Brain Structure Network Analysis in Patients with Obstructive Sleep Apnea

PLoS One. 2015 Sep 28;10(9):e0139055. doi: 10.1371/journal.pone.0139055. eCollection 2015.

Abstract

Childhood obstructive sleep apnea (OSA) is a sleeping disorder commonly affecting school-aged children and is characterized by repeated episodes of blockage of the upper airway during sleep. In this study, we performed a graph theoretical analysis on the brain morphometric correlation network in 25 OSA patients (OSA group; 5 female; mean age, 10.1 ± 1.8 years) and investigated the topological alterations in global and regional properties compared with 20 healthy control individuals (CON group; 6 females; mean age, 10.4 ± 1.8 years). A structural correlation network based on regional gray matter volume was constructed respectively for each group. Our results revealed a significantly decreased mean local efficiency in the OSA group over the density range of 0.32-0.44 (p < 0.05). Regionally, the OSAs showed a tendency of decreased betweenness centrality in the left angular gyrus, and a tendency of decreased degree in the right lingual and inferior frontal (orbital part) gyrus (p < 0.005, uncorrected). We also found that the network hubs in OSA and controls were distributed differently. To the best of our knowledge, this is the first study that characterizes the brain structure network in OSA patients and invests the alteration of topological properties of gray matter volume structural network. This study may help to provide new evidence for understanding the neuropathophysiology of OSA from a topological perspective.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Area Under Curve
  • Brain / pathology*
  • Brain / physiopathology
  • Case-Control Studies
  • Child
  • Cognition
  • Demography
  • Female
  • Humans
  • Male
  • Nerve Net / pathology
  • Nerve Net / physiopathology
  • Sleep Apnea, Obstructive / pathology*
  • Sleep Apnea, Obstructive / physiopathology

Grants and funding

The work described in this paper was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK 475711, CUHK 416712, CUHK 411811, CUHK 473012, CUHK 14113214), a grant from Shenzhen Science and Technology Innovation Committee (Project No. CXZZ20140606164105361), grants from the National Natural Science Foundation of China (Project No. 81271653 and 81201157), and a grant from the Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong (Project No. BME-p2-13/BME-CUHK).