Artificial neural network based characterization of the volume of tissue activated during deep brain stimulation.
Artificial neural network based characterization of the volume of tissue activated during deep brain stimulation.
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DOI：
10.1088/1741-2560/10/5/056023
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发表时间：
2013-10
影响因子：
4
通讯作者：
McIntyre CC
中科院分区：
文献类型：
--
作者：
Chaturvedi A;Luján JL;McIntyre CC
临床脑深部电刺激（DBS）系统可以使用数千种不同的刺激参数组合（例如电极触点、电压、脉冲宽度、频率）进行编程。我们的目标是开发新的计算工具来表征DBS刺激参数调整的影响。激活组织体积（VTA）代表用于估计给定参数设置的DBS空间范围的度量。用于计算VTA的传统方法依赖于基于激活函数（AF）的方法，并且当通过多个电极触点施加刺激时，倾向于高估神经响应。因此，我们创造了一种新的方法VTA计算，依赖于人工神经网络（ANN）。与基于AF的单极刺激方法相比，基于ANN的预测器提供了更准确的激活空间分布描述。此外，人工神经网络能够准确地估计VTA响应于多触点电极配置。基于ANN的方法可以代表在具有有限计算资源的情况下快速计算VTA的有用方法，诸如平板计算机上的临床DBS编程应用。
Clinical deep brain stimulation (DBS) systems can be programmed with thousands of different stimulation parameter combinations (e.g. electrode contact(s), voltage, pulse width, frequency). Our goal was to develop novel computational tools to characterize the effects of stimulation parameter adjustment for DBS. The volume of tissue activated (VTA) represents a metric used to estimate the spatial extent of DBS for a given parameter setting. Traditional methods for calculating the VTA rely on activation function (AF)-based approaches and tend to overestimate the neural response when stimulation is applied through multiple electrode contacts. Therefore, we created a new method for VTA calculation that relied on artificial neural networks (ANNs). The ANN-based predictor provides more accurate descriptions of the spatial spread of activation compared to AF-based approaches for monopolar stimulation. In addition, the ANN was able to accurately estimate the VTA in response to multi-contact electrode configurations. The ANN-based approach may represent a useful method for fast computation of the VTA in situations with limited computational resources, such as a clinical DBS programming application on a tablet computer.