Kinematics in the brain: unmasking motor control strategies?

Abstract

In rhythmical movement performance, our brain has to sustain movement while correcting for biological noise-induced variability. Here, we explored the functional anatomy of brain networks during voluntary rhythmical elbow flexion/extension using kinematic movement regressors in fMRI analysis to verify the interest of method to address motor control in a neurological population. We found the expected systematic activation of the primary sensorimotor network that is suggested to generate the rhythmical movement. By adding the kinematic regressors to the model, we demonstrated the potential involvement of cerebellar-frontal circuits as a function of the irregularity of the variability of the movement and the primary sensory cortex in relation to the trajectory length during task execution. We suggested that different functional brain networks were related to two different aspects of rhythmical performance: rhythmicity and error control. Concerning the latter, the partitioning between more automatic control involving cerebellar-frontal circuits versus less automatic control involving the sensory cortex seemed thereby crucial for optimal performance. Our results highlight the potential of using co-registered fine-grained kinematics and fMRI measures to interpret functional MRI activations and to potentially unmask the organisation of neural correlates during motor control.

Keywords: Error corrections; Kinematics; Motor control; Neural networks; Upper limb; fMRI.

Fig. 1

Schematic representation of experimental setup, with a plot of the right elbow movement amplitude during the fMRI acquisition over time (left lower corner) and a 3D representation of movement in space (right lower corner)

Fig. 2

Functional basis network: the main effect of task (flexion/extension of the elbow), FWE corrected, p < 0.05 at voxel level and the condition-specific activations p < 0.001, FWE corrected at cluster level, 22 degrees of freedom. R right sided, L left sided, B bilateral, U unilateral movement, RH right hemisphere, LH left hemisphere

Fig. 3

Overview of positive correlations between fMRI activity (BOLD signal intensity) and movement kinematics. Left image correlated clusters for the grouped movement conditions. T-contrast [0 1], p < 0.005, uncorrected with cluster >30 voxels, df = 10). RH right hemisphere, LH left hemisphere, R right sided, L left sided, B bilateral movement. Right graphs regression plots between each kinematic variable (x-axis) and the response intensity (y-axis) of the most pertinent cluster ([x, y, z] coordinates provided). FREQ (frequency): r = 0.71, F _(1,34) = 32.9, p < 0.0001; NPV (number of velocity peaks): r = 0.62, F _(1,34) = 21.18, p < 0.0001; nTL (trajectory length): r = 0.66, F _(1,34) = 25.39, p < 0.0001; SampEn (sample entropy): r = 0.70, F _(1,34) = 31.39, p < 0.0001