Normative Diffusion Autoencoders: Application to Amyotrophic Lateral Sclerosis

TL;DR

This paper introduces a novel normative diffusion autoencoder framework that combines generative and normative modeling techniques, significantly improving survival prediction accuracy in ALS using MRI data.

Contribution

It is the first to apply normative modeling within a diffusion autoencoder and to use this approach for ALS prognosis, enhancing predictive performance.

Findings

Outperforms existing normative and generative models in ALS survival prediction

Demonstrates the effectiveness of combining generative and normative modeling techniques

Provides a new approach for leveraging MRI data in neurodegenerative disease prognosis

Abstract

Predicting survival in Amyotrophic Lateral Sclerosis (ALS) is a challenging task. Magnetic resonance imaging (MRI) data provide in vivo insight into brain health, but the low prevalence of the condition and resultant data scarcity limit training set sizes for prediction models. Survival models are further hindered by the subtle and often highly localised profile of ALS-related neurodegeneration. Normative models present a solution as they increase statistical power by leveraging large healthy cohorts. Separately, diffusion models excel in capturing the semantics embedded within images including subtle signs of accelerated brain ageing, which may help predict survival in ALS. Here, we combine the benefits of generative and normative modelling by introducing the normative diffusion autoencoder framework. To our knowledge, this is the first use of normative modelling within a diffusion autoencoder, as well as the first application of normative modelling to ALS. Our approach outperforms generative and non-generative normative modelling benchmarks in ALS prognostication, demonstrating enhanced predictive accuracy in the context of ALS survival prediction and normative modelling in general.