Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson's disease.
Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson's disease.
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帕金森病患者脑深部电刺激参数优化的半自动化方法
DOI：
10.1186/s12984-021-00873-9
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发表时间：
2021-05-21
影响因子：
5.1
通讯作者：
Netoff TI
中科院分区：
文献类型：
--
作者：
Louie KH;Petrucci MN;Grado LL;Lu C;Tuite PJ;Lamperski AG;MacKinnon CD;Cooper SE;Netoff TI
脑深部电刺激（DBS）是帕金森病患者在药物治疗不足以控制其症状时的治疗选择。DBS可以是一种高效治疗，但仅在耗时的试错刺激参数调整过程之后，该过程易受临床医生偏差的影响。随着现在市场上可买到的分段电极的引入，这种试错过程将进一步延长。需要优化患者的刺激参数的新方法，其还可以处理新电极和刺激器设计的日益增加的复杂性。为了改善DBS参数编程，我们探索了两种半自动优化方法：贝叶斯优化（BayesOpt）算法，以有效地确定患者的最佳刺激参数，以最大限度地减少刚度，和概率高斯过程（pGP），以评估患者的偏好。在两次访问中，使用机器人操作器在两名参与者中获得量化的刚度测量。在第一次访视时在10- 185 Hz（共30-36个频率）之间以5 Hz增量测量刚度，在第二次访视时以8个BayesOpt算法选择的频率测量刚度。参与者还被问及他们对当前和先前刺激频率的偏好。首先，我们比较了访问之间的最佳频率与参与者的首选频率。接下来，我们评估了BayesOpt算法的效率，将其与随机和等间隔频率选择进行比较。BayesOpt算法估计最佳频率为最高可容忍频率，与第一次访问期间找到的最佳频率相匹配。然而，参与者的pGP模型表明他们偏好70-110 Hz之间的频率。在此，刺激频率最低，达到几乎最大的刚性抑制。BayesOpt是有效的，与较长的蛮力方法相比，估计对刺激的刚度响应曲线几乎无法区分。这些结果提供了使用BayesOpt确定最佳频率的可行性的初步证据，而pGP患者的偏好包括更难以测量的结果。这两种新方法都可以缩短DBS程控时间，并且可以扩展到包括多种症状和参数。
Deep brain stimulation (DBS) is a treatment option for Parkinson’s disease patients when medication does not sufficiently manage their symptoms. DBS can be a highly effect therapy, but only after a time-consuming trial-and-error stimulation parameter adjustment process that is susceptible to clinician bias. This trial-and-error process will be further prolonged with the introduction of segmented electrodes that are now commercially available. New approaches to optimizing a patient’s stimulation parameters, that can also handle the increasing complexity of new electrode and stimulator designs, is needed. To improve DBS parameter programming, we explored two semi-automated optimization approaches: a Bayesian optimization (BayesOpt) algorithm to efficiently determine a patient’s optimal stimulation parameter for minimizing rigidity, and a probit Gaussian process (pGP) to assess patient’s preference. Quantified rigidity measurements were obtained using a robotic manipulandum in two participants over two visits. Rigidity was measured, in 5Hz increments, between 10–185Hz (total 30–36 frequencies) on the first visit and at eight BayesOpt algorithm-selected frequencies on the second visit. The participant was also asked their preference between the current and previous stimulation frequency. First, we compared the optimal frequency between visits with the participant’s preferred frequency. Next, we evaluated the efficiency of the BayesOpt algorithm, comparing it to random and equal interval selection of frequency. The BayesOpt algorithm estimated the optimal frequency to be the highest tolerable frequency, matching the optimal frequency found during the first visit. However, the participants’ pGP models indicate a preference at frequencies between 70–110 Hz. Here the stimulation frequency is lowest that achieves nearly maximal suppression of rigidity. BayesOpt was efficient, estimating the rigidity response curve to stimulation that was almost indistinguishable when compared to the longer brute force method. These results provide preliminary evidence of the feasibility to use BayesOpt for determining the optimal frequency, while pGP patient’s preferences include more difficult to measure outcomes. Both novel approaches can shorten DBS programming and can be expanded to include multiple symptoms and parameters.
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影响因子：
5.7
作者：
Butson CR;Cooper SE;Henderson JM;Wolgamuth B;McIntyre CC
通讯作者：
McIntyre CC
影响因子：
158.5
作者：
Follett, Kenneth A.;Weaver, Frances M.;Reda, Domenic J.
通讯作者：
Reda, Domenic J.
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作者：
Hamel, Wolfgang;Koeppen, Johannes A.;Lozano, Andres M.
通讯作者：
Lozano, Andres M.
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作者：
Chaturvedi, Ashutosh;Foutz, Thomas J.;McIntyre, Cameron C.
通讯作者：
McIntyre, Cameron C.
影响因子：
6.1
作者：
KAHNEMAN, D;TVERSKY, A
通讯作者：
TVERSKY, A