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Features of threatening cues and the associated context influence the perceived imminence of threat and the defensive responses evoked. To provide additional knowledge about how the directionality of a threat (i.e. directed-towards or away from the viewer) might impact defensive responses in humans, participants were shown pictures of a man carrying a gun (threat) or nonlethal object (neutral) directed-away from or towards the participant. Cardiac and electrodermal responses were collected. Compared to neutral images, threatening images depicting a gun directed-towards the participant induced sustained bradycardia and an increased electrodermal response, interpreted as immobility under attack. This defensive immobility reaction is evoked by high perceived threat and inescapable situations and indicates intense action preparation. Pictures of guns directed-away from the participant induced shorter bradycardia and no significant modulation of the electrodermal response compared to neutral pictures, which might be consistent with the perception of a less threatening situation. The results show that the directionality of threat stimuli is a key factor that prompts different patterns of defensive responses.

Background: The present study aimed to apply multivariate pattern recognition methods to predict posttraumatic stress symptoms from whole-brain activation patterns during two contexts where the aversiveness of unpleasant pictures was manipulated by the presence or absence of safety cues., Methods: Trauma-exposed participants were presented with neutral and mutilation pictures during functional magnetic resonance imaging (fMRI) collection. Before the presentation of pictures, a text informed the subjects that the pictures were fictitious ("safe context") or real-life scenes ("real context"). We trained machine learning regression models (Gaussian process regression (GPR)) to predict PTSD symptoms in real and safe contexts., Results: The GPR model could predict PTSD symptoms from brain responses to mutilation pictures in the real context but not in the safe context. The brain regions with the highest contribution to the model were the occipito-parietal regions, including the superior parietal gyrus, inferior parietal gyrus, and supramarginal gyrus. Additional analysis showed that GPR regression models accurately predicted clusters of PTSD symptoms, nominal intrusion, avoidance, and alterations in cognition. As expected, we obtained very similar results as those obtained in a model predicting PTSD total symptoms., Conclusion: This study is the first to show that machine learning applied to fMRI data collected in an aversive context can predict not only PTSD total symptoms but also clusters of PTSD symptoms in a more aversive context. Furthermore, this approach was able to identify potential biomarkers for PTSD, especially in occipitoparietal regions., (© 2023. BioMed Central Ltd., part of Springer Nature.)

The Coronavirus Disease 2019 (COVID-19) pandemic has exposed healthcare workers (HCW) to traumatic situations that might lead to the development of posttraumatic stress disorder (PTSD). An important vulnerability factor for PTSD is the peritraumatic tonic immobility (TI) reaction, an involuntary and reflexive defensive response evoked by an intense and inescapable threat. TI is largely understudied in humans and has not been investigated during trauma related to COVID-19. For HCW, the pandemic context might be experienced as an intense and potentially inescapable threat, i.e., an overwhelming situation. Here, we investigated if TI response occurred during traumatic events related to the pandemic and its association with posttraumatic stress symptoms (PTSS). An online survey of 1001 HCW investigated COVID-19-related traumatic experiences, TI and PTSS. TI was reported for all types of traumatic events, and multivariate regression models revealed that TI was significantly associated with PTSS severity. HCW who reported high TI scores exhibited an increase of 9.08 times the probability of having a probable diagnosis of PTSD. Thus, TI was evoked by pandemic-related traumatic situations and associated with PTSS severity and higher odds of a PTSD diagnosis. Tonic immobility occurrence should be screened, and psychoeducation about its reflexive biological nature should be introduced., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19. Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers. Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r 2 ), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels. Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms. Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Portugal, Gama, Gonçalves, Mendlowicz, Erthal, Mocaiber, Tsirlis, Volchan, David, Pereira and Oliveira.)

