Coupled Eulerian-Lagrangian model prediction of neural tissue strain during microelectrode insertion

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computational Mechanics

Background:

• Implanted neural microelectrodes are vital for brain recording and stimulation.
• Tissue response to chronic implants often hinders device performance.
• Previous models overlooked insertion-induced tissue damage, a key inflammation factor.

Purpose of the Study:

• To computationally evaluate the impact of electrode geometry, insertion speed, and friction on brain tissue strain during insertion.
• To identify design parameters that may minimize initial tissue trauma and subsequent inflammation.

Main Methods:

• Developed a 3D finite element model (FEM) using a coupled Eulerian-Lagrangian approach.
• Simulated dynamic insertion of neural microelectrodes with varied geometries (tip bluntness, cross-section, shank thickness).
• Analyzed effects of varying insertion velocities (1-8 m/s) and friction coefficients (0-0.4) on tissue strain and hemorrhage radius.

Main Results:

• Sharper tips increased strain near the tip; blunter tips with square cross-sections caused stress concentrations.
• Smaller shank diameters reduced radial strain distribution, but square shanks with specific tapers increased damage radius.
• Faster insertion increased tip strain but decreased shank strain; higher friction increased strain along the shank and tip, while frictionless conditions caused tearing.

Conclusions:

• This is the first dynamic FEM study of neural electrode insertion.
• Electrode geometry, insertion speed, and friction significantly influence tissue strain and potential damage.
• Findings identify design factors to reduce initial tissue trauma during neural microelectrode implantation.