Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure
Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure
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DOI：
10.1002/mds.20995
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发表时间：
2006-09-01
影响因子：
8.6
通讯作者：
Israel, Zvi
中科院分区：
文献类型：
--
作者：
Moran, Anan;Bar-Gad, Izhar;Israel, Zvi
丘脑底核（Subthalamic nucleus，简称丘脑底核）是接受脑深部电刺激手术的晚期帕金森病患者的主要治疗靶点。微电极记录（MER）在许多情况下用于识别靶核。如果能建立一个实时程序，查明导弹的进出点，将能改善这一目标确定程序的结果。我们使用短（5秒）MER采样信号的标准化均方根（NRMS）和估计的靶解剖距离（EDT）作为该程序的基础。术中由神经生理学专家将电极头端位置定义为术前、术中或术后。来自27名患者的46个轨迹的数据用于计算在这些位置中的每一个中的贝叶斯后验概率，给出RMS-EDT对值。我们使用自举技术测试了我们对每个轨迹的预测，其余的轨迹作为训练集，发现预测的轨迹进入的误差为（平均值+/- D）0.18 +/- 0.84，轨迹退出点的误差为0.50 +/- 0.59 mm，与专家的目标中心偏差为0.30 +/- 0.28 mm。RMS计算的简单性和计算容易性、其尖峰分选独立性质和对该贝叶斯预测器的电极参数的耐受性，可以直接导致开发全自动术中生理程序，用于改进边缘的成像估计。(c)2006年，《社会运动》创刊。
The subthalamic nucleus (STN) is a major target for treatment of advanced Parkinson's disease patients undergoing deep brain stimulation surgery. Microelectrode recording (MER) is used in many cases to identify the target nucleus. A real-time procedure for identifying the entry and exit points of the STN would improve the outcome of this targeting procedure. We used the normalized root mean square (NRMS) of a short (5 seconds) MER sampled signal and the estimated anatomical distance to target (EDT) as the basis for this procedure. Electrode tip location was defined intraoperatively by an expert neurophysiologist to be before, within, or after the STN. Data from 46 trajectories of 27 patients were used to calculate the Bayesian posterior probability of being in each of these locations, given RMS-EDT pair values. We tested our predictions on each trajectory using a bootstrapping technique, with the rest of the trajectories serving as a training set and found the error in predicting the STN entry to be (mean +/- D) 0.18 +/- 0.84, and 0.50 +/- 0.59 mm for STN exit point, which yields a 0.30 +/- 0.28 mm deviation from the expert's target center. The simplicity and computational ease of RMS calculation, its spike sorting-independent nature and tolerance to electrode parameters of this Bayesian predictor, can lead directly to the development of a fully automated intraoperative physiological procedure for the refinement of imaging estimates of STN borders. (c) 2006 Movement Disorder Society.