Small-world EEG network analysis of functional connectivity in developmental dyslexia after visual training intervention

Abstract

Aberrations in functional connectivity in children with developmental dyslexia have been found in electroencephalographic studies using graph analysis. How training with visual tasks can modify the functional semantic network in developmental dyslexia remains unclear. We investigate local and global topological properties of functional networks in multiple EEG frequency ranges based on a small-world propensity method in controls, pre- and post-training dyslexic children during visual word/pseudoword processing. Results indicated that the EEG network topology in dyslexics before the training was more integrated than controls, and after training - more segregated and similar to that of the controls in the theta (θ: 4-8), alpha (α: 8-13), beta (β1: 13-20; β2: 20-30), and gamma (γ1: 30-48; γ2: 52-70 Hz) bands for three graph measures. The pre-training dyslexics exhibited a reduced strength and betweenness centrality of the left anterior temporal and parietal regions in the θ, α, β1 and γ1-frequency bands, compared to the controls. The simultaneous appearance of hubs in the left hemisphere (or both hemispheres) at temporal and parietal (α-word/γ-pseudoword discrimination), temporal and middle frontal cortex (θ, α-word), parietal and middle frontal cortex (β1-word), parietal and occipitotemporal cortices (θ-pseudoword), identified in the EEG-based functional networks of normally developing children were not present in the networks of dyslexics. The hub distribution for dyslexics in the θ, α, and β1 bands became similar to that of the controls. The topological organization of functional networks and the less efficient network configuration (long characteristic path length) in dyslexics compared to the more optimal global organization in the controls was studied for the first time after remediation training.

Keywords: EEG; connectome; developmental dyslexia; frequency oscillations; functional connectivity; graph theory; neural computation.