Identifying EEG-based Functional Networks for Whole-Brain Models

Brain activity differs according to the state of consciousness. Whole-brain models, typically based on functional magnetic resonance imaging (fMRI) data, provide valuable insight into these changes by utilizing structural connectivity and differential equations to model the functional connectivity between brain regions. The goal of this work is to adapt fMRI-based functional connectivity models to electroencephalography data. A key step in this process is to determine the number of clusters and to estimate the coupling parameters. We turn to amplitude envelope correlation, a time-domain measure, to better match functional connectivity patterns observed in fMRI. By analyzing 64-channel electroencephalogram data from 25 male subjects over the age of 60 from the AlphaMax study, we investigate wakefulness and the transition to unconsciousness under anesthesia. Using A;-means clustering, we identify optimal brain network configurations, focusing on whether they match known fMRI-based networks. Clustering is evaluated using the Calinski-Harabasz criterion for different thresholds and numbers of cluster. The results show that two clusters are predominantly optimal for both awake and the mixed half awake, half unconscious scenario. Misplaced electrodes are mainly found in parietal regions. Since we determined the number of differential equations, this work lays the foundation for further development of electroencephalography-based whole-brain models that can track functional connectivity changes during anesthesia.