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Journal Articles Network Neuroscience Year : 2021

A covariate-constraint method to map brain feature space into lower dimensional manifolds

Abstract

Human brain connectome studies aim at both exploring healthy brains, and 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 machine learning technique that exhibits flexibility, gives the investigator grip on the features and covariates, allows visualization and exploration, and yields insight into the data and the biological phenomena at stake. The retained approach is dimension reduction in a manifold learning methodology, the originality lying in that one (or several) reduced variables be chosen by the investigator. The proposed method is illustrated on two studies, the first one addressing comatose patients, the second one addressing young versus elderly population comparison. The method sheds light on the differences between brain connectivity graphs using graph metrics and potential clinical interpretations of theses differences.
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Dates and versions

hal-03165916 , version 1 (11-03-2021)

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Félix Renard, Christian Heinrich, Marine Bouthillon, Maleka Schenck, Francis Schneider, et al.. A covariate-constraint method to map brain feature space into lower dimensional manifolds. Network Neuroscience, 2021, 5 (1), pp.252-273. ⟨10.1162/netn_a_00176⟩. ⟨hal-03165916⟩
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