polak, T. Transcranial Direct Current Stimulation (tDCS) of the Right Inferior Frontal Gyrus Attenuates Skin Conductance Responses to Unpredictable Threat Conditions. Front. Hum. Neurosci. 10 , (2016). Gold, A. L., Morey, R. A. & McCarthy, G. Amygdala–Prefrontal Cortex Functional Connectivity During Threat-Induced Anxiety and Goal Distraction. Biol. Psychiatry 77 , 394–403 (2015). Madsen, M. K. et al. Threat-related amygdala functional connectivity is associated with 5-HTTLPR genotype and neuroticism. Soc. Cogn. Affect. Neurosci. 11 , 140–149 (2016). Kirk, P. A., Holmes, A. J. & Robinson, O. J. Threat vigilance and intrinsic amygdala connectivity. Hum. Brain Mapp. 43 , 3283–3292 (2022). Phelps, E. A. & LeDoux, J. E. Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron 48 , 175–187 (2005). Purves, D. et al. The Premotor Cortex. in Neuroscience. 2nd edition (Sinauer Associates, 2001). Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4139730","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":288065592,"identity":"2c9f5b06-0aa3-4602-a8a0-4e0f6dbac16d","order_by":0,"name":"Carolina Lopes","email":"","orcid":"","institution":"University of Coimbra","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Lopes","suffix":""},{"id":288065593,"identity":"df3213ea-5c90-4385-a8be-9ae508500159","order_by":1,"name":"Jaime Godinho","email":"","orcid":"","institution":"Centro Hospitalar e Universitário de Coimbra, EPE","correspondingAuthor":false,"prefix":"","firstName":"Jaime","middleName":"","lastName":"Godinho","suffix":""},{"id":288065594,"identity":"38e15a91-641a-46e7-81ed-2251ced3b710","order_by":2,"name":"César Teixeira","email":"","orcid":"","institution":"University of Coimbra","correspondingAuthor":false,"prefix":"","firstName":"César","middleName":"","lastName":"Teixeira","suffix":""},{"id":288065595,"identity":"e76c0d6d-89f1-47cc-b4a2-0c4c32b54c2e","order_by":3,"name":"Lorena Petrella","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACPhTegwIGORB94AEeLWworAQDBmOwlgRStCQ2gDh4tbD3PnxcUMOQxy/f+/BBgoFd+vywww+BttjJ6Tbg0MJz3Nh4xjGGYsk2dmODBIPk3I230wyAWpKNzQ7g0CKRxibNw8aQuOEYG5tEggFz7sbZCSAtBxK34dbC/pvnH0Pi/mNs7D8SDOrTDWenfyCkhY2Ztw1oCxsbG9D7hxPkpXMI2MJzjFmat0+iWOJYGjPQYccNN0jnFBwAegqnX/jZ2xg/83yzyeNvPsb44UNFtbz87PTNQIadHC4tUCCRAGcagFUa4FUOBggt8g2EVY+CUTAKRsHIAgBOJVUQ9KzRjQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Coimbra","correspondingAuthor":true,"prefix":"","firstName":"Lorena","middleName":"","lastName":"Petrella","suffix":""}],"badges":[],"createdAt":"2024-03-20 22:59:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4139730/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4139730/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54266773,"identity":"061ed5d8-8d76-426c-ac0b-122b5698e3a8","added_by":"auto","created_at":"2024-04-08 05:17:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93195,"visible":true,"origin":"","legend":"\u003cp\u003eMain steps involved in EEG signals analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4139730/v1/1049761b43f0d456eacbcb44.png"},{"id":54266774,"identity":"462e799c-8c59-4686-ae82-504a6896f59e","added_by":"auto","created_at":"2024-04-08 05:17:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":559031,"visible":true,"origin":"","legend":"\u003cp\u003eExample of four frames of the VR video; a) presents a neutral part of the video; b) and c) present the moment of the rock appearance (threat stimulus); d) presents the moment of the bird crossing the scene (neutral visual stimulus).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4139730/v1/658bd70ec9dd9f0fce92e1c5.png"},{"id":54266524,"identity":"6de32d12-39d2-4e4b-9f39-5c75dbe7b134","added_by":"auto","created_at":"2024-04-08 05:09:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85998,"visible":true,"origin":"","legend":"\u003cp\u003eExample of a participant in the experimental procedure, with the EEG cap and VR headset connected. Informed consent was obtained to publish the image in an online open-access publication.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4139730/v1/46f6a1fc95bca1cc98f61f01.png"},{"id":54266525,"identity":"6db1c3ff-f8a4-4579-a475-594a3ed8ecbf","added_by":"auto","created_at":"2024-04-08 05:09:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42540,"visible":true,"origin":"","legend":"\u003cp\u003eExample of the Kruskal-Wallis test for the F8 delta band, and for the threat stimulus time windows. The instants that present significant differences compared to the neutral state are marked in red.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4139730/v1/8e3800a6ad3861847d755c66.png"},{"id":54266775,"identity":"933f0346-29cf-4916-83b4-40bfdb08aaff","added_by":"auto","created_at":"2024-04-08 05:17:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83132,"visible":true,"origin":"","legend":"\u003cp\u003eRelevant channels and frequency bands activated in the threat stimulus situation. Relevant channels are marked in blue.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4139730/v1/726679cb5ad8e02903446c23.png"},{"id":54266529,"identity":"05ce3c20-02ed-480a-a40a-78f69790f41a","added_by":"auto","created_at":"2024-04-08 05:10:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":51689,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the brain connectivity and directionality (represented by arrows) found for the threat situation.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4139730/v1/0c7bcfe25b95ea11353b5c74.png"},{"id":56437080,"identity":"4a0da65c-82f7-40cd-9015-261bc961e81c","added_by":"auto","created_at":"2024-05-14 07:40:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1509021,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4139730/v1/ff1eae2b-d6d6-4df1-9776-6014718e4160.