Revisiting the Multilayer Network Framework for Electrophysiological Networks.

Abstract

Background: The multilayer network framework has emerged as an innovative approach for analyzing electrophysiological networks, providing insights into complex neuronal interactions by integrating connectivity across different frequency bands in electroencephalography (EEG) and magnetoencephalography (MEG) data. Current Limitations: Traditionally, multilayer networks have treated canonical frequency bands (e.g., delta, theta, alpha, beta, gamma) as distinct layers. Recent findings could raise potential concerns regarding this approach, emphasizing the need to incorporate the distinction between periodic (oscillatory) and aperiodic (broadband) signal components. Conceptual Advance: Aperiodic signals may reflect excitation-inhibition balance and scale-free dynamics, while periodic signals capture oscillatory rhythms, both contributing uniquely to brain network interactions. A multilayer network framework in the current context could be applicable in the case of genuine coupling between these components, termed "aperiodic-to-periodic coupling." This necessitates novel connectivity metrics and analytical methods that can handle broadband data. Furthermore, challenges remain in decomposing these components in the time domain and developing robust metrics for broadband connectivity that account for signal leakage. Outlook: Addressing these issues will enhance multilayer frameworks, enabling better insights into brain network integrity, cognitive dysfunction, and neurological conditions.