Functional disconnection of associative cortical areas predicts performance during BCI training

Marie-Constance Corsi (1, 2) , Mario Chavez (2) , Denis Schwartz (2) , Nathalie George (2) , Laurent Hugueville (2) , Ari Kahn (3) , Sophie Dupont (2) , Danielle Bassett (4) , Fabrizio de Vico Fallani (1, 2)

1 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
2 ICM - Institut du Cerveau = Paris Brain Institute
3 University of Pennsylvania
4 Perelman School of Medicine

Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeed-back in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neuralprocesses that enable humans to achieve good control remain poorly understood. To address this question, weperformed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) re-cordings in a motor imagery-based BCI training involving a group of healthy subjects. After reconstructing thesignals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skillacquisition is paralleled by a progressive disconnection of associative areas which were not directly targetedduring the experiments. Notably, these network connectivity changes reflected growing automaticity associatedwith BCI performance and predicted future learning rate. Altogether, ourfindings provide new insights into thelarge-scale cortical organizational mechanisms underlying BCI learning, which have implications for theimprovement of this technology in a broad range of real-life applications.

Mots clés: Learning; Motor imagery; Brain-computer interface; EEG; MEG; Network

Domaines: Neurosciences; Psychologie; Neurosciences [q-bio.NC]; Psychologie et comportements; Traitement du signal et de l'image [eess.SP]

Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Nathalie George, Laurent Hugueville, et al.. Functional disconnection of associative cortical areas predicts performance during BCI training. NeuroImage, 2020, 209, pp.116500. ⟨10.1016/j.neuroimage.2019.116500⟩. ⟨hal-03006210⟩