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

The overgeneralization of pain-related fear in individuals with higher pain sensitivity: A behavioral and event-related potential study

Fear generalization contributes to the development and maintenance of pain. Pain sensitivity has been proposed to predict the strength of fear responses to aversive stimuli. However, whether individual variation in pain sensitivity affects pain-related fear generalization and its underlying cognitive processing remains unclear. To address this gap, we recorded behavioral and event-related potential (ERP) data among 22 high pain sensitivity (HPS) and 22 low pain sensitivity (LPS) healthy adults when exposed to a fear generalization paradigm. The behavioral results indicate that the HPS group displayed higher unconditioned stimulus expectancy and greater fear, arousal, and anxiety ratings to conditioned stimulus and generalization stimulus than the LPS group (all p values < 0.05). The ERP results showed that the HPS group exhibited a larger late positive potential evoked by GS2, GS3 and CS- (all p < 0.005) but a smaller N1 evoked by all CS and GSs (all p values < 0.05) relative to the LPS group. These findings suggest that individuals with a high level of pain sensitivity allocate more attention resources to pain-related threatening stimuli, which contributes to an overgeneralization of pain-related fear.

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"). 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.