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Brain Connect. 2018 Nov 29. doi: 10.1089/brain.2018.0607. [Epub ahead of print]

Tracking the development of functional connectomes for face processing.

Author information

1
Medical University of South Carolina, Neurosciences , 96 Jonathan Lucas St. , CSB 325E MSC 606 , Charleston, South Carolina, United States , 29425 ; josep@musc.edu.
2
Medical University of South Carolina, Neuroscience, Charleston, South Carolina, United States ; d.vanderweyen@gmail.com.
3
Medical University of South Carolina, Neuroscience, Charleston, South Carolina, United States ; joshua.swearingen@gmail.com.
4
Medical University of South Carolina, Neurosciences, Charleston, South Carolina, United States ; vaughanb@musc.edu.
5
Medical University of South Carolina, Neuroscience, Charleston, South Carolina, United States ; dnnovo@yahoo.com.
6
Shihezi University, 70586, Psychology, Shihezi, Xinjiang, China ; ryanzhu@outlook.com.
7
Medical University of South Carolina, Public Health Sciences, Charleston, South Carolina, United States ; gebregz@musc.edu.
8
Medical University of South Carolina, Neurology, Charleston, South Carolina, United States ; bonilha@musc.edu.
9
University of Ketucky, Psychology, Lexington, Kentucky, United States ; rbhatt@email.uky.edu.
10
Medical University of South Carolina, Neuroscience, Charleston, South Carolina, United States ; tnaselar@musc.edu.
11
Clemson University, School of Computing, Clemson, South Carolina, United States ; bcdean@clemson.edu.

Abstract

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.

KEYWORDS:

Brain networks; Cognition; Connectome; Graph theory

PMID:
30489152
DOI:
10.1089/brain.2018.0607

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