Background: Transcranial magnetic stimulation (TMS) is a powerful tool to investigate neurophysiology of the human brain and treat brain disorders. Traditionally, therapeutic TMS has been applied in a one-size-fits-all approach, disregarding inter- and intra-individual differences. Brain state-dependent EEG-TMS, such as coupling TMS with a pre-specified phase of the sensorimotor mu-rhythm, enables the induction of differential neuroplastic effects depending on the targeted phase. But this approach is still user-dependent as it requires defining an a-priori target phase. Objectives: To present a first realization of a machine-learning-based, closed-loop real-time EEG-TMS setup to identify user-independently the individual mu-rhythm phase associated with high- vs. low-corticospinal excitability states. Methods: We applied EEG-TMS to 25 participants targeting the supplementary motor area-primary motor cortex network and used a reinforcement learning algorithm to identify the mu-rhythm phase associated with high- vs. low corticospinal excitability. We employed linear mixed effects models and Bayesian analysis to determine effects of reinforced learning on corticospinal excitability indexed by motor evoked potential amplitude, and functional connectivity indexed by the imaginary part of resting-state EEG coherence. Results: Reinforcement learning effectively identified the mu-rhythm phase associated with high- vs. low-excitability states, and their repetitive stimulation resulted in long-term increases vs. decreases in functional connectivity in the stimulated sensorimotor network. Conclusions: We demonstrated for the first time the feasibility of closed-loop EEG-TMS in humans, a critical step towards individualized treatment of brain disorders.

翻译：背景：经颅磁刺激（TMS）是研究人脑神经生理学和治疗脑部疾病的有力工具。传统上，治疗性TMS采用“一刀切”的方法，忽视了个体间和个体内的差异。脑状态依赖的脑电图-TMS，例如将TMS与感觉运动μ节律的特定预设相位耦合，能够根据目标相位诱导不同的神经可塑性效应。但这种方法仍依赖于用户，因为它需要预先定义一个目标相位。目标：首次实现一种基于机器学习的闭环实时脑电图-TMS系统，以独立于用户的方式识别与高皮质脊髓兴奋性状态和低皮质脊髓兴奋性状态相关的个体μ节律相位。方法：我们对25名参与者针对辅助运动区-初级运动皮层网络进行了脑电图-TMS，并使用强化学习算法来识别与高皮质脊髓兴奋性状态和低皮质脊髓兴奋性状态相关的μ节律相位。我们采用线性混合效应模型和贝叶斯分析来确定强化学习对皮质脊髓兴奋性（以运动诱发电位波幅为指标）和功能连接性（以静息态脑电图相干性的虚部为指标）的影响。结果：强化学习有效地识别了与高兴奋性状态和低兴奋性状态相关的μ节律相位，对这些相位的重复刺激分别导致受刺激的感觉运动网络中的功能连接性长期增加与减少。结论：我们首次证明了在人体中实现闭环脑电图-TMS的可行性，这是迈向脑部疾病个体化治疗的关键一步。