睡眠阶段转变的朗道-金兹堡现象学
A Landau-Ginzburg Phenomenology of Sleep-Stage Transitions
AI总结：
本研究构建朗道-金兹堡现象学模型解释睡眠阶段转变的特性，通过EEG/PSG数据验证其一致性，为睡眠阶段转变的机制研究提供可测试的理论框架。
AI中文摘要：
睡眠分期提供了可重复的临床描述，但它本身无法解释为何部分边界是突变的而另一些是渐变的，也无法解释为何转变窗口包含不稳定性、同步性和明显的状态共存。我们开发了一种局域朗道-金兹堡现象学，其中每个边界由空间延展的、含噪声的耗散神经场的有效势运动来表示。通过旨在避免循环性的测量模型，从预先指定的EEG/PSG观测值中推断出潜在的皮层有序坐标φ。我们分别处理典型边界：现有数据支持睡眠起始时觉醒稳定性的类折叠损失；该折叠是否位于具有滞后的全局双稳态尖点上仍待确定。N1到N2、N2到N3被视为类连续有序交叉，NREM到REM被视为候选的类一级去同步开关，而N3内可能存在的混合或三叉临界类 regime 是推测性假设。金兹堡项增加了标量睡眠起始模型中不存在的空间预测——相关长度的增长和局域到全局的招募。我们明确了区分分岔、共存、噪声驱动逃逸、平滑交叉和评分诱导不连续性所需的证据。 illustrative 时变金兹堡-朗道模拟再现了所提出的 signature 类别。一项合成分类实验部分区分了六种原型（交叉验证准确率0.49±0.005；平衡基线0.17），在噪声 regime 转变下变化很小。这些分析确立了该框架的内部一致性和可测试性，而非人类睡眠的拟议分类法。在推进临床或神经调节应用之前，需要以转变为中心的EEG验证。
英文摘要：
Sleep staging provides a reproducible clinical description, but it does not by itself explain why some boundaries are abrupt while others are graded, or why transition windows contain instability, synchrony, and apparent state coexistence. We develop a local Landau-Ginzburg phenomenology in which each boundary is represented by motion in an effective potential of a spatially extended, noisy, dissipative neural field. A latent cortical-ordering coordinate phi is inferred from prespecified EEG/PSG observables through a measurement model designed to avoid circularity. The canonical boundaries are treated separately. Existing data support a fold-like loss of wake stability at sleep onset; whether that fold lies on a globally bistable cusp with hysteresis remains open. N1-to-N2 and N2-to-N3 are posed as continuous-like ordering crossovers, NREM-to-REM as a candidate first-order-like desynchronizing switch, and a possible within-N3 mixed or tricritical-like regime as a speculative hypothesis. The Ginzburg term adds spatial predictions - growth of correlation length and local-to-global recruitment - that are absent from scalar sleep-onset models. We specify the evidence needed to distinguish bifurcation, coexistence, noise-driven escape, smooth crossover, and scoring-induced discontinuity. Illustrative time-dependent Ginzburg-Landau simulations reproduce the proposed signature classes. A synthetic classification experiment partially distinguished six archetypes (cross-validated accuracy 0.49 +/- 0.005; balanced baseline 0.17), with little change under a noise-regime shift. These analyses establish the internal consistency and testability of the framework, not the proposed taxonomy in human sleep. Transition-centered EEG validation is required before clinical or neuromodulation applications are pursued.
