用可解释的3D脑部MRI表征预测多发性硬化认知衰退
Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis
- 通过最大化图像与潜在变量互信息，生成结构化紧凑表征
- 在904例患者中准确预测认知测试得分，优于其他VAE模型
- 结果可直观聚类，适合临床医生理解神经退行性变化
我们提出InfoVAE-Med3D，一种用于3D脑部MRI的潜在表征学习方法，旨在发现认知衰退的可解释生物标志物。标准统计模型和浅层机器学习通常效力不足，而多数深度学习方法如同黑箱。本方法将InfoVAE扩展至显式最大化图像与潜在变量间的互信息，生成保留临床意义内容的紧凑、结构化嵌入。在两个队列上评估：包含6527名健康对照的大型队列（含年龄信息），以及捷克查尔斯大学提供的904名多发性硬化患者队列（含年龄和符号数字模态测验，SDMT分数）。所学潜在表示支持高精度脑龄与SDMT回归，保留关键医学特征，并形成直观聚类以辅助解读。在重建与下游预测任务中，InfoVAE-Med3D持续优于其他VAE变体，表明其嵌入空间具有更强的信息捕获能力。通过结合预测性能与可解释性，该方法为基于MRI的生物标志物开发及神经疾病认知衰退的透明分析提供了可行路径。
原文摘要 · Abstract (English)
We present InfoVAE-Med3D, a latent-representation learning approach for 3D brain MRI that targets interpretable biomarkers of cognitive decline. Standard statistical models and shallow machine learning often lack power, while most deep learning methods behave as black boxes. Our method extends InfoVAE to explicitly maximize mutual information between images and latent variables, producing compact, structured embeddings that retain clinically meaningful content. We evaluate on two cohorts: a large healthy-control dataset (n=6527) with chronological age, and a clinical multiple sclerosis dataset from Charles University in Prague (n=904) with age and Symbol Digit Modalities Test (SDMT) scores. The learned latents support accurate brain-age and SDMT regression, preserve key medical attributes, and form intuitive clusters that aid interpretation. Across reconstruction and downstream prediction tasks, InfoVAE-Med3D consistently outperforms other VAE variants, indicating stronger information capture in the embedding space. By uniting predictive performance with interpretability, InfoVAE-Med3D offers a practical path toward MRI-based biomarkers and more transparent analysis of cognitive deterioration in neurological disease.
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