期刊
FRONTIERS IN AGING NEUROSCIENCE
卷 8, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2016.00176
关键词
semiparametric model; graphical model; penalized regression methods; structural covariance; functional connectivity
资金
- McKnight Brain Research Foundation
- Center for Cognitive Aging and Memory at the University of Florida
- Claude D. Pepper Center at the University of Florida [P30 AG028740]
- Simons Foundation [354917]
Neuroimaging studies of cognitive and brain aging often yield massive datasets that create many analytic and statistical challenges. In this paper, we discuss and address several limitations in the existing work. (1) Linear models are often used to model the age effects on neuroimaging markers, which may be inadequate in capturing the potential nonlinear age effects. (2) Marginal correlations are often used in brain network analysis, which are not efficient in characterizing a complex brain network. (3) Due to the challenge of high-dimensionality, only a small subset of the regional neuroimaging markers is considered in a prediction model, which could miss important regional markers. To overcome those obstacles, we introduce several advanced statistical methods for analyzing data from cognitive and brain aging studies. Specifically, we introduce semiparametric models for modeling age effects, graphical models for brain network analysis, and penalized regression methods for selecting the most important markers in predicting cognitive outcomes. We illustrate these methods using the healthy aging data from the Active Brain Study.
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