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

TL;DR

This paper introduces a probabilistic matrix factorization model using Gaussian processes and spatial priors to analyze and stage Alzheimer's disease progression from short-term brain imaging data, revealing key temporal and spatial patterns.

Contribution

It presents a novel generative model combining Gaussian processes and spatial priors for disease progression analysis, accommodating short-term data and individual patient timing.

Findings

Identifies differential progression patterns in brain regions affected by AD.

Reveals a disease-specific time scale linked to biomarker decline.

Demonstrates the model's ability to map disease stages from structural brain images.

Abstract

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.