Dynamic brain connectivity predicts emotional arousal during naturalistic movie-watching

Jin Ke, Hayoung Song, Zihan Bai, Monica D Rosenberg and Yuan Chang Leong

PLOS Computational Biology, 2025, vol. 21, issue 4, 1-26

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 represented in the brain and whether the representations are shared across different individuals and diverse situational contexts. In this study, we first utilized two publicly available functional MRI datasets of participants watching movies to build predictive models of moment-to-moment emotional arousal and valence from dynamic functional brain connectivity. We tested the models by predicting emotional arousal and valence both within and across datasets. Our results revealed a generalizable arousal representation characterized by the interactions between multiple large-scale functional networks. The arousal representation generalized to two additional movie-watching datasets with different participants viewing different movies. In contrast, we did not find evidence of a generalizable valence representation. Taken together, our findings reveal a generalizable representation of emotional arousal embedded in patterns of dynamic functional connectivity, suggesting a common underlying neural signature of emotional arousal across individuals and situational contexts. We have made our model and analysis scripts publicly available to facilitate its use by other researchers in decoding moment-to-moment emotional arousal in novel datasets, providing a new tool to probe affective experience using fMRI.Author summary: This study explores how the brain represents two key dimensions of emotional experience: valence (how positive or negative an experience feels) and arousal (the level of emotional activation). Using publicly available brain imaging data from people watching movies spanning different genres, storylines, and characters, we built computational models to predict moment-to-moment changes in emotional valence and arousal from patterns of brain connectivity. Testing these models across datasets, we identified a common set of brain connections that capture a shared neural signature of emotional arousal across different individuals and movies. These results suggest that the brain may represent emotional arousal in a consistent manner across people and situations. However, we did not find evidence for a similarly generalizable neural pattern for emotional valence. By sharing our model and analysis tools openly, we aim to support other researchers in using brain imaging to better understand emotional experiences in naturalistic, real-world settings. Our findings contribute to a growing body of research focused on uncovering how emotional experiences are represented in the brain.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1012994

DOI: 10.1371/journal.pcbi.1012994

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