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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    ABSTRACT: Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.
    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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    ABSTRACT: Recent interest in human brain connectivity has led to the application of graph theoretical analysis to human brain structural networks, in particular white matter connectivity inferred from diffusion imaging and fiber tractography. While these methods have been used to study a variety of patient populations, there has been less examination of the reproducibility of these methods. A number of tractography algorithms exist and many of these are known to be sensitive to user-selected parameters. The methods used to derive a connectivity matrix from fiber tractography output may also influence the resulting graph metrics. Here we examine how these algorithm and parameter choices influence the reproducibility of proposed graph metrics on a publicly available test-retest dataset consisting of 21 healthy adults. The dice coefficient is used to examine topological similarity of constant density subgraphs both within and between subjects. Seven graph metrics are examined here: mean clustering coefficient, characteristic path length, largest connected component size, assortativity, global efficiency, local efficiency, and rich club coefficient. The reproducibility of these network summary measures is examined using the intraclass correlation coefficient (ICC). Graph curves are created by treating the graph metrics as functions of a parameter such as graph density. Functional data analysis techniques are used to examine differences in graph measures that result from the choice of fiber tracking algorithm. The graph metrics consistently showed good levels of reproducibility as measured with ICC, with the exception of some instability at low graph density levels. The global and local efficiency measures were the most robust to the choice of fiber tracking algorithm.
    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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    ABSTRACT: Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain's anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4T. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant's co-registered anatomical scans. The proportion of fiber connections between all pairs of cortical regions, or nodes, was found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70×70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.
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