Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data - Román, Claudio; Guevara, Miguel; Valenzuela, Ronald; Figueroa, Miguel; Houenou, Josselin; Duclap, Delphine; Poupon, Cyril; Mangin, Jean-François; Guevara, Pamela (Frontiers in Neuroinformatics, 2017)
Human brain connectivity is extremely complex and variable across subjects. While long association and projection bundles are stable and have been deeply studied, short association bundles present higher inter-subject variability and few studies have been carried out to adequately describe the structure, shape and reproducibility of these bundles. However, their analysis is crucial in order to understand brain function and better characterize the human connectome. In this work, we propose an automatic method to identify reproducible short association bundles of the superficial white matter, based on inter-subject hierarchical clustering. The method is applied to the whole brain and finds representative clusters of similar fibers belonging to a group of subjects, according to a distance metric between fibers. We experimented with both affine and a non-linear registration and, due to better reproducibility, chose the results obtained from non-linear registration. Once the clusters are calculated, our method performs automatic labeling of the most stable connections based on individual cortical parcellations. We compare results between two independent groups of subjects from a HARDI database in order to generate reproducible connections for the creation of an atlas. To perform a better validation of the results, we used a bagging strategy that uses pairs of groups of 27 subjects from a database of 74 subjects. The result is an atlas with 44 bundles in the left hemisphere and 49 in the right hemisphere, of which 33 bundles are found in both hemispheres. Finally, we use the atlas to automatically segment 78 new subjects from a different HARDI database and analyze stability and lateralization results.