Functional disconnection of associative cortical areas predicts performance during BCI training
Marie-Constance Corsi,
Mario Chavez,
Denis Schwartz,
Nathalie George,
Laurent Hugueville,
Ari E. Kahn,
Sophie Dupont,
Danielle S. Bassett,
Fabrizio De Vico Fallani
Affiliations
Marie-Constance Corsi
Inria Paris, Aramis Project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle Epinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, F-75013, Paris, France; Corresponding author. Inria Paris, Aramis project-team, F-75013, Paris, France.
Mario Chavez
CNRS, UMR 7225, F-75013, Paris, France
Denis Schwartz
Institut du Cerveau et de la Moelle Epinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Ecole Normale Supérieure, ENS, Centre MEG-EEG, F-75013, Paris, France
Nathalie George
Institut du Cerveau et de la Moelle Epinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, F-75013, Paris, France; Institut du Cerveau et de la Moelle Epinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Ecole Normale Supérieure, ENS, Centre MEG-EEG, F-75013, Paris, France
Laurent Hugueville
Institut du Cerveau et de la Moelle Epinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Ecole Normale Supérieure, ENS, Centre MEG-EEG, F-75013, Paris, France
Ari E. Kahn
Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
Sophie Dupont
Institut du Cerveau et de la Moelle Epinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, F-75013, Paris, France
Danielle S. Bassett
Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Santa Fe Institute, Santa Fe, NM, 87501, USA
Fabrizio De Vico Fallani
Inria Paris, Aramis Project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle Epinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, F-75013, Paris, France; Corresponding author. Institut du Cerveau et de la Moelle Epinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, F-75013, Paris, France.
Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neural processes that enable humans to achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings in a motor imagery-based BCI training involving a group of healthy subjects. After reconstructing the signals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skill acquisition is paralleled by a progressive disconnection of associative areas which were not directly targeted during the experiments. Notably, these network connectivity changes reflected growing automaticity associated with BCI performance and predicted future learning rate. Altogether, our findings provide new insights into the large-scale cortical organizational mechanisms underlying BCI learning, which have implications for the improvement of this technology in a broad range of real-life applications.