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain Activity During Defensive Reactions to Virtual Threats","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe study of brain activity under the appearance of an unexpected visual threat can give some insights into how the brain reacts to potential dangers and how the defensive response originates. In humans, the visual system plays an important role in protecting the body from threats once it is the primary sense used to evaluate and respond to the surroundings and potential sources of danger. It has been proved that threat stimuli evoke defensive responses\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, which consist of survival mechanisms that help to protect and prepare the body from dangers, and include avoidance responses, flight, freezing, and risk assessment. An avoidance behavior consists of fast actions directed to avoid or minimize the negative consequences of the perceived threat\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Also, the perception of the motion of the threatening stimulus is needed to adapt the behavior to the situation \u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Indeed, past research has shown that the direction of the threat, that is, if it is coming towards or away from the person, can alter brain activity and psychological responses\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn humans, some studies found diverse brain regions that activate under threatening stimuli namely the premotor cortex, the pre-supplementary motor area, and the inferior frontal gyrus\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Threat-related studies have been conducted in humans, monkeys, and rodents. To identify the activated brain areas, electroencephalogram (EEG)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and functional magnetic resonance imaging (fMRI)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e have been used. fMRI has a good spatial resolution, but a poor temporal resolution, which limits the study of fast responses. On the other hand, EEG presents good temporal resolution, but more limited spatial resolution compared to fMRI\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Regarding the threatening stimulus used in human studies, most of the studies resort to images, videos played on a monitor, and sounds, representing a certain threat (mostly animals considered threatening, such as spiders and sharks, or sudden objects and sounds). However, in these previous studies, emotional responses are inherent to visual stimuli recognition and interpretation. Moreover, observing an image or a scene projected on a monitor, reduces the person\u0026rsquo;s feeling of being in a threatening situation. In this sense, virtual reality (VR) videos present as a promising solution to induce a more natural response.\u003c/p\u003e \u003cp\u003eVirtual reality (VR) technology uses interactive computer-generated graphics to create a simulated environment and give the user an immersive feel of a virtual world. It gives the possibility to present complex stimuli in a controlled manner and has attracted the attention of researchers and psychotherapists in the last few years\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Also, it has been proved that VR can activate the threat-processing circuitry in the brain, and it is already used in the treatment of phobias and post-traumatic stress disorder (PTSD), once it allows the creation of highly realistic and immersive simulations of potentially threatening scenarios in a controlled environment\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, a VR video is used to provide an immersive scenario where an unexpected visual threat is presented to invoke a defensive reaction, while brain activity is measured using EEG. To invoke a defensive reaction, a neutral stimulus was used, overcoming some bias of previous studies, where the threatening stimuli involved an emotional perception. On the other hand, the immersive scene provided by VR is more efficient in generating a defensive response under the threatening situation. With this approach is expected to depict the flux of information involved in an escape response.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe general experimental setup comprises the following steps: the setup of the VR headset, the development of the VR video, the acquisition of EEG signals along video visualization, and data analysis. The main steps involved in data analysis are presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and detailed in the next sections.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eVisual Stimulus\u003c/h2\u003e\n \u003cp\u003eThe VR video was created recurring to the cross-platform game engine Unity (Unity Software Inc., San Francisco) and consists of a calm scenario of a forest, at dusk light, containing typical elements such as trees, flowers, and grass (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.a), light breeze movements, and soft background forest sounds. The goal was to create a calm scenario with few distracting elements to reduce strong modifications in brain activity, besides the ones related to the threat stimulus. The scene was presented as if the observer were walking straight ahead along the forest path. The visual stimulus was projected using a VR headset (Reverb G2, HP, Palo Alto, California) comprising lenses and headphones.