Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors.

ABSTRACT: Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ₁ -type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ₂ -norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alz

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC10115907 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature