Connectome sorting by Consensus Clustering increases separability in group neuroimaging studies

Javier Rasero, Ibai Diez, Jesus M Cortes, Daniele Marinazzo and Sebastiano Stramaglia. Connectome sorting by Consensus Clustering increases separability in group neuroimaging studies. Network Neuroscience , 2018 [pdf]

A fundamental challenge in preprocessing pipelines for neuroimaging datasets is to increase the signal-to-noise ratio for subsequent analyses. In the same line, we suggest here that the application of the consensus clustering approach to brain connectivity matrices can be a valid additional step for connectome processing to find subgroups of subjects with reduced intra-group variability and therefore increasing the separability of the distinct subgroups when connectomes are used as a biomarker. Moreover, by partitioning the data with consensus clustering before any group comparison (for instance, between a healthy population vs a pathological one), we demonstrate that unique regions within each cluster arise and bring new information that could be relevant from a clinical point of view.

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