\u003c/p\u003e\n \u003cp\u003eThe video lasts 5 m 35 s, and at 2 m 3 s the threatening stimulus is presented (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.b and 2.c). It consists of an unexpected rock coming towards the viewer that ends up obscuring the scene and then disappears. The rock is in the scene for about 1 s and is also accompanied by a crashing sound. To identify whether the participant\u0026apos;s reaction to the threat stimulus and the associated brain activity relate to defensive mechanisms or just to the sensory inputs, two additional stimuli were introduced. To segregate the effect of the crashing sound accompanying the threat stimulus, at 3 m 14 s, the same sound was reproduced but without the visual stimulus (without the rock). Also, at 4 m 45 s, a small bird crosses the scene (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.d), representing a neutral visual input.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eEEG Data Acquisition\u003c/h2\u003e\n \u003cp\u003eTo register the participant\u0026apos;s brain activity, an electrode cap (Electro-Cap International, Eaton, Ohio, United States) following the international 10\u0026ndash;20 electrode placement system was used. Activity was registered from 19 channels including: Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2. Signals were acquired at a sampling frequency of 512 Hz and stored on a computer for posterior analysis. Both, VR video and EEG records were synchronized to identify the stimuli\u0026rsquo; times.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants and Protocol\u003c/h2\u003e\n \u003cp\u003eThe study was approved by the Ethics Committee of the Faculty of Medicine of the University of Coimbra under the protocol CE-017/2023, and all procedures were performed in accordance with the relevant guidelines and regulations. Participants were informed about the study protocols, except for the content of the video. Each participant who agreed to enroll signed an informed consent. The inclusion criteria consisted of young adults between 18 and 40 years old. The exclusion criteria were the presence of visual deficits, neuropathological conditions, cardiac pathologies, and the use of neuropharmaceuticals. No further gender, social, or educational conditions were considered. The study included 26 eligible volunteers, and data was anonymized with a numeric code to preserve participants\u0026apos; identity.\u003c/p\u003e\n \u003cp\u003eThe involvement of each participant contemplated only one session, where the participant was connected to the EEG system to record brain activity while simultaneously viewing the VR video through the VR headset (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The whole procedure (including questionnaires, participant preparation, and video projection) took about one hour, and no posterior follow-up was required. During the procedure, participants were comfortably seated in a chair and were instructed to stay as still as possible, not talk, and stay calm during the video visualization. At the end, they were asked about their personal experiences while watching the video, specifically, if the threat stimulus was observed and if they felt threatened by it.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eSignal Processing and Feature Extraction\u003c/h2\u003e\n \u003cp\u003eAll processing and subsequent analysis of the acquired EEG data was implemented using MATLAB programming language.\u003c/p\u003e\n \u003cp\u003eThe EEG frequency range of interest was considered between 0.5 Hz and 80 Hz. An infinite impulse response (IIR) notch digital filter of second order was applied to exclude the frequency component of 50 Hz related to the power-line interference. Additionally, a second IIR notch filter of second order was applied at 37 Hz since this frequency component, related to external sources, was also evidenced in the signal spectrum. To eliminate the lower (\u0026lt;\u0026thinsp;0.5 Hz) and higher (\u0026gt;\u0026thinsp;80 Hz) frequency components, the data was bandpass filtered, applying a finite impulse response (FIR) filter of minimum-order (i.e., the order is automatically computed to be the minimum the FIR filter must have to meet the specifications).\u003c/p\u003e\n \u003cp\u003eTo remove the remaining artifacts (including eye blinking, eye movements, muscular activity, and cardiac artifacts) it was used independent component analysis (ICA). The number of computed independent components was the same as the number of EEG channels (i.e., 19). The identified components related to artifacts were eliminated, and the EEG was again reconstructed.\u003c/p\u003e\n \u003cp\u003eThe following step consisted of extracting features from the filtered data. The relative power spectral density (rPSD) values were obtained for each EEG channel and for each frequency band: delta (0.5 to 4 Hz), theta (4 to 7 Hz), alpha (8 to 12 Hz), beta (13 to 30 Hz) and gamma (30 to 80 Hz). To compute the PSD values, the signals were first segmented in 1 s windows with an overlap of 50%, using a Hamming window. Then, for each segment, the PSD value of each frequency band was computed by adding all the PSD values within each frequency band. Finally, the rPSD value for each band resulted from dividing the corresponding PSD values by the sum of all the PDS values. Therefore, 95 features were obtained, corresponding to each frequency band (5 bands) and each EEG channel (19 channels).