A Latent Auto-Regressive Approach for Bayesian Structural Equation Modeling of Spatially or Socially Dependent Data.

Spatial analytic approaches are classic models in econometric literature, but relatively new in social sciences. Spatial analysis models are synonymous with social network auto-regressive models which are also gaining popularity in the behavioral sciences. These models have two major benefits. First, dependent data, either socially or spatially, must be accounted for to acquire unbiased results. Second, analysis of the dependence provides rich additional information such as spillover effects. Structural Equation Models (SEM) are widely used in psychological research for measuring and testing multi-faceted constructs. So far, SEM that allow for spatial or social dependency are limited with regard to their flexibility, for example, when estimating nonlinear effects. Here, we provide a cohesive framework which can simultaneously estimate latent interaction/polynomial effects and account for spatial effects with both exogenous and endogenous latent variables, the Bayesian Spatial Auto-Regressive Structural Equation Model (BARDSEM). First, we briefly outline classic auto-regressive models. Next, we present the BARDSEM and introduce simulation results to exemplify its performance. Finally, we provide an empirical example using the spatially dependent extended US southern homicide data to show the rich interpretations that are possible using the BARDSEM. Finally, we discuss implications, limitations, and future research.

Lifestyle Affects Amyloid Burden and Cognition Differently in Men and Women.

OBJECTIVE: Evidence on associations of lifestyle factors with Alzheimer's pathology and cognition are ambiguous, potentially because they rarely addressed inter-relationships of factors and sex effects. While considering these aspects, we examined the relationships of lifestyle factors with brain amyloid burden and cognition. METHODS: We studied 178 cognitively normal individuals (women, 49%; 65.0 [7.6] years) and 54 individuals with mild cognitive impairment (women, 35%; 71.3 [8.3] years) enrolled in a prospective study of volunteers who completed 18 F-Flutemetamol amyloid positron emission tomography. Using structural equation modeling, we examined associations between latent constructs representing metabolic/vascular risk, physical activity, and cognitive activity with global amyloid burden and cognitive performance. Furthermore, we investigated the influence of sex in this model. RESULTS: Overall, higher cognitive activity was associated with better cognitive performance and higher physical activity was associated with lower amyloid burden. The latter association was weakened to a nonsignificant level after excluding multivariate outliers. Examination of the moderating effect of sex in the model revealed an inverse association of metabolic/vascular risk with cognition in men, whereas in women metabolic/vascular risk trended toward increased amyloid burden. Furthermore, a significant inverse association between physical activity and amyloid burden was found only in men. Inheritance of an APOE4 allele was associated with higher amyloid burden only in women. INTERPRETATION: Sex modifies effects of certain lifestyle-related factors on amyloid burden and cognition. Notably, our results suggest that the negative impact of metabolic/vascular risk influences the risk of cognitive decline and Alzheimer's disease through distinct paths in women and men. ANN NEUROL 2022;92:451-463.