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.
Highlights
Combining magnetic resonance imaging (MRI) of the human brain with graph theory analysis has emerged as a powerful approach to studying large-scale networks of both structural and functional connectivity
Diffusion tensor imaging (DTI) based studies using deterministic tractography have included the examination of tractography seed density (Cheng et al, 2012a), anatomic label density (Bassett et al, 2011), and studies examining a variety of network measures (Cheng et al, 2012a; Irimia and Van Horn, 2012)
NETWORK TOPOLOGY a number of studies have examined the reproducibility of graph metrics on structural brain networks derived from DTI-based fiber tractography, there are no known papers that focus on the selection of deterministic tracking algorithm
Summary
Combining magnetic resonance imaging (MRI) of the human brain with graph theory analysis has emerged as a powerful approach to studying large-scale networks of both structural and functional connectivity. The use of graph theoretical analysis to study the topology and structure of these large scale networks is an increasingly active topic of research (Hagmann et al, 2008; Zalesky et al, 2010; Sporns, 2011; Bastiani et al, 2012; Cheng et al, 2012b; Fornito et al, 2012; Irimia and Van Horn, 2012). These methods have been used to examine the structural consequences of neurological disorders (Guye et al, 2010; Martin, 2012; Xie and He, 2012) as well as the relationship between structure and function (Honey et al, 2007, 2009; Hagmann et al, 2008). A recent review article discussed the reproducibility of these graph metrics as used to examine both functional and structural networks across a variety of conditions (Telesford et al, 2013)
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