Learning Metabolic Brain Networks in MCI and AD by Robustness and Leave-One-Out Analysis: An FDG-PET Study

Zhijun Yao, Bin Hu*, Xuejiao Chen, Yuanwei Xie, Jürg Gutknecht, Dennis Majoe

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

This study attempted to better understand the properties associated with the metabolic brain network in mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Graph theory was employed to investigate the topological organization of metabolic brain network among 86 patients with MCI, 89 patients with AD, and 97 normal controls (NCs) using 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) data. The whole brain was divided into 82 areas by Brodmann atlas to construct networks. We found that MCI and AD showed a loss of small-world properties and topological aberrations, and MCI showed an intermediate measurement between NC and AD. The networks of MCI and AD were vulnerable to attacks resulting from the altered topological pattern. Furthermore, individual contributions were correlated with Mini-Mental State Examination and Clinical Dementia Rating. The present study indicated that the topological patterns of the metabolic networks were aberrant in patients with MCI and AD, which may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI and AD.

Original languageEnglish
Pages (from-to)42-54
Number of pages13
JournalAmerican Journal of Alzheimer's Disease and other Dementias
Volume33
Issue number1
DOIs
Publication statusPublished - 1 Feb 2018
Externally publishedYes

Keywords

  • AD
  • FDG-PET
  • Leave-one-out
  • MCI
  • Metabolic brain network
  • Network robustness

Fingerprint

Dive into the research topics of 'Learning Metabolic Brain Networks in MCI and AD by Robustness and Leave-One-Out Analysis: An FDG-PET Study'. Together they form a unique fingerprint.

Cite this