Investigating the depth electrode-brain interface in deep brain stimulation using finite element models with graded...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computational Modeling

Background:

• Deep brain stimulation (DBS) is a surgical therapy for neurological disorders, but its mechanisms remain unclear.
• The electrode-brain interface (EBI) properties change post-implantation, potentially affecting stimulation efficacy.
• Previous research defined the EBI's three structural components: DBS electrode, peri-electrode space, and brain tissue.

Purpose of the Study:

• To investigate the biophysical properties of the electrode-brain interface (EBI) in deep brain stimulation (DBS).
• To model the effects of the EBI on the electric field generated by DBS.
• To understand how EBI characteristics influence neuronal activation during DBS.

Main Methods:

• Developed structural computational models of the electrode-brain interface (EBI).
• Utilized coupled axon models to estimate stimulation effects.
• Employed finite element modeling with varying complexity, including quasi-static and frequency-dependent models.
• Incorporated anatomical features like the ventricular system into models.

Main Results:

• Quasi-static models effectively differentiate acute and chronic EBI stages.
• Frequency-dependent models are essential for understanding waveform shaping's impact on neuronal activation.
• Anatomical variations, such as the ventricular system, influence the electric field distribution.
• Models visualize static, dynamic, and target-specific properties of the DBS-induced electric field.

Conclusions:

• Computational modeling provides insights into the complex electrode-brain interface (EBI) in deep brain stimulation (DBS).
• Model findings highlight the importance of EBI properties and stimulation parameters for effective DBS.
• This approach aids in visualizing and understanding the DBS electric field for improved therapeutic outcomes.