Threat directionality modulates defensive reactions in humans: cardiac and electrodermal responses.

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.

Tonic Immobility is Associated with PTSD Symptoms in Traumatized Adolescents.

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.

Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach.

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 (r2), 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.

Long-Term Influence of Incidental Emotions on the Emotional Judgment of Neutral Faces.

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.

Predicting Bipolar Disorder Risk Factors in Distressed Young Adults From Patterns of Brain Activation to Reward: A Machine Learning Approach.

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.

Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach.

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.

Decoding negative affect personality trait from patterns of brain activation to threat stimuli.

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.

Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging?

INTRODUCTION: High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points. METHODS: A sample of fifty-seven youth (mean age: 14.5 years; 32 males) was selected from a multi-site study of youth with parent-reported behavioral and emotional dysregulation. Participants performed a block-design reward paradigm during functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Relevance Vector Regression (RVR) and two cross-validation strategies implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Medication was treated as a binary confounding variable. Decoded and actual clinical scores were compared using Pearson's correlation coefficient (r) and mean squared error (MSE) to evaluate the models. Permutation test was applied to estimate significance levels. RESULTS: Relevance Vector Regression identified patterns of neural activity associated with symptoms of behavioral and emotional dysregulation at the initial study screen and close to the fMRI scanning session. The correlation and the mean squared error between actual and decoded symptoms were significant at the initial study screen and close to the fMRI scanning session. However, after controlling for potential medication effects, results remained significant only for decoding symptoms at the initial study screen. Neural regions with the highest contribution to the pattern regression model included cerebellum, sensory-motor and fronto-limbic areas. CONCLUSIONS: The combination of pattern regression models and neuroimaging can help to determine the severity of behavioral and emotional dysregulation in youth at different time points.

Exposure to trauma-relevant pictures is associated with tachycardia in victims who had experienced an intense peritraumatic defensive response: the tonic immobility.

Tonic immobility is an involuntary, last-ditch defensive reaction characterized by physical inactivity in a context of inescapable threat that has been described in many species, including humans. The occurrence of this defensive response is a predictor of the severity of psychiatric disorders and may be considered as an index of an intense reaction to a traumatic event. Here, we investigated whether the retrospective reports of peritraumatic tonic immobility reaction in participants exposed to a traumatic event would modify their cardiac responses to pictures related to their trauma. Using a questionnaire of life-threating events, we selected students who experienced violent crime as their most intense trauma and students who had never experienced a violent crime trauma, but experienced other traumatic events. All participants completed a questionnaire that estimated the intensity of tonic immobility during their most intense trauma. Electrocardiographic recordings were collected during exposure to pictures. Participants viewed emotional pictures (human attack with guns) and neutral pictures. These emotional stimuli were selected to be trauma-relevant to the violent crime group and non trauma-relevant to the no violent crime trauma group. Violent crime group showed a positive correlation between heart rate changes after viewing trauma-related pictures and tonic immobility scores. We observed that low tonic immobility scores were associated with bradycardia and high scores with tachycardia in response to trauma-relevant pictures. For the no violent crime group, no significant correlation was detected. These results suggest that the relevance of the stimuli and the magnitude of the defensive response during a previous trauma event were important factors triggering more intense defensive responses.

How you perceive threat determines your behavior.

The prioritization of processing emotional stimuli usually produces deleterious effects on task performance when it distracts from a task. One common explanation is that brain resources are consumed by emotional stimuli, diverting resources away from executing the task. Viewing unpleasant stimuli also generates defensive reactions, and these responses may be at least partially responsible for the effect of the emotional modulation observed in various reaction time (RT) paradigms. We investigated whether modulatory effects on RT vary if we presented threat stimuli to prompt different defensive responses. To trigger different responses, we manipulated threat perception by moving the direction of threatening stimuli. Threatening or neutral stimuli were presented as distractors during a bar orientation discrimination task. The results demonstrated that threat stimuli directed toward the observer produced a decrease in RT; in contrast, threat stimuli directed away from the observer produced an increase in RT, when compared to neutral stimuli. Accelerated RT during directed toward threat stimuli was attributed to increased motor preparation resulting from strong activation of the defense response cascade. In contrast, directed away threat stimuli likely activated the defense cascade, but less intensively, prompting immobility. Different threat stimuli produced varying effects, which was interpreted as evidence that the modulation of RT by emotional stimuli represents the summation of attentional and motivational effects. Additionally, participants who had been previously exposed to diverse types of violent crime were more strongly influenced by threat stimuli directed toward the observer. In sum, our data support the concept that emotions are indeed action tendencies.