Functional connectivity in the resting-state motor networks influences the kinematic processes during motor sequence learning

Abstract

Neuroimaging studies support the involvement of the cerebello-cortical and striato-cortical motor loops in motor sequence learning. Here, we investigated whether the gain of motor sequence learning could depend on a-priori resting-state functional connectivity (rsFC) between motor areas and structures belonging to these circuits. Fourteen healthy subjects underwent a resting-state functional magnetic resonance imaging session. Afterward, they were asked to reproduce a verbally-learned sequence of finger opposition movements as fast and as accurately as possible. All subjects increased their movement rate with practice, by reducing the touch duration and/or intertapping interval. The rsFC analysis showed that, at rest, the left and right primary motor cortex (M1) and left and right supplementary motor area (SMA) were mainly connected with other motor areas. The covariate analysis taking into account the different kinematic parameters indicated that the subjects achieving greater movement rate increase were those showing stronger rsFC of the left M1 and SMA with the right lobule VIII of the cerebellum. Notably, the subjects with greater intertapping interval reduction showed stronger rsFC of the left M1 and SMA with the association nuclei of the thalamus. Conversely, the regression analysis with the right M1 and SMA seeds showed only a few significant clusters for the different covariates not located in the cerebellum and thalamus. No common clusters were found between the right M1 and SMA. All of these findings indicated important functional connections at rest of those neural circuits responsible for motor learning improvement, involving the motor areas related to the hemisphere directly controlling the finger movements, the thalamus and cerebellum.

Keywords: cerebellum; finger movement; human; learning; motor areas; thalamus.

Figure 1

Experimental protocol. Participants were shown a dummy task on the computer screen immediately before the rs-fMRI session, then they had to perform the motor sequence learning task.

Figure 2

Group-averaged motor parameters over the course of the motor sequence learning task (mean ± SE): A RATE, B Touch Duration (TD), C Inter Tapping Interval (ITI). The solid lines indicate the best fits to the data, representing the mean group learning curve: A Gaussian (y = y₀ + (A/(w×√(π/2)))×exp(-2×((x-x_c)/w)²), x_c = 45.79 ± 3.841, w = 119.53 ± 99.83, R² = 0.99), B Exponential (y = A₁×exp(-x/t₁) + y0, t₁ = 11.83 ± 1.15, R² = 0.98), C Exponential (y = A₁×exp(-x/t₁) + y0, t₁ = 12.14 ± 1.34, R² = 0.94).

Figure 3

Whole-brain seed-based resting functional connectivity maps of the selected seeds in the motor areas, displayed on a volumetric brain surface: A left M1, B right M1, C left SMA, D right SMA. Significant clusters are displayed in neurological convention (the left side of the image corresponds to the left side of the brain). See Tables 1, 4, 6, and 8.

Figure 4

Regression analysis between the resting functional connectivity maps of the M1 areas and the kinematic parameters indicating motor learning improvement: A left M1 with deltaRATE as covariate, B left M1 with deltaTD, C left M1 with deltaITI, D right M1 with deltaTD. Significant clusters are displayed in neurological convention; color bar shows a scale of T values. See Tables 2 and 5.

Figure 5

Regression analysis between the resting functional connectivity maps of the SMAs and the kinematic parameters indicating motor learning improvement: A left SMA with deltaRATE as covariate, B left SMA with deltaTD, C left SMA with deltaITI, D right SMA with deltaRATE. Significant clusters are displayed in neurological convention; color bar shows a scale of T values. See Tables 7 and 9.