Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data.

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 intersubject variability, and few studies have been carried out to adequately describe the structure, shape, and reproducibility of these bundles. However, their analysis is crucial to understand brain function and better characterize the human connectome. In this study, we propose an automatic method to identify reproducible short association bundles of the superficial white matter, based on intersubject 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 non-linear registrations 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 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 to analyze stability and lateralization results.

Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas.

This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the algorithm first preprocesses the data using a simplified distance metric to rapidly discard candidate streamlines in multiple stages, while guaranteeing that no false negatives are produced. The smaller set of remaining streamlines is then segmented using the original metric, thus eliminating any false positives from the preprocessing stage. As a result, a single-thread implementation of the algorithm can segment a dataset of almost 9 million streamlines in less than 6 minutes. Moreover, parallel versions of our algorithm for multicore processors and graphics processing units further reduce the segmentation time to less than 22 seconds and to 5 seconds, respectively. This performance enables the use of the algorithm in truly interactive applications for visualization, analysis, and segmentation of large white matter tractography datasets.

Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography.

Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of pathogenesis triggered by abnormal connectivity. In this work we automatically created a multi-subject atlas of SWM diffusion-based bundles of the whole brain. For each subject, the complete cortico-cortical tractogram is first split into sub-tractograms connecting pairs of gyri. Then intra-subject shape-based fiber clustering performs compression of each sub-tractogram into a set of bundles. Proceeding further with shape-based clustering provides a match of the bundles across subjects. Bundles found in most of the subjects are instantiated in the atlas. To increase robustness, this procedure was performed with two independent groups of subjects, in order to discard bundles without match across the two independent atlases. Finally, the resulting intersection atlas was projected on a third independent group of subjects in order to filter out bundles without reproducible and reliable projection. The final multi-subject diffusion-based U-fiber atlas is composed of 100 bundles in total, 50 per hemisphere, from which 35 are common to both hemispheres.