A baseline drift detrending technique for fast scan cyclic voltammetry

Abstract

Fast scan cyclic voltammetry (FSCV) has been commonly used to measure extracellular neurotransmitter concentrations in the brain. Due to the unstable nature of the background currents inherent in FSCV measurements, analysis of FSCV data is limited to very short amounts of time using traditional background subtraction. In this paper, we propose the use of a zero-phase high pass filter (HPF) as the means to remove the background drift. Instead of the traditional method of low pass filtering across voltammograms to increase signal to noise ratio, a HPF with a low cutoff frequency was applied to temporal data set at each voltage point to remove background drift. As a result, the HPF utilizing cutoff frequencies between 0.001 Hz to 0.01 Hz could be effectively used to a set of FSCV data for removing drifting patterns while preserving the temporal kinetics of the phasic dopamine response recorded in vivo. In addition, compared to a drift removal method using principal component analysis, this was found to be significantly more effective in reducing drift (unpaired t-test p<0.0001, t=10.88) when applied to data collected from tris buffer over 24 hours although a drift removal method using principal component analysis also showed the effective background drift reduction. The HPF was also applied to 5 hours of FSCV in vivo data. Electrically evoked dopamine peaks, observed in the nucleus accumbens, were clearly visible even without background subtraction. This technique provides a new, simple, and yet robust, approach to analyse FSCV data with unstable background.

Tracking tonic dopamine levels in vivo using multiple cyclic square wave voltammetry

“…DOPAC and AA are known for low or non-adsorptive species at CFMs resulting in low sensitivity and fast equilibrium states in multiple pulses (Atcherley et al, 2013). Transient local pH changes in the brain is an accompanying phenomenon with neuronal activity and it has been a major contributor to capacitive background drift in voltammetry (DeWaele et al, 2017). The features of M-CSWV allow the removal of capacitive charging currents which includes the background drift caused by pH changes.…”

Real-Time Fast Scan Cyclic Voltammetry Detection and Quantification of Exogenously Administered Melatonin in Mice Brain

“…Previously, we have demonstrated detection of exogenously administered MT in anesthetized mouse brain using square wave voltammetry (SWV) (Castagnola et al, 2020). Fast scan cyclic voltammetry (FSCV) is an electroanalytical technique with higher temporal resolution (usually 100 ms) (Robinson et al, 2003;Swamy and Venton, 2007;Wood and Hashemi, 2013;Oh et al, 2016;Ou et al, 2019;Puthongkham and Venton, 2020) and, when used in combination with carbon fiber electrodes (CFEs), can achieve detection of sub-second fluctuations in neurotransmitter concentrations in real-time in the brain (Keithley et al, 2011;Wood and Hashemi, 2013;Nguyen and Venton, 2014;Mark DeWaele et al, 2017;Castagnola et al, 2018;Puthongkham and Venton, 2020). However, being primarily a background subtraction technique, FSCV measurements are limited to short time intervals (<90 s) due to the instability of the background currents, i.e., background drifting (Oh et al, 2016;Oh et al, 2016;Mark DeWaele et al, 2017;Meunier et al, 2019).…”

“…Fast scan cyclic voltammetry (FSCV) is an electroanalytical technique with higher temporal resolution (usually 100 ms) (Robinson et al, 2003;Swamy and Venton, 2007;Wood and Hashemi, 2013;Oh et al, 2016;Ou et al, 2019;Puthongkham and Venton, 2020) and, when used in combination with carbon fiber electrodes (CFEs), can achieve detection of sub-second fluctuations in neurotransmitter concentrations in real-time in the brain (Keithley et al, 2011;Wood and Hashemi, 2013;Nguyen and Venton, 2014;Mark DeWaele et al, 2017;Castagnola et al, 2018;Puthongkham and Venton, 2020). However, being primarily a background subtraction technique, FSCV measurements are limited to short time intervals (<90 s) due to the instability of the background currents, i.e., background drifting (Oh et al, 2016;Oh et al, 2016;Mark DeWaele et al, 2017;Meunier et al, 2019). This background drift can be attributed to a number of factors, comprising the changes occurring at the carbon surface itself-i.e., chemical reaction of electrode material, non-specific absorption of proteins, deposition of byproducts of electrochemical reactions (Harreither et al, 2016;Hensley et al, 2018;Puthongkham and Venton, 2020)-and changes in the surrounding chemical and biological neuroenvironment-i.e., pH and local blood flow fluctuations (Mark DeWaele et al, 2017;Roberts and Sombers, 2018;Meunier et al, 2019).…”

“…However, being primarily a background subtraction technique, FSCV measurements are limited to short time intervals (<90 s) due to the instability of the background currents, i.e., background drifting (Oh et al, 2016;Oh et al, 2016;Mark DeWaele et al, 2017;Meunier et al, 2019). This background drift can be attributed to a number of factors, comprising the changes occurring at the carbon surface itself-i.e., chemical reaction of electrode material, non-specific absorption of proteins, deposition of byproducts of electrochemical reactions (Harreither et al, 2016;Hensley et al, 2018;Puthongkham and Venton, 2020)-and changes in the surrounding chemical and biological neuroenvironment-i.e., pH and local blood flow fluctuations (Mark DeWaele et al, 2017;Roberts and Sombers, 2018;Meunier et al, 2019). To predict the noise and extract the signal contribution from the drifting background, signal filtering (Mark DeWaele et al, 2017;Puthongkham and Venton, 2020), multivariate analyses (Hermans et al, 2008;Meunier et al, 2019), and waveform manipulations combined with mathematical techniques (Oh et al, 2016;Meunier et al, 2019) have been investigated, but they require training sets (Johnson et al, 2016;Puthongkham and Venton, 2020) and/or data preprocessing (Puthongkham and Venton, 2020).…”

“…This background drift can be attributed to a number of factors, comprising the changes occurring at the carbon surface itself-i.e., chemical reaction of electrode material, non-specific absorption of proteins, deposition of byproducts of electrochemical reactions (Harreither et al, 2016;Hensley et al, 2018;Puthongkham and Venton, 2020)-and changes in the surrounding chemical and biological neuroenvironment-i.e., pH and local blood flow fluctuations (Mark DeWaele et al, 2017;Roberts and Sombers, 2018;Meunier et al, 2019). To predict the noise and extract the signal contribution from the drifting background, signal filtering (Mark DeWaele et al, 2017;Puthongkham and Venton, 2020), multivariate analyses (Hermans et al, 2008;Meunier et al, 2019), and waveform manipulations combined with mathematical techniques (Oh et al, 2016;Meunier et al, 2019) have been investigated, but they require training sets (Johnson et al, 2016;Puthongkham and Venton, 2020) and/or data preprocessing (Puthongkham and Venton, 2020). On the other hand, different electrochemical procedures have been shown to restore the sensitivity of the carbon surface and preserve their current stability and sensitivity both in vitro and in vivo (Hafizi et al, 1990;Heien et al, 2003;Takmakov et al, 2010), suggesting a potential alternative to stabilize the FSCV current background.…”