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Prognostic Role of Hemispherical Functional Connectivity in Patients With Stroke
TAKE-HOME MESSAGE
- Electroencephalography (EEG) is an easy test that shows real-time electrical activity of the brain. Although previous studies suggest that EEG might have a predictive value in poststroke recovery, the utility of EEG is not well-established. This study investigated the prognostic value of EEG performed in the acute period in predicting motor recovery after stroke. The affected hemisphere showed less high-frequency activity compared with that in healthy controls. Additionally, acute EEG-based metrics (small-world index and total coherence) of the affected hemisphere predicted motor and functional improvements in 1 month.
- EEG appears to be promising in predicting motor recovery after a stroke. Given the widespread availability and ease of use, it may become a part of acute stroke care for prognostication.
abstract
This abstract is available on the publisher's site.
Access this abstract nowBACKGROUND
The objective of the present study is to explore whether acute stroke may result in changes in brain network architecture by electroencephalography functional coupling analysis and graph theory.
METHODS
Ninety acute stroke patients and 110 healthy subjects were enrolled in different clinical centers in Rome, Italy, starting from 2013, and for each one electroencephalographies were recorded within <15 days from stroke onset. All patients were clinically evaluated through National Institutes of Health Stroke Scale, Barthel Index, and Action Research Arm Test in the acute stage and during the follow-up. Functional connectivity was assessed using Total Coherence and Small World (SW) by comparing the affected and the unaffected hemisphere between groups (Stroke versus Healthy). Correlations between connectivity and poststroke recovery scores have been carried out.
RESULTS
In stroke patients, network hemispheric asymmetry, in terms of Total Coherence, was mainly detected in the affected hemisphere with lower values in Delta, Theta, Alpha1, and Alpha2 (P=0.000001), whereas the unaffected hemisphere showed lower Total Coherence only in Delta and Theta (P=0.000001). SW revealed a significant difference only in the affected hemisphere in all electroencephalography bands (lower SW in Delta (P=0.000003), Theta (P=0.000003), Alpha1 (P=0.000203), and Alpha2 (P=0.028) and higher SW in Beta2 (P=0.000002) and Gamma (P=0.000002)). We also found significant correlations between SW and improvement in National Institutes of Health Stroke Scale (Theta SW: r=-0.2808), Barthel Index (Delta SW: r=0.3692; Theta SW: r=0.3844, Beta2 SW: r=-0.3589; Gamma SW: r=-04948), and Action Research Arm Test (Beta2 SW: r=-0.4274; Gamma SW: r=-0.4370).
CONCLUSIONS
These findings demonstrated changes in global functional connectivity and in the balance of network segregation and integration induced by acute stroke. The findings on the correlations between clinical outcome(s) and poststroke network architecture indicate the possibility to identify a predictive index of recovery useful to address and personalize the rehabilitation program.
Additional Info
Disclosure statements are available on the authors' profiles:
Prognostic Role of Hemispherical Functional Connectivity in Stroke: A Study via Graph Theory Versus Coherence of Electroencephalography Rhythms
Stroke 2022 Nov 23;[EPub Ahead of Print], F Vecchio, C Pappalettera, F Miraglia, G Deinite, R Manenti, E Judica, P Caliandro, PM RossiniFrom MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.
Poststroke alterations in neural networks spread to remote brain regions, including the unaffected hemisphere. Functional neuroimaging via MRI has been widely used to characterize impairment and recovery after stroke. Furthermore, electroencephalography (EEG) is also a potential neuroimaging tool because it facilitates high temporal resolution, and it is being used widely. However, few researchers have evaluated the affected and unaffected hemispheres separately using only EEG-based neuroimaging.
Through a longitudinal study, Vecchio et al explored neural network alterations in 90 patients with acute ischemic stroke and 110 healthy controls using EEG. They assessed hemispheric asymmetry using functional connectivity (total coherence [TotCoh]) and the graph theory parameter (small world [SW]). TotCoh of the affected hemisphere exhibited lower values in the Delta, Theta, Alpha1, and Alpha2 bands, whereas TotCoh of the unaffected hemisphere exhibited lower values in the Delta and Theta bands. In contrast, SW showed a significant difference only in the affected hemisphere (lower values in the Delta, Theta, Alpha1, and Alpha2 bands and higher values in the Beta2 and Gamma bands). Moreover, SW showed a significant correlation with improvement of commonly used clinical scales: National Institutes of Health Stroke Scale (positive correlation in the Theta band), Barthel Index (positive correlation in the Delta and Theta bands and negative correlation in the Beta2 and Gamma bands), and the Action Research Arm Test (negative correlation in the Beta2 and Gamma bands).
This study revealed that EEG-based biomarkers could be used to assess alterations in global neural networks and predict clinical recovery. As the three aforementioned clinical scales assess combined activities rather than a single impairment, using hemispheric TotCoh and SW is reasonable to assess global neural network functions. Importantly, the authors did not prioritize the outcome that is often confounded by initial impairment but rather considered the clinical improvement associated with neuroplastic network alterations. SW reflects the balance between the integration and segregation of networks in the affected hemisphere and can, therefore, be considered a prognostic biomarker. On the other hand, TotCoh can indicate neural network alterations in the unaffected hemisphere. Concurrently, this study demonstrated the usefulness of EEG-based hemispheric parameters in evaluating the affected and nonaffected hemispheres after stroke. Future studies elucidating the effects of lesion location on the assessment will extend the usability of EEG as a neuroimaging tool.