title: Use of multimetric statistical analysis to characterize and discriminate between the performance of four Alzheimer's transgenic mouse lines differing in Abeta 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 beta-amyloid (Abeta) deposition measures from four PDAPP-derived transgenic mouse lines that differ in extent of Abeta 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 beta-amyloid (Abeta) 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 Abeta 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. Abeta deposition measures loaded exclusively on this primary factor. In individual animals, only factor scores derived from this primary factor were correlated with Abeta deposition. Both of these findings again underscore the association between cognitive impairment and Abeta 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 Abeta 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.
authors.affiliation: Memory and Aging Research Laboratory, SCA 110, University of South Florida, Tampa, FL 33620, USA.
authors.forename: Ralph E
authors.initials: RE
authors.lastname: Leighty
authors.forename: Lars N G
authors.initials: LN
authors.lastname: Nilsson
authors.forename: Huntington
authors.initials: H
authors.lastname: Potter
authors.forename: David A
authors.initials: DA
authors.lastname: Costa
authors.forename: Mark A
authors.initials: MA
authors.lastname: Low
authors.forename: Kelly R
authors.initials: KR
authors.lastname: Bales
authors.forename: Steven M
authors.initials: SM
authors.lastname: Paul
authors.forename: Gary W
authors.initials: GW
authors.lastname: Arendash
mesh_terms: D000544:Alzheimer Disease / Q000378:metabolism / Q000503:physiopathology*; D016229:Amyloid beta-Peptides / Q000235:genetics / Q000378:metabolism*; D016564:Amyloid beta-Protein Precursor / Q000235:genetics; D000818:Animals; D001362:Avoidance Learning / Q000502:physiology; D001522:Behavior, Animal / Q000502:physiology; D003071:Cognition / Q000502:physiology; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D004195:Disease Models, Animal; D005163:Factor Analysis, Statistical; D005824:Genetics, Behavioral; D006624:Hippocampus / Q000378:metabolism; D006801:Humans; D018782:Maze Learning / Q000502:physiology; D051379:Mice; D008822:Mice, Transgenic; D009043:Motor Activity / Q000235:genetics; D011597:Psychomotor Performance / Q000502:physiology; D012044:Regression Analysis; D013028:Space Perception / Q000502:physiology
chemical_list: D016229:Amyloid beta-Peptides; D016564:Amyloid beta-Protein Precursor