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of Relevant Brain Areas\u003c/h2\u003e\n \u003cp\u003eThe next step concerns the study of the activated brain areas during a threatening situation. To discard activations associated exclusively with sensorial perception, the activated brain areas for the two control stimuli (crashing sound and bird stimuli) described in the \u003cspan class=\"InternalRef\"\u003e\u003cem\u003eVisual Stimulus\u003c/em\u003e\u003c/span\u003e section were also considered. Being so, the neutral state (pre-stimulus) and each of the stimulus states (post-stimulus) were analyzed. Related to the time window for the neutral state, it was used the average of the rPSD values between 20 s and 100 s (before the appearance of the first stimulus), aiming to reduce variability in the neutral condition. Regarding the time window for each stimulus, several consecutive post-stimulus windows were considered to account for the total duration of the stimuli, as well as for possible delays in reaching the different brain areas. For the threat stimulus, the time windows considered were 122.5 s to 123.5 s, 123 s to 124 s, and 123.5 s to 124.5 s. Meanwhile, for the crashing sound stimulus, the intervals were 193.5 s to 194.5 s, 194 s to 195 s, and 194.5 s to 195.5 s. Lastly, in the case of the bird stimulus, the time windows included were 285.5 s to 286.5 s, 286 s to 287 s, 286.5 s to 287.5 s, and 287 s to 288 s (the additional window accounting for the longer duration of this stimulus).\u003c/p\u003e\n \u003cp\u003eFirstly, the One-sample Kolmogorov-Smirnov test (with a significance level of 0.05) was applied to the rPSD values of each frequency band and EEG channel of all the participants, to depict if the data followed a normal distribution.\u003c/p\u003e\n \u003cp\u003eThe subsequent step consisted of identifying the relevant EEG channels and frequency bands. A channel is considered relevant if the associated rPSD values exhibit significant differences between the neutral state (time interval without stimuli) and the stimuli states (time intervals starting at the beginning of each applied stimulus). For this, the Kruskal-Wallis nonparametric multiple comparison test was applied, with a significance level of 0.05. For each of the three stimuli separately (threat, crashing sound, and bird), the Kruskal-Wallis test was conducted among the rPSD data concerning the neutral condition and each of the consecutive post-stimulus time windows, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eAfter the multiple comparison tests, a multiple comparison correction using the Bonferroni method was applied, with \u0026alpha; set to 0.05.\u003c/p\u003e\n \u003cp\u003eOf the 26 participants, 6 did not react or feel threatened by the stimulus and were excluded from the analyses. However, the data from these 6 participants were posteriorly used for comparison, following the same statistical analysis first performed for the 20 participants who felt threatened.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eFunctional Connectivity Between Relevant Brain Areas\u003c/h2\u003e\n \u003cp\u003eThe next step was to find the existence and strength of connections between the activated brain areas (i.e., the areas identified in the previous step as being involved in the defensive reaction). In this sense, five methods of functional connectivity were implemented: coherence (COH)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, imaginary part of coherence (iCOH)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, weighted phase lag index (wPLI)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, mean phase coherence (MPC)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, and direct transfer function (DTF)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The connectivity methods iCOH and wPLIi overcome the problems of volume conduction and signal-to-noise ratio (SNR). The volume conduction problem refers to the electrical activity of a source being present in more than one EEG channel due to bone and other scalp structures conduction. The SNR quantifies the relative noise content in the signal\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The COH, iCOH, wPLI, and MPC methods were used to find the relevant connections while the DTF method was used to find the direction of these connections.\u003c/p\u003e\n \u003cp\u003eFor each connectivity method, the connectivity values for each channel pair were evaluated under two conditions: neutral state (pre-stimulus) and threat stimulus state (post-stimulus). Once again, to reduce variance within the neutral state, the connectivity was calculated for the time series data corresponding to each second of the interval comprehended between 20 s to 100 s (corresponding to a neutral part of the video), and then the resultant connectivity values were averaged. Regarding the threat stimulus state, the associated connectivity values were obtained for the time series data corresponding to the instant of the threat appearance on the video, i.e. the 122.8 s \u0026minus;\u0026thinsp;123.8 s time window. It is to be noted that this connectivity is independent of frequency bands, encompassing the entire spectrum of the signals (0.5 Hz to 80 Hz).\u003c/p\u003e\n \u003cp\u003eFor each connectivity method, the connectivity values of each channel pair were stored in three matrices containing the average of these values across participants: one for the neutral state (pre-stimulus), one for the threat stimulus state (post-stimulus), and one for the difference between the connectivity values of the stimulus and the neutral states (post-pre). Using the post-pre matrices, for each connectivity method, the pairs of channels whose post-pre values were greater than a threshold corresponding to the 0.65-quantile (that is, the value above which 35% of the post-pre values are) were obtained. These correspond to the pairs of channels with a relevant connection. To obtain more reliable results, only the relevant channel connections that were present in at least three of the four connectivity methods were considered relevant.