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.

翻译：随着阿尔茨海默病（AD）患病率的上升，从非侵入性生物标志物中提升机制性洞察变得日益关键。近期研究表明，环路水平的脑部改变会表现为脑电图（EEG）频谱特征的变化，这些变化可通过机器学习检测。然而，基于EEG的AD检测所采用的常规深度学习方法计算成本高且机制不透明。脉冲神经网络（SNNs）提供了一种生物可信且高能效的替代方案，但其在AD诊断中的应用仍基本未被探索。我们提出一种神经桥接框架，该框架将数据驱动学习与最小化的、基于生物物理的模拟相连接，从而在机器学习特征与AD的环路水平机制之间实现双向解释。利用静息态临床EEG，我们训练了一个SNN分类器，该分类器取得了有竞争力的性能（AUC = 0.839），并将非周期性的1/f斜率识别为一个关键的判别性标志物。1/f斜率反映了兴奋-抑制平衡。为了从机制上解释这一点，我们构建了脉冲网络模拟，其中系统地改变抑制性到兴奋性突触的比例以模拟健康、轻度认知障碍和AD样状态。使用基于膜电位和基于突触电流的EEG代理指标，我们重现了经验观察到的频谱减慢和改变的alpha节律组织。将经验性功能连接先验信息整合到多子网络模拟中，进一步增强了频谱区分度，这表明大规模网络拓扑结构对EEG特征的约束作用比单独的兴奋-抑制平衡更强。总体而言，这种神经桥接方法将基于SNN的分类与可解释的环路模拟联系起来，推进了对EEG生物标志物的机制性理解，同时实现了可扩展、可解释的AD检测。