Background: Evidence indicates that the processing of facial stimuli may be influenced by incidental factors, and these influences are particularly powerful when facial expressions are ambiguous, such as neutral faces. However, limited research investigated whether emotional contextual information presented in a preceding and unrelated experiment could be pervasively carried over to another experiment to modulate neutral face processing. Objective: The present study aims to investigate whether an emotional text presented in a first experiment could generate negative emotion toward neutral faces in a second experiment unrelated to the previous experiment. Methods: Ninety-nine students (all women) were randomly assigned to read and evaluate a negative text (negative context) or a neutral text (neutral text) in the first experiment. In the subsequent second experiment, the participants performed the following two tasks: (1) an attentional task in which neutral faces were presented as distractors and (2) a task involving the emotional judgment of neutral faces. Results: The results show that compared to the neutral context, in the negative context, the participants rated more faces as negative. No significant result was found in the attentional task. Conclusion: Our study demonstrates that incidental emotional information available in a previous experiment can increase participants' propensity to interpret neutral faces as more negative when emotional information is directly evaluated. Therefore, the present study adds important evidence to the literature suggesting that our behavior and actions are modulated by previous information in an incidental or low perceived way similar to what occurs in everyday life, thereby modulating our judgments and emotions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Nudelman, Portugal, Mocaiber, David, Rodolpho, Pereira and Oliveira.)

Purpose: Growing evidence suggests that peritraumatic tonic immobility, an involuntary defensive response that involves extreme physical immobility and the perceived inability to escape, is a significant predictor of post-traumatic stress disorder (PTSD) symptomatology. However, this issue has not been specifically addressed in adolescents. Here, we investigated whether tonic immobility response experienced during the worst childhood or adolescent trauma is associated with PTSD symptom severity in a non-clinical student sample., Methods: The sample was composed of students in 9th grade who were attending public and private schools. Symptoms of post-traumatic stress and tonic immobility were assessed using questionnaires. We performed bivariate and multivariate negative binomial regressions to examine whether tonic immobility was associated with PTSD symptomatology after controlling for confounders (peritraumatic dissociation, peritraumatic panic reactions, gender, age and time since trauma)., Results: We found an association between tonic immobility and PTSD symptom severity, even after controlling for confounders. Therefore, tonic immobility is associated with PTSD symptoms in trauma-exposed adolescents., Conclusion: These findings highlight tonic immobility as a possible risk factor that could be used to provide direction for more targeted trauma interventions for individuals, particularly those at risk for developing PTSD. Therefore, it contributes to preventing and reducing the psychiatric burden in adolescence and later in life., Competing Interests: The authors report no conflicts of interest in this work., (© 2021 Magalhaes et al.)

Background: Childhood maltreatment (CM) is unfortunately widespread globally and has been linked with an increased risk of a variety of psychiatric disorders in adults, including posttraumatic stress disorder (PTSD). These associations are well established in the literature for some maltreatment forms, such as sexual and physical abuse. However, the effects of emotional maltreatment are much less explored, even though this type figures among the most common forms of childhood maltreatment. Thus, the present study aims to investigate the impact of each type of childhood maltreatment, both individually and conjointly, on revictimization and PTSD symptom severity using a nonclinical college student sample., Methods: Five hundred and two graduate and undergraduate students participated in the study by completing questionnaires assessing lifetime traumatic experiences in general, maltreatment during childhood and PTSD symptoms. Bivariate and multivariate negative binomial regressions were applied to examine the associations among childhood maltreatment, revictimization, and PTSD symptom severity., Results: Our results showed that using bivariate models, all types of CM were significantly associated with revictimization and PTSD symptom severity. Multivariate models showed that emotional abuse was the type of maltreatment associated with the highest incidence rates of revictimization and PTSD symptom severity., Conclusions: These data provide additional evidence of the harmful effects of childhood maltreatment and its long-term consequences for individuals' mental health. Notably, the findings highlight the importance of studying the impacts of emotional abuse, which seems to be a highly prevalent, understudied, and chronic form of maltreatment that is as toxic as other maltreatment forms.

Since the Coronavirus disease 2019 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was announced, we had an unprecedented change in the way we organize ourselves socially and in our daily routine. Children and adolescents were also greatly impacted by the abrupt withdrawal from school, social life and outdoor activities. Some of them also experienced domestic violence growing. The stress they are subjected to directly impacts their mental health on account of increased anxiety, changes in their diets and in school dynamics, fear or even failing to scale the problem. Our aim is to bring up a discussion under different aspects and to alert public health and government agents about the need for surveillance and care of these individuals. We hope that the damage to their mental health as a result of the side effect of this pandemic can be mitigated by adequate and timely intervention., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Whether subtle differences in the emotional context during threat perception can be detected by multi-voxel pattern analysis (MVPA) remains a topic of debate. To investigate this question, we compared the ability of pattern recognition analysis to discriminate between patterns of brain activity to a threatening versus a physically paired neutral stimulus in two different emotional contexts (the stimulus being directed towards or away from the viewer). The directionality of the stimuli is known to be an important factor in activating different defensive responses. Using multiple kernel learning (MKL) classification models, we accurately discriminated patterns of brain activation to threat versus neutral stimuli in the directed towards context but not during the directed away context. Furthermore, we investigated whether it was possible to decode an individual's subjective threat perception from patterns of whole-brain activity to threatening stimuli in the different emotional contexts using MKL regression models. Interestingly, we were able to accurately predict the subjective threat perception index from the pattern of brain activation to threat only during the directed away context. These results show that subtle differences in the emotional context during threat perception can be detected by MVPA. In the directed towards context, the threat perception was more intense, potentially producing more homogeneous patterns of brain activation across individuals. In the directed away context, the threat perception was relatively less intense and more variable across individuals, enabling the regression model to successfully capture the individual differences and predict the subjective threat perception.

