title: Dynamics of motor-related functional integration during motor sequence learning.
abstract: Motor skill learning is associated with profound changes in brain activation patterns over time. Associative and rostral premotor cortical and subcortical regions are mostly recruited during the early phase of explicit motor learning, while sensorimotor regions may increase their activity during the late learning phases. Distinct brain networks are therefore engaged during the early and late phases of motor skill learning. How these regions interact with one another and how information is transferred from one circuit to the other has been less extensively studied. In this study, we used functional MRI (fMRI) at 3T to follow the changes in functional connectivity in the associative/premotor and the sensorimotor networks, during extended practice (4 weeks) of an explicitly known sequence of finger movements. Evolution of functional connectivity was assessed using integration, a measure that quantifies the total amount of interaction within a network. When comparing the integration associated with a complex finger movement sequence to that associated with a simple sequence, we observed two patterns of decrease during the 4 weeks of practice. One was not specific as it was observed for all sequences, whereas a specific decrease was observed only for the execution of the learned sequence. This second decrease was a consequence of a relative decrease in associative/premotor network integration, together with a relative increase in between-network integration. These findings are in line with the hypothesis that information is transferred from the associative/premotor circuit to the sensorimotor circuit during the course of motor learning.
authors.affiliation: INSERM and UPMC Univ Paris 06, UMR-S 678 Laboratoire d'Imagerie Fonctionnelle, CHU Pitié-Salpêtrière, 91 Boulevard de l'Hôpital, Paris, France. david.coynel@imed.jussieu.fr
authors.forename: David
authors.initials: D
authors.lastname: Coynel
authors.forename: Guillaume
authors.initials: G
authors.lastname: Marrelec
authors.forename: Vincent
authors.initials: V
authors.lastname: Perlbarg
authors.forename: Mélanie
authors.initials: M
authors.lastname: Pélégrini-Issac
authors.forename: Pierre-François
authors.initials: PF
authors.lastname: Van de Moortele
authors.forename: Kamil
authors.initials: K
authors.lastname: Ugurbil
authors.forename: Julien
authors.initials: J
authors.lastname: Doyon
authors.forename: Habib
authors.initials: H
authors.lastname: Benali
authors.forename: Stéphane
authors.initials: S
authors.lastname: Lehéricy
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D004525:Efferent Pathways / Q000502:physiology; D005260:Female; D005385:Fingers / Q000294:innervation / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009048:Motor Skills / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology; D025341:Principal Component Analysis; D011597:Psychomotor Performance / Q000502:physiology; D012691:Serial Learning / Q000502:physiology*; D055815:Young Adult