Hierarchical organization of brain functional network during visual task

TL;DR

This study investigates the hierarchical modular organization of brain functional networks during visual tasks using EEG data, revealing alignment with anatomical structures and functional segmentation.

Contribution

It introduces a method to analyze hierarchical modularity of EEG-derived brain networks and links network architecture to anatomical and functional brain organization.

Findings

Modular architecture aligns with brain anatomy.

EEG time series correlations are stronger within functional groups.

Hierarchical organization reflects cortical functional segmentation.

Abstract

In this paper, the brain functional networks derived from high-resolution synchronous EEG time series during visual task are generated by calculating the phase synchronization among the time series. The hierarchical modular organizations of these networks are systematically investigated by the fast Girvan-Newman algorithm. At the same time, the spatially adjacent electrodes (corresponding to EEG channels) are clustered into functional groups based on anatomical parcellation of brain cortex, and this clustering information are compared to that of the functional network. The results show that the modular architectures of brain functional network are in coincidence with that from the anatomical structures over different levels of hierarchy, which suggests that population of neurons performing the same function excite and inhibit in identical rhythms. The structure-function relationship further reveals that the correlations among EEG time series in the same functional group are much stronger than those in different ones and that the hierarchical organization of brain functional network may be a consequence of functional segmentation of brain cortex.