Data-Driven Approaches to Executive Function Performance and Structure in Aging: Integrating Person-Centered Analyses and Machine Learning Risk Prediction

Objective: Executive function (EF) performance and structure in nondemented aging are frequently examined with variable-centered approaches. Person-centered analytics can contribute unique information about classes of persons by simultaneously considering EF performance and structure. The risk predictors of these classes can then be determined by machine learning technology. Using data from the Victoria Longitudinal Study we examined two goals: (a) detect different underlying subgroups (or classes) of EF performance and structure and (b) test multiple risk predictors for best discrimination of these detected subgroups. Method: We used a classification sample (n = 778; Mage = 71.42) for the first goal and a prediction subsample (n = 570; Mage = 70.10) for the second goal. Eight neuropsychological measures represented three EF dimensions (inhibition, updating, shifting). Fifteen predictors represented five domains (genetic, functional, lifestyle, mobility, demographic). Results: First, we observed two distinct classes: (a) lower EF performance and unidimensional structure (Class 1) and (b) higher EF performance and multidimensional structure (Class 2). Second, Class 2 was predicted by younger age, more novel cognitive activity, more education, lower body mass index, lower pulse pressure, female sex, faster balance, and more physical activity. Conclusions: Data-driven modeling approaches tested the possibility of an EF aging class that displayed both preserved EF performance levels and sustained multidimensional structure. The two observed classes differed in both performance level (lower, higher) and structure (unidimensional, multidimensional). Machine learning prediction analyses showed that the higher performing and multidimensional class was associated with multiple brain health-related protective factors.