Paper
30 March 2012 Predicting brain tissue deformation around an implantable electrode due to dynamic micromotion
Michael Polanco, Hargsoon Yoon, Keejoo Lee, Sebastian Bawab
Author Affiliations +
Abstract
Brain cells located adjacent to an implantable electrode are susceptible to both insertion and mechanical damage due to micromotion as the tissue undergoes cyclic periods of pulsation and breathing. The brain cells inevitably interface with electrodes that are typically much lighter and stiffer in comparison. As a result, the brain's high sensitivity to deformation poses a great challenge in designing a neuron probe that is durable throughout time, as mechanical damage in the brain can reduce the usefulness of the electrode. A number of electrode design parameters need to be examined to determine how the brain's high susceptibility to deformation can be minimized, such as material properties and geometry. Objectively, a neuron probe may need to be designed such that it can conform to motion of the brain while electrical functionality is maintained during deformation. To better understand the design enhancements needed for the neuron probe, a series of dynamic simulations are conducted which represent the motion the brain is expected to undergo over time. This motion will, in turn, influence the motion of the neuron probe throughout time. Of interest is how the brain tissue deformation near the interface of the neuron probe will be affected by micromotion of the probe. The nonlinear transient explicit finite element code LS-DYNA is used to carry out the analyses.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael Polanco, Hargsoon Yoon, Keejoo Lee, and Sebastian Bawab "Predicting brain tissue deformation around an implantable electrode due to dynamic micromotion", Proc. SPIE 8344, Nanosensors, Biosensors, and Info-Tech Sensors and Systems 2012, 83441I (30 March 2012); https://doi.org/10.1117/12.917477
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Brain

3D modeling

Electrodes

Neurons

Tissues

Interfaces

Motion models

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