MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer's Disease

Characterizing Alzheimer's disease (AD) progression remains a significant clinical challenge. The initial stages of AD are marked by the accumulation of amyloid-beta plaques and Tau tangles, with cognitive functions often appearing normal, and clinical symptoms may not manifest until up to 20 years after the prodromal period begins. Comprehensive longitudinal studies analyzing brain-wide structural connectomics in the early stages of AD, especially those with large sample sizes, are scarce. In this study, we investigated a longitudinal diffusion-weighted imaging dataset of 264 subjects to assess the predictive potential of diffusion data for AD. Our findings indicate the potential of a simple prognostic biomarker for disease progression based on the hemispheric lateralization of mean tract volume for tracts originating from the supramarginal and paracentral regions, achieving an accuracy of 86%, a sensitivity of 86%, and a specificity of 93% when combined with other clinical indicators. However, diffusion-weighted imaging measurements alone did not provide strong predictive accuracy for clinical variables, disease classification, or disease conversion. By conducting a comprehensive tract-by-tract analysis of diffusion-weighted characteristics contributing to the characterization of AD and its progression, our research elucidates the potential of diffusion MRI as a tool for the early detection and monitoring of neurodegenerative diseases and emphasizes the importance of integrating multi-modal data for enhanced predictive analytics.