通过脑电轨迹几何结构，无监督揭示麻醉中大脑的隐藏脆弱性
ReMAP: Self-supervised learning to unveil brain representations and vulnerability
- 用自监督学习分析原始脑电数据，构建低维表示空间
- 准确预测麻醉深度（误差3.2，决定系数0.82），且小模型胜过大模型
- 脑电路径形状可预判术后认知与死亡风险，适合临床前瞻性研究
全身麻醉为观察人类大脑在标准化扰动下的状态提供了独特机会。然而术中脑电图（EEG）通常仅简化为单一专有深度指数，将丰富的动态变化压缩成一个数值，丢失了大脑在不同状态间迁移的信息。本文探讨该轨迹的几何结构是否蕴含临床意义。基于原始双电极额区脑电数据，采用无标签的相似性自监督学习，将每个记录嵌入低维空间，其中麻醉深度成为可读轴，而患者轨迹形状则编码额外结构。在两个队列、两种采集系统共超过1000名患者中验证该表示。麻醉深度预测准确（BIS平均绝对误差=3.2，R²=0.82）；在稀疏导联设置下，仅68k参数的小模型仍优于参数量达400万至1.57亿的脑电基础模型，表明匹配数据特征比模型规模更重要。所学空间能无监督地组织年龄梯度，并与前额α波、慢delta波及爆发抑制等已知神经生理标志对齐。在独立队列的纵向随访中，早期轨迹几何可区分30个月后的认知与死亡结局（AUROC=0.86），优于年龄预测。结果提示，大脑在麻醉中走过的路径是潜在脆弱性的高效指标，值得进一步前瞻性验证。
原文摘要 · Abstract (English)
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.
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