RESEARCH ARTICLE A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect

Neuroimaging has identified many correlates of emotion but has not yet yielded brain repre-sentations predictive of the intensity of emotional experiences in individuals. We used ma-chine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual par-ticipants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92 % discriminative accu-racy), 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. Further-more, 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 identi-fies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes. Author Summar