  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.
