SPLTRAK Abstract Submission
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510 SA-PM Reproducibility of Small World Metrics from Diffusion Tensor Tractography at 3 Tesla M.J. Vaessen1,2, P.A. Hofman1,2, J.F. Jansen1, H.N. Tijssen1,3, A. Aldenkamp2, W.H. Backes1 1Department of Radiology, Maastricht University Medical Centre, Maastricht, Netherlands/2Epilepsy Centre Kempenhaeghe, Heeze, Netherlands/3Centre for functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom Introduction: Advances in computational network analysis have enabled the characterization of topological properties in large scale networks such as the human brain. Recent studies demonstrated that structural brain networks have small-world attributes (Bassett and Bullmore, 2006, Sporns and Zwi, 2004, Hagmann et al., 2008). Small-world networks are characterized by a topology in which most nodes are not neighbors of one another, but can be reached through a small number of steps. Their attributes may serve as clinical markers in a variety of pathologies (Liu et al., 2008). However, little is known about their accuracy and reproducibility, which have consequences for detectable clinical differences. Moreover, acquisition parameters, such as number of gradient directions and diffusion strength, possibly influence network metrics derived from tractography data. The aim of the present study is twofold: (i) to determine the effect of clinically available DTI sampling schemes on small world metrics derived from tractography data and (ii) to asses the reproducibility of small world metrics. Methods: Diffusion Tensor Imaging experiments were conducted on six healthy volunteers on a 3T Philips Achieva whole body scanner. Every subject was scanned twice. Six sampling schemes, which varied in directional resolution (Ndir = 32, 15 and 6) and gradient strength (Gamp = 31 and 44 mT/m), were employed in random order. Sampling schemes were matched for total scan time (12 min.) by setting number of averages appropriately. Whole brain connectivity data was obtained by tracking from Brodmann areas, defined on the greywhite matter interface, using probabilistic tractography (PICo (Parker et al., 2003)) (Fig. 1).
From the connectivity data the following small world network metrics were computed: average node degree (K), characteristic path length (L), and cluster coefficient (C). Effect of gradient sampling scheme was analyzed using a two-way ANOVA test. Calculated reproducibility measures were: Coefficient of Variation (CV) and Reproducibility Coefficient (RC). Results: The values of the small-world metrics for each sampling scheme are shown in Fig 2. Node degree K and cluster coefficient C increased significantly (p<0.05) with directional resolution. Characteristic path length L showed significant decrease (p<0.05) with directional resolution. Lower directional resolution is associated with less long range tracts (Fig. 3). Gradient amplitude did not have a significant effect on any of the metrics. There was no significant deviation in CV and RC values for either Ndir or Gamp. Conclusions: Small world metrics derived from tractography data are influenced by the employed gradient scheme. Less long range tracts (Fig. 3) imply that distant brain regions are less connected, this
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SPLTRAK Abstract Submission
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could explain differences in small world metrics. Small world metrics show high reproducibility (Fig. 4), advocating the applicability in clinical studies. References: Basset, D.S. (2006), 'Small-world brain networks', Neuroscientist, vol. 12, no. 6, pp. 512-523. Hagmann, P (2008), 'Mapping the structural core of human cerebral cortex', PLoS Biology, vol. 6, no. 7, pp. e159. Liu, Y (2008), 'Disrupted small-world networks in schizophrenia', Brain, vol. 131, no. 4, pp. 945-961. Parker, G.J. (2003), 'A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements', Journal of Magnetic Resonance Imaging, vol. 18, no. 2, pp. 242-254. Sporns, O (2004), 'The small world of the cerebral cortex', Neuroinformatics, vol. 2, no. 2, pp. 145-62.
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