Multi-Scale Anti-Correlated Neural States Dominate Naturalistic Whole-Brain Activity

Abstract

The human brains response to naturalistic stimuli is characterized by complex spatiotemporal dynamics. Within these dynamics there is a transitioning structure between sets of anti-correlated neural states that is frequently observed but has not been systematically investigated across scales. In this paper, we use three different naturalistic fMRI datasets to quantify anti-correlation in global and local neural states during naturalistic viewing or listening and investigate their interdependence and their relationship to changes in the stimuli. We demonstrate that continuous naturalistic brain activity shows an anti-correlational structure that spans both global and local spatial scales, with regions in the dorsal attention network showing strong alignment between local and global state transitions. On the global scale, ongoing dynamics are dominated by two antagonistic states that correspond to Default Mode Network and Task Positive Network configurations, with a third transitional state mediating between them. On the local scale, we observe anti-correlated neural states that are associated with periods of relatively high and low brain activity. Across the brain, these are driven by subsets of voxels that are systematically anti-correlated with their areas dominant activity pattern. This antagonism is related to stimulus changes, which tend to trigger a switch to the TPN state globally and to high activity states locally. On the local scale we also see a modality-specific pattern, with visual changes mostly driving transitions in visual cortical regions and auditory changes predominantly affecting auditory and language-related areas. The consistency of these findings across datasets with different stimulus types (audiovisual and purely auditory) indicates that anti-correlated neural states represent a domain-general organizational principle of brain function. We propose that anti-correlated dynamics functionally represent a convergent solution to the fundamental challenge of maintaining coherent internal representations while remaining responsive to meaningful changes in the environment.