Open AccessParcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study
Author(s) -
Wang Jinhui,
Wang Liang,
Zang Yufeng,
Yang Hong,
Tang Hehan,
Gong Qiyong,
Chen Zhang,
Zhu Chaozhe,
He Yong
Publication year - 2009
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20623
Subject(s) - resting state fmri , neuroscience , functional connectivity , thresholding , human brain , neuroimaging , degree distribution , complex network , small world network , psychology , artificial intelligence , pattern recognition (psychology) , computer science , world wide web , image (mathematics)
Abstract Recent studies have demonstrated small‐world properties in both functional and structural brain networks that are constructed based on different parcellation approaches. However, one fundamental but vital issue of the impact of different brain parcellation schemes on the network topological architecture remains unclear. Here, we used resting‐state functional MRI (fMRI) to investigate the influences of different brain parcellation atlases on the topological organization of brain functional networks. Whole‐brain fMRI data were divided into ninety and seventy regions of interest according to two predefined anatomical atlases, respectively. Brain functional networks were constructed by thresholding the correlation matrices among the parcellated regions and further analyzed using graph theoretical approaches. Both atlas‐based brain functional networks were found to show robust small‐world properties and truncated power‐law connectivity degree distributions, which are consistent with previous brain functional and structural networks studies. However, more importantly, we found that there were significant differences in multiple topological parameters (e.g., small‐worldness and degree distribution) between the two groups of brain functional networks derived from the two atlases. This study provides quantitative evidence on how the topological organization of brain networks is affected by the different parcellation strategies applied. Hum Brain Mapp 2009. © 2008 Wiley‐Liss, Inc.