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Manifold learning for brain connectivity
[article]
2020
arXiv
pre-print
Human brain connectome studies aim at extracting and analyzing relevant features associated to pathologies of interest. Usually this consists in modeling the brain connectome as a graph and in using graph metrics as features. A fine brain description requires graph metrics computation at the node level. Given the relatively reduced number of patients in standard cohorts, such data analysis problems fall in the high-dimension low sample size framework. In this context, our goal is to provide a
arXiv:2005.00469v1
fatcat:bvc2zj5vibblpecdrbs5egckvi