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

Abstract

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants 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 accuracy), 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”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

Self-regulation via neural simulation

“…Likewise, participants who exhibited a greater difference in PINES scores for the tough vs. sensitive target subsequently exhibited a greater difference in their behavioral evaluations of the affective states of these targets (r = 0.39, P = 0.014). Interestingly, there was no correlation between the PINES effect and amygdala effect (r = 0.01), as would be expected based on prior work establishing the PINES that suggested they could be independent predictors of negative affect (23). A multiple regression with both the PINES effect and amygdala effect as predictors and the behavioral effect as the dependent variable showed a significant effect for the PINES (b = 1.193, SE = 0.562, 95% CI: 0.023, 2.364, P = 0.046) and a marginally significant effect for the amygdala (b = 0.742, SE = 0.363, 95% CI: −0.103, 1.499, P = 0.053; R 2 = 0.295).…”

“…First, amygdala activity was up-regulated for the sensitive (vs. tough) perspective. Second, a multivoxel, wholebrain pattern of activity that has been independently shown to accurately predict participants' affective state (PINES) (23) indicated up-regulated negative affectivity when taking a sensitive (vs. tough) perspective. Third, participants who behaviorally predicted a greater difference in the affective responses of the sensitive and tough targets also exhibited a greater difference in their PINES and amygdala response when adopting the sensitive (vs. tough) perspectives.…”

“…In light of this, we sought to strengthen our claim that emotional perspective-taking modulates negative affective processing by using a recently identified PINES (23). The PINES is a whole brain activation pattern developed using machine learning techniques that can reliably predict self-reported emotional responses to aversive images.…”

“…We examined the effect of perspective-taking on multiple behavioral and brain markers of affective responding, including reports of the target's predicted affective reactions to stimuli, activation in the amygdala (which is the brain region most strongly associated with detecting, encoding, and promoting responses to affectively relevant and especially potentially threatening stimuli) (21,22), and finally, a recently identified picture-induced negative emotion signature (PINES) (23). PINES is a distributed, wholebrain multivoxel activation pattern developed using machine learning techniques that can reliably predict levels of negative affect elicited by aversive images.…”

Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations

“…Machine learning might lock onto features that correlate with pain, such as salience, rather than pain itself -the reverse inference problem 72,73 (discussed further below). Second, successful prediction of pain does not imply that the predictive brain biomarker is specific to the experience of pain; such a neuromarker must be tested in many types of painful and non-painful conditions to empirically establish what it does and does not respond to 54,[74][75][76] . Third, an imaging neuromarker might not generalize to all types of pain, or to all individuals; this aspect must also be tested empirically.…”