Journal Article

DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders

Garbarino, Sara, Lorenzi, Marco, Oxtoby, Neil P., Eshaghi, Arman, Marinescu, Răzvan V., Young, Alexandra L., Alexander, Daniel C., Crutch, Sebastian J.

2019

Overview

Current models of progression in neurodegenerative diseases use neuroimaging measures that are averaged across pre-defined regions of interest (ROIs). Such models are unable to recover fine details of atrophy patterns; they tend to impose an assumption of strong spatial correlation within each ROI and no correlation among ROIs. Such assumptions may be violated by the influence of underlying brain network connectivity on pathology propagation – a strong hypothesis e.g. in Alzheimer's Disease. 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 (i.e. point-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. The DRC cohort contains 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). We demonstrate that DIVE stages have potential clinical relevance, despite being based only on imaging data, by showing that the stages correlate with cognitive test scores. 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 tensor imaging (DTI) or other PET measures. [Display omitted] •DIVE estimates fine-grained spatial patterns of pathology in neurodegenerative diseases, not available with ROI-based methods.•Further estimates the temporal evolution of brain pathology and subject specific disease stages.•Forecasts the evolution of pathology at population- and subject-level.•Finds plausible patterns of pathology in four distinct datasets comprising different diseases and modalities.•DIVE, available online, can be applied to any registered voxelwise images (e.g.MRI, PET, DTI).

• Publisher: Elsevier Inc,Elsevier Limited,Elsevier
• Subject: Age / Alzheimer's disease / Amyloid / Anisotropy / Atrophy / Biomarkers / Brain mapping / Brain research / Clinical trials / Cognitive ability / Compression / Computer Science / Cortical thickness / Dementia / Dementia disorders / Disease Progression / Disease progression model / Grants / Humans / Magnetic resonance imaging / Medical Imaging / Models, Neurological / Neural networks / Neurodegenerative diseases / Neurodegenerative Diseases - diagnostic imaging / Neurodegenerative Diseases - pathology / Neuroimaging / Neuroimaging - methods / NMR / Nuclear magnetic resonance / Pathology / Positron emission tomography / Posterior cortical atrophy / Scholarships & fellowships / Spatial distribution / Temporal cortex / Vertex-wise measures