Graph Theory Metrics-based Functional Brain Network Analysis in Dyslexia

OpenAlex Turkish

Developmental dyslexia, a neurodevelopmental disorder affecting 5-10% of school-aged children, involves persistent reading, decoding, and spelling difficulties. Electroencephalography (EEG), especially when combined with graph theory, offers a powerful, non-invasive method for exploring the neural basis of dyslexia. This study examines neuroplastic changes in 14 subjects with dyslexia during a 60-day cognitive training protocol, utilizing EEG-based functional connectivity and brain network analysis. The international 10–20 system is used to capture EEG signals from 14 scalp locations that encompass the frontal, temporal, parietal, and occipital lobes. The EEG recordings from Days 1 and 60 are used to extract a variety of connectivity metrics, including correlation coefficients, covariances, cross-power spectral densities, magnitude-squared coherence, and entropy-based metrics. Additionally, seven graph theory metrics (assortativity, transitivity, clustering coefficient, global efficiency, local efficiency, modularity, and characteristic path length) are calculated using connectivity measures to assess changes in large-scale brain networks. In order to evaluate the impact of cognitive training, EEG-derived features are classified using machine learning models such as logistic regression, decision trees, bagging trees, k-nearest neighbor, and support vector machines. By Day 60, graph-based connectivity tests revealed enhanced long-range functional connectivity and better brain network integration. These results imply that children with dyslexia may have quantifiable neuroplasticity as a result of cognitive instruction. EEG-based graph metrics are also highlighted in the paper as possible indicators for tracking dyslexic children’s cognitive development and the effectiveness of their treatment.