阿尔茨海默病（Alzheimer’s disease，AD）是一种常见的进行性加重的神经系统退行性疾病，以近事记忆力减退为突出表现，常伴发精神行为症状及不同程度的日常生活能力受损。AD发病隐匿且进展缓慢，这给临床早期诊断带来了极大的挑战。在2024年美国国家衰老研究所和阿尔茨海默病协会（National Institute on Aging and Alzheimer’s Association，NIA-AA）提出的 β 淀粉样蛋白-Tau 蛋白-神经退行性病变（amyloid β-tauneurodegeneration，ATN）诊断框架下，AD 的生物学诊断主要依赖脑脊液检测与正电子发射体层成像（positron emission tomography，PET），但前者具有侵入性，后者成本高昂且设备依赖度高，两者的临床普及性均严重受限。因此，探寻一种无创、经济且能动态反映神经退行性病变（即“N”）的功能性生物标志物，成为该领域亟待解决的问题。脑电图（electroencephalogram，EEG）以其低成本、无创、高时间分辨率及良好的可重复性，在认知功能障碍的研究中展现出独特的优势，其信号对突触功能障碍和网络振荡异常高度敏感，能够实时捕捉大脑网络功能的失调，直接映射神经退行性病变的电生理基础。近年来，EEG与经颅磁刺激（transcranial magnetic stimulation，TMS）、功能磁共振成像（functional magnetic resonance imaging，fMRI）及功能性近红外光谱（functional near-infrared spectroscopy，fNIRS）等多模态神经成像技术的联合应用发展迅速，可从皮质兴奋性、因果网络连接、振荡-血流耦合及神经血管耦合等多个维度，为 AD 的早期识别提供丰富的功能学信息。本文系统梳理了静息态 EEG 频谱与微状态分析、TMS-EEG、EEG-fMRI 及 EEG-fNIRS在AD及轻度认知功能障碍（mild cognitive impairment，MCI）中的应用进展，重点探讨了这些无创技术如何与现有ATN诊断框架互补整合，同时还分析了当前研究在样本量、标准化及临床转化方面所面临的局限，并对未来的技术优化、多中心验证及框架整合路径进行了展望。

Alzheimer’s disease (AD) is a common progressive neurodegenerative disease characterized prominently by recent memory impairment, often accompanied by psychiatric and behavioral symptoms as well as varying degrees of functional impairment in daily living abilities. The insidious onset and gradual progression of AD pose substantial challenges to early clinical diagnosis. Under the amyloid β -tau-neurodegeneration (ATN) diagnostic framework proposed by the National Institute on Aging and Alzheimer’s Association (NIAAA) in 2024, the biological diagnosis of AD mainly relies on cerebrospinal fluid examination and positron emission tomography (PET), but the former is invasive and the latter is expensive and equipment dependent, severely limiting their clinical popularity. Therefore, exploring a non-invasive, economical, and dynamic functional biomarker that can reflect neurodegeneration (i. e., “N”) has become an urgent problem in this field. Electroencephalogram (EEG) has shown unique advantages in the study of cognitive impairment due to its low cost, non-invasive nature, high temporal resolution, and good reproducibility. Its signals are highly sensitive to synaptic dysfunction and abnormal network oscillations, and can capture real-time brain network dysfunction and directly map the electrophysiological basis of neurodegeneration. In recent years, the combined application of EEG with multimodal neuroimaging techniques, such as transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), and functional nearinfrared spectroscopy (fNIRS), has developed rapidly, providing rich functional information for early identification of AD from multiple dimensions, including cortical excitability, causal network connectivity, oscillation blood flow coupling, and neurovascular coupling. This article systematically reviews the application progress of resting-state EEG spectrum and microstate analysis, TMS-EEG, EEG-fMRI, and EEG-fNIRS in AD and mild cognitive impairment (MCI), focusing on how these non-invasive techniques complement and integrate with existing ATN diagnostic frameworks. At the same time, it analyzes the limitations faced by current research in sample size, standardization, and clinical translation, and looks forward to future technological optimization, multi-center validation, and potential pathway for intergrating the ATN diagnostic network.