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Brain network development for face processing involves complex changes. Maturation of the face network (FN) shows more reductions than increases in information flow, suggesting increased specialization.

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Area of Science:

  • Neuroscience
  • Developmental Neuroscience
  • Cognitive Neuroscience

Background:

  • Face processing abilities develop and specialize throughout childhood and adolescence.
  • The precise neural mechanisms driving this specialization, particularly the roles of different brain states, remain unclear.

Purpose of the Study:

  • To investigate how task-positive (face viewing) and task-negative (resting) brain states contribute to the maturation of face processing networks.
  • To test if developmental changes in network properties are progressive, regressive, or a combination of both.

Main Methods:

  • Applied graph theory network analysis to functional magnetic resonance imaging (fMRI) data from children and adults.
  • Utilized predictive modeling to link network properties (eigenvector centrality, betweenness centrality) to age in whole-brain and face networks.
  • Compared network dynamics during face viewing versus resting states.

Main Results:

  • Face network (FN) maturation was characterized by more regressive than progressive changes in information diffusion (eigenvector centrality) during face viewing, indicating increased specialization.
  • Whole-brain network (WBN) maturation showed a balance of progressive and regressive changes in hub connectivity (betweenness centrality) during rest.
  • Age could be predicted from network properties in both task-positive and task-negative states.

Conclusions:

  • Specialized network development, like the face network, is influenced by domain-specific processing and involves dynamic changes, including reductions in information diffusion.
  • Maturation of the brain overall can be predicted from resting-state network properties.
  • Developmental changes in brain networks are complex, involving both increases and decreases in connectivity and information flow.