Exploring the Sensorimotor Network Using Functional Connectivity and Graph Theory

Abstract

Background: Performing a motor task activates the sensorimotor network. Functional connectivity (FC) analysis can determine connections between distinct neural regions of a network. Graph theory can then be applied to quantify the network’s connections. Establishing the network in non-disabled participants can be used as a comparator in future neuroimaging research. Purpose: To determine the sensorimotor network in a group of non-disabled participants. Methods: Nineteen participants were scanned using magnetoencephalography while they performed a unilateral upper-limb visuomotor task. FC was compared between rest and task conditions to determine significant connections during task only. These connections were quantified using graph theory. Results: FC significantly increased between 118 node pairs during the task state compared to rest. Graph theory quantitatively highlighted 40 nodes as important, including regions of the pre-established sensorimotor network (contralateral primary motor and somatosensory cortex among others). This network can be used as a template for comparison in future studies.