Removal of Differential Capacitive Interferences in Fast-Scan Cyclic Voltammetry

Area of Science:

• Neuroscience
• Analytical Chemistry
• Electrochemistry

Background:

• Fast-scan cyclic voltammetry (FSCV) offers high spatiotemporal resolution for in vivo neurotransmitter monitoring.
• Accurate signal analysis in FSCV requires digital background subtraction to remove capacitive and faradaic currents.
• Electrode state and ionic environment changes cause background current fluctuations, complicating FSCV data interpretation.

Purpose of the Study:

• To investigate the origins of background current shifts in FSCV caused by cation changes.
• To develop a model explaining the shape of these background current shifts.
• To introduce a novel convolution-based method for removing differential capacitive contributions in FSCV.

Main Methods:

• Explored background current shifts due to local cation concentration changes.
• Developed a model to characterize the shape of background current fluctuations.
• Implemented a convolution method using a small-amplitude pulse to probe system impedance and predict non-faradaic currents.

Main Results:

• A convolution-based method was developed to accurately predict and subtract non-faradaic currents in FSCV.
• This technique effectively removes artifacts caused by changes in electrode impedance.
• The method demonstrated successful removal of capacitive artifacts both in vitro and in vivo during spreading depression events.

Conclusions:

• The developed convolution method significantly improves the accuracy of FSCV data analysis by correcting for electrode impedance variations.
• This advancement enables more reliable localized in vivo monitoring of subsecond neurotransmitter fluctuations.
• The technique offers a robust solution for mitigating nonspecific contributions in FSCV, advancing neuroscience research.