Mapping dyslexia abnormalities onto hub areas

Two decades of neuroimaging research have allowed a relative consensus to form as to which brain regions are commonly dysfunctional in dyslexia. To determine whether there was any pattern related to network architecture in these areas, we gathered all activations from three meta-analyses comparing fMRI responses for individuals with dyslexia to typical readers [7–9]. The meta-analyses encompassed a total of 68 studies comparing dyslexic and typically developing individuals. Sample populations included children and adults under different experimental conditions. All brain areas that showed atypical activation in dyslexia (either greater or less activity) were included. When an activation spanned a large area, all reported local maxima were included.

To get measures of hubness across the brain, we used data from a connectomics study by Power and colleagues [36]. That study reports the participation coefficient for each of the 264 nodes previously described. The participation coefficient reflects the diversity of a node’s connectivity to different RSNs, where a higher value indicates that the node is correlated with many different RSNs. Activations from the dyslexia meta-analyses were then mapped to the geometrically closest node from this dataset, resulting in a small set of dyslexia-related nodes and a larger, unaffected set. The nodes and descriptions, along with their suggested system and atlas label, are available as an Additional file 1.

The distribution of participation coefficients across the 264 nodes was non-normal, with a large group of areas having low participation coefficients (i.e., affiliated with few RSNs) and a smaller hub-like group. Therefore, a Wilcoxon rank-sum test was performed on the participation coefficients for the two groups, which tests for the equivalence of two distributions in a non-parametric fashion.