MEG-Based Functional Connectivity Biomarkers of Dyslexia

Dyslexia a learning disability related to reading, often characterized by difficulty with accurate word recognition, decoding, and spelling. The disorder affects approximately 10% of the population and it is typically diagnosed using neuropsychological evaluation. The main objective of this thesis has been the development of unique measures based on fast neurophysiological recordings that may used to improve detection and allow intervention at an earlier age, with improved outcomes. We used functional connectivity analysis to identify brain connectivity networks in task-free, resting-state Magnetoencephalographic recordings of brain activity obtained in two groups of participants, namely 21 dyslexia patients and 20 age-matched normal controls. In an attempt to quantify interaction among brain regions and understand how brain networks are affected by dyslexia, we used Granger causality, which can estimate cause-and-effect relationships both in terms of strength and direction. A Granger connectivity matrix was computed for each subject individually, and then group templates were estimated by averaging all matrices in each group. Furthermore, we performed classification of the subjects using support vector machines and Fisher's criterion to rank the features and identify the best subset for maximum separation of the two groups. Our results show that a combined model based on connectivity matrices and graph theory measures can provide 100% classification accuracy in separating the two groups, with 100% sensitivity and specificity. These findings suggest that analysis of functional connectivity patterns may provide a valuable tool for the early detection of dyslexia.