Identification of the subthalamic nucleus in deep brain stimulation surgery with a novel wavelet-derived measure of neural background activity Clinical article
Identification of the subthalamic nucleus in deep brain stimulation surgery with a novel wavelet-derived measure of neural background activity Clinical article
复制标题
DOI：
10.3171/2008.11.jns08392
复制
发表时间：
2009-10-01
影响因子：
4.1
通讯作者：
Aldridge, J. Wayne
中科院分区：
文献类型：
--
作者：
Snellings, Andre;Sagher, Oren;Aldridge, J. Wayne
Object.作者开发了一种基于小波的测量方法，用于术中神经生理记录过程中神经背景活动的定量评估，以便更容易地定位丘脑底核（subthalamic nucleus，ENUB）的边界以进行电极定位。在脑深部刺激器植入手术的靶定位部分，记录了14例多巴胺敏感性特发性帕金森病患者（20个轨迹和275个单独记录部位）的神经电生理数据。在术中记录过程中，根据对神经放电模式的听觉和视觉监测、动觉测试以及神经行为与靶核的已知特征之间的比较来识别神经元。使用市售软件离线应用基于小波的定量测量来测量神经背景活动的幅度，并将该分析的结果与术中结论进行比较。小波衍生的估计也进行了比较，功率谱密度测量。在临床估计的脊髓边界所涵盖的区域中，小波衍生的背景水平显著高于周围区域（脊髓，225 +/- 61 mu V;脊髓腹侧，112 +/- 32 mu V;脊髓背侧，136 +/- 66 mu V）。在每个磁道中，绝对最大幅度都在临床识别的范围内。与功率谱密度测量相比，小波背景水平提供了更一致的指数，其变异性更小。小波衍生的背景活性可以快速计算，不需要尖峰分选，并且可以用于可靠地识别荧光，需要很少的主观解释。这种方法可以促进术中快速识别肿瘤边界。(DOI：10.3171/2008.11.JNS08392）
Object. The authors developed a wavelet-based measure for quantitative assessment of neural background activity during intraoperative neurophysiological recordings so that the boundaries of the subthalamic nucleus (STN) can be more easily localized for electrode implantation.Methods. Neural electrophysiological data were recorded in 14 patients (20 tracks and 275 individual recording sites) with dopamine-sensitive idiopathic Parkinson disease during the target localization portion of deep brain stimulator implantation surgery. During intraoperative recording, the STN was identified based on audio and visual monitoring of neural firing patterns, kinesthetic tests, and comparisons between neural behavior and the known characteristics of the target nucleus. The quantitative wavelet-based measure was applied offline using commercially available software to measure the magnitude of the neural background activity, and the results of this analysis were compared with the intraoperative conclusions. Wavelet-derived estimates were also compared with power spectral density measurements.Results. The wavelet-derived background levels were significantly higher in regions encompassed by the clinically estimated boundaries of the STN than in the surrounding regions (STN, 225 +/- 61 mu V; ventral to the STN, 112 +/- 32 mu V; and dorsal to the STN, 136 +/- 66 mu V). In every track, the absolute maximum magnitude was found within the clinically identified STN. The wavelet-derived background levels provided a more consistent index with less variability than measurements with power spectral density.Conclusions. Wavelet-derived background activity can be calculated quickly, does not require spike sorting, and can be used to identify the STN reliably with very little subjective interpretation required. This method may facilitate the rapid intraoperative identification of STN borders. (DOI: 10.3171/2008.11.JNS08392)