Normative models in neuroimaging learn the brain patterns of healthy
population distribution and estimate how disease subjects like Alzheimer's
Disease (AD) deviate from the norm. Existing variational autoencoder
(VAE)-based normative models using multimodal neuroimaging data aggregate
information from multiple modalities by estimating product or averaging of
unimodal latent posteriors. This can often lead to uninformative joint latent
distributions which affects the estimation of subject-level deviations. In this
work, we addressed the prior limitations by adopting the
Mixture-of-Product-of-Experts (MoPoE) technique which allows better modelling
of the joint latent posterior. Our model labelled subjects as outliers by
calculating deviations from the multimodal latent space. Further, we identified
which latent dimensions and brain regions were associated with abnormal
deviations due to AD pathology.

通过采用混合乘积专家（MoPoE）技术，我们提出了一种改进的范式模型，该模型利用多模态神经影像数据对联合潜在后验进行更好的建模，并计算从多模态潜在空间由于阿尔茨海默病（AD）病理而产生的异常偏差。

基于混合乘积专家变分自编码器的多模态神经影像数据规范建模改进

Improving Normative Modeling for Multi-modal Neuroimaging Data using  mixture-of-product-of-experts variational autoencoders

Despite the impact of psychiatric disorders on clinical health, early-stage
diagnosis remains a challenge. Machine learning studies have shown that
classifiers tend to be overly narrow in the diagnosis prediction task. The
overlap between conditions leads to high heterogeneity among participants that
is not adequately captured by classification models. To address this issue,
normative approaches have surged as an alternative method. By using a
generative model to learn the distribution of healthy brain data patterns, we
can identify the presence of pathologies as deviations or outliers from the
distribution learned by the model. In particular, deep generative models showed
great results as normative models to identify neurological lesions in the
brain. However, unlike most neurological lesions, psychiatric disorders present
subtle changes widespread in several brain regions, making these alterations
challenging to identify. In this work, we evaluate the performance of
transformer-based normative models to detect subtle brain changes expressed in
adolescents and young adults. We trained our model on 3D MRI scans of
neurotypical individuals (N=1,765). Then, we obtained the likelihood of
neurotypical controls and psychiatric patients with early-stage schizophrenia
from an independent dataset (N=93) from the Human Connectome Project. Using the
predicted likelihood of the scans as a proxy for a normative score, we obtained
an AUROC of 0.82 when assessing the difference between controls and individuals
with early-stage schizophrenia. Our approach surpassed recent normative methods
based on brain age and Gaussian Process, showing the promising use of deep
generative models to help in individualised analyses.

本文介绍如何通过使用深度生成模型实现困难诊断的心理障碍，包括早期精神分裂症，对脑区的遍及性微小变化进行检测，产生了良好的效果，优于基于脑年龄和高斯过程的最近的规范方法。