Gianmarco Cafaro — Università degli Studi di Salerno #
Temporal Organization of Neuronal Avalanches Constrained by Structural Connectivity in MEG #
The study of neuronal activity through electrophysiological recordings provides a framework to investigate brain function and how biological alterations may influence disease onset. Different analytical approaches across multiple brain scales (single neurons, neural networks, neural mass, and whole-brain activity) yield distinct biomarkers, offering insight into where and how system-level disruptions emerge in specific pathologies. Neuronal avalanches, consistently observed across these scales, suggest that brain dynamics operate near a critical point, leading to the emergence of scale-free distributions in both the size of recorded activity and the duration of bursts of coordinated activations. The occurrence of simultaneous activations across multiple neuronal components (e.g., brain regions in whole-brain analyses) correlates with the underlying connectivity among these components, providing a natural interpretation in terms of network interactions grounded in biologically measurable architectures. However, how neuronal activity, organized into avalanches, is structured in time—particularly in relation to the sequence of brain states (microstates) that the system traverses—and how the topological properties of brain architecture shape this temporal organization remain open questions. In this study, we analyzed magnetoencephalography (MEG) recordings from 30 healthy male and female subjects with no certified neurological disorders, during eyes-closed resting-state activity, within the framework of neuronal avalanche analysis. We find that the size of burst activations (defined as the integrated signal above threshold across all active brain regions during a bursting event) and the inter-avalanche silent time are not independent variables. The joint probability distribution of avalanche size conditioned on a given preceding silent time was estimated and compared with a reshuffled surrogate dataset, revealing significant temporal correlations. In particular, small avalanches are more likely to follow long silent periods, whereas large avalanches preferentially follow short silent intervals. These results provide the opportunity to distinguish between small- and large-size avalanches. We
find that brain regions with higher white matter node degree connectivity are more likely to be active during small avalanches, whereas less connected regions require higher levels of activity to be recruited. This suggests that the underlying structural connectivity plays a role in shaping the temporal and spatial organization of avalanche dynamics.