The goal of this project is to investigate cortical plasticity induced by brain-computer interface (BCI) learning in a tetraplegic patient suffering from limb girdle muscular dystrophy. This EEG BCI will be non invasive, asynchronous and based on imagery. To examine these changes, we will employ a longitudinal design in which the subject will go through BCI training sessions. This learning will be composed of 24 one-hour sessions spread over 3 months, 2 days per week. The subject will also undergo testing composed of two neurophysiological exams and one psychological test. Neurophysiological testing will occur at four time points: (i) onset of training (pre-test), (ii) end of training (post-test), (iii) halfway through the training period, and (iv) 3 weeks after the end of the BCI training (retention test). As for the psychological test, before each training session, the subject will complete the 24-item PANAS test which assesses individual positive and negative affects [2Crawford J.R., Henry J.D. The positive and negative affect schedule (PANAS): construct validity, measurement properties and normative data in a large non-clinical sample Br J Clin Psychol 2004 ;  43 : 245-265 [cross-ref]

Cliquez ici pour aller à la section Références]. To investigate motor representation alterations, we will use transcranial magnetic stimulation in combination with a visually guided technique. The resulting motor-evoked potentials will be used to map the motor cortex functionally [1Ahdab R., Ayache S.S., Farhat W.H., Mylius V., Schmidt S., Brugières P., et al. Reappraisal of the anatomical landmarks of motor and premotor cortical regions for image-guided brain navigation in TMS practice Hum Brain Mapp 2014 ;  3 : 2435-2447 [cross-ref]

Cliquez ici pour aller à la section Références]. To probe reorganization of neural networks, we will record resting-state EEG. Pair-wise synchronization for EEG signals will be computed using phase lag index (PLI). Then, PLI weighted matrices will be calculated from which minimum spanning trees (MST) will be constructed [3Stam C.J., Nolte G., Daffertshofer A. Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources Hum Brain Mapp 2007 ;  28 : 1178-1193 [cross-ref]

Cliquez ici pour aller à la section Références, 4Stam C.J., Tewarie P., van Dellen E., van Straaten E.C.W., Hillebrand A., van Mieghem P. The trees and the forest: characterization of complex brain networks with minimum spanning trees Int J Psychophysiol 2014 ;  92 : 129-138 [cross-ref]

Cliquez ici pour aller à la section Références]. MST represents constructs of the most important routes for information flow in a network. From these MST, several indicators will be calculated such as degree, leaf number, eccentricity, betweenness centrality, degree correlation, and hierarchy. PLI and MST indexes will be obtained from Brainwave software (CS, brainwave.html). We expect changes, resulting from the BCI learning, in the motor cortex functional maps and in the topological properties of the MST.