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Background: Repetitive mild traumatic brain injury (rmTBI) often occurs in individuals engaged in contact sports, particularly boxing. This study aimed to elucidate the effects of rmTBI on phase-locking value (PLV)-based graph theory and functional network architecture in individuals with boxing-related injuries in five frequency bands by employing resting-state electroencephalography (EEG). Methods: Twenty-fore professional boxers and 25 matched healthy controls were recruited to perform a resting-state task, and their noninvasive scalp EEG data were collected simultaneously. Based on the construction of PLV matrices for boxers and controls, phase synchronization and graph-theoretic characteristics were identified in each frequency band. The significance of the calculated functional brain networks between the two populations was analyzed using a network-based statistical (NBS) approach. Results: Compared to controls, boxers exhibited an increasing trend in PLV synchronization and notable differences in the distribution of functional centers, especially in the gamma frequency band. Additionally, attenuated nodal network parameters and decreased small-world measures were observed in the theta, beta, and gamma bands, suggesting that the functional network efficiency and small-world characteristics were significantly weakened in boxers. NBS analysis revealed that boxers exhibited a significant increase in network connectivity strength compared to controls in the theta, beta, and gamma frequency bands. The functional connectivity of the significance subnetworks exhibited an asymmetric distribution between the bilateral hemispheres, indicating that the optimized organization of information integration and segregation for the resting-state networks was imbalanced and disarranged for boxers. Conclusions: This is the first study to investigate the underlying deficits in PLV-based graph-theoretic characteristics and NBS-based functional networks in patients with rmTBI from the perspective of whole-brain resting-state EEG. Joint analyses of distinctive graph-theoretic representations and asymmetrically hyperconnected subnetworks in specific frequency bands may serve as an effective method to assess the underlying deficiencies in resting-state network processing in patients with sports-related rmTBI.
Wu et al. (Thu,) studied this question.
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