Speaker
Description
Alzheimer’s disease (AD) is the most common form of progressive neurodegenerative dementia and a leading cause of death worldwide. The definitive cause of AD remains unknown, but its development is a multifaceted etiology. Early AD diagnosis is crucial as pathology begins decades before symptoms appear, and its diagnosis can only be confirmed post-mortem. Imaging techniques such as magnetic resonance imaging (MRI) provide insight into the distinct patterns of brain inflammation, followed by degeneration. In both clinical and preclinical imaging, MRI enables non-invasive, longitudinal, volume-based quantification of regions of interest with sensitivity to multiple tissue properties. Despite advances in preclinical imaging, there is limited consensus on standardized, quantitative methods for assigning anatomical labels to brain regions in mouse models relating to cognitive decline. From a medical physics perspective, reliable anatomical labeling is essential for quantitative MRI, yet automated pipelines remain under-validated in preclinical disease models.
This project addresses this challenge using the most aggressive disease model, 5XFAD transgenic mice, to detect early differences in brain anatomy. The primary objective is to determine whether MRI can be used as an early diagnostic tool by detecting age-dependent structural changes across the lifespan using a 1 T M2™ Compact High-Performance MRI System (Aspect Imaging) and a 3D imaging method. A major challenge in pre-clinical studies is the lack of accessibility and interrater variability in quantifying neurodegeneration. This project explores diverse MRI processing strategies, including manual segmentation using the Allen Mouse Brain Atlas and the computational pipeline AIDAmri. By exploring a variety of techniques, the anticipated outcome is to evaluate the robustness through cross-method agreement, age-dependent consistency, and sensitivity to preprocessing choices in the 5XFAD model, not previously characterized by the AIDAmri pipeline. This research advances techniques for automated anatomical labeling in preclinical MRI and supports the development of standardized imaging pipelines applicable to longitudinal therapeutic investigations of neurodegeneration.
| Keyword-1 | Magnetic Resonance Imaging |
|---|---|
| Keyword-2 | Alzheimer's Disease |
| Keyword-3 | Diagnostic Methodology |