Network asymmetry of motor areas revealed by resting-state functional magnetic resonance imaging

There are ample functional magnetic resonance imaging (fMRI) studies on functional brain asymmetries, and the asymmetry of cerebral network in the resting state may be crucial to brain function organization. In this paper, a unified schema of voxel-wise functional connectivity and asymmetry analysis was presented and the network asymmetry of motor areas was studied. Twelve healthy male subjects with mean age 29.8 ± 6.4 were studied. Functional network in the resting state was described by using functional connectivity magnetic resonance imaging (fcMRI) analysis. Motor areas were selected as regions of interest (ROIs). Network asymmetry, including intra- and inter-network asymmetries, was formulated and analyzed. The intra-network asymmetry was defined as the difference between the left and right part of a particular functional network. The inter-network asymmetry was defined as the difference between the networks for a specific ROI in the left hemisphere and its homotopic ROI in the right hemisphere. Primary motor area (M1), primary sensory area (S1) and premotor area (PMA) exhibited higher functional correlation with the right parietal–temporal–occipital circuit and the middle frontal gyrus than they did with the left hemisphere. Right S1 and right PMA exhibited higher functional correlation with the ipsilateral precentral and supramarginal areas. There exist the large-scale hierarchical network asymmetries of the motor areas in the resting state. These asymmetries imply the right hemisphere dominance for predictive motor coding based on spatial attention and higher sensory processing load for the motor performance of non-dominant hemisphere.

► Network asymmetry, including intra- and inter-network asymmetries, was formulated.
► A unified schema of functional connectivity and asymmetry analysis was presented.
► There existed hierarchical network asymmetries of motor areas in the resting state.
► Right hemisphere was dominant for predictive motor coding based on spatial attention.
► Sensory processing load for the motor performance of non-dominant hemisphere was higher.