Abstract
Alzheimer's disease (AD) is characterized by amyloidosis of brain tissues. This phenomenon is studied with genetically-modified mouse models. We propose a method to quantify amyloidosis in whole 5xFAD mouse brains, a model of AD. We use optical projection tomography (OPT) and a random forest voxel classifier to segment and measure amyloid plaques. We validate our method in a preliminary cross-sectional study, where we measure 6136 ± 1637, 8477 ± 3438, and 17267 ± 4241 plaques (AVG ± SD) at 11, 17, and 31 weeks. Overall, this method can be used in the evaluation of new treatments against AD.
Highlights
Amyloidosis in brain tissues is associated with several neurodegenerative diseases, including Parkinson’s and Alzheimer’s disease (AD)
We propose a supervised learning pipeline relying on random forest voxel classifiers to segment and quantify amyloid plaques in whole 5xFAD mouse brains of different ages acquired with optical projection tomography (OPT)
The pipeline is applied to whole brain images of 5xFAD mice, a mouse model of AD
Summary
Amyloidosis in brain tissues is associated with several neurodegenerative diseases, including Parkinson’s and AD. In AD, toxic extracellular aggregates of a truncated and misfolded amyloid precursor protein form deposits known as amyloid plaques [1, 2]. These plaques have a spherical shape, and their size varies between approximately ten and one hundred micrometers. The mechanisms of plaque formation and their consequences remain elusive and are commonly studied in genetically modified rodent models, such as the 5xFAD mouse model [4]. Such models are designed to reproduce the age-dependent amyloid deposition observed in humans [5]
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