Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms.

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").

Impact of textual warnings on emotional brain responses to ultra-processed food products.

Background And Objectives: Ultra-processed food products (UPF) have been associated with numerous non-communicable diseases. Despite this, the addictive nature of UPF, and the aggressive marketing strategies used to promote them, has created a strong emotional connection between UPF and consumers, and supports their increasing UPF global consumption. In view of the emotional link that consumers often have with UPF, modulating emotional reactions to UPF (by using strategies such as textual warnings) is important in changing consumers' behavior.

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

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. 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.

Pictures of social interaction prompt a sustained increase of the smile expression and induce sociability.

Viewing pictures of social interaction can facilitate approach behaviors. We conducted two studies to investigate if social interaction cues, empathy, and/or social touch modulate facial electromyographic (EMG) reactivity (as evidenced by the zygomaticus major and corrugator supercilii muscles) and mood states. We presented bonding pictures (depicting social interaction) and control pictures (without social interaction) while continuously recording zygomatic and corrugator EMG activities.

The invisible scars of emotional abuse: a common and highly harmful form of childhood maltreatment.

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.

Interactions between emotion and action in the brain.

A growing literature supports the existence of interactions between emotion and action in the brain, and the central participation of the anterior midcingulate cortex (aMCC) in this regard. In the present functional magnetic resonance imaging study, we sought to investigate the role of self-relevance during such interactions by varying the context in which threating pictures were presented (with guns pointed towards or away from the observer). Participants performed a simple visual detection task following exposure to such stimuli.