Exploring Brain Connectivity EEG Correlates of Gait Kinematic Parameters

Understanding the relationship between brain connectivity and motor performance is gaining increasing attention in neuroscience as it provides deeper insights into brain functioning in health and disease conditions. The present study aimed to explore the possible correlations between gait parameters and EEG-derived brain functional connectivity as well as the performances a general linear model (GLM) predicting network graph theory (NGT) connectomic metrics with kinematic features. To this aim, 16 healthy volunteers were enrolled in this study. EEG signals were first acquired in the resting-state conditions. Then, kinematic parameters were collected during a motor task, using wearable devices. Brain sources were reconstructed from EEG signals, and phase-locking values were computed between each pair of sources to generate the functional brain connectivity matrices, from which global NGT metrics were computed. Several statistically significant correlations were found between kinematic parameters and NGT metrics. Among kinematic parameters, the foot strike angle emerged as the most influential feature, showing a significant correlation with 4 NGT metrics: clustering coefficient, global efficiency, strength and transitivity. The significance of these correlations was further confirmed via the GLM analysis, where the best model, achieved an R-squared of 0.824 in the prediction of global efficiency. These findings suggest that neural network organization may play a specific role in motor control, offering valuable insights into how brain connectivity may influence gait and conversely. Future studies on enriched populations, also including patients with neurological disorders, along with simultaneous data acquisition, could provide a deeper understanding of the complex interplay between gait performances and brain network organization.