Wang Kai-Liang, Burns Mathew, Xu Dan, Hu Wei, Fan Shi-Ying, Han Chun-Lei, Wang Qiao, Michitomo Shimabukuro, Xia Xiao-Tong, Zhang Jian-Guo, Wang Feng, Meng Fan-Gang
Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Department of Neurology, Fixel Center for Neurological Diseases, Program in Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.
Front Neurol. 2020 Feb 26;11:142. doi: 10.3389/fneur.2020.00142. eCollection 2020.
Deep brain stimulation (DBS) is an effective therapy for resting tremor in Parkinson's disease (PD). However, quick and objective biomarkers for quantifying the efficacy of DBS intraoperatively are lacking. Therefore, we aimed to study how DBS modulates the intraoperative neuromuscular pattern of resting tremor in PD patients and to find predictive surface electromyography (sEMG) biomarkers for quantifying the intraoperative efficacy of DBS. Intraoperative sEMG of 39 PD patients with resting tremor was measured with the DBS on and off, respectively, during the intraoperative DBS testing stage. Twelve signal features (time and frequency domains) were extracted from the intraoperative sEMG data. These sEMG features were associated with the clinical outcome to evaluate the efficacy of intraoperative DBS. Also, an sEMG-based prediction model was established to predict the clinical improvement rate (IR) of resting tremor with DBS therapy. A typical resting tremor with a peak frequency of 4.93 ± 0.98 Hz (mean ± SD) was measured. Compared to the baseline, DBS modulated significant neuromuscular pattern changes in most features except for the peak frequency, by decreasing the motor unit firing rate, amplitude, or power and by changing the regularity pattern. Three sEMG features were detected with significant associations with the clinical improvement rate (IR) of the tremor scale: peak frequency power ( = 0.37, = 0.03), weighted root mean square ( = 0.42, = 0.01), and modified mean amplitude power ( = 0.48, = 0.003). These were adopted to train a Gaussian process regression model with a leave-one-out cross-validation procedure. The prediction values from the trained sEMG prediction model (1,000 permutations, = 0.003) showed a good correlation ( = 0.47, = 0.0043) with the true IR of the tremor scale. DBS acutely modulated the intraoperative resting tremor, mainly by suppressing the amplitude and motor unit firing rate and by changing the regularity pattern, but not by modifying the frequency pattern. Three features showed strong robustness and could be used as quick intraoperative biomarkers to quantify and predict the efficacy of DBS in PD patients with resting tremor.
深部脑刺激（DBS）是治疗帕金森病（PD）静止性震颤的有效方法。然而，目前缺乏快速、客观的生物标志物来术中量化DBS的疗效。因此，我们旨在研究DBS如何调节PD患者术中静止性震颤的神经肌肉模式，并寻找用于量化DBS术中疗效的预测性表面肌电图（sEMG）生物标志物。在术中DBS测试阶段，分别在DBS开启和关闭状态下测量了39例患有静止性震颤的PD患者的术中sEMG。从术中sEMG数据中提取了12个信号特征（时域和频域）。这些sEMG特征与临床结果相关联，以评估术中DBS的疗效。此外，还建立了基于sEMG的预测模型，以预测DBS治疗静止性震颤的临床改善率（IR）。测量到一种典型的静止性震颤，其峰值频率为4.93±0.98Hz（平均值±标准差）。与基线相比，DBS通过降低运动单位发放率、幅度或功率以及改变规律性模式，在除峰值频率外的大多数特征上调节了显著的神经肌肉模式变化。检测到三个sEMG特征与震颤量表的临床改善率（IR）有显著关联：峰值频率功率（r = 0.37，p = 0.03）、加权均方根（r = 0.42，p = 0.01）和修正平均幅度功率（r = 0.48，p = 0.003）。采用这些特征通过留一法交叉验证程序训练高斯过程回归模型。训练后的sEMG预测模型的预测值（1000次排列，p = 0.003）与震颤量表的真实IR显示出良好的相关性（r = 0.47，p = 0.0043）。DBS急性调节术中静止性震颤，主要通过抑制幅度和运动单位发放率以及改变规律性模式，而不是通过改变频率模式。三个特征显示出很强的稳健性，可作为快速的术中生物标志物来量化和预测DBS对患有静止性震颤的PD患者的疗效。
