Comparison of Characteristics between Region- and Voxel-Based Network Analyses in Resting-State fMRI Data

By Satoru Hayasaka, Paul J. Laurienti. NeuroImage, 2009

384 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/76aeedee3bc0bcb045b6cf6ce80801038cfedc44

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Citations in the corpus

Schizophrenia, neuroimaging and connectomics (2012, background): “Evidence that differences in node definitions can affect network properties (Fornito et al., 2010; Hayasaka and Laurienti, 2010; Wang et al., 2009a; Zalesky et al., 2010b), and that invalid node definitions can distort estimates of true network interactions (Smith et al., 2011), underscores the…”
Functional network organization of the human brain (2011, background): “, 2011), or random interpolations between voxels and parcels (Hayasaka and Laurienti, 2010; Meunier et al., 2009b).”
Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data (2011, background): “Previous studies have demonstrated that network properties were sensitive to nodal definition based on parcellation strategies [32,36] and spatial scales [33,34,35].”
Altered Resting State Complexity in Schizophrenia (2011, background): “We note that while fine-grained analyses of voxel-wise connectivity have also been reported (Ferrarini et al., 2011; Hayasaka and Laurienti, 2010), here we focused our examination on average connectivity properties of resting state function derived from large anatomically-specified regions.”
Network-based statistic: Identifying differences in brain networks (2010, background): “…MRI, electroencephalography or magnetoencephalography, temporal correlations (Achard et al., 2006; Deuker et al., 2009; Fair et al., 2009; Hayasaka & Laurienti, 2010; Honey et al., 2009; Li et al., 2009) or mutual information (Bassett et al., 2009) in the node-averaged signals are…”
Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures (2012, methodology): “We decided on using correlation rather than covariance, since this is the most straightforward and most commonly used method (Fair et al., 2008; Hayasaka and Laurienti, 2010; Smith et al., 2011; van den Heuvel et al., 2008).”
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, background): “We note that down-sampling is indeed known to affect network characteristics [49–53].”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, positive): “Inconsistent or imprecise node definitions can have a major impact on subsequent analyses (Wang, et al., 2009; Antiqueira, et al., 2010; Fornito, et al., 2010; Hayasaka and Laurienti, 2010; Zalesky, Fornito, Harding, et al., 2010).”
Graph-Based Network Analysis of Resting-State Functional MRI (2010, positive): “Recent evidence has demonstrated the meaningful influence of node choice on the properties of resulting networks (Hayasaka and Laurienti, 2009; Wang et al., 2009a; Zalesky et al., 2009).”
Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data (2010, positive): “One recent study suggests that the network properties of systems, in terms of ‘small-world’ characteristics inherent in connectivity relationships between nodes, are better approximated by using single voxel seeds rather than larger ROI seeds (Hayasaka and Laurienti, 2009).”
Conserved and variable architecture of human white matter connectivity (2011, background): “, 2009) and robust to spatial resolution in both anatomical and functional networks (Zalesky et al., 2010; Hayasaka and Laurienti, 2010).”
Plasticity of Brain Networks in a Randomized Intervention Trial of Exercise Training in Older Adults (2010, neutral): “…examination of the effects of aerobic training on functional connectivity in the context of aging, an important area for future research will be characterizing the multidimensional nature of age-related changes in patterns of brain activation (Cole et al., 2010; Hayasaka and Laurienti, 2010).”
On the use of correlation as a measure of network connectivity (2012, background): “All doi:10.1016/j.neuroimage.2012.02.001 a b s t r a c t a r t i c l e i n f o”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Another challenge in graph theory research is developing a consensus about which of the brain parcellation schemes is optimal for defining network nodes and constructing the brain network (Hayasaka and Laurienti, 2010).”