Background: The aim of this study was to apply multivariate pattern recognition to predict the severity of behavioral traits and symptoms associated with risk for bipolar spectrum disorder from patterns of whole-brain activation during reward expectancy to facilitate the identification of individual-level neural biomarkers of bipolar disorder risk., Methods: We acquired functional neuroimaging data from two independent samples of transdiagnostically recruited adults (18-25 years of age; n = 56, mean age 21.9 ± 2.2 years, 42 women; n = 36, mean age 21.2 ± 2.2 years, 24 women) during reward expectancy task performance. Pattern recognition model performance in each sample was measured using correlation and mean squared error between actual and whole-brain activation-predicted scores on behavioral traits and symptoms., Results: In the first sample, the model significantly predicted severity of a specific hypo/mania-related symptom, heightened energy, measured by the energy manic subdomain of the Mood Spectrum Structured Interviews (r = .42, p = .001; mean squared error = 9.93, p = .001). The region with the highest contribution to the model was the left ventrolateral prefrontal cortex. Results were confirmed in the second sample (r = .33, p = .01; mean squared error = 8.61, p = .01), in which the severity of this symptom was predicted using a bilateral ventrolateral prefrontal cortical mask (r = .33, p = .009, mean squared error = 9.37, p = .04)., Conclusions: The severity of a specific hypo/mania-related symptom was predicted from patterns of whole-brain activation in two independent samples. Given that emerging manic symptoms predispose to bipolar disorders, these findings could provide neural biomarkers to aid early identification of individual-level bipolar disorder risk in young adults., (Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogeneity poses challenges for identifying biological markers associated with dimensions of symptoms and behaviour that could provide targets to guide treatment choice and novel treatment. In response, the research domain criteria (RDoC) (Insel et al., 2010) was developed to advocate a dimensional approach which omits any disease definitions, disorder thresholds, or cut-points for various levels of psychopathology to understanding the pathophysiological processes underlying psychiatry disorders. In the present study we aimed to apply pattern regression analysis to identify brain signatures during dynamic emotional face processing that are predictive of anxiety and depression symptoms in a continuum that ranges from normal to pathological levels, cutting across categorically-defined diagnoses., Methods: The sample was composed of one-hundred and fifty-four young adults (mean age=21.6 and s.d.=2.0, 103 females) consisting of eighty-two young adults seeking treatment for psychological distress that cut across categorically-defined diagnoses and 72 matched healthy young adults. Participants performed a dynamic face task involving fearful, angry and happy faces (and geometric shapes) while undergoing functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Gaussian Process Regression (GPR) implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r) and normalized mean squared error (MSE) to evaluate the models' performance. Permutation test was applied to estimate significance levels., Results: GPR identified patterns of neural activity to dynamic emotional face processing predictive of self-report anxiety in the whole sample, which covered a continuum that ranged from healthy to different levels of distress, including subthreshold to fully-syndromal psychiatric diagnoses. Results were significant using two different cross validation strategies (two-fold: r=0.28 (p-value=0.001), MSE=4.47 (p-value=0.001) and five fold r=0.28 (p-value=0.002), MSE=4.62 (p-value=0.003). The contributions of individual regions to the predictive model were very small, demonstrating that predictions were based on the overall pattern rather than on a small combination of regions., Conclusions: These findings represent early evidence that neuroimaging techniques may inform clinical assessment of young adults irrespective of diagnoses by allowing accurate and objective quantitative estimation of psychopathology., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample., Methods: fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli., Results: The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions)., Conclusion: These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)