Use of multimetric statistical analysis to characterize and discriminate between the performance of four Alzheimer’s transgenic mouse lines differing in Aβ deposition

Abstract Behavioral assessment of genetically-manipulated mouse lines for Alzheimer’s disease has become an important index for determining the efficacy of therapeutic interventions and examining disease pathogenesis. However, the potential for higher level statistical analyses to assist in these goals remains largely unexplored. The present study thus involved multimetric statistical analyses of behavioral and β-amyloid (Aβ) deposition measures from four PDAPP-derived transgenic mouse lines that differ in extent of Aβ deposition. For all four lines, multiple behavioral measures obtained from a comprehensive task battery administered at 15–16 months of age were collectively examined by correlation, factor, and discriminant function analyses. In addition, both compact and total β-amyloid (Aβ) histologic measures were determined from the same animals. Widespread intra- and inter-task correlations were evident, with impairment in all three water tasks (Morris maze, platform recognition, and radial arm water maze) correlating extensively with Aβ deposition in hippocampus and cerebral cortex. By elucidating the underlying relationships among measures, factor analysis revealed a single primary factor (Factor 1) that loaded most cognitive measures, particularly those for working memory and recognition. Aβ deposition measures loaded exclusively on this primary factor. In individual animals, only factor scores derived from this primary factor were correlated with Aβ deposition. Both of these findings again underscore the association between cognitive impairment and Aβ deposition. Finally, discriminant function analysis (step-wise forward method) was able to distinguish between all four AD transgenic lines based on behavioral performance alone, as well as when Aβ deposition measures were included. Our results demonstrate the utility of higher level, multimetric analysis of behavioral measures from AD transgenic mice. Analyses such as these will be very beneficial for the functional evaluation of therapeutic interventions against AD and for finding behavioral measures that can serve as predictors of pathology.