  Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is
an image-based disease progression model with single-vertex resolution,
designed to reconstruct long-term patterns of brain pathology from short-term
longitudinal data sets. DIVE clusters vertex-wise biomarker measurements on the
cortical surface that have similar temporal dynamics across a patient
population, and concurrently estimates an average trajectory of vertex
measurements in each cluster. DIVE uniquely outputs a parcellation of the
cortex into areas with common progression patterns, leading to a new signature
for individual diseases. DIVE further estimates the disease stage and
progression speed for every visit of every subject, potentially enhancing
stratification for clinical trials or management. On simulated data, DIVE can
recover ground truth clusters and their underlying trajectory, provided the
average trajectories are sufficiently different between clusters. We
demonstrate DIVE on data from two cohorts: the Alzheimer's Disease Neuroimaging
Initiative (ADNI) and the Dementia Research Centre (DRC), UK, containing
patients with Posterior Cortical Atrophy (PCA) as well as typical Alzheimer's
disease (tAD). DIVE finds similar spatial patterns of atrophy for tAD subjects
in the two independent datasets (ADNI and DRC), and further reveals distinct
patterns of pathology in different diseases (tAD vs PCA) and for distinct types
of biomarker data: cortical thickness from Magnetic Resonance Imaging (MRI) vs
amyloid load from Positron Emission Tomography (PET). Finally, DIVE can be used
to estimate a fine-grained spatial distribution of pathology in the brain using
any kind of voxelwise or vertexwise measures including Jacobian compression
maps, fractional anisotropy (FA) maps from diffusion imaging or other PET
measures. DIVE source code is available online:
https://github.com/mrazvan22/dive
