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Tracking the Development of Functional Connectomes for Face Processing.
Jane E Joseph1, Davy Vanderweyen1, Joshua Swearingen1
11 Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina.
Brain Connectivity
|November 30, 2018
Summary
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.
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.
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