Cluster structure of functional networks estimated from high-resolution EEG data

TL;DR

This study analyzes the topological properties of cortical functional networks derived from high-resolution EEG data in spinal cord injured patients and healthy controls, revealing differences in network efficiency and community structure related to injury.

Contribution

It introduces a detailed analysis of network topology and community structure differences in EEG-derived cortical networks between injured and healthy subjects during movement preparation.

Findings

Injured patients show higher density of efficient clusters.

Distinct community structures observed in alpha band related to injury.

Functional separation suggests compensatory mechanisms in injured patients.

Abstract

We study the topological properties of functional connectivity patterns among cortical areas in the frequency domain. The cortical networks were estimated from high-resolution EEG recordings in a group of spinal cord injured patients and in a group of healthy subjects, during the preparation of a limb movement. We first evaluate global and local efficiency, as indicators of the structural connectivity respectively at a global and local scale. Then, we use the Markov Clustering method to analyse the division of the network into community structures. The results indicate large differences between the injured patients and the healthy subjects. In particular, the networks of spinal cord injured patient exhibited a higher density of efficient clusters. In the Alpha (7-12 Hz) frequency band, the two observed largest communities were mainly composed by the cingulate motor areas with the supplementary motor areas, and by the pre-motor areas with the right primary motor area of the foot. This functional separation strengthens the hypothesis of a compensative mechanism due to the partial alteration in the primary motor areas because of the effects of the spinal cord injury.