Magnetic Resonance Diffusion Tensor Imaging (DTI) has opened the way to a variety of white matter analysis approaches which leverage the axon networks in the brain. Even if tractography algorithms are widely used to reconstruct these networks, their topology is seldom employed to evaluate differences between patient and control groups, unless there is prior knowledge on the areas of interest. Using a sparse approach, we have developed a multivariate method to automatically identify the most significant connections bundles able to characterise differences between two groups. This will allow neuroscientist to explore inter-group differences in white-matter topology in an unbiased-fashion, and without the need of a priori knowledge. Here, we performed a preliminary test of the approach with serotonin dysfunctional mice and a control group. The results allowed us to identify inter-group differences in the density of white matter tracts originating from serotonergic areas, thus corroborating the predictive validity of the method.
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