Abstract：
Brain aging is associated with a decline in cognitive performance, motor function and sensory perception, even in the absence of neurodegeneration. The underlying pathophysiological mechanisms remain incompletely understood, though alterations in neurogenesis, neuronal senescence and synaptic plasticity are implicated. Recent years have seen advancements in neurophysiological techniques such as electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP) and transcranial magnetic stimulation (TMS), offering insights into physiological and pathological brain aging. These methods provide real-time information on brain activity, connectivity and network dynamics. Integration of Artificial Intelligence (AI) techniques promise as a tool enhancing the diagnosis and prognosis of age-related cognitive decline. Our review highlights recent advances in these electrophysiological techniques (focusing on EEG, ERP, TMS and TMS-EEG methodologies) and their application in physiological and pathological brain aging. Physiological aging is characterized by changes in EEG spectral power and connectivity, ERP and TMS parameters, indicating alterations in neural activity and network function. Pathological aging, such as in Alzheimer\'s disease, is associated with further disruptions in EEG rhythms, ERP components and TMS measures, reflecting underlying neurodegenerative processes. Machine learning approaches show promise in classifying cognitive impairment and predicting disease progression. Standardization of neurophysiological methods and integration with other modalities are crucial for a comprehensive understanding of brain aging and neurodegenerative disorders. Advanced network analysis techniques and AI methods hold potential for enhancing diagnostic accuracy and deepening insights into age-related brain changes.
摘要：
大脑老化与认知能力下降有关，运动功能和感官知觉，即使没有神经变性.潜在的病理生理机制仍未完全了解，虽然神经发生改变，涉及神经元衰老和突触可塑性。近年来，在脑电图（EEG）等神经生理学技术方面取得了进步，脑磁图（MEG），事件相关电位（ERP）和经颅磁刺激（TMS），提供对生理和病理性大脑衰老的见解。这些方法提供了大脑活动的实时信息，连通性和网络动态。人工智能（AI）技术的整合有望成为增强与年龄相关的认知能力下降的诊断和预后的工具。我们的评论重点介绍了这些电生理技术的最新进展（重点是EEG，ERP,TMS和TMS-EEG方法）及其在生理和病理性脑老化中的应用。生理老化的特点是脑电频谱功率和连通性的变化，ERP和TMS参数，指示神经活动和网络功能的改变。病理性老化,比如在老年痴呆症中，与脑电图节律的进一步中断有关，ERP组件和TMS措施，反映潜在的神经退行性过程。机器学习方法在分类认知障碍和预测疾病进展方面显示出希望。神经生理学方法的标准化以及与其他方式的整合对于全面了解大脑衰老和神经退行性疾病至关重要。先进的网络分析技术和人工智能方法具有提高诊断准确性和加深对年龄相关大脑变化的见解的潜力。
