CHANGES OF RESTING-STATE OSCILLATORY NETWORK DYNAMICS AFTER MOTOR LEARNING: A M.E.G. DEVELOPMENTAL STUDY

Introduction : Neuroimaging studies have shown that in adults, the motor learning induced alterations of the functional connectivity assessed during Resting State Networks (RSN) is age-dependent (Mary et al., 2017). Motor learning relies on the build-up of new sensori-motor representations, which has been studied using the bar-man task in adults (Barlaam, Vaugoyeau, Fortin, Assaiante, & Schmitz, 2016; Paulignan, Dufossé, Hugon, & Massion, 1989) and in children (Schmitz et al, 2002).. The aim of this study was to investigate the modulations of functional connectivity after a motor learning task in the childs resting state network. Method : 20 children aged 7 to 12 (12 boys; age 9y 9m ; age 1y et 8m) took part in the study. The resting state tasks consisted of a 3 session closed-eyes recording of 45 seconds. The first one was used as a baseline and the next two have been set around the motor learning task to evaluate the effect of motor learning on the connectivity in the RSN. The motor learning task was a load-lifting task where the participant was asked to lift a weight using its right hand which triggered the fall of a weight attached to the supporting left arm (Paulignan et al, 1989). We continuously recorded the neuromagnetic signals using a 275 channels CTFMEG system. To quantify the functional connectivity between brain regions, coherency analyses have been conducted, using the imaginary part of the coherency, corresponding to the correlation coefficient between two signals in the alpha and beta frequency bands. Moreover, graph theory analysis has provided an overview of the network organisation after the motor task. All resultshave been analysed using data-related permutation statistic with a 0.005 significance threshold (calculated as a relation between the number of conditions (n=2) and the number of subjects (N=20); = 1/, therefore < 0.005). 2 of 3 Results : The motor learning behavioural performances were assessed using a learning curve model throughout the trials which revealed a significant global learning effect (F(7 ;19) = 50,62 ; p <0,0001). In the alpha band (8-12Hz) : Permutation analysis showed an increase of the functional connectivity in the RSN (<0.005) when contrasting before and after the sensorimotor learning in the primary motor cortices (M1) along with the inferior frontal gyrus (IFG) and the premotor cortices. In the beta band (15-29Hz) : Significant increase of the functional connectivity was also found in this frequency band when contrasting before and after the sensorimotor learning in the somatosensory cortex andin the precuneus gyrus. Interestingly, we found that functional connectivity measured in pairs of brain areas (such as the premotor cortex and the cerebellum) in the pre-learning RSN was predictive of the behavioural learning performance. Conclusion : Our study showed that, after a motor learning task, the functional connectivity measured in the RSN increases between regions involved in the build-up of sensorimotor representations (such as the supplementary motor area (SMA); the primary motor dorsal area (PMd) ; the primary motor cortex (M1) and even the somatosensory cortexs (S1 & S2)), in children. References : Barlaam, F., Vaugoyeau, M., Fortin, C., Assaiante, C., & Schmitz, C. (2016). Shift of the muscular inhibition latency during on-line acquisition of anticipatory postural adjustments. PLoS ONE, 11(5). https://doi.org/10.1371/journal.pone.0154775 Mary, A., Wens, V., Op De Beeck, M., Leproult, R., De Tiège, X., & Peigneux, P. (2017). Resting-state Functional Connectivity is an Age-dependent Predictor of Motor Learning Abilities. Cerebral Cortex, 27(10), 49234932. https://doi.org/10.1093/cercor/bhw286 Paulignan, Y., Dufossé, M., Hugon, M., & Massion, J. (1989). Acquisition of co-ordination between posture and movement in a bimanual task. Experimental Brain Research, 77(2), 337348. https://doi.org/10.1007/BF00274991