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The maturation and cognitive relevance of structural brain network organization from early infancy to childhood.
Woodburn, Mackenzie; Bricken, Cheyenne L; Wu, Zhengwang; Li, Gang; Wang, Li; Lin, Weili; Sheridan, Margaret A; Cohen, Jessica R.
Affiliation
  • Woodburn M; Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States. Electronic address: mawoodbu@live.unc.edu.
  • Bricken CL; Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States.
  • Wu Z; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States.
  • Li G; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States.
  • Wang L; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States.
  • Lin W; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States.
  • Sheridan MA; Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, United Sta
  • Cohen JR; Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, United Sta
Neuroimage ; 238: 118232, 2021 09.
Article in En | MEDLINE | ID: mdl-34091033
ABSTRACT
The interactions of brain regions with other regions at the network level likely provide the infrastructure necessary for cognitive processes to develop. Specifically, it has been theorized that in infancy brain networks become more modular, or segregated, to support early cognitive specialization, before integration across networks increases to support the emergence of higher-order cognition. The present study examined the maturation of structural covariance networks (SCNs) derived from longitudinal cortical thickness data collected between infancy and childhood (0-6 years). We assessed modularity as a measure of network segregation and global efficiency as a measure of network integration. At the group level, we observed trajectories of increasing modularity and decreasing global efficiency between early infancy and six years. We further examined subject-based maturational coupling networks (sbMCNs) in a subset of this cohort with cognitive outcome data at 8-10 years, which allowed us to relate the network organization of longitudinal cortical thickness maturation to cognitive outcomes in middle childhood. We found that lower global efficiency of sbMCNs throughout early development (across the first year) related to greater motor learning at 8-10 years. Together, these results provide novel evidence characterizing the maturation of brain network segregation and integration across the first six years of life, and suggest that specific trajectories of brain network maturation contribute to later cognitive outcomes.
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Full text: 1 Database: MEDLINE Main subject: Brain / Brain Cortical Thickness / Nerve Net Type of study: Observational_studies / Prognostic_studies Limits: Child / Child, preschool / Female / Humans / Infant / Male / Newborn Language: En Journal: Neuroimage Journal subject: DIAGNOSTICO POR IMAGEM Year: 2021 Type: Article

Full text: 1 Database: MEDLINE Main subject: Brain / Brain Cortical Thickness / Nerve Net Type of study: Observational_studies / Prognostic_studies Limits: Child / Child, preschool / Female / Humans / Infant / Male / Newborn Language: En Journal: Neuroimage Journal subject: DIAGNOSTICO POR IMAGEM Year: 2021 Type: Article