Introduction
Stroke is a major cause of functional disability, affecting brain connectivity and oscillatory activity measured by electroencephalography (EEG). Social network analysis offers a promising model to characterize structural and functional changes after stroke. This study aimed to systematically review the literature and perform a meta-analysis on changes in brain connectivity in post-stroke patients using EEG, using complex network approaches.
Methods
A systematic search was performed in the PubMed, Cochrane and ScienceDirect databases up to October 2021, following the PRISMA criteria. Experimental studies with adults who suffered stroke and that used EEG and complex network analysis to assess brain connectivity were included. Methodological quality was assessed by the Newcastle-Ottawa Scale, and the meta-analysis used the random-effects model to calculate the effect size.
Results
Ten studies were analyzed, of which 9 were cohort studies and 1 was cross-sectional. Most had good methodological quality and low risk of death. The studies specifically altered functional connectivity post-stroke, particularly in networks described by:
Reduction in functional connectivity in injured regions, with decreased transmission of information between electrical electrodes.
Increased “small-world” index in some frequency bands (beta and gamma), while other regions refer to altered reorganization in the alpha band.
Changes in clustering coefficients and mean path length establish heterogeneous patterns in post-injury reorganization.
Quantitative analysis indicated a small and non-significant effect size in favor of the healthy control group (Hedges’ g = 0.189; p = 0.592), indicating that there is no universal pattern in the reorganization of financial networks after stroke. Furthermore, results varied according to the methods used to calculate connectivity, highlighting the need for standardization in methodological approaches.
Discussion
Changes in brain connectivity after stroke demonstrate structural and functional changes in the neural network. Increased connectivity in non-injured regions suggests compensatory mechanisms of neuroplasticity, while disruption in information transmission may be associated with motor and cognitive deficits. The results on the variability of the small-world index and the modulation of specific bands (delta, theta, alpha, beta and gamma) reinforce the complexity of brain reorganization after injury. However, no specific distribution of networks capable of consistently differentiating post-stroke patients from healthy individuals has been identified.
Conclusion
Complex network analysis applied to EEG provides a promising approach for characterizing specific post-stroke dysfunctions. However, the current results do not establish a definitive pattern of connectivity reorganization, emphasizing the need for future studies with greater methodological standardization and larger samples to validate neurophysiological markers of post-stroke recovery.
Reference:
Asadi, B., Cuenca-Zaldivar, J. N., Nakhostin Ansari, N., Ibáñez, J., Herrero, P., & Calvo, S. (2023). Brain analysis with a complex network approach in stroke patients based on electroencephalography: a systematic review and meta-analysis . Healthcare, 11 (5), 666. DOI: 10.3390/healthcare11050666
