Meta-network analysis of structural correlation networks provides insights into brain network development

Xiaohua Xu, Ping He, Pew Thian Yap, Han Zhang, Jingxin Nie, Dinggang Shen

Research output: Contribution to journalArticle

Abstract

Analysis of developmental brain networks is fundamentally important for basic developmental neuroscience. In this paper, we focus on the temporally-covarying connection patterns, called meta-networks, and develop a new mathematical model for meta-network decomposition. With the proposed model, we decompose the developmental structural correlation networks of cortical thickness into five meta-networks. Each meta-network exhibits a distinctive spatial connection pattern, and its covarying trajectory highlights the temporal contribution of the meta-network along development. Systematic analysis of the meta-networks and covarying trajectories provides insights into three important aspects of brain network development.

Original languageEnglish
Article number93
JournalFrontiers in Human Neuroscience
Volume13
DOIs
Publication statusPublished - 2019 Feb 1

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Keywords

  • Brain network development
  • Cortical thickness
  • Low rank
  • Meta-network analysis
  • Temporal smoothness

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Neurology
  • Psychiatry and Mental health
  • Biological Psychiatry
  • Behavioral Neuroscience

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