Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Department of Psychiatry and Behavioral Science, Emory University, Atlanta, GA, USA.
Neuromodulation. 2019 Jun;22(4):403-415. doi: 10.1111/ner.12929. Epub 2019 Feb 18.
Detailed biophysical modeling of deep brain stimulation (DBS) provides a theoretical approach to quantify the cellular response to the applied electric field. However, the most accurate models for performing such analyses, patient-specific field-cable (FC) pathway-activation models (PAMs), are so technically demanding to implement that their use in clinical research is greatly limited. Predictive algorithms can simplify PAM calculations, but they generally fail to reproduce the output of FC models when evaluated over a wide range of clinically relevant stimulation parameters. Therefore, we set out to develop a novel driving-force (DF) predictive algorithm (DF-Howell), customized to the study of DBS, which can better match FC results.
We developed the DF-Howell algorithm and compared its predictions to FC PAM results, as well as to the DF-Peterson algorithm, which is currently the most accurate and generalizable DF-based method. Comparison of the various methods was quantified within the context of subthalamic DBS using activation thresholds of axons representing the internal capsule, hyperdirect pathway, and cerebellothalamic tract for various combinations of fiber diameters, stimulus pulse widths, and electrode configurations.
The DF-Howell predictor estimated activation of the three axonal pathways with less than a 6.2% mean error with respect to the FC PAM for all 21 cases tested. In 15 of the 21 cases, DF-Howell outperformed DF-Peterson in estimating pathway activation, reducing mean-errors up to 22.5%.
DF-Howell represents an accurate predictor for estimating axonal pathway activation in patient-specific DBS models, but errors still exist relative to FC PAM calculations. Nonetheless, the tractability of DF algorithms helps to reduce the technical barriers for performing accurate biophysical modeling in clinical DBS research studies.
对深部脑刺激（DBS）进行详细的生物物理建模，为量化细胞对所施加电场的反应提供了一种理论方法。然而，用于执行此类分析的最准确模型，即针对患者的场-线（FC）通路激活模型（PAM），在技术上要求很高，以至于在临床研究中的应用受到了极大限制。预测算法可以简化 PAM 计算，但在评估广泛的临床相关刺激参数时，它们通常无法重现 FC 模型的输出。因此，我们着手开发一种新的驱动力（DF）预测算法（DF-Howell），该算法针对 DBS 研究进行了定制，可以更好地匹配 FC 结果。
我们开发了 DF-Howell 算法，并将其预测结果与 FC PAM 结果以及当前最准确和最具通用性的基于 DF 的方法 DF-Peterson 算法进行了比较。在使用不同纤维直径、刺激脉冲宽度和电极配置的情况下，通过代表内囊、直接超皮质通路和小脑丘脑束的轴突激活阈值，在丘脑底核 DBS 的背景下对各种方法进行了量化比较。
DF-Howell 预测器对所有 21 个测试案例的三种轴突通路的激活估计误差均小于 6.2%，与 FC PAM 相比。在 21 个案例中的 15 个案例中，DF-Howell 在估计通路激活方面优于 DF-Peterson，平均误差降低了 22.5%。
DF-Howell 是一种准确的预测器，可用于估计特定于患者的 DBS 模型中的轴突通路激活，但与 FC PAM 计算相比仍存在误差。尽管如此，DF 算法的可处理性有助于降低在临床 DBS 研究中进行精确生物物理建模的技术障碍。
