Improving Fast Ripples Recording With Model-Guided Design of Microelectrodes.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Materials Science

Background:

• High impedance of microelectrodes causes noise, hindering neural recordings.
• Accurate detection of Fast Ripples (FRs) is vital for identifying epileptogenic networks in drug-resistant epilepsy.
• Improved signal-quality is essential for better surgical outcomes in epilepsy patients.

Purpose of the Study:

• To propose a novel model-based approach for designing microelectrodes optimized for Fast Ripples (FRs) recording.
• To analyze the impact of microelectrode geometry and material properties on FRs detection.
• To enhance the observability and detectability of FRs for presurgical evaluation.

Main Methods:

• Developed a 3D microscale computational model simulating hippocampal FRs.
• Coupled the model with an Electrode-Tissue Interface accounting for biophysical properties.
• Validated the model using in vivo recordings (LFPs) with various electrode materials (stainless steel, gold, Au:PEDOT/PSS).

Main Results:

• Optimal wire microelectrode radius for FRs recording identified between 65 and 120 μm.
• PEDOT/PSS coated microelectrodes demonstrated improved FRs observability in both in silico and in vivo studies.
• Model-based simulations correlated well with experimental data.

Conclusions:

• Microelectrode design optimization enhances FRs observability and detectability, key markers of epileptogenicity.
• The model-based approach aids in designing hybrid electrodes for presurgical evaluation of drug-resistant epilepsy.
• This research contributes to improving diagnostic accuracy and surgical planning for epilepsy management.