EEG reveals brain network alterations in chronic aphasia during natural speech listening

Abstract

Aphasia is a common consequence of a stroke which affects language processing. In search of an objective biomarker for aphasia, we used EEG to investigate how functional network patterns in the cortex are affected in persons with post-stroke chronic aphasia (PWA) compared to healthy controls (HC) while they are listening to a story. EEG was recorded from 22 HC and 27 PWA while they listened to a 25-min-long story. Functional connectivity between scalp regions was measured with the weighted phase lag index. The Network-Based Statistics toolbox was used to detect altered network patterns and to investigate correlations with behavioural tests within the aphasia group. Differences in network geometry were assessed by means of graph theory and a targeted node-attack approach. Group-classification accuracy was obtained with a support vector machine classifier. PWA showed stronger inter-hemispheric connectivity compared to HC in the theta-band (4.5-7 Hz), whilst a weaker subnetwork emerged in the low-gamma band (30.5-49 Hz). Two subnetworks correlated with semantic fluency in PWA respectively in delta- (1-4 Hz) and low-gamma-bands. In the theta-band network, graph alterations in PWA emerged at both local and global level, whilst only local changes were found in the low-gamma-band network. As assessed with the targeted node-attack, PWA exhibit a more scale-free network compared to HC. Network metrics effectively discriminated PWA and HC (AUC = 83%). Overall, we showed for that EEG-network metrics are effective biomarkers to assess natural speech processing in chronic aphasia. We hypothesize that the detected alterations reflect compensatory mechanisms associated with recovery.

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

“…Specifically, weaker connectivity over the left prefrontal region is associated with worse performance in the Dutch naming test. Association between delta-band connectivity and language performance agrees with the results reported in our previous study, 9 and further favors the relevance of delta oscillation for the assessment of the severity of the pathology. As discussed in the previous paragraph, a network pattern in the delta band is likely to reflect neurophysiological mechanisms associated with perception of rhythm, acoustic modulation and lexical processing.…”

“…Our cohort comprised 24 HC (72 ± 7 years old, 16 males) and 40 PWA (69 ± 12 years old, 26 males). Demographic and behavioral information was assessed through standardized clinical tests as described in our recent works 9,27,34 and reported in Table 1. Participants' cognition was assessed with the Oxford Cognitive Screen (OCS), 35 and language tests comprised the Nederlandse Benoem Test (NBT), i.e.…”

“…These results support the hypothesis of ongoing compensatory processes in PWA associated with recovery and resonate with pathological reorganization of language-and auditory-related functional networks 12,13 towards a more functionally centered architecture. 9 Language processing and related disorders can also be investigated through the analysis of neural tracking of natural speech. This is performed by quantitatively assessing the statistical relationship between the EEG-recorded neural signal and the speech stimulus.…”

“…Our analyses focuses specifically on the delta (0.5-4 Hz), theta (4-8 Hz) and low-gamma (30-49 Hz) frequency bands, since these reportedly reflect diverse aspects of language processing and its disorders. 9,12,13,27,28 2. Materials and methods…”

Neural substrates and behavioral relevance of speech envelope tracking: evidence from post-stroke aphasia

“…The presented sample is a subset of prior studies (Kries et al, 2023a;Mehraram et al, 2024), comprising 39 native Flemish IWA (13 female participants, mean age = 69.6 y/o, sd = 12.4 y/o) in the chronic phase (≥ 6 months) post-stroke. Compared to these prior studies, one participant was excluded as we lacked accessible brain scan data.…”