Chen He, Liu Tao, Song Yinglu, Ding Zhaohuan, Li Xiaoli
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.
Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510640, China.
Brain Sci. 2025 Jul 8;15(7):731. doi: 10.3390/brainsci15070731.
Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) has emerged as a transformative tool for probing cortical dynamics with millisecond precision. This review examines the state-dependent nature of TMS-EEG, a critical yet underexplored dimension influencing measurement reliability and clinical applicability. By integrating TMS's neuromodulatory capacity with EEG's temporal resolution, this synergy enables real-time analysis of brain network dynamics under varying neural states. We delineate foundational mechanisms of TMS-evoked potentials (TEPs), discuss challenges posed by temporal and inter-individual variability, and evaluate advanced paradigms such as closed-loop and task-embedded TMS-EEG. The former leverages real-time EEG feedback to synchronize stimulation with oscillatory phases, while the latter aligns TMS pulses with task-specific cognitive phases to map transient network activations. Current limitations-including hardware constraints, signal artifacts, and inconsistent preprocessing pipelines-are critically analyzed. Future directions emphasize adaptive algorithms for neural state prediction, phase-specific stimulation protocols, and standardized methodologies to enhance reproducibility. By bridging mechanistic insights with personalized neuromodulation strategies, state-dependent TMS-EEG holds promise for advancing both basic neuroscience and precision medicine, particularly in psychiatric and neurological disorders characterized by dynamic neural dysregulation.
经颅磁刺激结合脑电图（TMS-EEG）已成为一种具有变革性的工具，能够以毫秒级精度探测皮层动力学。本综述探讨了TMS-EEG的状态依赖性本质，这是一个关键但尚未充分探索的维度，会影响测量可靠性和临床适用性。通过将TMS的神经调节能力与EEG的时间分辨率相结合，这种协同作用能够在不同神经状态下对脑网络动力学进行实时分析。我们阐述了TMS诱发电位（TEP）的基础机制，讨论了时间和个体间变异性带来的挑战，并评估了诸如闭环和任务嵌入型TMS-EEG等先进范式。前者利用实时EEG反馈使刺激与振荡相位同步，而后者将TMS脉冲与特定任务的认知相位对齐，以绘制瞬态网络激活情况。对当前的局限性，包括硬件限制、信号伪迹和不一致的预处理流程进行了批判性分析。未来的方向强调用于神经状态预测的自适应算法、相位特异性刺激方案以及提高可重复性的标准化方法。通过将机制性见解与个性化神经调节策略相结合，状态依赖性TMS-EEG有望推动基础神经科学和精准医学的发展，特别是在以动态神经调节异常为特征的精神和神经疾病中。
