The overall goal of the proposed research is to develop in vivo imaging methods for plaque detection and classification in transgenic mice using SPECT or alternative nuclear imaging modalities. The reduction of beta-amyloid (A2) plaque burden is one of the main therapeutic objectives for the treatment of Alzheimer's disease (AD). Transgenic mouse models of AD have been created, and in vivo imaging of A2 in a mouse brain would facilitate the study of plaque development and testing of drugs that could potentially cure AD in human. However, the heterogeneous plaque structure of micro-scale range makes an in vivo approach to detecting and quantifying the plaque distribution challenging due to insufficient sensitivity and/or resolution of imaging systems or a lack of good contrast mechanism or radiotracer specificity. These problems can be largely overcome by using novel computational algorithms based on mathematical observers and stochastic object models as an alternative technique to image reconstruction. These algorithms take advantage of prior knowledge about plaque distributions and the noise characteristics of an imaging system to make classification decisions that are based on the best match between the observed data and the underlying stage of plaque burden. The candidate's training in the K99 phase will provide skills to establish herself in this interdisciplinary field, and is also a logical extension of her past experiences in SPECT/PET instrumentation and image reconstruction. This training is adapted to the candidate's needs in acquiring knowledge and skills in neuroscience. In addition to didactic training, she will obtain laboratory training in plaque studies and small-animal imaging of transgenic mouse models of AD under the supervision of mentors at Vanderbilt. This training is well-coordinated with the current research of these mentors, and will be carried out as a part of specific aim I.a, where the stereological plaque studies will help to define a statistical object model of plaque distributions.
Specific aim I. a will also include computational studies of binary classification methods to detect the presence of simulated plaque distributions from noisy projection data. The objective of aim I.b is to further advance the candidate's skill in developing system models for SPECT scanners. This training together with he past training in PET systems will allow her to perform the R00 research with the most suitable SPECT or, alternatively, PET systems. By the end of the K99 phase, she will have the skills for developing object models and classification methods.
Specific aims II. a and II.b outline R00 studies to explore the feasibility of using a three-class observer analysis with both experimental phantoms and animal scans. The ultimate goal of this study is to acquire scans of transgenic mice from different age groups and to accurately classify the stage of plaque burden. The candidate's long-term goal is to develop signal detection and pattern recognition tools to overcome the practical limitations of current radiotracers while maintaining her focus on their implementation in AD research.
My studies propose new computational techniques for conducting molecular imaging of Alzheimer's disease (AD) using transgenic mouse models. These techniques are likely to be applicable for studying new radiotracers and therapies for AD. There is a potential for translating these methods to human for detecting AD at very early stages.
|Shokouhi, Sepideh; Claassen, Daniel; Kang, Hakmook et al. (2013) Longitudinal progression of cognitive decline correlates with changes in the spatial pattern of brain 18F-FDG PET. J Nucl Med 54:1564-9|