(Invited) Invasive Cortical Microelectrode Array Longitudinal Performance: Temporal Dynamics of Electrical Impedance Spectroscopy and Multiunit Activity

Tuesday, 3 October 2017: 11:00
National Harbor 11 (Gaylord National Resort and Convention Center)
C. Welle (University of Colorado Denver, Anschutz Medical Campus), M. G. Street (US FDA), K. Ruda (Duke University), E. Civillico (National Institutes of Health), and P. A. Takmakov (US FDA)
Addition of neural sensing elements to neurostimulation devices can ‘close the loop’ to provide dynamic, patient-specific neuromodulation. Recently approved medical devices, such as the Neuropace RNS System for drug-resistant epilepsy and the Inspire Upper Airway Stimulation device monitor the patient’s electrophysiological activity and titrate stimulation accordingly, improving therapeutic outcomes. Current devices rely on a few, large electrode contacts; however, high-density electrode interfaces will provide greater information content to maximize the therapeutic benefit.

High-density neural interfaces have been explored in the context of brain computer interface devices, which utilize microelectrode arrays to detect neural activity used to control prosthetic devices. Although the high-density multielectrode arrays implanted in human cortex can record from hundreds of neurons simultaneously, the device performance gradually declines within six to twelve months post-implantation as the number of recorded neurons dwindles. Preclinical investigations have identified several key factors implicated in this failure, including poor tissue integration resulting in inflammation, blood-brain barrier compromise and electrode material breakdown.

To differentiate between tissue-related, biological failure modalities and material-related physical modalities, we performed electrical impedance spectroscopy (EIS) before, during and after the long-term implantation of high-density electrode arrays in the mouse motor cortex. Three different types of commercial research-grade electrodes were sterilized with ethylene oxide and implanted into the neocortex (Blackrock, Neuronexus and Microprobes for Life Science). Implant duration lasted for between 6-12 months, and EIS was collected across the implant duration. EIS was measured over frequencies ranging from 1Hz to 1MHz with 5 frequency points per decade using a Gamry Reference 600 potentiostat (Gamry Instruments). Multi-unit activity was also collected throughout the course of implantation during periods of spontaneous movement in awake, behaving animals using a 16-channel data acquisition system (Neuralynx).

As expected, electrode implantation lead to an overall increase in impedance across three device types. However, the largest increase in impedance was not centered around 1kHz (a common frequency for impedance spectroscopy in vivo), but instead centered between 10-100kHz for all array types. Much of this increase was reduced when EIS was measured after the arrays were explanted from the brain, suggesting that changes in the resistive component of the tissue medium is the largest contribution to in vivo impedance dynamics. Interestingly, all arrays demonstrated a considerable change in impedance phase, centered between 100kHz and 1MHz during the course of implantation.

The electrophysiological signals recorded from each animal were analyzed using custom routines (MATLAB, Igor). Mechanical and EMG artifacts were subtracted using coherence-based common average referencing, and then two metrics of neural firing were calculated; the event rate of signals 3.5SD above the mean, and power in the spectral band between 1000 and 1500 Hz.

Temporal dynamics of electrophysiological and EIS signals were compared for each electrode across the implant duration. In addition, scanning electron microscopy (SEM) images of the explanted arrays were scored for measures of physical damage, including tip breakage, metallization loss, insulation delamination, cracking or loss and broken connectivity. Characteristics from each of these separate modalities were combined into custom machine learning algorithms to identify relationships between factors.

Through this work, we were able to characterize the longitudinal impedance dynamics up to a year post implantation, to dissociate the contribution of biological and physical factors to the impedance change, and perform electrode-by-electrode correlation between impedance, physical damage and electrophysiological signal quality.