Fabrication and Dose–Response Simulation of Soft Dual-Sided Deep Brain Stimulation Electrode
A 16-channel dual-sided flexible electrode based on a polyimide substrate was designed and fabricated using micro-electromechanical system (MEMS) technology. The electrode exhibited an average impedance of 5.9 kΩ at 1 kHz and a charge storage capacity (CSC) of 10.63 mC/cm<sup>2</sup>. Concurrently, a three-dimensional finite element model incorporating electrical stimulation and micromotion-induced damage was established. The simulation results demonstrated that the implantation trauma caused by the bilateral electrode was significantly lower compared with silicon-based and cylindrical electrodes, while also enabling directional stimulation. Furthermore, leveraging the design of experiments (DOE) methodology, a multivariate regression model was developed to investigate the influence of key stimulation parameters-namely, current amplitude, frequency, and pulse width-on the volume of tissue activated (VTA). The results indicated that the regression model provided accurate predictions of VTA (R<sup>2</sup> = 0.912). Among the parameters, current amplitude and pulse width exerted a statistically significant influence on VTA size (<i>p</i> < 0.001), whereas the effect of frequency was comparatively minor (<i>p</i> = 0.387 > 0.05). This study presents the first successful fabrication and comprehensive dose-response analysis of a flexible bilateral DBS electrode. Its attributes of low implantation trauma, multi-channel capability, and directional stimulation offer a novel paradigm for precise neuromodulation. Additionally, the established stimulation parameter-VTA response model provides a robust theoretical foundation for optimizing therapeutic parameters in subsequent clinical applications.
Authors
- Bei Tong (ORCID: https://orcid.org/0000-0002-6863-6019)
- Li Huang (ORCID: https://orcid.org/0000-0003-1432-2054)
- Guoting Fu
- Dengfei Yang
- Jian Zhang (ORCID: https://orcid.org/0009-0001-0623-2422)
- Changmao Ni
Institutions
- Wuhan University of Technology (CN)
Publication Details
- Journal
- Micromachines
- Published
- 2025-08-18
- DOI
- https://doi.org/10.3390/mi16080945
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00