ReMAP：用于揭示大脑表征与脆弱性的自监督学习

中文摘要 AI 辅助

全身麻醉为观察标准化受控扰动下的人类大脑提供了难得的机会，但术中脑电图（EEG）几乎总是被简化为单一的专有深度指数，将丰富的轨迹压缩为一个数字，忽略了大脑在不同状态间的变化过程。本研究探究该轨迹的几何特征（而非仅其达到的深度）是否包含临床意义信息。研究采用基于相似性的自监督学习方法，对原始双电极额叶EEG进行无标签处理，将每个记录映射到低维空间，其中麻醉深度为可解读的轴，而患者路径的形状则编码额外结构。研究在两个队列、两套采集系统共超1000名患者上验证该表征：准确预测麻醉深度（BIS平均绝对误差=3.2，R²=0.82）；在稀疏导联设置下，本研究的紧凑模型（约6.8万个参数）仍可与参数规模大几个数量级的EEG基础模型（400万至1.57亿个参数）相媲美，表明表征与记录的匹配度优于原始规模。该学习空间还在无监督情况下沿自身梯度组织年龄信息，且与额叶α波、慢δ波、爆发抑制等可解释的麻醉特征对齐，将数据驱动的表征与已确立的神经生理学关联起来。在含纵向随访的独立队列中，早期轨迹的几何特征可区分30个月的认知与死亡结局，其性能与年龄互补（AUROC=0.86）。这些结果表明，大脑在麻醉过程中走过的路径是潜在脆弱性的标签高效关联物，值得开展前瞻性验证。

英文摘要

General anesthesia offers a rare opportunity to observe the human brain under a standardized, controlled perturbation. Yet intraoperative electroencephalography (EEG) is almost always reduced to a single proprietary depth index, collapsing a rich trajectory into one number and discarding how a brain moves between states. Here we ask whether the geometry of that trajectory, not merely the depth it reaches, carries clinically meaningful information. Using similarity-based self-supervised learning on raw, two-electrode frontal EEG, with no labels, we place each recording within a low-dimensional space in which anesthetic depth becomes one readable axis while the shape of a patient's path encodes additional structure. We validate the representation across two cohorts and two acquisition systems totaling more than 1,000 patients. Depth of anesthesia is predicted accurately (BIS mean absolute error = 3.2, R2 = 0.82), and in the sparse-montage setting our compact ( 68k parameter) model remains competitive with EEG foundation models orders of magnitude larger (4M-157M parameters), indicating that matching the representation to the recording dominates raw scale. The learned space organizes age along its own gradient, independent from depth, without supervision. The same space also aligns with interpretable anesthetic signatures like frontal alpha, slow-delta, and burst suppression, linking this data-driven representation to established neurophysiology. On an independent cohort with longitudinal follow-up, the geometry of the early trajectory separates 30- month cognitive and mortality outcomes complementary to age (AUROC 0.86). These results suggest that the path a brain traces through anesthesia is a label-efficient correlate of latent vulnerability, motivating prospective validation.

发表机构

• AP-HP（巴黎公立医院集团）
• Inserm（法国国家健康与医学研究院）
• Université Paris Cité（巴黎城市大学）
• Université Paris-Saclay（巴黎-萨克雷大学）
• Inria（法国国家信息与自动化研究所）
• CEA（法国原子能和替代能源委员会）
• Sorbonne Université（索邦大学）

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