\u003c/p\u003e\n \u003cp\u003eThe next step consisted of depicting the direction of the identified connections, that is, the direction of the information flow. In this sense, the DTF method was applied to the post-stimulus data to discover the direction of each of the previously found significant connections. The post-stimulus data was the same used for the other connectivity methods (corresponding to the 122.8 s \u0026minus;\u0026thinsp;123.8 s time window), and a DTF matrix containing the DTF values of each channel pair averaged over all the participants was obtained.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eRelevant Brain Areas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause the One-sample Kolmogorov-Smirnov test indicated that the data did not follow a normal distribution, the Kruskal-Wallis nonparametric multiple comparison test was applied in the following steps. The features exhibiting significant differences between the neutral state and the stimulus state, for each of the three stimuli (threat, crashing sound, and bird), are presented in Table 1. Significant differences were observed in almost all the consecutive post-stimulus time windows compared to the neutral window.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1.\u003c/strong\u003e EEG channels and frequency bands presenting significant differences between the neutral state and each of the three stimuli, using a significance level of 0.05. The results were corrected for multiple comparison effects.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"375\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThreat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSound (crashing sound)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisual (bird)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003eT5- beta, alpha, delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003eT4- theta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003eT4- theta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003eT4- beta, delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003eCz- beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003eF8- beta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003eCz- beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003eF8- beta, delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003eC3- gamma, beta, delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003eT3- gamma, delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003eF8- gamma, beta, delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003eFz- beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003eF7- gamma, delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003eFp2- gamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.2%\"\u003e\n \u003cp\u003eFp1- beta, delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.466666666666665%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSome brain areas were activated with both, the threat stimulus, and the sound stimulus, including Cz-beta, F8-beta, and F8-delta. Comparing the threat stimulus with the visual stimulus results, only F8-beta activation was common to both situations. As such, the activation of F8-beta and F8-delta will not be deemed to play a role in an escape reaction. However, Cz activation is possibly related to the motor activity resulting from the unexpected appearance of threatening or sound stimuli, so the Cz-beta will be kept. This way, Figure 5 represents the relevant channels and frequency bands activated under the threat stimulus, mentioned above. It is possible to observe that most discriminative brain regions between the neutral and threat states locate in the front half of the brain and slightly more concentrated in the left hemisphere.\u003c/p\u003e\n\u003cp\u003eAlso, regarding the threatening stimulus, it is possible to observe the predominance of the delta, beta, and gamma bands in several channels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Connectivity Between the Relevant Brain Areas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows the connections that presented significant post-pre connectivity values (higher than the 0.65-quantile), for each of the connectivity methods. It includes the channel pairs and the corresponding connectivity values for each method, as well as the connections identified as significant in at least three of the methods (last column).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eSignificant connections (higher than the 0.65-quantile) related to the threat stimulus; the values correspond to the difference between the connectivity values under threat and neutral states. Results for each of the implemented methods are presented in each column, and connections considered significant for at least three of the methods are identified in the last column.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"455\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003e\u003cstrong\u003eConnections\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u003cstrong\u003eiCOH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u003cstrong\u003ewPLI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMPC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommon Significant Connections\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eFp1-T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eFp1-Cz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eFp1-F7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eFp1-F8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n 