Topographic hub maps of the human structural neocortical network.

Hubs within the neocortical structural network determined by graph theoretical analysis play a crucial role in brain function. We mapped neocortical hubs topographically, using a sample population of 63 young adults. Subjects were imaged with high resolution structural and diffusion weighted magnetic resonance imaging techniques. Multiple network configurations were then constructed per subject, using random parcellations to define the nodes and using fibre tractography to determine the connectivity between the nodes. The networks were analysed with graph theoretical measures. Our results give reference maps of hub distribution measured with betweenness centrality and node degree. The loci of the hubs correspond with key areas from known overlapping cognitive networks. Several hubs were asymmetrically organized across hemispheres. Furthermore, females have hubs with higher betweenness centrality and males have hubs with higher node degree. Female networks have higher small-world indices.

Partition-based mass clustering of tractography streamlines.

We describe a novel scalable clustering framework for streamlines obtained from diffusion tractography. Clustering is an attractive means of segmenting a large set of streamlines into anatomically relevant bundles. For most existing methods, however, the large datasets produced in high resolution or multiple subject studies are problematical. To achieve good scalability, our method repeatedly divides the data into subsets, which are then partitioned using hierarchical clustering. A final partition is obtained by recombining the subsets. In addition, the recombination scheme provides a consistency measure for cluster assignment of individual streamlines, which is used to clean up the final result. The clusters have good anatomical plausibility and we show that three clusters corresponding to the three known segments of the arcuate fasciculus show excellent agreement with literature. A major advantage of the method is the fact that it can find clusters in datasets of essentially arbitrary size. This fact is exploited to find consistent clusters in concatenated tractography data from multiple subjects. We expect the identification of bundles across subjects to be an important application of the method.