Dynamic functional connectivity encodes generalizable representations of emotional arousal across individuals and situational contexts

Abstract

Human affective experience varies along the dimensions of valence (positivity or negativity) and arousal (high or low activation). It remains unclear how these dimensions are encoded in the brain and if the representations generalize across diverse situations. Here we utilized two publicly available functional MRI datasets of participants watching movies to build predictive models of moment-to-moment valence and arousal from functional correlations in brain activity. We tested the models both within and across datasets and identified a situation-general arousal representation characterized by the interaction between multiple large-scale functional networks. The arousal representation generalized to two additional datasets. Predictions based on multivariate patterns of activation underperformed connectome-based predictions and did not generalize. In contrast, we found no evidence of a situation-general valence representation. Together, our findings reveal a generalizable representation of arousal encoded in patterns of dynamic functional connectivity, revealing an underlying similarity in how arousal is encoded across affective contexts.