University Institute of Intelligent Systems and Numerical Applications in Engineering (SIANI), University of Las Palmas de Gran Canaria, Las Palmas, Spain.
Department of Otolaryngology, Head and Neck Surgery, Complejo Hospitalario Universitario Insular Materno Infantil de Gran Canaria, Las Palmas, Spain.
PLoS Comput Biol. 2022 May 27;18(5):e1010134. doi: 10.1371/journal.pcbi.1010134. eCollection 2022 May.
There is a growing interest in biomedical engineering in developing procedures that provide accurate simulations of the neural response to electrical stimulus produced by implants. Moreover, recent research focuses on models that take into account individual patient characteristics. We present a phenomenological computational model that is customized with the patient's data provided by the electrically evoked compound action potential (ECAP) for simulating the neural response to electrical stimulus produced by the electrodes of cochlear implants (CIs). The model links the input currents of the electrodes to the simulated ECAP. Potentials and currents are calculated by solving the quasi-static approximation of the Maxwell equations with the finite element method (FEM). In ECAPs recording, an active electrode generates a current that elicits action potentials in the surrounding auditory nerve fibers (ANFs). The sum of these action potentials is registered by other nearby electrode. Our computational model emulates this phenomenon introducing a set of line current sources replacing the ANFs by a set of virtual neurons (VNs). To fit the ECAP amplitudes we assign a suitable weight to each VN related with the probability of an ANF to be excited. This probability is expressed by a cumulative beta distribution parameterized by two shape parameters that are calculated by means of a differential evolution algorithm (DE). Being the weights function of the current density, any change in the design of the CI affecting the current density produces changes in the weights and, therefore, in the simulated ECAP, which confers to our model a predictive capacity. The results of the validation with ECAP data from two patients are presented, achieving a satisfactory fit of the experimental data with those provided by the proposed computational model.
人们对于生物医学工程越来越感兴趣，希望开发出能够准确模拟植入物产生的电刺激对神经反应的程序。此外，最近的研究还侧重于考虑个体患者特征的模型。我们提出了一种现象学计算模型，该模型可以根据电诱发复合动作电位 (ECAP) 提供的患者数据进行定制，以模拟耳蜗植入物 (CI) 电极产生的电刺激对神经的反应。该模型将电极的输入电流与模拟的 ECAP 联系起来。通过有限元方法 (FEM) 求解麦克斯韦方程组的准静态近似来计算电势和电流。在 ECAP 记录中，一个活动电极产生电流，使周围的听神经纤维 (ANF) 产生动作电位。这些动作电位的总和由其他附近的电极记录。我们的计算模型通过引入一组线电流源来模拟这种现象，用一组虚拟神经元 (VN) 代替 ANF。为了拟合 ECAP 幅度，我们为每个 VN 分配一个合适的权重，该权重与激发 ANF 的概率相关。该概率由两个形状参数表示，通过差分进化算法 (DE) 进行参数化。作为电流密度的权重函数，CI 设计的任何变化都会影响电流密度，从而改变权重，进而改变模拟的 ECAP，这使我们的模型具有预测能力。我们使用来自两个患者的 ECAP 数据进行了验证，结果表明，该计算模型能够很好地拟合实验数据。
