Generalizable neural models of emotional engagement and disengagement

Emotional experiences are never static but continuously evolve in response to internal and external contexts. Little is known about how neural patterns change as a function of these experiences, particularly in response to complex, real-world stimuli. This study aimed to identify generalizable neural patterns as individuals collectively engage and disengage from emotions dynamically. To do so, we analyzed functional magnetic resonance imaging along with subjective emotional annotations from two independent studies as individuals watched negative and neutral movie clips. We used predictive modeling to test if a model trained to predict a group emotional signature response in one study generalizes to the other study and vice versa. Disengagement patterns generalized specifically across intense clips. They were supported by connections within and between the sensorimotor and salience networks, maybe reflecting the processing of feeling states as individuals regulate their emotions. Prediction success for the engagement signature was mixed, but primarily linked to connections within the visual and between the visual and dorsal attention networks, maybe supporting visual attention orienting as emotions intensify. This work offers potential pathways for identifying generalizable neural patterns contributing to future affective research and clinical applications aiming to better understand dynamic emotional responses to naturalistic stimuli.