Computational protocol: Scale-integrated Network Hubs of the White Matter Structural Network

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Protocol publication

[…] DTI data was processed using the FMRIB Software Library (http://www.fmrib.ox.ac.uk/fsl). Motion artifacts and eddy current distortions were corrected by normalizing each diffusion-weighted volume to the baseline volume (b0) using the affine registration method in the FMRIB’s Linear Image Registration Tool (FLIRT). Diffusion tensor matrices from the sets of diffusion-weighted images were generated using a general linear fitting algorithm.The DTI tractography was performed using the FACT algorithm implemented in the Diffusion Toolkit in the diffusion MR space, and about 100,000 fibers were extracted in each subject (Fig. ). An angle less than 45° between each fiber tracking step and minimum/maximum path length of 20/200 mm were included in a threshold set of the terminating condition. The tractography result was masked by the classified WM map. [...] T1-weighted images were co-registered to the b0 images using FLIRT. Reconstructed whole-brain fiber tracts were inversely transformed into the T1 space, and fiber tracts and surface-based parcellated regions at various scales were located in the same space. Two nodes were considered to be structurally connected by an edge when at least the end points of three fiber tracts were located in these two regions, and the edge was defined by the number of fiber tracts. We selected the threshold of fiber number as three, which was commonly adopted in the previous network studies, – to eliminate spurious connections. Finally, weighted structural networks were constructed for each individual at each scale (Fig. ).Nodal betweenness centrality was adopted to examine the regional hub characteristics of the structural brain networks, . The betweenness centrality (BC) of a node i is defined as1BC(i)=∑j≠i≠kρjk(i)ρjkwhere ρjk is the number of the shortest paths from node j to node k, and ρjk(i) is the number of the shortest paths between node j and node k that pass through node i. Hence, BC(i) captures the influence of a node over information flow between other nodes in the network. Regions with a high betweenness centrality indicate high interconnectivity with other regions in the network.The betweenness centrality map was calculated twenty times for each pre-defined individual connectivity matrix based on random network nodes at each scale using the Brain Connectivity Toolbox (http://www.brain-connectivity-toolbox.net). The individual betweenness centrality map was taken as an average of the twenty betweenness centrality maps. We registered all individual betweenness centrality maps to a group template using a 2-dimensional surface-based registration algorithm, . They were blurred with a 20 mm full width at half maximum surface-based diffusion kernel to decrease spatial variability between subjects. This procedure was repeated for all nodal scales (Fig. ). [...] A two-sample t-test was applied at multiple nodal scales to determine the statistical significance of the difference in local efficiency between the H IS regions and the non H IS regions at each nodal scale. The non H IS region was defined as the network hub at each single scale that was not included in the H IS regions. The network hubs tend to have a high level of local efficiency. The local efficiency was defined as:5E(i)=∑j≠ind−1ijn−1where d−1ij is the reciprocal of the shortest paths from node j to node i. We tested whether the H IS regions showed a higher level of local efficiency than the non H IS regions for each nodal scale. In addition, we used Kolmogorov–Smirnov normality test to check normality of their distribution using the SPSS Statistics 18 software (http://www.spss.com/software/statistics/). […]

Pipeline specifications

Software tools BCT, SPSS
Applications Miscellaneous, Connectivity analysis
Organisms Homo sapiens