Graph analysis of EEG resting state functional networks in dyslexic readers

Abstract

Objective: Neuroimaging research suggested a mixed pattern of functional connectivity abnormalities in developmental dyslexia. We examined differences in the topological properties of functional networks between 29 dyslexics and 15 typically reading controls in 3rd grade using graph analysis. Graph metrics characterize brain networks in terms of integration and segregation.

Method: We used EEG resting-state data and calculated weighted connectivity matrices for multiple frequency bands using the phase lag index (PLI). From the connectivity matrices we derived minimum spanning tree (MST) graphs representing the sub-networks with maximum connectivity. Statistical analyses were performed on graph-derived metrics as well as on the averaged PLI connectivity values.

Results: We found group differences in the theta band for two graph metrics suggesting reduced network integration and communication between network nodes in dyslexics compared to controls.

Conclusion: Collectively, our findings point to a less efficient network configuration in dyslexics relative to the more proficient configuration in the control group.

Significance: Graph metrics relate to the intrinsic organization of functional brain networks. These metrics provide additional insights on the cognitive deficits underlying dyslexia and, thus, may advance our knowledge on reading development. Our findings add to the growing body literature suggesting compromised networks rather than specific dysfunctional brain regions in dyslexia.

Keywords: Dyslexia; Electroencephalography (EEG); Functional connectivity; Graph theory; Minimum spanning tree; Network.