Department of Information Technology, Uppsala University, Box 337, 75105 Uppsala, Sweden.
J Neural Eng. 2019 Feb;16(1):016020. doi: 10.1088/1741-2552/aaeeb7. Epub 2018 Nov 6.
Deep brain stimulation (DBS) consists of delivering electrical stimuli to a brain target via an implanted lead to treat neurological and psychiatric conditions. Individualized stimulation is vital to ensure therapeutic results, since DBS may otherwise become ineffective or cause undesirable side effects. Since the DBS pulse generator is battery-driven, power consumption incurred by the stimulation is important. In this study, target coverage and power consumption are compared over a patient population for clinical and model-based patient-specific settings calculated by constrained optimization.
Brain models for five patients undergoing bilateral DBS were built. Mathematical optimization of activated tissue volume was utilized to calculate stimuli amplitudes, with and without specifying the volumes, where stimulation was not allowed to avoid side effects. Power consumption was estimated using measured impedance values and battery life under both clinical and optimized settings.
It was observed that clinical settings were generally less aggressive than the ones suggested by unconstrained model-based optimization, especially under asymmetrical stimulation. The DBS settings satisfying the constraints were close to the clinical values.
The use of mathematical models to suggest optimal patient-specific DBS settings that observe technological and safety constraints can save time in clinical practice. It appears though that the considered safety constraints based on brain anatomy depend on the patient and further research into it is needed. This work highlights the need of specifying the brain volumes to be avoided by stimulation while optimizing the DBS amplitude, in contrast to minimizing general stimuli overspill, and applies the technique to a cohort of patients. It also stresses the importance of considering power consumption in DBS optimization, since it increases with the square of the stimuli amplitude and also critically affects battery life through pulse frequency and duty cycle.
深部脑刺激（DBS）通过植入的导线将电刺激传递到脑目标，以治疗神经和精神疾病。个性化刺激至关重要，以确保治疗效果，因为否则 DBS 可能变得无效或引起不良副作用。由于 DBS 脉冲发生器由电池驱动，因此刺激产生的功耗很重要。在这项研究中，针对接受双侧 DBS 的患者群体，比较了临床和基于模型的患者特定设置下的目标覆盖范围和功耗，这些设置是通过约束优化计算得出的。
为 5 名接受双侧 DBS 的患者建立了大脑模型。利用激活组织体积的数学优化来计算刺激幅度，同时指定和不指定刺激不允许发生的区域的体积，以避免副作用。使用测量的阻抗值和电池寿命在临床和优化设置下估算功耗。
观察到临床设置通常比无约束模型优化建议的设置更为保守，尤其是在不对称刺激下。满足约束条件的 DBS 设置接近临床值。
使用数学模型来建议遵守技术和安全约束的最佳患者特定 DBS 设置可以在临床实践中节省时间。但是，考虑到基于大脑解剖结构的安全约束取决于患者，需要进一步研究。这项工作强调了在优化 DBS 幅度时需要指定要避免刺激的大脑体积，而不是最小化一般刺激溢出，并且将该技术应用于一组患者。它还强调了在 DBS 优化中考虑功耗的重要性，因为它与刺激幅度的平方成正比，并且通过脉冲频率和占空比严重影响电池寿命。
