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

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

Implicit Motivational Impact of Pictorial Health Warning on Cigarette Packs

Abstract Objective: The use of pictorial warning labels on cigarette packages is one of the provisions included in the first ever global health treaty by the World Health Organization against the tobacco epidemic. There is substantial evidence demonstrating the effectiveness of graphic health warning labels on intention to quit, thoughts about health risks and engaging in cessation behaviors. However, studies that address the implicit emotional drives evoked by such warnings are still underexplored. Here, we provide experimental data for the use of pictorial health warnings as a reliable strategy for tobacco control. Methods: Experiment 1 pre-tested nineteen prototypes of pictorial warnings to screen for their emotional impact. Participants (n = 338) were young adults balanced in gender, smoking status and education. Experiment 2 (n = 63) tested pictorial warnings (ten) that were stamped on packs. We employed an innovative set-up to investigate the impact of the warnings on the ordinary attitude of packs' manipulation, and quantified judgments of warnings' emotional strength and efficacy against smoking. Findings: Experiment 1 revealed that women judged the warning prototypes as more aversive than men, and smokers judged them more aversive than non-smokers. Participants with lower education judged the prototypes more aversive than participants with higher education. Experiment 2 showed that stamped warnings antagonized the appeal of the brands by imposing a cost to manipulate the cigarette packs, especially for smokers. Additionally, participants' judgments revealed that the more aversive a warning, the more it is perceived as effective against smoking. Conclusions: Health warning labels are one of the key components of the integrated approach to control the global tobacco epidemic. The evidence presented in this study adds to the understanding of how implicit responses to pictorial warnings may contribute to behavioral change

Behavioral modulation by mutilation pictures in women

Previous studies have shown that women are more emotionally expressive than men. It is unclear, however, if women are also more susceptible to the emotional modulation of behavior imposed by an affective stimulus. To investigate this issue, we devised a task in which female subjects performed six sequential trials of visual target detection following the presentation of emotional (mutilation and erotic) or neutral pictures (domestic utensils and objects) and compared the data obtained in the present study with those described in a previous study with male subjects. The experiment consisted of three blocks of 24 pictures and each block had an approximate duration of 4 min. Our sample consisted of 36 subjects (age range: 18 to 26 years) and each subject performed all blocks. Trials following the presentation of mutilation pictures (283 ms) had significantly slower reaction times than those following neutral (270 ms) pictures. None of the trials in the "pleasant block" (271 ms) was significantly different from those in the "neutral block". The increase in reaction time observed in the unpleasant block may be related in part to the activation of motivational systems leading to an avoidance behavior. The interference effect observed in this study was similar to the pattern previously described for men. Thus, although women may be more emotionally expressive, they were not more reactive to aversive stimuli than men, as measured by emotional interference in a simple reaction time task

Architectonic subdivisions of the amygdalar complex of a primitive marsupial (Didelphis aurita)

The architecture of the amygdaloid complex of a marsupial, the opossum Didelphis aurita, was analyzed using classical stains like Nissl staining and myelin (Gallyas) staining, and enzyme histochemistry for acetylcholinesterase and NADPH-diaphorase. Most of the subdivisions of the amygdaloid complex described in eutherian mammals were identified in the opossum brain. NADPH-diaphorase revealed reactivity in the neuropil of nearly all amygdaloid subdivisions with different intensities, allowing the identification of the medial and lateral subdivisions of the cortical posterior nucleus and the lateral subdivision of the lateral nucleus. The lateral, central, basolateral and basomedial nuclei exhibited acetylcholinesterase positivity, which provided a useful chemoarchitectural criterion for the identification of the anterior basolateral nucleus. Myelin stain allowed the identification of the medial subdivision of the lateral nucleus, and resulted in intense staining of the medial subdivisions of the central nucleus. The medial, posterior, and cortical nuclei, as well as the amygdalopiriform area did not exhibit positivity for myelin staining. On the basis of cyto- and chemoarchitectural criteria, the present study highlights that the opossum amygdaloid complex shares similarities with that of other species, thus supporting the idea that the organization of the amygdala is part of a basic plan conserved through mammalian evolution. (C) 2008 Elsevier Inc. All rights reserved

How do you perceive threat? It's all in your pattern of brain activity

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