用脉冲神经网络解析脑电图，揭示阿尔茨海默病的神经机制。
Learning Alzheimer's Disease Signatures by bridging EEG with Spiking Neural Networks and Biophysical Simulations
- 构建神经桥框架，将脑电数据与生物物理模拟结合。
- 识别1/f斜率是关键判别特征，反映兴奋抑制失衡。
- 适合关注脑电机制解释与可解释性诊断的研究者。
随着阿尔茨海默病（AD）患病率上升，从非侵入性生物标志物中获取机制性洞察变得愈发重要。近期研究表明，电路水平的脑改变会以脑电图（EEG）频谱特征的变化体现，可被机器学习检测。然而，传统深度学习方法计算量大且机制不透明。脉冲神经网络（SNNs）提供了生物合理且能效更高的替代方案，但其在AD诊断中的应用仍较少。本文提出一种神经桥框架，将数据驱动学习与最小化、基于生物物理的模拟相结合，实现机器学习特征与AD电路机制间的双向解释。基于静息态临床脑电图，训练的SNN分类器达到优异性能（AUC = 0.839），并识别出1/f斜率是关键判别特征，反映兴奋-抑制平衡变化。通过系统调整抑制性到兴奋性突触比例，构建脉冲网络模拟健康、轻度认知障碍及AD样状态。采用膜电位和突触电流两类脑电代理，重现了经验性的频谱减慢与α波组织改变。在多子网络模拟中引入经验功能连接先验，进一步增强了频谱区分能力，表明大规模网络拓扑对脑电特征的约束强于兴奋-抑制平衡本身。整体上，该神经桥方法将SNN分类结果与可解释的电路模拟相连，推进了对脑电生物标志物的机制理解，并实现了可扩展、可解释的AD检测。
原文摘要 · Abstract (English)
As the prevalence of Alzheimer's disease (AD) rises, improving mechanistic insight from non-invasive biomarkers is increasingly critical. Recent work suggests that circuit-level brain alterations manifest as changes in electroencephalography (EEG) spectral features detectable by machine learning. However, conventional deep learning approaches for EEG-based AD detection are computationally intensive and mechanistically opaque. Spiking neural networks (SNNs) offer a biologically plausible and energy-efficient alternative, yet their application to AD diagnosis remains largely unexplored. We propose a neuro-bridge framework that links data-driven learning with minimal, biophysically grounded simulations, enabling bidirectional interpretation between machine learning signatures and circuit-level mechanisms in AD. Using resting-state clinical EEG, we train an SNN classifier that achieves competitive performance (AUC = 0.839) and identifies the aperiodic 1/f slope as a key discriminative marker. The 1/f slope reflects excitation-inhibition balance. To interpret this mechanistically, we construct spiking network simulations in which inhibitory-to-excitatory synaptic ratios are systematically varied to emulate healthy, mild cognitive impairment, and AD-like states. Using both membrane potential-based and synaptic current-based EEG proxies, we reproduce empirical spectral slowing and altered alpha organization. Incorporating empirical functional connectivity priors into multi-subnetwork simulations further enhances spectral differentiation, demonstrating that large-scale network topology constrains EEG signatures more strongly than excitation-inhibition balance alone. Overall, this neuro-bridge approach connects SNN-based classification with interpretable circuit simulations, advancing mechanistic understanding of EEG biomarkers while enabling scalable, explainable AD detection.
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