Transcranial magnetic stimulation (TMS) can produce plastic changes within descending motor pathways and distributed brain networks [1,2]. It has been proposed that TMS could enhance post-stroke motor recovery by normalizing imbalanced sensorimotor network function and/or upregulating corticospinal output [3,4] but studies using TMS to boost motor recovery have shown heterogeneous results [5]. However, TMS has traditionally been delivered uncoupled from endogenous brain oscillatory activity, leading to indiscriminate application of individual TMS pulses across different, physiologically distinct brain states. Thus, failure to control for endogenous brain states during TMS application may have contributed to the overall weak effect sizes and high response variability often observed in TMS studies [6]. Phasedependent TMS, which involves delivering individual TMS pulses or trains of pulses during pre-defined brain oscillatory phases, attempts to address this limitation. Early results using phasedependent TMS in healthy individuals are promising: TMS applied during sensorimotor mu (8e12 Hz) trough phases reflecting increased sensorimotor cortical neuronal spiking [7] and interregional neuronal communication [8] enhances corticospinal output to a larger extent than TMS applied irrespective of these phases [9]. These findings raise the hypothesis that phasedependent TMS could be more effective than TMS uncoupled from sensorimotor mu phases. Yet, for phase-dependent TMS to be therapeutically useful after stroke, it must first be possible to accurately deliver TMS during pre-defined brain oscillatory phases in the lesioned brain. Why might accurate phase-dependent TMS delivery be challenging after stroke? In order to account for time-delays inherent to signal acquisition and processing, phase-dependent TMS approaches typically use autoregressive forward prediction in either the time or frequency domain to estimate the instantaneous oscillatory phase at some future time point [9,10]. These methods require the presence of a recordable mu rhythm that exhibits

中文翻译：

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中风后对病变半球的相位依赖性经颅磁刺激是准确的

经颅磁刺激 (TMS) 可在下行运动通路和分布式大脑网络中产生可塑性变化 [1,2]。有人提出，TMS 可以通过使不平衡的感觉运动网络功能正常化和/或上调皮质脊髓输出来增强中风后运动恢复 [3,4]，但使用 TMS 促进运动恢复的研究显示出不同的结果 [5]。然而，传统上 TMS 与内源性大脑振荡活动脱钩，导致在不同的生理上不同的大脑状态中不加区分地应用单个 TMS 脉冲。因此，在 TMS 应用过程中未能控制内源性大脑状态可能导致 TMS 研究中经常观察到的整体弱效应和高反应变异性 [6]。相依TMS，这涉及在预定义的大脑振荡阶段提供单个 TMS 脉冲或脉冲串，试图解决这一限制。在健康个体中使用相位依赖性 TMS 的早期结果是有希望的：在感觉运动 mu (8e12 Hz) 波谷阶段应用的 TMS 反映了感觉运动皮层神经元尖峰 [7] 和区域间神经元通信 [8] 的增强皮质脊髓输出，而不考虑应用这些阶段 [9]。这些发现提出了这样一种假设，即相依赖的 TMS 可能比与感觉运动 mu 相分离的 TMS 更有效。然而，对于在中风后具有治疗作用的相位依赖性 TMS，必须首先能够在受损大脑中预先定义的大脑振荡阶段准确地提供 TMS。为什么中风后准确的相位相关 TMS 传递可能具有挑战性？为了解决信号采集和处理固有的时间延迟，相位相关 TMS 方法通常在时域或频域中使用自回归前向预测来估计未来某个时间点的瞬时振荡相位 [9,10]。这些方法需要存在可记录的 mu 节奏，该节奏表现出