Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals.
In an article in PLOS Biology by Chang LJ et al Published: June 22, 2015 (DOI: 10.1371/journal.pbio.1002180 ) ; Authors used machine learning to identify a sensitive and specific signature of emotional responses to aversive images.
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”).
Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.