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width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eC3-T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eF8-T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eF8-C3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eF8-Cz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eF8-T5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eF8-T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eT3-T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eT3-Cz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eT3-C3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eT3-T5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.065934065934066%\"\u003e\n \u003cp\u003eT4-T5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.527472527472527%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.087912087912088%\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.142857142857146%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe most relevant channel connections (Table 2, last column) related to the threat situation are represented in Figure 6, as well as their directions obtained by analyzing the DTF(i,j) and DTF(j,i) values of the corresponding channel pairs. From them, stand out the connections Fp1-T4, Fp2-F7, Fz-T3, and F7-Cz which also presented the highest connectivity values for each method.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study it was observed that the cortical areas and frequency bands involved in threat evoked reaction correspond to T5-beta, T5-alpha, T5-delta, T4-beta, T4-delta, Cz-beta, C3-gamma, C3-beta, C3-delta, T3-gamma, T3-delta, F8-gamma, Fz-beta, F7-gamma, F7-delta, Fp2-gamma, Fp1-beta and Fp1-delta.\u003c/p\u003e \u003cp\u003eAlthough brain activity involves highly complex mechanisms that cannot be completely explored by EEG analysis, some insights are presented below. The T5 channel locates over the left associative visual cortex, and is responsible for complex processing of visual information concerning spatial orientation, depth perception, object recognition, location and movement of objects in space\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e; and the object moving towards the observer in this study can cause its activation. The T3 and T4 channels captures activity from the left and right temporal gyrus, respectively, both involved in sound processing and recognition, and their activation may relate to the crash sound involved in the threatening stimulus. However, T3 was not activated when only the sound stimulus was presented, and T4 was also activated when only the visual stimulus was presented. So that, the role of these areas under a threatening situation should be further explored\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The Cz and Fz channels locate over the motor cortex, with Cz near the primary motor cortex and Fz near the premotor and supplementary motor cortex. While the primary motor cortex and the supplementary motor cortex are more strongly related to the control of voluntary movement, the premotor cortex plays a key role in autonomic reactions and motor responses in defensive behaviors\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Overall, the activation of these regions may relate to a short movement (escape reaction) observed in the participants when the threat stimulus appears. Cz was also activated only with the sound stimulus, and it evidence that an unexpected sound can induce a defensive reaction by itself. The C3 channel is over the left primary somatosensory cortex, responsible for receiving and processing tactile information (touch, pressure, temperature, and pain), and although there is no evident relation with the threatening stimulus, past studies have also observed its activation in response to fear stimuli not consciously perceived\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The F7 and F8 channels are located near the left and right inferior frontal gyrus\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, involved in attentional processing\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, fear processing\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, anxiety regulation during threat exposure\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and auditory processing\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. On the other hand, it has been reported that the left frontal gyrus is involved in action preparation, and plays a role in threat processing, specifically where the threat is directed towards the person, which represents a direct relation with this study\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Fp2 and Fp1 channels are located over the right and left anterior prefrontal cortex, which are associative areas involved in complex functions like reasoning, planning, execution, decision-making, vigilance, and processing emotional stimuli (fear control). Given the complexity of the prefrontal cortex functions, there is a wide range of mechanisms that can lead to its activation under threat stimuli\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Interestingly, regarding the statistical analysis performed with the six participants who did not feel threatened by the stimulus, no significant differences were obtained between the neutral and threat situations for any of the brain areas.