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Journal Articles Journal of Neural Engineering Year : 2018

A review on mechanical considerations for chronically-implanted neural probes

Abstract

This review intends to present a comprehensive analysis of the mechanical considerations for chronically-implanted neural probes. Failure of neural electrical recordings or stimulation over time has shown to arise from foreign body reaction and device material stability. It seems that devices that match most closely with the mechanical properties of the brain would be more likely to reduce the mechanical stress at the probe/tissue interface, thus improving body acceptance. The use of low Young's modulus polymers instead of hard substrates is one way to enhance this mechanical mimetism, though compliance can be achieved through a variety of means. The reduction of probe width and thickness in comparison to a designated length, the use of soft hydrogel coatings and the release in device tethering to the skull, can also improve device compliance. Paradoxically, the more compliant the device, the more likely it will fail during the insertion process in the brain. Strategies have multiplied this past decade to offer partial or temporary stiffness to the device to overcome this buckling effect. A detailed description of the probe insertion mechanisms is provided to analyze potential sources of implantation failure and the need for a mechanically-enhancing structure. This leads us to present an overview of the strategies that have been put in place over the last ten years to overcome buckling issues. Particularly, great emphasis is put on bioresorbable polymers and their assessment for neural applications. Finally, a discussion is provided on some of the key features for the design of mechanically-reliable, polymer-based next generation of chronic neuroprosthetic devices.

Dates and versions

hal-01764292 , version 1 (11-04-2018)

Identifiers

Cite

Aziliz Lecomte, Emeline Descamps, Christian Bergaud. A review on mechanical considerations for chronically-implanted neural probes. Journal of Neural Engineering, 2018, 15 (3), pp.031001. ⟨10.1088/1741-2552/aa8b4f⟩. ⟨hal-01764292⟩
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