A network psychometric approach to neurocognition in early Alzheimer's disease.

In a typical pattern of Alzheimer's disease onset, episodic memory decline is predominant while decline in other neurocognitive domains is subsidiary or absent. Such descriptions refer to relationships between neurocognitive domains as well as deficits within domains. However, the former relationships are rarely statistically modelled. This study used psychometric network analysis to model relationships between neurocognitive variables in cognitive normality (CN), amnestic mild cognitive impairment (aMCI), and early Alzheimer's disease (eAD). Gaussian graphical models with extended Bayesian information criterion graphical lasso model selection and regularisation were used to estimate network models of neurocognitive and demographic variables in CN (n = 229), aMCI (n = 395), and eAD (n = 191) groups. The edge density, network strength and structure, centrality, and individual links of the network models were explored. Results indicated that while global strength did not differ, network structures differed across CN and eAD and aMCI and eAD groups, suggesting neurocognitive reorganisation across the eAD continuum. Episodic memory variables were most central (i.e., influential) in the aMCI network model, whereas processing speed and fluency variables were most central in the eAD network model. Additionally, putative clusters of memory, language and semantic variables, and attention, processing speed and working memory variables arose in the models for the clinical groups. This exploratory study shows how psychometric network analysis can be used to model the relationships between neurocognitive variables across the eAD continuum and to generate hypotheses for future (dis)confirmatory research. Crown