Researchers studied 182 participants who were shown negative photos (bodily injuries, acts of aggression, hate groups, car wrecks, human feces) and neutral photos. Thirty additional participants were also subjected to painful heat.
Using brain imaging and machine learning techniques, the researchers identified a neural signature of negative emotion—a single neural activation pattern distributed across the entire brain that accurately predicts how negative a person will feel after viewing unpleasant images.
This signature predicted the intensity of negative emotion in individual participants in cross validation. It was unresponsive to physical pain, demonstrating that it is not a representation of generalized arousal or salience.
The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience.
Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”).