University of Michigan, Department of Biomedical Engineering, Carl A. Gerstacker Building, 2200 Bonisteel Blvd, Ann Arbor, MI, 48109, USA; University of Michigan, Department of Neurosurgery, 1500 E Medical Center Drive, Ann Arbor, MI, 48109, USA.
University of Michigan, Department of Neurosurgery, 1500 E Medical Center Drive, Ann Arbor, MI, 48109, USA; University of Michigan, Department of Neurology, 1500 E Medical Center Drive, Ann Arbor, MI, 48109, USA.
Brain Stimul. 2020 Mar-Apr;13(2):412-419. doi: 10.1016/j.brs.2019.11.013. Epub 2019 Dec 4.
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.
Combine high-density, broadband microelectrode recordings with detailed computational models of tissue activation to describe and to predict regions of therapeutic tissue activation.
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.
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.
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.
通过激活精确的神经组织体积，丘脑底核深部脑刺激可缓解帕金森病的运动症状。虽然已经描述了与临床有效电极部位相关的电生理和解剖学相关性，但治疗性刺激可能通过多个不同的神经群体起作用，因此需要对整个组织激活范围进行特征描述。微电极记录尚未与治疗性组织激活体积相关联，并对其预测标记进行调查。
结合高密度、宽带微电极记录和组织激活的详细计算模型，描述和预测治疗性组织激活的区域。
从 16 名帕金森病患者的 23 个丘脑底核深部脑刺激植入物中的 23 个微电极记录中提取电生理特征。这些特征在空间上与治疗性刺激的组织激活体积进行映射，使用临床确定的刺激编程参数以及从 3T 弥散张量磁共振图像得出的完全个体化、与图谱无关的各向异性组织特性进行建模。逻辑 LASSO 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性刺激部位的预测因子。使用这些预测因子的支持向量机用于预测治疗性激活。使用 6 个植入物的测试集验证性能。
分析表明，在微电极记录定义的丘脑底核内，治疗性组织激活的分布存在广泛差异。应用于训练集的逻辑 LASSO 确定了 6 个治疗性组织激活的振荡预测因子：θ、α、β、高γ、高频振荡（HFO，200-400 Hz）和高频带（HFB，500-2000 Hz），以及交互项：θ×HFB、α×β、β×HFB 和高γ×HFO。使用这些特征的支持向量分类器在测试集中以 64%的敏感性和 82%的特异性预测治疗性激活部位，优于仅使用β的分类器。概率预测器在测试数据中获得了 0.87 的接收器操作特征曲线下面积。
总之，这些结果表明个性化靶向的重要性，并验证了一组可预测治疗性激活体积的微电极记录特征。这些特征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。
