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


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, Oct 04, 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 · 4.09 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 · 3.26 Impact Factor
    • "Promising targets for genetic analysis must be heritable, and reproducible to measure . Zhan et al. (2013b) and Dennis et al. (2012) studied the stability of connectomic measures at different field strengths and their repeatability over time; Jahanshad et al. (2012b) studied diffusion imaging protocol effects on genome-wide scanning results. While efforts to identify common variants influencing brain connectivity are progressing, far fewer studies have attempted to examine the impact of functional rare variation on these traits. "
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    ABSTRACT: Connectome genetics attempts to discover how genetic factors affect brain connectivity. Here we review a variety of genetic analysis methods - such as genome-wide association studies (GWAS), linkage and candidate gene studies - that have been fruitfully adapted to imaging data to implicate specific variants in the genome for brain-related traits. We then review studies of that emphasized the genetic influences on brain connectivity. Some of these perform genetic analysis of brain integrity and connectivity using diffusion MRI, and others have mapped genetic effects on functional networks using resting state functional MRI. Connectome-wide genome-wide scans have also been conducted, and we review the multivariate methods required to handle the extremely high dimension of genomic and the network data. We also review some consortium efforts, such as ENIGMA, that offer the power to detect robust common genetic associations using phenotypic harmonization procedures and meta-analysis. Current work on connectome genetics is advancing on many fronts and promises to shed light on how disease risk genes affect the brain. It is already discovering new genetic loci and even entire genetic networks that affect brain organization and connectivity.
    NeuroImage 05/2013; 80. DOI:10.1016/j.neuroimage.2013.05.013 · 6.36 Impact Factor
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