Probing Individual Differences in the Topological Landscape of Naturalistic Brain Dynamics

Abstract

Psychiatry seeks to unravel brain dysfunction and individual differences in real-world contexts. Naturalistic stimuli, like movie watching, are increasingly recognized for eliciting complex, context-dependent neural activity with high ecological validity. Yet, current methods often rely on standard paradigms that average data across time, limiting the full potential of such stimuli. Here, we present STIM, a Topological Data Analysis-based framework designed to dynamically track how individuals integrate complex contexts in real time. Applied to large-sample fMRI data from movie watching, STIM constructs a robust low-dimensional dynamical landscape that reflects group consensus while probing individual variations at both global (spanning narratives) and local (within specific narratives) levels. At the global level, individual differences emerge along a center-periphery gradient in the dynamical landscape, which significantly predicts fluid intelligence, underscoring the importance of neural adaptability and diversity. At finer scales, local geometric features correlate with context-specific psychological traits beyond cognition. STIM also captures developmental changes in the dynamical landscape and reveals abnormalities in conditions such as autism. These findings demonstrate that STIM leverages the rich information from movie stimuli and fMRI recordings as neural probes to assess individual differences in cognition and mental health.