CONNECTOME MAPPERを用いた脳構造ネットワーク解析におけるTRACTOGRAPHYパラメータの影響

The process of reconstructing the white matter tracts using Diffusion Tensor Imaging (DTI) refers to tractography which is a key to structural connectivity since it is the best non-invasive technique to investigate brain networks. The structural networks which generate from diffusion may influence by tractography parameters. Thus, examine the optimum parameters can be beneficial as it helps to create better connectomes. In this study, we examine the tractography parameters of the Connectome Mapper. We aimed to optimise parameters including the number of seeds, step size and turning angle in tractography of the Connectome Mapper (www.cmtk.org) which is a combination of sophisticated neuroimaging tools. Therefore, we could be able to use a better choice of tractography parameters in future clinical studies. DTI and T1 images of ten healthy subjects (3.0T, Philips, Achieva) were processed to construct connectivity matrices using the Connectome Mapper. The graph theory analysis was applied on connectivity matrices using Brain Connectivity Toolbox. Connectivity measures of five different number of seeds per voxel (15, 25, 35, 45, 55), step sizes (0.1 mm, 0.5 mm, 1 mm, 1.5 mm, 2 mm) and turning angles (40^0, 50^0, 60^0, 70^0, 80^0) were analysed for whole brain connectivity by estimating mean network measures including degree, betweenness centrality, local efficiency, cluster coefficient, eccentricity, strength, small-worldness and characteristic path length. Our study emphasised that more connections can be obtained when increasing the tractography parameters. We suggested that given parameters are not any more optimal than another in term of the number of seeds, turning angles or step size. While some cases showed drastic points that were not significant in many cases suggesting that the default values of tractography parameters would be appropriate to use in future studies. Moreover, future studies should give careful consideration to the choice of tractography parameter based upon the network measure that will be analysed.首都大学東京, 2018-09-30, 修士（放射線学）首都大学東