Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

By Jinhui Wang, Xi-Nian Zuo, Suril R. Gohel, Michael P. Milham, Bharat B. Biswal, Yong He. PLoS ONE, 2011

410 citations recorded by the source corpus.

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/be0adfea42acc755693e8d95291997201bc6c248

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Citations in the corpus

Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, methodology): “For more information regarding data collection methods, please refer to Shehzad et al. (2009), Zuo et al. (2010a,b,c, 2012), Zuo and Xing (2011), Wang et al. (2011a).”
Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity (2012, positive): “…2011; Seeley et al., 2007; van den Heuvel et al., 2009; Zhu et al., 2011) and to some extent reproducible across scanning sessions (Braun et al., 2012; Cohen et al., 2008; Mannfolk et al., 2011; Meindl et al., 2010; Shehzad et al., 2009; Van Dijk et al., 2010; Wang et al., 2011; Zuo et al., 2010).”
Network centrality in the human functional connectome. (2012, background): “those from a recent study on TRT reliability of large-scale functional brain networks (Wang et al. 2011).”
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, methodology): “…has been widely used in brain network studies because it tends to have higher test-retest (TRT) reliability than other nodal centrality metrics (Wang et al., 2011; Cao et al., 2014), and it is well in line with physiological measures, such as the rates of cerebral blood flow and glucose…”
Clinical applications of the functional connectome (2013, neutral): “…adults by multiple groups (Braun et al., 2012; Chou et al., 2012; Faria et al., 2012; Fiecas et al., 2013; Kristo et al., 2012; Liang et al., 2012; Mannfolk et al., 2011; Meindl et al., 2010; Shehzad et al., 2009; Van Dijk et al., 2010; Wang et al., 2011, 2013; Zuo et al., 2010a, 2010b, 2013).”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “Multiple studies have found evidence for limited test-retest reliability using a multitude of conventional rs-fcMRI network characteristics and analytic approaches (Braun et al., 2012; Wang et al., 2011; Wisner et al., 2013; Zuo et al., 2010; Zuo and Xing, 2014).”
Cognitive Network Neuroscience (2015, background): “, 2011) and functional (Braun et al., 2012; Wang et al., 2011; Telesford et al., 2010; Deuker et al., 2009) brain”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “Resting static FC can provide a robust estimate of an individual’s functional network architecture that is stable across multiple scans and imaging sessions, even months or years apart (Guo et al., 2012; Shehzad et al., 2009; J. H. Wang et al., 2011).”