Assessing graph models for description of brain networks

Yixuan Yuan, Lei Guo, Peili Lv, Xintao Hu, Degang Zhang, Junwei Han, Li Xie, Tianming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Both structural and functional brain networks have been investigated in the literature with enthusiasm via graph-theoretical methods. However, an important issue that has not been adequately addressed before is: what is the optimal graph model for describing brain networks, both in structural and functional aspects? We address this question in the following three aspects. First, multi-resolution structural brain networks are reconstructed via cortical surface parcellation based on white matter fiber density information. Second, the global and local graph properties of the constructed networks are measured using state-of-the-art graph analysis algorithms and tools, and are further compared with five popular random graph models. Third, a functional simulation study is conducted to evaluate the synchronizability of the five models. Our results suggest that the STICKY graph model fits brain networks the best in terms of global and local graph properties, and the fastest speed of functional synchronization.

Original languageEnglish
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages827-831
Number of pages5
DOIs
StatePublished - 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: 30 Mar 20112 Apr 2011

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/TerritoryUnited States
CityChicago, IL
Period30/03/112/04/11

Keywords

  • graph models
  • graph properties
  • multi-resolution structural brain networks

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