Measuring neuronal avalanches to inform brain-computer interfaces

Marie-Constance Corsi; Pierpaolo Sorrentino; Denis Schwartz; Nathalie George; Leonardo L Gollo; Sylvain Chevallier; Laurent Hugueville; Ari E Kahn; Sophie Dupont; Danielle S Bassett; Viktor Jirsa; Fabrizio De Vico Fallani

doi:10.1016/j.isci.2023.108734

Measuring neuronal avalanches to inform brain-computer interfaces

iScience. 2023 Dec 14;27(1):108734. doi: 10.1016/j.isci.2023.108734. eCollection 2024 Jan 19.

Authors

Affiliations

¹ Sorbonne Université, Institut du cerveau - Paris Brain Institute - ICM, CNRS, Inserm, APHP, Hôpital de la Pitié Salpêtrière, Paris, France.
² Inria, Aramis Team, Paris, France.
³ Institut de Neuroscience des Systèmes, Aix-Marseille University, Inserm, Marseille, France.
⁴ Institut du Cerveau - Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, CENIR, Centre MEG-EEG, Paris, France.
⁵ The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria 3168, Australia.
⁶ LISN-CNRS, Université Paris-Saclay, Orsay, France.
⁷ Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA.
⁸ University of Pennsylvania, Philadelphia, PA 19104, USA.

Abstract

Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.

Keywords: Computer science; Neuroscience.