title: Statistical Approaches for the Study of Cognitive and Brain Aging.
abstract: 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.
authors.affiliation: Department of Biostatistics, University of FloridaGainesville, FL, USA; Department of Aging and Geriatric Research, Center for Cognitive Aging and Memory, Institute on Aging, McKnight Brain Institute, University of FloridaGainesville, FL, USA.
authors.forename: Huaihou
authors.initials: H
authors.lastname: Chen
authors.affiliation: Department of Aging and Geriatric Research, Center for Cognitive Aging and Memory, Institute on Aging, McKnight Brain Institute, University of Florida Gainesville, FL, USA.
authors.forename: Bingxin
authors.initials: B
authors.lastname: Zhao
authors.affiliation: Department of Mathematics and Statistics, Auburn University Auburn, AL, USA.
authors.forename: Guanqun
authors.initials: G
authors.lastname: Cao
authors.affiliation: Department of Aging and Geriatric Research, Center for Cognitive Aging and Memory, Institute on Aging, McKnight Brain Institute, University of Florida Gainesville, FL, USA.
authors.forename: Eric C
authors.initials: EC
authors.lastname: Proges
authors.affiliation: Department of Aging and Geriatric Research, Center for Cognitive Aging and Memory, Institute on Aging, McKnight Brain Institute, University of Florida Gainesville, FL, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: O'Shea
authors.affiliation: Department of Aging and Geriatric Research, Center for Cognitive Aging and Memory, Institute on Aging, McKnight Brain Institute, University of Florida Gainesville, FL, USA.
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Woods
authors.affiliation: Department of Aging and Geriatric Research, Center for Cognitive Aging and Memory, Institute on Aging, McKnight Brain Institute, University of Florida Gainesville, FL, USA.
authors.forename: Ronald A
authors.initials: RA
authors.lastname: Cohen
keywords: functional connectivity; graphical model; penalized regression methods; semiparametric model; structural covariance