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首页> 外文期刊>Brain Sciences >Electrode Location in a Microelectrode Recording-Based Model of the Subthalamic Nucleus Can Predict Motor Improvement After Deep Brain Stimulation for Parkinson’s Disease
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Electrode Location in a Microelectrode Recording-Based Model of the Subthalamic Nucleus Can Predict Motor Improvement After Deep Brain Stimulation for Parkinson’s Disease

机译:丘脑底核的基于微电极记录的模型中的电极位置可以预测帕金森氏病的深度脑刺激后运动的改善。

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Motor improvement after deep brain stimulation (DBS) in the subthalamic nucleus (STN) may vary substantially between Parkinson’s disease (PD) patients. Research into the relation between improvement and active contact location requires a correction for anatomical variation. We studied the relation between active contact location relative to the neurophysiological STN, estimated by the intraoperative microelectrode recordings (MER-based STN), and contralateral motor improvement after one year. A generic STN shape was transformed to fit onto the stereotactically defined MER sites. The location of 43 electrodes (26 patients), derived from MRI-fused CT images, was expressed relative to this patient-specific MER-based STN. Using regression analyses, the relation between contact location and motor improvement was studied. The regression model that predicts motor improvement based on levodopa effect alone was significantly improved by adding the one-year active contact coordinates (R 2 change = 0.176, p = 0.014). In the combined prediction model (adjusted R 2 = 0.389, p 0.001), the largest contribution was made by the mediolateral location of the active contact (standardized beta = 0.490, p = 0.002). With the MER-based STN as a reference, we were able to find a significant relation between active contact location and motor improvement. MER-based STN modeling can be used to complement imaging-based STN models in the application of DBS.
机译:在帕金森氏病(PD)患者之间,丘脑底核(STN)的深层脑刺激(DBS)后的运动改善可能有很大差异。研究改善与主动接触位置之间的关系需要对解剖学变化进行校正。我们研究了术中微电极记录(基于MER的STN)估计的相对于神经生理性STN的主动接触位置与一年后对侧运动功能改善之间的关系。转换了普通的STN形状以适合立体定位的MER站点。相对于该患者特异性的基于MER的STN,表达了来自MRI融合CT图像的43个电极(26个患者)的位置。使用回归分析,研究了接触位置与运动能力改善之间的关系。通过添加一年的主动接触坐标,可以显着改善仅基于左旋多巴效应预测运动改善的回归模型(R 2变化= 0.176,p = 0.014)。在组合预测模型中(调整后的R 2 = 0.389,p <0.001),最大的贡献是由活动接触的中外侧位置(标准化的beta = 0.490,p = 0.002)引起的。以基于MER的STN作为参考,我们能够发现主动触点位置与电机改进之间的重要关系。基于MER的STN建模可用于在DBS应用程序中补充基于成像的STN模型。

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