Figure 5
From: Scale-integrated Network Hubs of the White Matter Structural Network
Flowchart of the processing pipeline. (a) The T1-weighted image was rigidly coregistered to the averaged b0 image in the native diffusion space. The whole-brain WM tracts were reconstructed using the FACT algorithm. (b) Each neocortical hemisphere was parcellated 20 times into 100, 200, 300, 400, 500, or 600 regions of interest (ROIs) as nodes using the k-means algorithm determined with the Euclidean distances between coordinates on the sphere model (left). The parcellated ROIs were transformed to the WM surface matched with the sphere model (right). (c) 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 weighted structural networks were constructed for each individual node at each scale. (d) The betweenness centrality map was calculated 20 times for each pre-defined individual connectivity matrix dataset using the Brain Connectivity Toolbox (http://www.brain-connectivity-toolbox.net). Individual betweenness centrality maps were averaged to create the group hub map. This procedure was repeated for all nodal scales. (e) The scale-integrated hub strength (H IS _ ST ) is defined as the sum of all normalized group hub scores divided by the total number of nodal scales in order to estimate the overall network hub pattern between multiple nodal scales. (f) The scale-integrated hub score (H IS _ SC ) captures how ‘well connected’ node i is to other nodes in the H IS _ ST map.
