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Title: Flexural bending to approximate cortical forces exerted by electrocorticography (ECoG) arrays
Abstract Objective. The force that an electrocorticography (ECoG) array exerts on the brain manifests when it bends to match the curvature of the skull and cerebral cortex. This force can negatively impact both short-term and long-term patient outcomes. Here we provide a mechanical characterization of a novel liquid crystal polymer (LCP) ECoG array prototype to demonstrate that its thinner geometry reduces the force potentially applied to the cortex of the brain. Approach. We built a low-force flexural testing machine to measure ECoG array bending forces, calculate their effective flexural moduli, and approximate the maximum force they could exerted on the human brain. Main results. The LCP ECoG prototype was found to have a maximal force less than 20% that of any commercially available ECoG arrays that were tested. However, as a material, LCP was measured to be as much as 24× more rigid than silicone, which is traditionally used in ECoG arrays. This suggests that the lower maximal force resulted from the prototype’s thinner profile (2.9×–3.25×). Significance. While decreasing material stiffness can lower the force an ECoG array exhibits, our LCP ECoG array prototype demonstrated that flexible circuit manufacturing techniques can also lower these forces by decreasing ECoG array thickness. Flexural tests of ECoG arrays are necessary to accurately assess these forces, as material properties for polymers and laminates are often scale dependent. As the polymers used are anisotropic, elastic modulus cannot be used to predict ECoG flexural behavior. Accounting for these factors, we used our four-point flexure testing procedure to quantify the forces exerted on the brain by ECoG array bending. With this experimental method, ECoG arrays can be designed to minimize force exerted on the brain, potentially improving both acute and chronic clinical utility.  more » « less
Award ID(s):
1752274
NSF-PAR ID:
10405899
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Journal of Neural Engineering
Volume:
19
Issue:
4
ISSN:
1741-2560
Page Range / eLocation ID:
046041
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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