Biosensors 2016, 6(2), 27; doi:10.3390/bios6020027
Computational Assessment of Neural Probe and Brain Tissue Interface under Transient Motion
1
Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA 23529, USA
2
Department of Engineering, Center for Materials Research, Norfolk State University, Norfolk, VA 23504, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Jeff D. Newman
Received: 6 February 2016 / Revised: 2 June 2016 / Accepted: 6 June 2016 / Published: 16 June 2016
(This article belongs to the Special Issue Neural Sensing and Interfacing Technology)
Abstract
The functional longevity of a neural probe is dependent upon its ability to minimize injury risk during the insertion and recording period in vivo, which could be related to motion-related strain between the probe and surrounding tissue. A series of finite element analyses was conducted to study the extent of the strain induced within the brain in an area around a neural probe. This study focuses on the transient behavior of neural probe and brain tissue interface with a viscoelastic model. Different stages of the interface from initial insertion of neural probe to full bonding of the probe by astro-glial sheath formation are simulated utilizing analytical tools to investigate the effects of relative motion between the neural probe and the brain while friction coefficients and kinematic frequencies are varied. The analyses can provide an in-depth look at the quantitative benefits behind using soft materials for neural probes. View Full-TextKeywords:
neural electrodes; brain; finite element analysis; viscoelasticity
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Polanco, M.; Bawab, S.; Yoon, H. Computational Assessment of Neural Probe and Brain Tissue Interface under Transient Motion. Biosensors 2016, 6, 27.
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