Jinhui Wang and Liang Wang contributed equally to this work.
Parcellation-dependent small-world brain functional networks: A resting-state fMRI study
Article first published online: 22 JUL 2008
Copyright © 2008 Wiley-Liss, Inc.
Human Brain Mapping
Volume 30, Issue 5, pages 1511–1523, May 2009
How to Cite
Wang, J., Wang, L., Zang, Y., Yang, H., Tang, H., Gong, Q., Chen, Z., Zhu, C. and He, Y. (2009), Parcellation-dependent small-world brain functional networks: A resting-state fMRI study. Hum. Brain Mapp., 30: 1511–1523. doi: 10.1002/hbm.20623
- Issue published online: 14 APR 2009
- Article first published online: 22 JUL 2008
- Manuscript Accepted: 12 MAY 2008
- Manuscript Revised: 14 APR 2008
- Manuscript Received: 23 JAN 2008
- National Natural Science Foundation (NSFC) of China. Grant Numbers: 30625024, 30728017, 30700256, 30621130074
- National Key Basic Research and Development Program (973 Program). Grant Numbers: 2003CB716101, 2007CB512305
- UK Royal Society International Joint Project with NSFC. Grant Numbers: 30530300, 2006/R3
- High Technology Program of China (863 Program). Grant Number: 2007AA02Z430
- brain networks;
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.