BACKGROUND Subthalamic deep brain stimulation alleviates motor symptoms of Parkinson disease by activating precise volumes of neural tissue. While electrophysiological and anatomical correlates of clinically effective electrode sites have been described, therapeutic stimulation likely acts through multiple distinct neural populations, necessitating characterization of the full span of tissue activation. Microelectrode recordings have yet to be mapped to therapeutic tissue activation volumes and surveyed for predictive markers. OBJECTIVE Combine high-density, broadband microelectrode recordings with detailed computational models of tissue activation to describe and to predict regions of therapeutic tissue activation. METHODS Electrophysiological features were extracted from microelectrode recordings along 23 subthalamic deep brain stimulation implants in 16 Parkinson disease patients. These features were mapped in space against tissue activation volumes of therapeutic stimulation, modeled using clinically-determined stimulation programming parameters and fully individualized, atlas-independent anisotropic tissue properties derived from 3T diffusion tensor magnetic resonance images. Logistic LASSO was applied to a training set of 17 implants out of the 23 implants to identify predictors of therapeutic stimulation sites in the microelectrode recording. A support vector machine using these predictors was used to predict therapeutic activation. Performance was validated with a test set of six implants. RESULTS Analysis revealed wide variations in the distribution of therapeutic tissue activation across the microelectrode recording-defined subthalamic nucleus. Logistic LASSO applied to the training set identified six oscillatory predictors of therapeutic tissue activation: theta, alpha, beta, high gamma, high frequency oscillations (HFO, 200-400 Hz), and high frequency band (HFB, 500-2000 Hz), in addition to interaction terms: theta x HFB, alpha x beta, beta x HFB, and high gamma x HFO. A support vector classifier using these features predicted therapeutic sites of activation with 64% sensitivity and 82% specificity in the test set, outperforming a beta-only classifier. A probabilistic predictor achieved 0.87 area under the receiver-operator curve with test data. CONCLUSIONS Together, these results demonstrate the importance of personalized targeting and validate a set of microelectrode recording signatures to predict therapeutic activation volumes. These features may be used to improve the efficiency of deep brain stimulation programming and highlight specific neural oscillations of physiological importance.

中文翻译：

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高密度微电极记录预测帕金森病下丘脑深部脑刺激治疗组织激活体积的跨度

背景下丘脑深部脑刺激通过激活精确体积的神经组织来减轻帕金森病的运动症状。虽然已经描述了临床有效电极部位的电生理学和解剖学相关性，但治疗刺激可能通过多个不同的神经群体起作用，需要对组织激活的整个跨度进行表征。微电极记录尚未映射到治疗组织激活体积并调查预测标记。目标将高密度、宽带微电极记录与详细的组织激活计算模型相结合，以描述和预测治疗性组织激活区域。方法从 16 名帕金森病患者的 23 个丘脑深部脑刺激植入物的微电极记录中提取电生理特征。这些特征相对于治疗刺激的组织激活体积映射在空间中，使用临床确定的刺激编程参数和完全个性化的、独立于图谱的各向异性组织特性建模，这些特性源自 3T 扩散张量磁共振图像。Logistic LASSO 被应用于 23 个植入物中的 17 个植入物的训练集，以识别微电极记录中治疗刺激部位的预测因子。使用这些预测器的支持向量机用于预测治疗激活。性能通过六个植入物的测试集进行了验证。结果 分析显示，微电极记录定义的丘脑底核中治疗性组织激活的分布存在很大差异。应用于训练集的 Logistic LASSO 确定了治疗组织激活的六个振荡预测因子：theta、alpha、beta、高 gamma、高频振荡（HFO，200-400 Hz）和高频带（HFB，500-2000 Hz），除了相互作用项：theta x HFB、alpha x beta、beta x HFB 和 high gamma x HFO。使用这些特征的支持向量分类器在测试集中以 64% 的灵敏度和 82% 的特异性预测激活的治疗位点，优于仅测试版的分类器。概率预测器在测试数据的接受者-操作者曲线下达到 0.87 面积。结论 一起，这些结果证明了个性化靶向的重要性，并验证了一组微电极记录特征以预测治疗激活量。这些功能可用于提高深部脑刺激编程的效率，并突出具有生理重要性的特定神经振荡。