The functional significance of EEG phase synchronization networks during information integration of left and right visual fields

Abstract

Objects moving between the left and right visual hemifields are naturally perceived as continuous entities, although early visual processing independently transmits information from the two hemifields. Therefore, interhemispheric integration of visual information is essential for maintaining an object’s identity. Additionally, brain function is thought to be maintained through a dynamic balance between integration and segregation. In this study, we investigated the functional neural architecture underlying visual hemifield integration in healthy adults, using electroencephalography (EEG) and a visual integration task. To capture neural oscillatory networks without relying on prior assumptions regarding electrode pairs or frequency bands, we applied a frequency-inclusive, data-driven network analysis based on an extended network-based statistic. This analysis identified a broadband EEG phase synchronization network that emerged specifically under task conditions with high interhemispheric integration demands. Furthermore, individual differences in behavioral performance were associated with modulation of interhemispheric synchronization, with this relationship differing according to participants’ relative performance across task conditions. These findings suggest that visual hemifield integration is supported by large-scale phase synchronization networks spanning multiple frequencies and are consistent with the importance of a balance between integration and segregation. Author Summary The brain continuously integrates visual information across the left and right visual fields, although early visual processing processes these inputs separately. Efficient brain function is also thought to depend on the balance between the integration and segregation of neural activity. However, the mechanism by which large-scale brain networks achieve this balance during visual information processing remains unclear. In this study, we used EEG and a visual tracking task to examine the neural dynamics during visual hemifield integration. Data-driven network analysis revealed a broadband phase synchronization network that emerged when the interhemispheric integration demands were high. Importantly, individual differences in task performance were associated with the strength of the interhemispheric synchronization. These results suggest that flexible changes in large-scale neural networks across multiple frequencies support visual information integration.