Neural oscillations reveal disrupted functional connectivity associated with impaired speech auditory feedback control in post-stroke aphasia

Abnormal resting-state neural activities of language and non-language cognitive function impairments in stroke patients with aphasia: A cross-sectional study

OBJECTIVE

Language impairments may mask non-language cognitive deficits in post-stroke aphasia (PSA) patients. Moreover, the underlying neural mechanisms of both language and non-language cognitive impairment remain unclear. This study aimed to investigate the activities and functional abnormalities of local and remote brain regions and their relationship with cognitive function in PSA patients, to provide more effective tips in future clinical therapy.

METHODS

This cross-sectional study included 46 PSA patients and 40 controls, who underwent language and non-language cognitive assessments, and resting-state functional magnetic resonance imaging (rs-fMRI). We then examined the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and functional connectivity (FC) based on a modest sample size (46 PSA patients and 40 normal controls (NCs)). Independent two-sample t-tests were used to identify differences in these measures between PSA patients and NCs. Moreover, partial correlation analyses were performed to determine the correlation between FC from the affected brain regions and language, and non-language cognitive performance in PSA patients.

RESULTS

This study revealed that both fALFF and ReHo in PSA patients presented significantly lower in the right superior cerebellum, left thalamus, and left middle frontal gyrus, along with increased values in the right superior frontal gyrus (dorsolateral part) (p < 0.05). Notably, decreased FC between the left middle frontal gyrus and orbital part of the left inferior frontal gyrus was significantly associated with both language and non-language cognitive performance (p < 0.05). In addition, PSA patients were further divided into fluent and non-fluent groups. The results revealed that non-fluent patients demonstrated worse overall cognitive functioning, accompanied by reduced FC between the left thalamus and the left supplementary motor area (p < 0.001).

CONCLUSION

This study provides new evidence that abnormal neural activities and functional connectivities within specific brain regions may play crucial roles in language and non-language cognitive function. The underlying mechanisms of non-language cognitive decline accompanied by impaired language function in PSA patients may be a partial overlap between language and cognitive networks. In the future, combining language and non-language functions and designing a comprehensive treatment plan will be the focus of rehabilitation.

HD-tACS over the left frontal aslant tract entrains theta activity associated with speech motor control

Transient disruption or permanent damage to the left Frontal Aslant Tract (FAT) is associated with deficits in speech production. The present study examined the application of theta (4 Hz) high-definition transcranial alternating current stimulation (HD-tACS) over the left SMA and IFG -as a part of FAT- as a potential multisite protocol to modulate neural and behavioral correlates of speech motor control. Twenty-one young adults participated in three counterbalanced sessions in which they received in-phase, anti-phase, and sham theta HD-tACS. In each session, 4 Hz stimulation was applied over the left IFG and SMA, and subsequently EEG data was recorded while participants performed a speech Go/No-Go task. Relative to sham and anti-phase, in-phase HD-tACS significantly improved speech reaction time. Neural data showed an increase in the power of frontal theta activity prior to speech initiation for the in-phase condition compared to sham. Moreover, in-phase stimulation increased the phase synchrony of theta activity between the left central and frontal electrodes. For speech inhibition, the power of theta activity increased following the in-phase condition over frontocentral electrodes. Furthermore, the in-phase condition enhanced the connectivity between the left central and frontal electrodes. Overall findings suggest that in-phase theta HD-tACS of FAT enhanced the neural markers of cognitive control required for motor preparation and inhibition during a speech task and have translational implications.

Aberrant neural oscillations in poststroke aphasia

Neural oscillations are electrophysiological indicators of synchronous neuronal activity in the brain. Recent work suggests aberrant patterns of neuronal activity in patients with poststroke aphasia. Yet, there is a lack of systematic explorations of neural oscillations in poststroke aphasia. Investigating changes in the dynamics of neuronal activity after stroke may be helpful to identify neural markers of aphasia and language recovery and increase the current understanding of successful language rehabilitation. This review summarizes research on neural oscillations in poststroke aphasia and evaluates their potential as biomarkers for specific linguistic processes. We searched the literature through PubMed, Web of Science, and EBSCO, and selected 31 studies that met the inclusion criteria. Our analyses focused on neural oscillation activity in each frequency band, brain connectivity, and therapy-induced changes during language recovery. Our review highlights potential neurophysiological markers; however, the literature remains confounded, casting doubt on the reliability of these findings. Future research must address these confounds to confirm the robustness of cross-study findings on neural oscillations in poststroke aphasia.

Integrating EEG and Machine Learning to Analyze Brain Changes during the Rehabilitation of Broca's Aphasia

The fusion of electroencephalography (EEG) with machine learning is transforming rehabilitation. Our study introduces a neural network model proficient in distinguishing pre- and post-rehabilitation states in patients with Broca's aphasia, based on brain connectivity metrics derived from EEG recordings during verbal and spatial working memory tasks. The Granger causality (GC), phase-locking value (PLV), weighted phase-lag index (wPLI), mutual information (MI), and complex Pearson correlation coefficient (CPCC) across the delta, theta, and low- and high-gamma bands were used (excluding GC, which spanned the entire frequency spectrum). Across eight participants, employing leave-one-out validation for each, we evaluated the intersubject prediction accuracy across all connectivity methods and frequency bands. GC, MI theta, and PLV low-gamma emerged as the top performers, achieving 89.4%, 85.8%, and 82.7% accuracy in classifying verbal working memory task data. Intriguingly, measures designed to eliminate volume conduction exhibited the poorest performance in predicting rehabilitation-induced brain changes. This observation, coupled with variations in model performance across frequency bands, implies that different connectivity measures capture distinct brain processes involved in rehabilitation. The results of this paper contribute to current knowledge by presenting a clear strategy of utilizing limited data to achieve valid and meaningful results of machine learning on post-stroke rehabilitation EEG data, and they show that the differences in classification accuracy likely reflect distinct brain processes underlying rehabilitation after stroke.