• DOI:10.1111/biom.13775
• Corpus ID: 232478917

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

@article{Zhao2021IdentifyingBH,
  title={Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors.},
  author={Yi Zhao and Bingkai Wang and Chin-Fu Liu and Andreia Vasconcellos Faria and Michael I. Miller and Brian S. Caffo and Xi Luo},
  journal={Biometrics},
  year={2021}
}

• Yi Zhao, Bingkai Wang, +4 authors Xi Luo
• Published 1 April 2021
• Psychology
• Biometrics

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 -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 -norm and the model selection is also consistent. When applied to a brain structural magnetic resonance imaging dataset acquired from the Alzheimer's Disease Neuroimaging Initiative, the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. This article is protected by copyright. All rights reserved.