Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes

by   Clement Abi Nader, et al.

Alzheimer's disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain's morphology. Although the disease evolution spans decades, to date we cannot rely on long-term data to model the pathological progression, since most of the available measures are on a short-term scale. It is therefore difficult to understand and quantify the temporal progression patterns affecting the brain regions across the AD evolution. In this work, we tackle this problem by presenting a generative model based on probabilistic matrix factorization across temporal and spatial sources. The proposed method addresses the problem of disease progression modelling by introducing clinically-inspired statistical priors. To promote smoothness in time and model plausible pathological evolutions, the temporal sources are defined as monotonic and independent Gaussian Processes. We also estimate an individual time-shift parameter for each patient to automatically position him/her along the sources time-axis. To encode the spatial continuity of the brain sub-structures, the spatial sources are modeled as Gaussian random fields. We test our algorithm on grey matter maps extracted from brain structural images. The experiments highlight differential temporal progression patterns mapping brain regions key to the AD pathology, and reveal a disease-specific time scale associated with the decline of volumetric biomarkers across clinical stages.


page 6

page 7


Monotonic Gaussian Process for Spatio-Temporal Trajectory Separation in Brain Imaging Data

We introduce a probabilistic generative model for disentangling spatio-t...

Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy

In order to find effective treatments for Alzheimer's disease (AD), we n...

A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments

In this study we propose a deformation-based framework to jointly model ...

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

Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIV...

Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling

Alzheimer's Disease (AD) is characterized by a cascade of biomarkers bec...

Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia

Event-based models (EBM) are a class of disease progression models that ...

A spatially varying change points model for monitoring glaucoma progression using visual field data

Glaucoma disease progression, as measured by visual field (VF) data, is ...

Please sign up or login with your details

Forgot password? Click here to reset