Connectivity-informed adaptive regularization for generalized outcomes.

ABSTRACT: One of the challenging problems in neuroimaging is the principled incorporation of information from different imaging modalities. Data from each modality are frequently analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, generalized ridgified Partially Empirical Eigenvectors for Regression (griPEER), to estimate associations between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER improves the regression coefficient estimation by providing a principled approach to use external information from the structural brain connectivity. Specifically, we incorporate a penalty term, derived from the structural connectivity Lapla

SUBMITTER: Brzyski D 

PROVIDER: S-EPMC8730330 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature