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 are shared across different individuals and diverse situational contexts. 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 dynamic functional brain connectivity. We tested the models both within and across datasets and identified a generalizable arousal representation characterized by the interaction between multiple large-scale functional networks. The arousal representation generalized to two additional movie-watching datasets. Predictions based on multivariate patterns of activation underperformed connectome-based predictions and did not generalize. In contrast, we found no evidence of a generalizable valence representation. Taken 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 individuals and situational contexts.