Home banner
A-Z Index

Quick way to the find the information that you need...

More button
Register with FRAME

Although you do not need to register, any information you provide will be confidential and used only by FRAME to improve the website

Register button
Account Login
Forgot password?

The Journal


Alternatives to Laboratory Animals - ATLA

Download latest issue button Download back issues button Subscribe to ATLA
Contact Us

Tel icon

Tel: +44 (0)115 9584740

Tel icon

Fax: +44 (0)115 9503570

Make an Enquiry

Statistical aspects of quantitative image analysis of beta-amyloid in the APP(V717F) transgenic mouse model of Alzheimer's disease.

Fishman, C.E., Cummins, D.J., Bales, K.R., DeLong, C.A., Esterman, M.A., Hanson, J.C., White, S.L., Paul, S.M. and Jordan, W.H.

Journal of Neuroscience Methods, 108(2), 145-152 (2001).

Cerebral beta -amyloidosis is a central part of the neuropathology of Alzheimer's disease (AD). Quantitation of beta -amyloid plaques in the human AD brain, and in animal models of AD, is an important study endpoint in AD research. Methodologic approaches to the measurement of beta -amyloid in the brain vary between investigators, and these differences affect outcome measures. Here, one quantitative approach to the measurement of beta -amyloid plaques in brain sections was analyzed for sources of variability due to sampling. Brain tissue was from homozygous APP(V717F) transgenic male mice. Sampling variables were at the mouse and microscopic slide and field levels. Results indicated that phenotypic variability in the mouse sample population was the largest contributor to the standard error of the analyses. Within each mouse, variability between slides or between fields within slides had smaller effects on the error of the analyses. Therefore, when designing studies of adequate power, in this and in other similar models of cerebral beta -amyloidosis, sufficient numbers of mice per group must be included in order for change in mean plaque burden attributable to an experimental variable to outweigh phenotypic variability.