自监督学习可挖掘更敏感的阿尔茨海默病影像生物标志物
A Cautionary Tale of Self-Supervised Learning for Imaging Biomarkers: Alzheimer's Disease Case Study
- 结合自由度特征的残差噪声对比估计提升信息提取能力
- 在疾病转化预测等任务上超越传统特征与现有自监督方法
- 结果具有生物学意义，关联神经退行性病变相关基因
阿尔茨海默病早期检测与监测依赖敏感且具生物学基础的生物标志物。结构MRI广泛可用，但通常依赖手工设计特征如皮层厚度或体积。我们探究自监督学习（SSL）能否从相同数据中发现更强大的生物标志物。现有SSL方法在疾病分类、转化预测和淀粉样蛋白状态预测任务中表现不及FreeSurfer衍生特征。本文提出残差噪声对比估计（R-NCE），在保留辅助FreeSurfer特征的同时最大化增广不变信息。R-NCE在多个基准测试中均优于传统特征与现有SSL方法，包括阿尔茨海默病转化预测。为评估生物学相关性，我们构建脑龄差（BAG）指标并开展全基因组关联分析。R-NCE-BAG显示高遗传度，与MAPT和IRAG1基因显著相关，并在星形胶质细胞和少突胶质细胞中富集，表明对神经退行性和脑血管过程高度敏感。
原文摘要 · Abstract (English)
Discovery of sensitive and biologically grounded biomarkers is essential for early detection and monitoring of Alzheimer's disease (AD). Structural MRI is widely available but typically relies on hand-crafted features such as cortical thickness or volume. We ask whether self-supervised learning (SSL) can uncover more powerful biomarkers from the same data. Existing SSL methods underperform FreeSurfer-derived features in disease classification, conversion prediction, and amyloid status prediction. We introduce Residual Noise Contrastive Estimation (R-NCE), a new SSL framework that integrates auxiliary FreeSurfer features while maximizing additional augmentation-invariant information. R-NCE outperforms traditional features and existing SSL methods across multiple benchmarks, including AD conversion prediction. To assess biological relevance, we derive Brain Age Gap (BAG) measures and perform genome-wide association studies. R-NCE-BAG shows high heritability and associations with MAPT and IRAG1, with enrichment in astrocytes and oligodendrocytes, indicating sensitivity to neurodegenerative and cerebrovascular processes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。