Tracking the development of functional connectomes for face processing.

Face processing capacities become more specialized and advanced during development but neural underpinnings of these processes are not fully understood. The present study applied graph-theory based network analysis to task-negative (resting blocks) and task-positive (viewing faces) fMRI data in children (5-17 years) and adults (18-42 years) to test the hypothesis that development of a specialized network for face processing is driven by task-positive processing (face viewing) more than by task-negative processing (visual fixation) and by both progressive and regressive changes in network properties. Predictive modeling was used to predict age from node-based network properties derived from task-positive and task-negative states in a whole-brain network and a canonical face network. The best fitting model indicated that face network maturation was marked by both progressive and regressive changes in information diffusion (eigenvector centrality) in the task-positive state, with regressive changes outweighing progressive changes. Hence, face network maturation was characterized by reductions in information diffusion potentially reflecting the development of more specialized modules. In contrast, whole-brain network maturation was marked by a balance of progressive and regressive changes in hub-connectivity (betweenness centrality) in the task-negative state. These findings suggest that development of specialized networks like the face network depend on dynamic developmental changes associated with domain-specific information (e.g., face processing) but maturation of the brain as whole can be predicted from task-free states.