Hierarchical Features of Brain EEG Edge-Centric Networks Predict Mild Brain Injury

Traumatic brain injury (TBI) is a global health issue that often leads to cognitive dysfunction and behavioral problems, affecting the quality of life of patients. Although traditional clinical imaging techniques can reveal structural damage to the brain, our understanding of brain functional networks remains limited. To explore the impact of TBI on brain functional connectivity, this study utilized 64-channel electroencephalography (EEG) data and employed edge functional connectivity analysis to compare the functional connectivity differences between 20 patients with mild TBI (mTBI) and 20 healthy controls. By calculating the functional connectivity matrix from resting-state EEG data, we analyzed the brain network characteristics of both groups using edge functional network methods. The results showed that the edge-centric functional connectivity (eFC) in the TBI group was significantly lower than that in the healthy group. Furthermore, hierarchical feature separation and integration values were used to predict brain injury, revealing that the brain functional networks of mTBI patients exhibited weaker coupling between brain regions and were relatively independent, indicating a significant impact on their brain synchrony and information integration capabilities, on the basis of which the hierarchical features successfully predicted brain injury.