Statistical approaches for the study of cognitive and brain aging

Frontiers in Aging Neuroscience

Guanqun Cao¹  Bingxin Zhao²  Ronald Cohen²  Andrew O'Shea²  Huaihou Chen²  Adam J Woods²  Eric C Porges² 

[1] Auburn University;University of Florida;

关键词: functional connectivity;    Graphical Model;    structural covariance;    Semiparametric model;    penalized regression methods;   

DOI  :  10.3389/fnagi.2016.00176

来源: DOAJ

【 摘 要 】

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 are inadequate in capturing the potential nonlinear age trends. 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 the 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.

【 授权许可】

Unknown