The effects of lesion and treatment-related recovery on functional network modularity in post-stroke dysgraphia.

A better understanding of the neural network properties that support cognitive recovery after a brain lesion is important for our understanding of human neuroplasticity and may have valuable clinical implications. In fifteen individuals with chronic, acquired written language deficits subsequent to left-hemisphere stroke, we used task-based functional connectivity to evaluate the relationship between the graph-theoretic measures (modularity, participation coefficient and within-module degree z-score) and written language production accuracy before and after behavioral treatment. A reference modular structure and local and global hubs identified from healthy controls formed the basis of the analyses. Overall, the investigation revealed that less modular networks with greater global and lower local integration were associated with greater deficit severity and lower response to treatment. Furthermore, we found treatment-induced increases in modularity and local integration measures. In particular, local integration within intact ventral occipital-temporal regions of the spelling network showed the greatest increase in local integration following treatment. This investigation significantly extends previous research by using task-based (rather than resting-state) functional connectivity to examine a larger set of network characteristics in the evaluation of treatment-induced recovery and by including comparisons with control participants. The findings demonstrate the relevance of network modularity for understanding the neuroplasticity supporting functional neural reorganization.