Improving Fast Ripples Recording With Model-Guided Design of Microelectrodes

Objective:
Microelectrodes allow the recording of neural activities with a high spatial resolution. However, their small sizes result in high impedance causing high thermal noise and poor signal-to-noise ratio. In drug-resistant epilepsy, the accurate detection of Fast Ripples (FRs) can help in the identification of epileptogenic networks. Consequently, good-quality recordings are instrumental in improving surgical outcomes. In this work, we propose a novel model-based approach for the design of microelectrodes optimized for FRs recording.
Methods:
A 3D microscale computational model was developed to simulate FRs generated in the hippocampus. It was coupled with a model of the Electrode-Tissue Interface that accounts for the biophysical properties of intracortical microelectrode. This hybrid model was used to analyze the microelectrode geometrical and physical characteristics and their impact on recorded FRs. For model validation, experimental signals (local field potentials, LFPs) were recorded from CA1 using different electrode materials: stainless steel, gold, and gold coated with poly(3,4-ethylene dioxythiophene)/Poly(styrene sulfonate) (Au:PEDOT/PSS).
Results:
results indicated that a radius between 65 and 120 μm for a wire microelectrode is the most optimal for recording FRs. In addition, in silico and in vivo quantified results showed a possible improvement in FRs observability using PEDOT/PSS coated microelectrodes.
Conclusion:
the optimization of the design of microelectrodes for FRs recording can improve the observability and detectability of FRs which are a recognized marker of epileptogenicity.
Significance:
This model-based approach can assist in the design of hybrid electrodes that can be used in the presurgical evaluation of epileptic patients with drug-resistant epilepsy.