Brain Laterality Dynamics Support Human Cognition

Abstract

Hemispheric lateralization constitutes a core architectural principle of human brain organization underlying cognition, often argued to represent a stable, trait-like feature. However, emerging evidence underlines the inherently dynamic nature of brain networks, in which time-resolved alterations in functional lateralization remain uncharted. Integrating dynamic network approaches with the concept of hemispheric laterality, we map the spatiotemporal architecture of whole-brain lateralization in a large sample of high-quality resting-state fMRI data (N=991, Human Connectome Project). We reveal distinct laterality dynamics across lower-order sensorimotor systems and higher-order associative networks. Specifically, we expose two aspects of the laterality dynamics: laterality fluctuations, defined as the standard deviation of laterality time series, and laterality reversal, referring to the number of zero-crossings in laterality time series. These two measures are associated with moderate and extreme changes in laterality over time, respectively. While laterality fluctuations depict positive association with language function and cognitive flexibility, laterality reversal shows a negative association with the same neurocognitive factors. These opposing interactions indicate a dynamic balance between intra- and inter-hemispheric communication, i.e., segregation and integration of information across hemispheres. Furthermore, in their time-resolved laterality index, the default-mode and language networks correlate negatively with visual/sensorimotor and attention networks, indicating flexible while parallel processing capabilities that are linked to better out-of-scanner cognitive performance. Finally, the laterality dynamics correlate with regional metabolism and structural connectivity and showed significant heritability. Our results provide insights into the adaptive nature of the lateralized brain and new perspectives for future studies of human cognition, genetics and brain disorders.