A new multiple-mediator model maximally uncovering the mediation pathway: Evaluating the role of neuroimaging measures in age-related cognitive decline

Abstract

Aging changes brain functions and structures in a downward trajectory and consequently leads to a decline in neurocognitive performance. Our research is motivated by understanding whether and to what extent the age-effect on cognitive decline can be explained by neuroimaging measures. We consider a new mediation model with age as an independent variable, while treating neuroimaging data and cognitive function as the multiple mediators and outcome, respectively. Given that the brain is the primary organ responsible for cognitive function, it is neurobiologically intuitive that the age-related decline in cognition is largely mediated through neuroimaging measures. Additionally, cognitive function is localized to certain regions of the brain rather than being a function of the entire brain. Taking these factors into account, we propose a novel mediation model with multiple mediators that aims to maximally uncover the mediation pathway while simultaneously identifying active neuroimaging mediators by imposing an ${\ell }_1$ penalty and ${\ell }_2$ constraint. We develop a computationally efficient algorithm to handle the nonconvex optimization problem of penalized mediation proportion maximization. We apply our method to a data example of 37,441 participants of UK Biobank with cortical gray-matter thickness and white-matter integrity measures and cognitive performance scores. Our results show that the mediation effect of brain-imaging variables can explain 97% of age-related cognitive decline.