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Prediction of STN-DBS Electrode Implantation Track in Parkinsons Disease by Using Local Field Potentials

机译:利用局部场势预测帕金森病STN-DBS电极植入轨迹

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摘要

Optimal electrophysiological placement of the DBS electrode may lead to better long term clinical outcomes. Inter-subject anatomical variability and limitations in stereotaxic neuroimaging increase the complexity of physiological mapping performed in the operating room. Microelectrode single unit neuronal recording remains the most common intraoperative mapping technique, but requires significant expertise and is fraught by potential technical difficulties including robust measurement of the signal. In contrast, local field potentials (LFPs), owing to their oscillatory and robust nature and being more correlated with the disease symptoms, can overcome these technical issues. Therefore, we hypothesized that multiple spectral features extracted from microelectrode-recorded LFPs could be used to automate the identification of the optimal track and the STN localization. In this regard, we recorded LFPs from microelectrodes in three tracks from 22 patients during DBS electrode implantation surgery at different depths and aimed to predict the track selected by the neurosurgeon based on the interpretation of single unit recordings. A least mean square (LMS) algorithm was used to de-correlate LFPs in each track, in order to remove common activity between channels and increase their spatial specificity. Subband power in the beta band (11–32 Hz) and high frequency range (200–450 Hz) were extracted from the de-correlated LFP data and used as features. A linear discriminant analysis (LDA) method was applied both for the localization of the dorsal border of STN and the prediction of the optimal track. By fusing the information from these low and high frequency bands, the dorsal border of STN was localized with a root mean square (RMS) error of 1.22 mm. The prediction accuracy for the optimal track was 80%. Individual beta band (11–32 Hz) and the range of high frequency oscillations (200–450 Hz) provided prediction accuracies of 72 and 68% respectively. The best prediction result obtained with monopolar LFP data was 68%. These results establish the initial evidence that LFPs can be strategically fused with computational intelligence in the operating room for STN localization and the selection of the track for chronic DBS electrode implantation.
机译:DBS电极的最佳电生理放置可能会导致更好的长期临床效果。受试者间的解剖变异性和立体定向神经影像学的局限性增加了在手术室中进行生理标测的复杂性。微电极单单位神经元记录仍然是最常见的术中标测技术,但需要大量的专业知识,并且充满潜在的技术难题,包括信号的可靠测量。相反,由于它们的振荡性和鲁棒性,并且与疾病症状更相关,因此局部场电势(LFP)可以克服这些技术问题。因此,我们假设从微电极记录的LFP中提取的多个光谱特征可用于自动识别最佳轨道和STN定位。在这方面,我们在22个患者的DBS电极植入手术期间,以不同深度记录了来自22个患者的三个轨迹中的微电极LFP,旨在根据单个单位记录的解释来预测神经外科医生选择的轨迹。为了消除通道之间的共同活动并提高其空间特异性,使用了最小均方(LMS)算法对每个轨道中的LFP进行去相关。从解相关的LFP数据中提取了beta频带(11–32 Hz)和高频范围(200–450 Hz)中的子带功率,并将其用作特征。线性判别分析(LDA)方法既用于STN背边界的定位,又用于预测最佳轨迹。通过融合来自这些低频段和高频段的信息,可以将STN的背边界定位为均方根(RMS)误差为1.22 mm。最佳轨道的预测精度为80%。单独的β波段(11–32 Hz)和高频振荡范围(200–450 Hz)分别提供了72%和68%的预测精度。使用单极LFP数据获得的最佳预测结果为68%。这些结果建立了最初的证据,即LFP可以在手术室中策略性地与STN本地化和慢性DBS电极植入的轨迹选择相融合。

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