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Revealing Brain Network Dynamics During the Emotional State of Suspense Using TDA
(2025)
Astrid A. Olave, Jose A. Perea, Francisco Gómez
Abstract
Suspense is an affective state that is ubiquitous in human life, from art to quotidian events. However, little is known about the behavior of large-scale brain networks during suspenseful experiences. To address this question, we examined the continuous brain responses of participants watching a suspenseful movie, along with reported levels of suspense from an independent set of viewers. We employ sliding window analysis and Pearson correlation to measure functional connectivity states over time. Then, we use Mapper, a topological data analysis tool, to obtain a graphical representation that captures the dynamical transitions of the brain across states; this representation enables the anchoring of the topological characteristics of the combinatorial object with the measured suspense. Our analysis revealed changes in functional connectivity within and between the salience, fronto-parietal, and default networks associated with suspense. In particular, the functional connectivity between the salience and fronto-parietal networks increased with the level of suspense. In contrast, the connections of both networks with the default network decreased. Together, our findings reveal specific dynamical changes in functional connectivity at the network level associated with variation in suspense, and suggest topological data analysis as a potentially powerful tool for studying dynamic brain networks.Suspense is a complex emotional experience whose neural bases remain poorly understood. This study analyzes fMRI data from participants watching a suspenseful Alfred Hitchcock film to track time-evolving brain connectivity patterns. The Mapper algorithm represented moment-to-moment functional connectivity, transforming high-dimensional data into a graph that retains local detail and global shape. During heightened suspense, connectivity between salience and fronto-parietal control networks increases, whereas links with the default-mode network diminish. These shifts trace recurring paths through a finite set of connectivity states, offering a topological view of large-scale neural dynamics and clarifying how distributed networks coordinate during emotionally engaging experiences.