Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors.
Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors.
复制标题
DOI：
10.1111/biom.13775
复制
发表时间：
2023-09
期刊：
影响因子：
1.9
通讯作者：
Luo, Xi
中科院分区：
文献类型：
--
作者：
Zhao, Yi;Wang, Bingkai;Liu, Chin-Fu;Faria, Andreia, V;Miller, Michael, I;Caffo, Brian S.;Luo, Xi
关键词：
不同层次的脑分割通常表示为层次树。特定水平的脑区域萎缩与阿尔茨海默病的预后关系不大。在这项研究中，我们提出了一个遵循层次树结构的预测器的E11型正则化。考虑到树作为一个有向无环图，我们解释模型参数从路径分析的角度。根据这一概念，拟议的惩罚规定了每个预测因素对结果的总体影响。在正则性条件下，证明了在所提出的正则化下，模型系数的估计在范数下是相容的，模型选择也是相容的。当应用于从阿尔茨海默病神经成像倡议（ADNI）获得的脑sMRI数据集时，所提出的方法识别出这些区域中的萎缩表明记忆衰退的脑区域。随着对总效应的规则化，研究结果表明，萎缩对记忆缺陷的影响局限于小的大脑区域，但在不同水平的大脑分割。本文的数据来自ADNI数据库。
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 Alzheimer’s Disease Neuroimaging Initiative (ADNI), 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 paper were obtained from the ADNI database.
登录
查看更多内容
影响因子：
3.2
作者：
Crane, Paul K.;Carle, Adam;Gibbons, Laura E.;Insel, Philip;Mackin, R. Scott;Gross, Alden;Jones, Richard N.;Mukherjee, Shubhabrata;Curtis, S. McKay;Harvey, Danielle;Weiner, Michael;Mungas, Dan
通讯作者：
Mungas, Dan
影响因子：
1.7
作者：
Shojaie, Ali;Michailidis, George
通讯作者：
Michailidis, George
影响因子：
5.8
作者：
Li, Caiyan;Li, Hongzhe
通讯作者：
Li, Hongzhe
影响因子：
5.8
作者：
Chen, Li;Liu, Han;Chen, Jun
通讯作者：
Chen, Jun
影响因子：
5.7
作者：
Doshi J;Erus G;Ou Y;Resnick SM;Gur RC;Gur RE;Satterthwaite TD;Furth S;Davatzikos C;Alzheimer's Neuroimaging Initiative
通讯作者：
Alzheimer's Neuroimaging Initiative