一种基于证据的脑电图微状态分析框架：对阿尔茨海默病和衰老的敏感性提高
An Evidence-Aware Framework for EEG Microstate Analysis: Improved Sensitivity to Alzheimer's Disease and Ageing
- Coventry University（考文垂大学）
- Sheffield Teaching Hospitals NHS Foundation Trust（谢菲尔德教学医院NHS基金会信托）
- University of Sheffield（谢菲尔德大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种基于证据的EEG微状态分析框架，保留模板证据轨迹，改进持续时间、复杂度和分类性能，对阿尔茨海默病和衰老更敏感。
AI中文摘要：
脑电图（EEG）微状态分析通常将每个头皮地形图转换为赢者通吃的硬标签，并使用持续时间、发生次数、覆盖率、转换和符号复杂性来总结所得序列。尽管这种读出方式具有可解释性，但它丢弃了证据强度、分配模糊性和低置信度时段。我们引入了一个模板证据轨迹框架，在每个采样的全局场功率（GFP）峰值处保留所有模板或受试者特定地形图社区的证据。传统的硬标签被视为该多变量轨迹的压缩读出。我们推导了经典描述符的两种基于证据的扩展：高证据发作持续时间和发作率，它们量化了强状态证据的时间聚类和碎片化，以及空状态Lempel-Ziv复杂度（LZC），它显式编码证据不足的时段。我们在四个静息态EEG数据集上评估了该框架，涵盖阿尔茨海默病和年龄相关变化。固定状态模型包括K-means、AAHC和HMM，K=4和K=7，以及自适应的受试者特定Leiden和Infomap社区。在32个数据集-模型案例中，在代表性设置下，轨迹导出的持续时间效应在所有案例中均超过匹配的硬标签效应，在更广泛的参数网格中，在30-32个案例中也是如此。空状态LZC在大多数情况下优于传统LZC，而分类显示轨迹或组合特征的适度但一致的增益。因此，保留模板证据提供了对EEG地形图状态动力学的更敏感读出，同时保持与传统微状态分析的兼容性。
英文摘要：
Electroencephalography (EEG) microstate analysis commonly converts each scalp topography into a winner-take-all hard label and summarises the resulting sequence using duration, occurrence, coverage, transitions, and symbolic complexity. Although interpretable, this readout discards evidence strength, assignment ambiguity, and low-confidence periods. We introduce a template evidence trajectory framework that retains, at each sampled Global Field Power (GFP) peak, the evidence for all templates or subject-specific topographic communities. Conventional hard labels are treated as a compressed readout of this multivariate trajectory. We derive two evidence-aware extensions of classical descriptors: high-evidence episode duration and episode rate, which quantify temporal clustering and fragmentation of strong state evidence, and null-state Lempel-Ziv complexity (LZC), which explicitly encodes insufficient-evidence periods. We evaluated the framework across four resting-state EEG datasets spanning Alzheimer's disease and age-related variation. Fixed-state models included K-means, AAHC, and HMMs at K = 4 and K = 7, together with adaptive subject-specific Leiden and Infomap communities. Across 32 dataset-model cases, trajectory-derived duration effects exceeded matched hard-label effects in all cases at the representative setting and in 30-32 cases across a broader parameter grid. Null-state LZC improved over traditional LZC in most cases, while classification showed modest but consistent gains for trajectory or combined features. Retaining template evidence therefore provides a more sensitive readout of EEG topographic state dynamics while remaining compatible with conventional microstate analysis.
