The effect and reproducibility of different clinical DTI gradient sets on small world brain connectivity measures

Advances in computational network analysis have enabled the characterization of topological properties in large scale networks including the human brain. Information on structural networks in the brain can be obtained in-vivo by performing tractography on diffusion tensor imaging (DTI) data. However, little is known about the reproducibility of network properties derived from whole brain tractography data, which has important consequences for minimally detectable abnormalities or changes over time. Moreover, acquisition parameters, such as the number of gradient directions and gradient strength, possibly influence network metrics and the corresponding reproducibility derived from tractography data. The aim of the present study is twofold: (i) to determine the effect of several clinically available DTI sampling schemes, differing in number of gradient directions and gradient amplitude, on small world metrics and (ii) to evaluate the interscan reproducibility of small world metrics. DTI experiments were conducted on six healthy volunteers scanned twice. Probabilistic tractography was performed to reconstruct structural connections between regions defined from an anatomical atlas. The observed reproducibility of the network measures was high, reflected by low values for the coefficient of variation (