Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2010-04, Vol.50 (2), p.499-508
• Main Authors: Hayasaka, Satoru, Laurienti, Paul J.
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Brain - anatomy & histology; Brain Mapping - methods; Data processing; Female; Functional connectivity; Graph theory; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging; Male; Medical research; Neighborhoods; Nerve Net - anatomy & histology; Network theory; Rest; Resting-state fMRI; Scale-free network; Small-world network
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2009.12.051

Small-world networks are a class of networks that exhibit efficient long-distance communication and tightly interconnected local neighborhoods. In recent years, functional and structural brain networks have been examined using network theory-based methods, and consistently shown to have small-world properties. Moreover, some voxel-based brain networks exhibited properties of scale-free networks, a class of networks with mega-hubs. However, there are considerable inconsistencies across studies in the methods used and the results observed, particularly between region-based and voxel-based brain networks. We constructed functional brain networks at multiple resolutions using the same resting-state fMRI data, and compared various network metrics, degree distribution, and localization of nodes of interest. It was found that the networks with higher resolutions exhibited the properties of small-world networks more prominently. It was also found that voxel-based networks were more robust against network fragmentation compared to region-based networks. Although the degree distributions of all networks followed an exponentially truncated power law rather than true power law, the higher the resolution, the closer the distribution was to a power law. The voxel-based analyses also enhanced visualization of the results in the 3D brain space. It was found that nodes with high connectivity tended have high efficiency, a co-localization of properties that was not as consistently observed in the region-based networks. Our results demonstrate benefits of constructing the brain network at the finest scale the experiment will permit.