One of the challenges of studying common neurological disorders is disease heterogeneity including differences in causes, neuroimaging characteristics, comorbidities, or genetic variation. Normative modelling has become a popular method for studying such cohorts where the 'normal' behaviour of a physiological system is modelled and can be used at subject level to detect deviations relating to disease pathology. For many heterogeneous diseases, we expect to observe abnormalities across a range of neuroimaging and biological variables. However, thus far, normative models have largely been developed for studying a single imaging modality. We aim to develop a multi-modal normative modelling framework where abnormality is aggregated across variables of multiple modalities and is better able to detect deviations than uni-modal baselines. We propose two multi-modal VAE normative models to detect subject level deviations across T1 and DTI data. Our proposed models were better able to detect diseased individuals, capture disease severity, and correlate with patient cognition than baseline approaches. We also propose a multivariate latent deviation metric, measuring deviations from the joint latent space, which outperformed feature-based metrics.

翻译：研究常见神经系统疾病的挑战之一在于疾病的异质性，包括病因差异、神经影像特征差异、共病差异或遗传变异差异。规范建模已成为研究此类人群的流行方法，该方法对生理系统的"正常"行为进行建模，并可在个体层面用于检测与疾病病理相关的偏差。对于许多异质性疾病，我们预期在多种神经影像和生物学变量中观察到异常。然而，迄今为止，规范模型主要针对单一成像模态开发。我们旨在构建一个多模态规范建模框架，在该框架中异常值跨多模态变量进行聚合，能够比单模态基线更有效地检测偏差。我们提出两种多模态VAE规范模型，用于检测T1和DTI数据中的个体水平偏差。与基线方法相比，我们提出的模型能更有效地识别患病个体、捕捉疾病严重程度，并与患者认知功能呈现更强相关性。我们还提出一种多变量潜在偏差度量指标，通过测量联合潜在空间中的偏差，其性能优于基于特征的度量指标。