Characterizing dynamic functional connectivity subnetwork contributions in narrative classification with Shapley values Open Access

Functional connectivity derived from functional magnetic resonance imaging (fMRI) data has been increasingly used to study brain activity. In this study, we model brain dynamic functional connectivity during narrative tasks as a temporal brain network and employ a machine learning model to classify in a supervised setting the modality (audio, movie), the content (airport, restaurant situations) of narratives, and both combined. Leveraging Shapley values, we analyze subnetwork contributions within Yeo parcellations (7- and 17-subnetworks) to explore their involvement in narrative modality and comprehension. This work represents the first application of this approach to functional aspects of the brain, validated by existing literature, and provides novel insights at the whole-brain level. Our findings suggest that schematic representations in narratives may not depend solely on preexisting knowledge of the top-down process to guide perception and understanding, but may also emerge from a bottom-up process driven by the temporal parietal subnetwork.

This study investigates how different brain subnetworks contribute to processing narratives. We used a machine learning model to analyze fMRI data from participants listening to or watching narratives that varied in modality (audio or movie) and thematic content (airport or restaurant). Our model accurately classified these different narrative aspects, and by using Shapley values, we identified the subnetworks most crucial for each classification. Consistent with existing neuroscience knowledge, our findings highlight the distinct roles of these subnetworks in narrative comprehension. This study provides a powerful approach for investigating brain function across various domains.