通过将脑电图与脉冲神经网络和生物物理模拟相结合学习阿尔茨海默病特征
Learning Alzheimer's Disease Signatures by bridging EEG with Spiking Neural Networks and Biophysical Simulations
AI总结：
本文提出神经桥接框架，通过结合EEG与脉冲神经网络和生物物理模拟，提升阿尔茨海默病的可解释性检测
摘要：
随着阿尔茨海默病的发病率上升，通过非侵入性生物标记物提高机制性洞察力变得越来越关键。最近的研究表明，电路层面的脑部变化会表现为可被机器学习检测到的脑电图（EEG）频谱特征变化。然而，传统的基于EEG的AD检测深度学习方法计算成本高且机制不透明。脉冲神经网络（SNN）提供了一种生物合理且能耗低的替代方案，但其在AD诊断中的应用仍鲜有探索。我们提出了一种神经桥接框架，将数据驱动的学习与最小、基于生物物理的模拟相结合，使机器学习特征与AD电路层面的机制之间能够实现双向解释。利用静息状态临床EEG数据，我们训练了一个SNN分类器，其性能（AUC = 0.839）具有竞争力，并识别出非周期性1/f斜率作为关键的区分标记。1/f斜率反映了兴奋-抑制平衡。为了机制性解释这一点，我们构建了脉冲网络模拟，在其中系统地调整抑制性到兴奋性突触比率以模拟健康、轻度认知障碍和AD样状态。使用基于膜电位和突触电流的EEG代理，我们重现了实证频谱变慢和改变的alpha组织。将经验功能连接性先验纳入多子网络模拟进一步增强了频谱区分，证明大规模网络拓扑对EEG特征的约束比单独的兴奋-抑制平衡更强。总体而言，这种神经桥接方法将SNN分类与可解释的电路模拟连接起来，推动了EEG生物标记物的机制理解，同时实现了可扩展且可解释的AD检测。
英文摘要：
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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