It is hypothesized that changes in brain tissue microstructure, particularly degradation of neurites (i.e,. axons and dendrites) and synapses, are early drivers of Alzheimer's disease (AD) pathogenesis. Quantitative magnetic resonance imaging (MRI) tools like diffusion tensor imaging (DTI) have long been used to study AD pathogenesis. Using DTI metrics, structural insights of neuro tissue can be inferred but not directly measured. DTI has proven to be an effective tool indicating fractional anisotrophy (FA) differences between groups of varying AD risk factor, but it does not explicitly quantify pathophysiologically-relevant features like neurite density and complexity. This study aims to develop and validate an advanced diffusion MRI acquisition and biophysical modeling platform that can be used to explicitly quantify changes to brain tissue microstructure, specifically neurite density and complexity. Ultimately, this platform will be used to study the pathogenic mechanisms that drive AD in the pre-clinical and clinical setting.

Alzheimer's disease (AD) is a neurodegenerative disease that damages the cognitive abilities of a patient. It is critical to diagnose AD early to begin treatment as soon as possible which can be done through biomarkers. One such biomarker is the beta-amyloid (Aβ) peptide which can be quantified using the centiloid (CL) scale. For identifying the Aβ biomarker, A deep learning model that can model AD progression by predicting the CL value for brain magnetic resonance images (MRIs) is proposed. Brain MRI images can be obtained through the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets, however a single model cannot perform well on both datasets at once. Thus, A regularization-based continuous learning framework to perform domain adaptation on the previous model is also proposed which captures the latent information about the relationship between Aβ and AD progression within both datasets.