Department of Information Technology (INTEC-WAVES/IMEC), Ghent University/IMEC, Technologypark 126, 9052 Zwijnaarde, Belgium.
Author to whom any correspondence should be addressed.
J Neural Eng. 2020 Oct 10;17(5):056010. doi: 10.1088/1741-2552/abb73d.
To design a computationally efficient model for ultrasonic neuromodulation (UNMOD) of morphologically realistic multi-compartmental neurons based on intramembrane cavitation.
A Spatially Extended Neuronal Intramembrane Cavitation model that accurately predicts observed fast Charge Oscillations (SECONIC) is designed. A regular spiking cortical Hodgkin-Huxley type nanoscale neuron model of the bilayer sonophore and surrounding proteins is used. The accuracy and computational efficiency of SECONIC is compared with the Neuronal Intramembrane Cavitation Excitation (NICE) and multiScale Optimized model of Neuronal Intramembrane Cavitation (SONIC).
Membrane charge redistribution between different compartments should be taken into account via fourier series analysis in an accurate multi-compartmental UNMOD-model. Approximating charge and voltage traces with the harmonic term and first two overtones results in reasonable goodness-of-fit, except for high ultrasonic pressure (adjusted R-squared ≥0.61). Taking into account the first eight overtones results in a very good fourier series fit (adjusted R-squared ≥0.96) up to 600 kPa. Next, the dependency of effective voltage and rate parameters on charge oscillations is investigated. The two-tone SECONIC-model is one to two orders of magnitude faster than the NICE-model and demonstrates accurate results for ultrasonic pressure up to 100 kPa.
Up to now, the underlying mechanism of UNMOD is not well understood. Here, the extension of the bilayer sonophore model to spatially extended neurons via the design of a multi-compartmental UNMOD-model, will result in more detailed predictions that can be used to validate or falsify this tentative mechanism. Furthermore, a multi-compartmental model for UNMOD is required for neural engineering studies that couple finite difference time domain simulations with neuronal models. Here, we propose the SECONIC-model, extending the SONIC-model by taking into account charge redistribution between compartments.
设计一种基于细胞膜内空化的计算效率高的超声神经调制（UNMOD）形态逼真的多室神经元模型。
设计了一个能准确预测观察到的快速电荷振荡（SECONIC）的空间扩展神经元细胞膜内空化模型。采用双层声子和周围蛋白的规则放电皮质霍奇金-赫胥黎型纳尺度神经元模型。将 SECONIC 的准确性和计算效率与神经元细胞膜内空化激发（NICE）和神经元细胞膜内空化的多尺度优化模型（SONIC）进行了比较。
在准确的多室 UNMOD 模型中，应通过傅里叶级数分析考虑不同隔室之间的膜电荷再分配。用谐波项和前两个泛音近似电荷和电压迹线，除了高超声压（调整后的 R 平方≥0.61）外，结果拟合度较好。考虑前八个泛音可得到非常好的傅里叶级数拟合（调整后的 R 平方≥0.96），最高可达 600kPa。接下来，研究了有效电压和率参数对电荷振荡的依赖性。双音 SECONIC 模型比 NICE 模型快一到两个数量级，在高达 100kPa 的超声压力下表现出准确的结果。
到目前为止，UNMOD 的潜在机制还不太清楚。在这里，通过设计一个多室 UNMOD 模型，将双层声子模型扩展到空间扩展神经元，将产生更详细的预测，可用于验证或否定这一暂定机制。此外，对于将有限差分时域模拟与神经元模型耦合的神经工程研究，需要一个 UNMOD 的多室模型。在这里，我们提出了 SECONIC 模型，通过考虑隔室之间的电荷再分配，扩展了 SONIC 模型。
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