Abstract
Latent growth curve (LGC) models estimate change over time in a cohort's serially obtained measurements. We have applied LGC techniques to a spatial distribution of Alzheimer's disease (AD) pathology using autopsy data from 435 participants in the Honolulu-Asia Aging Study. Neurofibrillary tangle (NFT) and neuritic plaques (NP) were distributed across differently ordered sets of anatomical regions. The gradient of spatial change in neuritic plaque (dNP), was significantly associated with that of neurofibrillary tangle (dNFT), but weakly and inversely (r = -0.12; p < 0.001). Both dNFT and dNP correlated significantly and inversely with Braak stage. Sixty-one percent of the variance in Braak stage was explained by dNFT independent of covariates. Only dNFT was significantly associated with longitudinal change in cognition. Only dNP was associated with apolipoprotein (APOE) e4 burden. This is the first application of LGC models to spatially-ordered data. The result is a quantification of the interindividual variation in the interregional vulnerability to Alzheimer's disease lesions.