Cortical excitability is a core feature of brain function shaped by microcircuitry, large-scale networks, and neurochemistry. In humans, it can be inferred from indirect electrophysiological measures, yet it remains unclear whether these capture a single construct or multiple processes. We systematically compared established excitability metrics using whole-head magneto-encephalography (MEG) recorded at rest and during 40 Hz auditory stimulation in a large sample of healthy adults.
These measures included stimulus-driven synchronization, spectral power, aperiodic (1/f) activity, signal complexity, long-range temporal correlations, and phase–gamma synchronization. Hierarchical clustering identified six distinct excitability dimensions with limited redundancy, indicating that cortical excitability is inherently multidimensional. Aperiodic and alpha-band measures were strongly coupled and most spatially aligned with stimulus responses, while temporal, complexity, and synchronization metrics were largely independent.
Regional differences were most pronounced in integrative and internally oriented areas. Each dimension also related differently to cortical morphology and neurotransmitter receptor density, highlighting diverse neurobiological substrates and supporting multimodal approaches to studying excitability. Ricerca Corrente 2026 from the Italian Ministry of Health.
G.P. is also funded by the AMOSO (Academic Medical Organization of Southwestern Ontario) Opportunities Fund, Western Strategic Support for CIHR Success Seed program, NSERC Discovery Grant, NSERC RTI Grant, Western CNS Internal Competition Grant, Lawson Internal Research Grant Fund, and CNS Department Starting Grant. These authors contributed equally: Giovanni Pellegrino, Gian Marco Duma. Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada Scientific Institute IRCCS E.Medea, Epilepsy and Clinical Neurophysiology Unit, Conegliano, Italy Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany Department of Data Analysis, Ghent University, Ghent, Belgium Dipartimento di Psicologia Generale, Università degli Studi di Padova, 35131, Padova, Italy Department of Physics and Astronomy, Western University, London, ON, Canada School of Data Science, University of Virginia, Charlottesville, USA The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Extract — continue reading at the source.
