Automated multi-subject fiber clustering of mouse brain using dominant sets

• Published in: Frontiers in neuroinformatics Vol. 8; p. 87
• Main Authors: Dodero, Luca, Vascon, Sebastiano, Murino, Vittorio, Bifone, Angelo, Gozzi, Alessandro, Sona, Diego
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 12.01.2015; Frontiers Media S.A
• Subjects: Algorithms; Anatomy; Automation; Brain architecture; Brain mapping; Cluster analysis; Clustering; Datasets; Diffusion; Diffusion Magnetic Resonance Imaging; DTI; fibers; Game theory; Knowledge; Magnetic resonance imaging; Methods; Neural networks; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Pattern recognition; Structure-function relationships; Substantia alba; tractography; white matter; Italy; white matter; diffusion magnetic resonance imaging; tractography; fibers segmentation; multi-subject; dominant sets; DTI; clustering
• DOI: 10.3389/fninf.2014.00087
• ISSN: 1662-5196

Mapping of structural and functional connectivity may provide deeper understanding of brain function and disfunction. Diffusion Magnetic Resonance Imaging (DMRI) is a powerful technique to non-invasively delineate white matter (WM) tracts and to obtain a three-dimensional description of the structural architecture of the brain. However, DMRI tractography methods produce highly multi-dimensional datasets whose interpretation requires advanced analytical tools. Indeed, manual identification of specific neuroanatomical tracts based on prior anatomical knowledge is time-consuming and prone to operator-induced bias. Here we propose an automatic multi-subject fiber clustering method that enables retrieval of group-wise WM fiber bundles. In order to account for variance across subjects, we developed a multi-subject approach based on a method known as Dominant Sets algorithm, via an intra- and cross-subject clustering. The intra-subject step allows us to reduce the complexity of the raw tractography data, thus obtaining homogeneous neuroanatomically-plausible bundles in each diffusion space. The cross-subject step, characterized by a proper space-invariant metric in the original diffusion space, enables the identification of the same WM bundles across multiple subjects without any prior neuroanatomical knowledge. Quantitative analysis was conducted comparing our algorithm with spectral clustering and affinity propagation methods on synthetic dataset. We also performed qualitative analysis on mouse brain tractography retrieving significant WM structures. The approach serves the final goal of detecting WM bundles at a population level, thus paving the way to the study of the WM organization across groups.