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The effect of EEG microstate on the characteristics of TMS-EEG

地方政府 脑电图 磁刺激 刺激 心理学 神经科学 计算机科学 人工智能
作者
Zhaohuan Ding,Yong Wang,Zikang Niu,Gaoxiang Ouyang,Xiaoli Li
出处
期刊:Computers in Biology and Medicine [Elsevier]
卷期号:173: 108332-108332 被引量:2
标识
DOI:10.1016/j.compbiomed.2024.108332
摘要

Differences in neural states at the time of transcranial magnetic stimulation (TMS) can lead to variations in the effectiveness of TMS stimulation. Strategies that aim to lock neural activity states and improve the precision of stimulation timing in TMS optimization should gradually receive attention. One feasible approach is to utilize microstate locking for TMS stimulation, and understanding the impact of microstates at the time of stimulation on TMS response forms the foundation of this approach. TMS-EEG data were extracted from 21 healthy subjects through experiments. Based on the different microstates at the time of stimulation, the trials were classified into four datasets. TMS-evoked potential (TEP), topographical distribution, and natural frequency, were computed for each dataset to explore the differences in TMS-EEG characteristics across different microstates. The N100 component of microstate C group (−2.376 μV) was significantly higher (p = 0.003) than of microstate D group (−1.739 μV), and the P180 component of microstate D group (2.482 μV) was significantly higher (p = 0.024) than of microstate B group (1.766 μV) and slightly higher (p = 0.058) than of microstate C group (1.863 μV) by calculating the ROI. The topographical distribution of TEP components during microstate C and microstate D still retained the template characteristics of the microstate at the time of stimulation, and the natural frequencies did not differ among the four classical microstates. This study showed the potential for future closed-loop TMS based on microstates and would guiding the development of microstate-based closed-loop TMS techniques.
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