Poster Presentation 7
DTI-based Analysis of APP/PS1 Mouse Brains as a Model of
Progressive Alzheimer’s Disease
1
David Hike1,2, Abdol Aziz Ould Ismail1,2 and Samuel C. Grant
Department of Chemical & Biomedical Engineering, FAMU–FSU College of Engineering
2
CIMAR, National High Magnetic Field Laboratory
Florida State University, Tallahassee FL, USA
Alzheimer’s disease is the most common form of dementia. It is characterized by memory loss, changes in
behavior, and difficulty thinking1. Currently, over 5 million people in the US have Alzheimer’s disease, and
there is no known cure1. In 2012, it was estimated that $200 billion was spent in direct costs for Alzheimer’s
disease while in 2011, unpaid care by family members and friends exceeded $210 billion2.
Magnetic Resonance Imaging potentially plays an important role in the diagnosis of Alzheimer’s disease (AD)
with its high resolution and non-invasive nature. Here, we focus on angular resolution to map water diffusion
using DTI. We used 18 diffusion directions and 4 unweighted directions. We used an 11.75-T, 500-MHz MRI
scanner located at the FAMU-FSU College of Engineering and collected data using APP/PS1 mouse models
with 2 variables (age and AD). Our in-plane resolution was 100 x 100 microns with a matrix size of 256 x 256.
Repetition timae of 2 sec and an echo time of 30 ms utilizing acquisition parameters of Δ=21ms and δ=3ms
with 15 averages provided us with a high signal-to-noise ratios and an approximate acquisition time of 47 hours
per sample. The study used all female brains fixed with 4% paraformaldehyde.
The six main areas we focused on were the dentate gyrus, fornix columns, corpus callosum, putamen, frontal
cortex, and temporal cortex. Our reports indicate a significant decrease in FA in the temporal cortex. As we
could not detect plaques utilizing gradient echo scans, we relied on detecting significant stages at early ages
using high resolution and high angular DTI approach. We have conducted a preliminary graph theoretical
analysis in which we use a binary approach to study the structural connectivity in the models. Additionally, we
detected significant increase in the c \