Distributed Cortical Network Dynamics of Binocular Convergent Eye Movements in Humans

Abstract

Neuroimaging studies in humans have localized brain functions to specific brain regions, but a recent shift toward distributed network-based models of brain function promises deeper insights into the network processes that generate brain functionality. Resting-state functional connectivity provides a rich mapping of the brains network architecture, linking with both underlying structure and task-evoked responses across the whole brain. In this study, we utilized a model based on propagation of task-evoked activations over resting-state functional connectivity networks to identify cortical contributions to localized functional brain activations associated with binocular convergent eye movements. Binocular vision is crucial for daily routine activities, with its impairment leading to significant challenges in daily life. The distributed network-level mechanisms of binocular convergent eye movements remain unknown. Results showed that mapping activity flow over brain connections accurately generated actual brain activations associated with convergent eye movements, which were distinct from those observed during control tasks. The visual and dorsal attention networks dominated the propagation of activations through resting-state connections during convergent eye movements. Submodel analyses further revealed that restricting activity flow to individual networks, such as the visual or dorsal attention systems alone, substantially reduced model accuracy, underscoring the necessity of distributed, whole-brain contributions. In conclusion, highly distributed network pathways are involved in convergent eye movements, with some pathways contributing much more than others, providing important implications for future clinical models of binocular dysfunction.