Joint graph embedding applied to networks estimated from correlations in volumetric data reveal Alzheimers neurodegeneration patterns

Neurodegeneration measured through volumetry in MRI is recognized as a potential Alzheimer’s Disease (AD) biomarker, but its utility is limited by lack of specificity. Quantifying spatial patterns of neurodegeneration, rather than a few structures of interest, may help address this. We turn to network based analyses developed for studying functional connectivity from signal correlations, measured through fMRI on the time scale of seconds. Our contribution applies these methods to studying volume‐change correlations, measured through structural MRI on the time scale of years.