Acquisition et consolidation de représentations distribuées de séquences motrices, mesurées par IRMf

Everyday, we produce a number of coordinated motor actions to achieve a variety of goals. As these skills are not innate, they thus need to be learned and integrated in a vast repertoire that develops along the lifetime in order to adapt to a constantly changing environment. Motor skills are generally composed of multiple movements organized in a sequence. Repeated training of such actions enables fluid, fast and precise chaining of movements. A plethora of studies indicates that the production of this type of skills improves not only during physical practice, but also through the intervention of neurobiological processes occurring out of these training sessions, notably during sleep, which allows this form of memory trace to become persistant. This mechanism is coined "memory consolidation" and recent accumulating results demonstrate that it is driven by the repeated reactivation of the networks supporting the expression of the motor skill, thus reinforcing the activity of neuronal structures implicated in the programming of the motor pattern necessary for their production, and reconfiguring large-scale networks recruited during subsequent expression. Such a reorganization has generally been demonstrated in humans through studies measuring changes in regional cerebral activations observed with functional MRI, but these results do not allow to reveal the genuine and specific plastic changes that occur during the acquisition of new motor sequential information. Therefore, the present thesis aimed at identifying, not only the brain regions specialized in encoding information specific to motor sequences, but also those involved in the consolidation process after a night of sleep. To do so, we carried out an experimentation in which participants learned different motor sequences over multiple days and used a representational approach to test the hypothesis that the existence of information allows to discriminate the diverse sequences. We demonstrate that sequential information is equally present in a large motor cortical network before and after consolidation. The latter, however, induces an important restructuring and strengthening of the representations in secondary cortical cortices, cerebellum and dorsolateral striatum, while the one implicating the hippocampus and dorsomedial striatum are fading. The results of these new multivariate statistical approaches contribute to entrench the models of brain plasticity in motor sequence learning, which conjecture the transient support of the hippocampus during the fast learning phase and the offline reactivation during sleep as well as the critical role of the putamen in the persistence of motor memories. In addition, to circumvent the limits of conventional fMRI preprocessing, methodological developments for the correction of motion and the noises inherent to fMRI acquisition in humans have been conducted in order to improve multivariate statistical analysis based on fine-grained activity patterns. We demonstrate a significant advantage of our method, in comparison to conventional preprocessing in terms of spatial accuracy and signal quality by analyzing both simulated and real datasets. In sum, this thesis contributes to different aspects of the interpretations of subtle signal variations by investigating the representational changes underlying plasticity in humans, significantly improving the knowledge of neuronal substrates underlying motor sequence learning.

Keyphrases: consolidation, MVPA, recalage, motor sequence learning, sleep, multivariate statistics, pattern, representational similarity, functional MRI, motion correction, realignment, denoising