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.

翻译：预测肌萎缩侧索硬化症（ALS）患者的生存期是一项具有挑战性的任务。磁共振成像（MRI）数据提供了大脑健康的体内洞察，但该疾病的低患病率及由此导致的数据稀缺性限制了预测模型的训练集规模。生存模型进一步受到ALS相关神经退行性病变的细微且通常高度局部化特征的阻碍。规范性模型提供了一种解决方案，因为它们通过利用大型健康队列来增强统计功效。另一方面，扩散模型擅长捕捉图像中嵌入的语义信息，包括加速大脑衰老的细微迹象，这可能有助于预测ALS患者的生存期。在此，我们通过引入规范性扩散自编码器框架，结合了生成模型与规范性建模的优势。据我们所知，这是首次在扩散自编码器中使用规范性建模，也是规范性建模在ALS中的首次应用。我们的方法在ALS预后预测中优于生成性和非生成性规范性建模基准，在ALS生存预测及一般性规范性建模的背景下展现了更高的预测准确性。