Connectome sorting by consensus clustering increases separability in group neuroimaging studies

Network Neuroscience
Javier RaseroSebastiano Stramaglia

Abstract

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 intragroup 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.

References

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Citations

Sep 26, 2019·Frontiers in Computational Neuroscience·Peter Beim GrabenSerafim Rodrigues

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Methods Mentioned

BETA
MDMR

Software Mentioned

FSL
Brain Connectivity Toolbox
R
AFNI
FMRIB Software Library
MDMR
MATLAB
simTB
FreeSurfer

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