Alterations in resting-state neural networks in subacute non-fluent aphasia after stroke: a fNIRS study

Post-stroke aphasia is a prevalent and often disabling language impairment. Despite its high incidence, the neuropathological mechanisms underlying post-stroke aphasia and the neural substrates of functional recovery remain poorly understood. Understanding these mechanisms is essential for developing targeted rehabilitation strategies.

In this study, we used fNIRS to collect resting-state data from three participant groups. Pearson correlation was applied to compute whole-brain functional connectivity, followed by graph theory analysis to compare global and regional network characteristics across groups.

Compared with the healthy control (HC) group, the aphasia group exhibited significantly reduced whole-brain functional connectivity and a lower global clustering coefficient. Further nodal analysis revealed alterations in both node degree and nodal efficiency within the aphasia group. Notably, the local efficiency of the right-hemisphere homologue of Broca's area and the right dorsolateral prefrontal cortex (DLPFC) was significantly higher than that in the HC group. Furthermore, network hub analysis identified the right supplementary motor area (SMA) as a prominent global hub, demonstrating its elevated centrality within the overall network of the aphasia group.

While global network topological metrics did not differ significantly after correction for multiple comparisons, exploratory analyses revealed trends toward reduced clustering and increased global efficiency. Regional nodal analysis identified specific alterations in right-hemisphere homologues, providing potential targets for neuroregulation in aphasia rehabilitation.