Cross-atlas Identification of Narrative Hubs via Multi-embedding Graph Models in fMRI Data

One of the main objectives of cognitive neuroscience is to investigate brain processes that underlie narrative comprehension. Furthermore, earlier studies that used naturalistic functional magnetic resonance imaging (fMRI) datasets, like Narratives, has advanced our knowledge of large-scale language and narrative networks, most studies have relied on correlation-based analyses or single-region importance measures, overlooking the dynamic and structural properties of brain networks. In this work, we present a new graph-based framework to identify important regions in narrative comprehension by combining a composite node importance scoring method with multiple node embedding algorithms. We first used controlled simulations with stochastic block models (SBM) with different hub nodes and community strengths to validate the framework. This made it possible to systematically assess seven embedding algorithms for node influence attribution, link prediction, and community detection. Applying the same framework to fMRI data, we analyzed two parcellation schemes, the Harvard-Oxford and Schaefer (100-parcel) atlases, to identify influential cortical regions. Our findings reveal consistent engagement of the default mode, salience, and limbic networks across stories and atlases, emphasizing their central role in narrative processing. Overall, this work offers a reliable, comprehensible method for identifying key brain regions, bridging the gap between graph representation learning and cognitive neuroscience. The framework provides a scalable basis for further research that connects naturalistic cognition, dynamic brain connectivity, and linguistic features.