在阿尔茨海默病中使用反思变分自编码器的多模态规范建模
Multimodal normative modeling in Alzheimers Disease with introspective variational autoencoders
AI总结：
本文提出mmSIVAE，通过结合MOPOE聚合提升多模态数据的规范建模效果，提高参考分布保真度和多模态整合能力，用于阿尔茨海默病的偏差分析。
摘要：
规范建模学习一个健康的参考分布，并量化受试者特异性偏差以捕捉异质性疾病效应。在阿尔茨海默病（AD）中，多模态神经影像学提供了互补的信号，但基于VAE的规范模型往往（i）拟合健康的参考分布不完美，导致假阳性增加，（ii）使用后验聚合（例如，PoE/MoE）可能导致共享潜在空间中的弱多模态融合。我们提出了mmSIVAE，一种结合混合产品专家（MOPOE）聚合的多模态软反思变分自编码器，以提高参考保真度和多模态整合。我们计算潜在空间和特征空间中的偏差分数，并将统计显著的潜在偏差映射到区域异常以提高可解释性。在ADNI MRI区域体积和淀粉样蛋白PET SUVR上，mmSIVAE在留出的对照组上提高了重建效果，并比VAE基线产生更具判别力的偏差分数用于异常检测，具有更高的似然比和更清晰的区分度。偏差图突出显示了与已确立的AD相关变化一致的区域模式。更广泛地说，我们的结果强调了训练目标应优先考虑参考分布保真度和稳健的多模态后验聚合对于规范建模的重要性，这对多模态临床数据的偏差分析具有启示意义。
英文摘要：
Normative modeling learns a healthy reference distribution and quantifies subject-specific deviations to capture heterogeneous disease effects. In Alzheimers disease (AD), multimodal neuroimaging offers complementary signals but VAE-based normative models often (i) fit the healthy reference distribution imperfectly, inflating false positives, and (ii) use posterior aggregation (e.g., PoE/MoE) that can yield weak multimodal fusion in the shared latent space. We propose mmSIVAE, a multimodal soft-introspective variational autoencoder combined with Mixture-of-Product-of-Experts (MOPOE) aggregation to improve reference fidelity and multimodal integration. We compute deviation scores in latent space and feature space as distances from the learned healthy distributions, and map statistically significant latent deviations to regional abnormalities for interpretability. On ADNI MRI regional volumes and amyloid PET SUVR, mmSIVAE improves reconstruction on held-out controls and produces more discriminative deviation scores for outlier detection than VAE baselines, with higher likelihood ratios and clearer separation between control and AD-spectrum cohorts. Deviation maps highlight region-level patterns aligned with established AD-related changes. More broadly, our results highlight the importance of training objectives that prioritize reference-distribution fidelity and robust multimodal posterior aggregation for normative modeling, with implications for deviation-based analysis across multimodal clinical data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
