Applying a network framework to the neurobiology of reading and dyslexia

Abstract

Background: There is a substantial literature on the neurobiology of reading and dyslexia. Differences are often described in terms of individual regions or individual cognitive processes. However, there is a growing appreciation that the brain areas subserving reading are nested within larger functional systems, and new network analysis methods may provide greater insight into how reading difficulty arises. Yet, relatively few studies have adopted a principled network-based approach (e.g., connectomics) to studying reading. In this study, we combine data from previous reading literature, connectomics studies, and original data to investigate the relationship between network architecture and reading.

Methods: First, we detailed the distribution of reading-related areas across many resting-state networks using meta-analytic data from NeuroSynth. Then, we tested whether individual differences in modularity, the brain's tendency to segregate into resting-state networks, are related to reading skill. Finally, we determined whether brain areas that function atypically in dyslexia, as identified by previous meta-analyses, tend to be concentrated in hub regions.

Results: We found that most resting-state networks contributed to the reading network, including those subserving domain-general cognitive skills such as attention and executive function. There was also a positive relationship between the global modularity of an individual's brain network and reading skill, with the visual, default mode and cingulo-opercular networks showing the highest correlations. Brain areas implicated in dyslexia were also significantly more likely to have a higher participation coefficient (connect to multiple resting-state networks) than other areas.

Conclusions: These results contribute to the growing literature on the relationship between reading and brain network architecture. They suggest that an efficient network organization, i.e., one in which brain areas form cohesive resting-state networks, is important for skilled reading, and that dyslexia can be characterized by abnormal functioning of hub regions that map information between multiple systems. Overall, use of a connectomics framework opens up new possibilities for investigating reading difficulty, especially its commonalities across other neurodevelopmental disorders.

Keywords: Brain network; Dyslexia; Functional MRI; Graph theory; Individual differences; Language; Reading.

Fig. 1

Reading areas are distributed across many resting-state networks. On the left is the volumetric breakdown of the “reading” network, pulled from a NeuroSynth automated meta-analysis (forward-inference: p<0.01, FDR-corrected) [22], according to the 7-network cortical parcellation from Yeo and colleagues [23]. On the right is a surface plot of the same data. Reading areas are well-distributed across different networks and load highly onto attention and executive networks. Several important reading areas, including the inferior frontal gyrus and temporo-parietal junction, sit at points where multiple networks converge, i.e., likely hub areas

Fig. 2

Modularity metrics at rest predict reading skill. Global modularity, the degree to which a whole-brain network separates into RSNs, was positively related to reading skill across all subjects (N=65). Modularity for individual nodes was also positive overall (r_avg=0.134), but was significantly higher for nodes in the visual, default mode and cingulo-opercular RSNs (p<0.01). RSN colors correspond to the dominant Yeo RSN displayed in Fig. 1

Fig. 3

Dyslexia disproportionately impacts hub areas. Among the brain areas examined in Power and colleagues [36], nodes implicated in dyslexia have higher participation coefficients (32 nodes) compared to the rest of the brain (232 nodes)