\u003c/p\u003e \u003cp\u003eRegarding the connectivity analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the prefrontal areas, with complex functions like reasoning and planning\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, communicates with the inferior frontal gyrus involved in actions preparation (F7), and it connect to the primary motor cortex. Together it unveils part of the information flux involved in action planning in threatening situations. It is observed that the area involved in sound processing and recognition (left middle temporal gyrus, T3) acts over the premotor cortex (Fz), identified as a key cortical area in defensive response, since here it is monitored the peri personal space favoring a quick reaction. So that, sound recognition seems to play a main role in human autonomic reaction. Indeed, along the development of the threatening stimulus, it was observed that the visual stimulus alone (without sound), induced few or non-reaction. Also, the role of sound in triggering a defensive action has been reported in previous studies\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. A connection from the premotor cortex to the primary motor cortex (Cz) was also evidenced, and despite no considered significant in this analysis, a connection between the associative visual cortex to the primary motor cortex was depicted by two connectivity methods. Intriguingly, it was also evidenced a connection from the prefrontal regions to the somatosensory cortex (C3). Despite this observation should be further explored, we hypothesize that it may relate to some mechanism for alerting potential body injuries.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe goal of this study was to explore the brain areas involved in defensive reactions to a threatening stimulus, as well as the functional connectivity between them, using a combined VR and EEG approach. The brain areas activated by the applied threat stimulus consisted of the inferior frontal gyrus, premotor cortex, supplementary motor cortex, prefrontal cortex, temporal gyrus, associative visual cortex, primary motor cortex, and primary somatosensory cortex. Regarding the connectivity analysis, our results suggest that the threat stimulus triggers a defensive reaction mediated by sound recognition (connectivity from T3 to Fz) which acts over the premotor cortex, and that action planning involves a complex brain network including the Cz, F7, FP1 and FP2 related areas. These results contribute to the understanding of defensive reactions in a threatening situation and may, in the future, support the development of treatments for certain psychopathological conditions. Future work consists of finding the temporal sequence of activation of the brain areas involved and incorporating techniques able to study the activity of subcortical structures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is founded by FCT – Foundation for Science and Technology, I.P., within the scope of the projects: CISUC – UID/CEC/00326/2020 with founds from the European Social Fund, through the Regional Operational Program Centro 2020; and project RECoD – PTDC/EEI-EEE/5788/2020 financed with national funds (PIDDAC) via the Portuguese State Budget.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: C.L., L.P., and C.T.; experiment design: C.L. and L.P.; VR video development: C.L.; instrumentation setup and data acquisition: C.L., J.G. and C.T.; data analysis and interpretation: C.L., L.P., and C.T.; manuscript revision: C.L., L.P., and C.T.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePichon, S., de Gelder, B. \u0026amp; Gr\u0026egrave;zes, J. 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A. \u0026amp; LeDoux, J. E. Contributions of the amygdala to emotion processing: from animal models to human behavior. \u003cem\u003eNeuron \u003c/em\u003e\u003cstrong\u003e48\u003c/strong\u003e, 175\u0026ndash;187 (2005).\u003c/li\u003e\n\u003cli\u003ePurves, D. \u003cem\u003eet al.\u003c/em\u003e The Premotor Cortex. in \u003cem\u003eNeuroscience. 2nd edition\u003c/em\u003e (Sinauer Associates, 2001).\u003c/li\u003e\n\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-4139730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4139730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study of brain activity under the appearance of an unexpected visual threat can give some insights into how the brain reacts to potential dangers, and how the consequent defensive response is originated. In this study, a virtual reality (VR) scene is used to present an unexpected threat aiming to invoke a defensive reaction, as well as non-threatening stimuli as control. The brain activity is measured along the pre and post stimuli conditions using electroencephalography (EEG). The goal is to identify how the information propagates between cortical regions once the threatening situation is presented. The functional connectivity study evidenced a flux of information from the left middle temporal gyrus to the premotor cortex, evidencing a defensive response induced by the sound involved in the stimulus. Additional connections involving diverse cortical areas as the left inferior frontal gyrus, the primary motor cortex, the prefrontal cortex, beside the premotor cortex may represent part of the information flux involved in action planning. Other activated cortical areas were the supplementary motor cortex, the right temporal gyrus, the associative visual cortex, and primary somatosensory cortex. Concluding, the immersive scenario provided by VR allowed to induce more natural defensive response, and consequently the identification of relevant brain activity.\u003c/p\u003e","manuscriptTitle":"Brain Activity During Defensive Reactions to Virtual Threats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 05:09:55","doi":"10.21203/rs.3.rs-4139730/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":"bd515a67-ed79-4a4d-8751-8430324213f4","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30333170,"name":"Biological sciences/Neuroscience"},{"id":30333171,"name":"Biological sciences/Computational biology and bioinformatics/Data processing"}],"tags":[],"updatedAt":"2024-05-14T07:32:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 05:09:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4139730","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4139730","identity":"rs-4139730","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}