Courtney G E Hilderman
Jane Z Wang
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AbstractThe functional networks that support action observation are of great interest in understanding the development of social cognition and motor learning. How infants learn to represent and understand the world around them remains one of the most intriguing questions in developmental cognitive neuroscience. Recently, mathematical measures derived from graph theory have been used to study connectivity networks in the developing brain. Thus far, this type of analysis in infancy has only been applied to the resting state. In this study, we recorded electroencephalography (EEG) from infants (ages 4-11 months of age) and adults while they observed three types of actions: a) reaching for an object, b) walking and c) object motion. Graph theory based analysis was applied to these data to evaluate changes in brain networks. Global metrics that provide measures of the structural properties of the network (characteristic path, density, global efficiency, and modularity) were calculated for each group and for each condition. We found statistically significant differences in measures for the observation of walking condition only. Specifically, in comparison to adults, infants showed increased density and global efficiency in combination with decreased modularity during observation of an action that is not within their motor repertoire (i.e. independent walking), suggesting a less structured organization. There were no group differences in global metric measures for observation of object motion or for observation of actions that are within the repertoire of infants (i.e. reaching). These preliminary results suggest that infants and adults may share a basic functional network for action observation that is sculpted by experience. Motor experience may lead to a shift towards a more efficient functional network.