It is commonly assumed that the long-term functionality of electrodes used for recording and stimulation in the central nervous system (CNS) is highly dependent on the composition of the electrode-tissue interface (ETI). The ETI consists of various ions, proteins, and cells adhered directly to the electrode contact with a surrounding encapsulation layer. However, there is a lack of quantitative details and experimentally-validated models describing the ETI. This project exploited both experimental and theoretical techniques to provide a more detailed description of the ETI for recording and stimulation applications in the CNS.
In this study, the ETI was characterized for chronic recording with intracortical microelectrode arrays that are typically used in brain machine interface (BMI) applications. This study utilized a detailed cortical recording model incorporating a volume conductor model with an explicit representation of the recording microelectrode and a neural source model. Thermal and biological noise sources were also incorporated into the modeling infrastructure. Model analysis showed electrode design along with a number of other factors (e.g. recording bandwidth, neural density, neural firing rate, etc.) can significantly affect the recording quality.
This study also examined changes at the ETI of chronically implanted deep brain stimulation (DBS) electrodes with electrode impedance spectroscopy (EIS) measurements. Microelectrode voltage recordings showed that changes in the composition of the ETI significantly affected the voltage distributions generated in the brain during voltage-controlled stimulation. However, the effect of these interface changes was minimized during current-controlled stimulation.
This study produced a detailed characterization of the complex environment of the ETI for chronic recording and stimulation applications in the CNS. The details of this complex environment are often oversimplified or neglected; however, the results of this study show that consideration of these details is necessary to understand the confounding factors that can limit the success of recording and stimulation applications in the CNS. This study provides a significant step towards improving the technologies and therapies for BMI and DBS applications and the results of the study can also be applied to the general fields of neural recording and stimulation.