Amorphous silicon carbide probe mechanics for insertion in the cerebral cortex of rats, pigs, and macaques

Abstract Objective:
Intracortical microelectrode arrays (MEAs) are implantable devices used for neural recording and stimulation. However, their long-term performance is often compromised due, at least in part, to glial scar formation, initiated by microglial migration and astrocyte activation following implantation. To address this issue, ultra-thin microelectrode arrays (UMEAs) have been proposed as an alternative due to their reduced cross-sectional area and enhanced flexibility that minimizes the mechanical mismatch between brain tissue and the electrode. These properties are expected to mitigate the persistent foreign-body response associated with micromotion. However, unaided implantation of UMEAs can be challenging, as their high flexibility may increase the likelihood of buckling and curtail the precise penetration into the brain. Approach: We investigated flexible amorphous silicon carbide (a-SiC) probe designs with varying cross-sectional areas and lengths to identify geometries that enable unsupported implantation into the cerebral cortices of rat, pig, and macaques. The critical buckling force of the a-SiC probes was experimentally determined as a function of geometry and formally described via finite element modeling (FEM), which predicted buckling behavior. Additionally, the penetration behavior of a-SiC probes was evaluated by measuring force-displacement responses during insertion into cortical tissues across species.
Main results:
Our findings demonstrated that the penetration force and cortical dimpling depth were not significantly influenced by the range of probe geometries tested. However, we observed that penetration force and dimpling depth were significantly lower in rat cortices than in larger species. Importantly, probe geometries with a higher ratio (≥2) of critical buckling force to penetration force exhibited a 100% success rate for unaided insertion. Significance: This study provides a framework for designing and evaluating UMEA geometries to optimize unsupported implantation in both small and large animal brains.