The Detrending of FSCV Data Through the Use of a High Pass Filter Along With Improved Limits of Detection Through a New Application of a Conventional Low Pass Filter

Abstract

Fast scan cyclic voltammetry (FSCV) is commonly used to measure extracellular neurotransmitter concentrations in the brain. Due to the constantly changing background currents recorded during FSCV experiments, experiment recording times are limited. Another issue that FSCV research faces is lowering the detection limits of target neurotransmitters in the brain. Here, we propose solutions to both these problems. First, we propose the use of a zero-phase high pass filter (HPF) to remove the drifting background currents and extend usable recording times. A HPF with a very low cutoff frequency was applied to temporal FSCV data at each recorded voltage point. We found that a range of cutoff frequencies from 0.001Hz to 0.01Hz could be utilized to remove background drift while preserving signals’ temporal kinetics. When compared with principal component regression (PCR), it was found to be significantly more effective in removing the drifting background current (unpaired t-test p<0.0001, t=22.87) when applied to data recorded over 24 hours in Tris buffer. Without any conventional background subtraction, applying the HPF to in vivo data, electrically evoked dopamine peaks were clearly visible with no temporal distortion. Secondly, propose a new method of low pass filtering (LPF) to smooth FSCV data by applying a conventional LPF in the frequency domain. Instead of applying a LPF across a set of time data, we compile the Fourier Transforms of each time series and filter the compiled frequencies. A range of normalized cutoff frequencies from 0.02 to 0.03 was found to increase signal to noise ratio in recordings and also decrease detection limits of target neurotransmitters. This method also does not eliminate any frequencies. In this way, it only eliminates random noise and not any meaningful data. These two techniques can provide very simple, and yet robust tools for researchers to more easily analyze FSCV data.