Functional disconnection of associative cortical areas predicts performance during BCI training

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2020-04, Vol.209, p.116500-116500, Article 116500
• Main Authors: Corsi, Marie-Constance, Chavez, Mario, Schwartz, Denis, George, Nathalie, Hugueville, Laurent, Kahn, Ari E., Dupont, Sophie, Bassett, Danielle S., De Vico Fallani, Fabrizio
• Format: Article
• Language: English
• Subjects: Adult; Automation; Brain-computer interface; Brain-Computer Interfaces; Cerebral Cortex - physiology; Cognitive science; Computer Science; Connectome; Couples; Digitization; EEG; Electroencephalography; Experiments; Feedback; Female; Humans; Imagination - physiology; Interfaces; Learning; Learning - physiology; Life Sciences; Longitudinal Studies; Magnetoencephalography; Male; Maximum entropy method; MEG; Mental task performance; Motor Activity - physiology; Motor imagery; Motor skill learning; Nerve Net - physiology; Network; Neural networks; Neurons and Cognition; Neuroscience; Psychology; Psychology and behavior; Reinforcement, Psychology; Signal and Image Processing; Young Adult
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.116500

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. •We discovered that BCI skill acquisition depends on specific brain network changes.•These network changes predict the BCI learning rate in a longitudinal training.•Our results could pave the way to an improvement of the BCI performance.