Altered Dynamic Functional Network Connectivity in Post-Stroke Aphasia

Previous studies examining post-stroke aphasia (PSA) patients via resting-state functional magnetic resonance imaging (rs-fMRI) have predominantly focused on static functional connectivity. In contrast, the current investigation aims to elucidate the alterations in dynamic functional network connectivity (dFNC) among PSA patients.

We recruited 40 PSA patients and 41 age-, gender-, and education-matched normal controls (NCs). The participants underwent rs-fMRI and the Western Aphasia Battery (WAB) test. Independent component analysis (ICA) was used to identify resting-state networks (RSNs), and dFNC was constructed using a sliding window technique followed by k-means clustering to classify distinct dynamic network states. We compared the dFNC differences between the PSA and NC groups and examined their relationships with clinical outcomes.

The dynamic analysis identified 4 distinct dFNC states: state 1 (modular network state), state 2 (pan-network hyperconnectivity state), state 3 (intra-network coordinated state), and state 4 (sparse connectivity state). Notable group differences were observed: compared with NCs, the PSA group demonstrated significantly reduced connectivity within the language network (LN) and increased connectivity between the cerebellar network (CN) and the default mode network (DMN) in state 3; significantly reduced connectivity changes within the LN and between the LN and executive control network (ECN) / CN were noted in state 4. Additionally, fraction time and mean dwell time in the modular network state were positively correlated with the WAB score.

Alteration in dFNC could serve as a sensitive biomarker toward language impairment in PSA patients, with implications for diagnostic assessment and therapeutic intervention planning.