Structural and maturational covariance in early childhood brain development

Xiujuan Geng, Gang Li, Zhaohua Lu, Wei Gao, Li Wang, Dinggang Shen, Hongtu Zhu, John H. Gilmore

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and sensorimotor networks in a longitudinal population of 118 children after birth to 2 years old and compared them with intrinsic functional connectivity networks. We found that structural covariance of all networks exhibit strong correlations mostly limited to their seed regions. By Age 2, default and dorsal attention structural networks are much less distributed compared with their functional maps. The maturational covariance maps, however, revealed significant couplings in rates of change between distributed regions, which partially recapitulate their functional networks. The structural and maturational covariance of the primary visual and sensorimotor networks shows similar patterns to the corresponding functional networks. Results indicate that functional networks are in place prior to structural networks, that correlated structural patterns in adultmay arise in part fromcoordinated corticalmaturation, and that regional co-activation in functional networks may guide and refine the maturation of SCNs over childhood development.

Original languageEnglish (US)
Pages (from-to)1795-1807
Number of pages13
JournalCerebral Cortex
Volume27
Issue number3
DOIs
StatePublished - Mar 1 2017

Keywords

  • Cortical thickness
  • Early brain development
  • Functional connectivity
  • Maturational covariance
  • Structural covariance

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

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