|Title||Long-term recording reliability of liquid crystal polymer µECoG arrays.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||V Woods, M Trumpis, B Bent, K Palopoli-Trojani, C-H Chiang, C Wang, C Yu, MN Insanally, RC Froemke, and J Viventi|
|Journal||Journal of Neural Engineering|
OBJECTIVE:The clinical use of microsignals recorded over broad cortical regions is largely limited by the chronic reliability of the implanted interfaces. APPROACH:We evaluated the chronic reliability of novel 61-channel micro-electrocorticographic (µECoG) arrays in rats chronically implanted for over one year and using accelerated aging. Devices were encapsulated with polyimide (PI) or liquid crystal polymer (LCP), and fabricated using commercial manufacturing processes. In vitro failure modes and predicted lifetimes were determined from accelerated soak testing. Successful designs were implanted epidurally over the rodent auditory cortex. Trends in baseline signal level, evoked responses and decoding performance were reported for over one year of implantation. MAIN RESULTS:Devices fabricated with LCP consistently had longer in vitro lifetimes than PI encapsulation. Our accelerated aging results predicted device integrity beyond 3.4 years. Five implanted arrays showed stable performance over the entire implantation period (247-435 d). Our regression analysis showed that impedance predicted signal quality and information content only in the first 31 d of recordings and had little predictive value in the chronic phase (>31 d). In the chronic phase, site impedances slightly decreased yet decoding performance became statistically uncorrelated with impedance. We also employed an improved statistical model of spatial variation to measure sensitivity to locally varying fields, which is typically concealed in standard signal power calculations. SIGNIFICANCE:These findings show that µECoG arrays can reliably perform in chronic applications in vivo for over one year, which facilitates the development of a high-density, clinically viable interface.
|Short Title||Journal of Neural Engineering|