Dual temporal pathway model of emotion processing based on dynamic network reconfiguration analysis of EEG signals

Emotion is crucial for the quality of daily life. Recent findings suggest that the cooperation and integration of multiple brain regions are essential for effective emotion processing. Additionally, network reconfiguration has been observed during various cognitive tasks. However, it remains unclear how the brain responds to different emotional categories under natural stimuli from the perspective of network reconfiguration, or whether this reconfiguration can predict subjective rating scores. To address this question, 28 video clips were used to evoke eight distinct emotion categories, and the participants' electroencephalogram (EEG) signals were recorded. Dynamic network reconfiguration analysis was performed on brain networks extracted from band-limited EEG signals using the phase locking value (PLV) across multiple non-overlapping time windows. Robust dynamic community detection was applied to these networks, followed by quantification of integration and segregation at both node- and community-level changes. Multidimensional rating scores were collected for each clip. The analysis revealed that the metrics of dynamic network reconfiguration could predict subjective ratings. Specifically, longer EEG segments improved predictions for positive emotions, whereas shorter segments were more effective for negative emotions. Our study provides empirical evidence integrating the dual-process model and the theory of constructed emotion. Based on observed spatiotemporal patterns of global integration and segregation across the brain, we propose the dual temporal pathway model for emotional processing across various emotion categories, highlighting fast and slow neural processes associated with negative and positive emotions, respectively. These findings offer valuable support for developing temporally targeted diagnostic and therapeutic strategies for emotion-related brain disorders.