IEEE Trans Biomed Eng. 2023 Aug;70(8):2496-2505. doi: 10.1109/TBME.2023.3250763. Epub 2023 Jul 18.
Microelectrodes allow the recording of neural activities with a high spatial resolution. However, their small sizes result in high impedance causing high thermal noise and poor signal-to-noise ratio. In drug-resistant epilepsy, the accurate detection of Fast Ripples (FRs) can help in the identification of epileptogenic networks. Consequently, good-quality recordings are instrumental in improving surgical outcomes. In this work, we propose a novel model-based approach for the design of microelectrodes optimized for FRs recording.
A 3D microscale computational model was developed to simulate FRs generated in the hippocampus. It was coupled with a model of the Electrode-Tissue Interface that accounts for the biophysical properties of intracortical microelectrode. This hybrid model was used to analyze the microelectrode geometrical and physical characteristics and their impact on recorded FRs. For model validation, experimental signals (local field potentials, LFPs) were recorded from CA1 using different electrode materials: stainless steel, gold, and gold coated with poly(3,4-ethylene dioxythiophene)/Poly(styrene sulfonate) (Au:PEDOT/PSS).
results indicated that a radius between 65 and 120 μm for a wire microelectrode is the most optimal for recording FRs. In addition, in silico and in vivo quantified results showed a possible improvement in FRs observability using PEDOT/PSS coated microelectrodes.
the optimization of the design of microelectrodes for FRs recording can improve the observability and detectability of FRs which are a recognized marker of epileptogenicity.
This model-based approach can assist in the design of hybrid electrodes that can be used in the presurgical evaluation of epileptic patients with drug-resistant epilepsy.
微电极能够以高空间分辨率记录神经活动。然而，它们的小尺寸导致高阻抗，从而产生高热噪声和低信噪比。在耐药性癫痫中，准确检测快速尖波（FRs）有助于识别致痫网络。因此，高质量的记录对于改善手术结果至关重要。在这项工作中，我们提出了一种新的基于模型的方法，用于设计优化 FRs 记录的微电极。
开发了一个 3D 微尺度计算模型来模拟海马体中产生的 FRs。它与电极-组织界面模型耦合，该模型考虑了颅内微电极的生物物理特性。该混合模型用于分析微电极的几何和物理特性及其对记录的 FRs 的影响。为了模型验证，使用不同的电极材料（不锈钢、金和金涂有聚（3,4-亚乙基二氧噻吩）/聚（苯乙烯磺酸盐）（Au：PEDOT/PSS））从 CA1 记录实验信号（局部场电位，LFP）。
结果表明，用于记录 FRs 的线微电极的半径在 65 到 120 μm 之间是最优化的。此外，体内和体内的量化结果表明，使用 PEDOT/PSS 涂覆的微电极可以提高 FRs 的可观测性。
优化 FRs 记录的微电极设计可以提高 FRs 的可观测性和可检测性，FRs 是致痫性的公认标志物。
这种基于模型的方法可以协助设计混合电极，可用于耐药性癫痫患者的术前评估。
