Functional connectivity of stimulus-evoked brain responses to natural speech in post-stroke aphasia

Objective. One out of three stroke-patients develop language processing impairment known as aphasia. The need for ecological validity of the existing diagnostic tools motivates research on biomarkers, such as stimulus-evoked brain responses. With the aim of enhancing the physiological interpretation of the latter, we used EEG to investigate how functional brain network patterns associated with the neural response to natural speech are affected in persons with post-stroke chronic aphasia. Approach. EEG was recorded from 24 healthy controls and 40 persons with aphasia while they listened to a story. Stimulus-evoked brain responses at all scalp regions were measured as neural envelope tracking in the delta (0.5-4 Hz), theta (4-8 Hz) and low-gamma bands (30-49 Hz) using mutual information. Functional connectivity between neural-tracking signals was measured, and the Network-Based Statistics toolbox was used to: (1) assess the added value of the neural tracking vs EEG time series, (2) test between-group differences and (3) investigate any association with language performance in aphasia. Graph theory was also used to investigate topological alterations in aphasia. Main results. Functional connectivity was higher when assessed from neural tracking compared to EEG time series. Persons with aphasia showed weaker low-gamma-band left-hemispheric connectivity, and graph theory-based results showed a greater network segregation and higher region-specific node strength. Aphasia also exhibited a correlation between delta-band connectivity within the left pre-frontal region and language performance. Significance. We demonstrated the added value of combining brain connectomics with neural-tracking measurement when investigating natural speech processing in post-stroke aphasia. The higher sensitivity to language-related brain circuits of this approach favors its use as informative biomarker for the assessment of aphasia.