Article

Test-retest reliability of graph theory measures of structural brain connectivity

Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA, CA, USA.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 01/2012; 15(Pt 3):305-12. DOI: 10.1007/978-3-642-33454-2-38
Source: PubMed

ABSTRACT The human connectome has recently become a popular research topic in neuroscience, and many new algorithms have been applied to analyze brain networks. In particular, network topology measures from graph theory have been adapted to analyze network efficiency and 'small-world' properties. While there has been a surge in the number of papers examining connectivity through graph theory, questions remain about its test-retest reliability (TRT). In particular, the reproducibility of structural connectivity measures has not been assessed. We examined the TRT of global connectivity measures generated from graph theory analyses of 17 young adults who underwent two high-angular resolution diffusion (HARDI) scans approximately 3 months apart. Of the measures assessed, modularity had the highest TRT, and it was stable across a range of sparsities (a thresholding parameter used to define which network edges are retained). These reliability measures underline the need to develop network descriptors that are robust to acquisition parameters.

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Available from: Emily L. Dennis, Aug 28, 2015
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    • "While many network properties – such as high clustering, short path-length, core-periphery structure, and modularity – have consistently been found to characterize connectivity patterns extracted from many types of non-invasive neuroimaging data, these properties all vary among people. There is mounting evidence that one can reliably identify individual differences in structural (Bassett et al., 2011; Dennis et al., 2012; Owen et al., 2013) and functional (Braun et al., 2012; Deuker et al., 2009; Telesford et al., 2010; Wang et al., 2011) brain network organization, suggesting that individual variation in such architectures can be linked to individual variation in cognitive performance. "
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    Journal of Cognitive Neuroscience 08/2015; 27(8):1471-1491. DOI:10.1162/jocn_a_00810 · 4.69 Impact Factor
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    • "A number of studies have also examined reproducibility in structural networks, each focusing on various aspects of the complex processing pipeline that is a prerequisite for these measures. These have included studies of diffusion spectrum imaging (Bassett et al., 2011; Cammoun et al., 2012) and high angular resolution diffusion imaging (Dennis et al., 2012). Some studies have examined probabilistic tractography (Vaessen et al., 2010; Owen et al., 2013). "
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    Frontiers in Neuroinformatics 05/2014; 8:46. DOI:10.3389/fninf.2014.00046
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    • "To minimize any effects of arbitrary thresholding, we calculated our network measures over a range of thresholds (Achard and Bullmore, 2007; Bassett et al., 2008; He et al., 2008; Khundrakpam et al., 2012) and integrated over that range. We have shown that this can improve their robustness and test–retest reliability (Dennis et al., 2012c). We selected the range 0.2–0.3 to calculate and integrate these measures, as that range is biologically plausible (Sporns, 2011) and more stable (Dennis et al., 2012a). "
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