利用混合专家积变分自编码器改进多模态神经影像数据的规范建模
Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders
AI总结：
本文提出利用混合专家积变分自编码器改进多模态神经影像数据的规范建模，以更准确地识别异常个体及异常脑区。
摘要：
神经影像中的规范模型学习健康人群大脑模式的分布，并估计如阿尔茨海默病（AD）等疾病患者的偏离程度。现有的基于变分自编码器（VAE）的规范模型使用多模态神经影像数据时，通过估计单模态潜在后验的乘积或平均来聚合信息。这通常会导致信息量不足的联合潜在分布，影响对个体偏离程度的估计。在本工作中，我们通过采用混合专家积（MoPoE）技术来解决先前的限制，该技术能够更好地建模联合潜在后验。我们的模型通过计算个体在多模态潜在空间中的偏离程度来标记异常个体。此外，我们识别出哪些潜在维度和脑区与AD病理学导致的异常偏离有关。
英文摘要：
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.
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