Multivariate Curve Resolution for Signal Isolation from Fast-Scan Cyclic Voltammetric Data

Abstract

The use of multivariate analysis techniques, such as principal component analysis-inverse least-squares (PCA-ILS), has become standard for signal isolation from in vivo fast-scan cyclic voltammetric (FSCV) data due to its superior noise removal and interferent-detection capabilities. However, the requirement of collecting separate training data for PCA-ILS model construction increases experimental complexity and, as such, has been the source of recent controversy. Here, we explore an alternative method, multivariate curve resolution-alternating least-squares (MCR-ALS), to circumvent this issue while retaining the advantages of multivariate analysis. As compared to PCA-ILS, which relies on explicit user definition of component number and profiles, MCR-ALS relies on the unique temporal signatures of individual chemical components for analyte-profile determination. However, due to increased model freedom, proper deployment of MCR-ALS requires careful consideration of the model parameters and the imposition of constraints on possible model solutions. As such, approaches to achieve meaningful MCR-ALS models are characterized. It is shown, through use of previously reported techniques, that MCR-ALS can produce similar results to PCA-ILS and may serve as a useful supplement or replacement to PCA-ILS for signal isolation from FSCV data.

Next-Generation Diamond Electrodes for Neurochemical Sensing: Challenges and Opportunities

“…Another drawback of these approaches is the need to obtain a separate training set for model generation, which can result in prediction errors if the training set is not carefully constructed from consistent data measured within the same animal with the same electrode. Multivariate curve resolution-alternating least squares (MCR-ALS) has been discussed [ 229 ] as an alternate approach to deal with this issue, which uses raw data itself to define component spectral and concentration profiles needing only the definition of number of components. Other considerations are the processing time for data analysis and errors due to bias.…”

Defining a Path Toward the Use of Fast-Scan Cyclic Voltammetry in Human Studies

“…Data analysis, including calibration and signal extraction algorithms designed to distinguish neurochemical signals from interferents and noise, depend mainly on the electrochemical properties of the FSCV recording system. For example, the material composition, configuration, and surface properties of FSCV electrodes define the electrochemical properties of the recording system and can affect the recorded signals ( Bucher and Wightman, 2015 ; Ganesana et al, 2017 ; Johnson et al, 2017 ; Meunier et al, 2018 ; Puthongkham and Venton, 2019 ). Additionally, electrode materials and recording system commonly used in small animal models must be modified to ensure biocompatibility and the ability to reach deep brain targets.…”

“…Consistency and accuracy can be improved with automated multivariate statistical data analyses, such as principal component regression, partial least squares regression, and statistical models. Typically, information across the scan-potential window can be used to separate overlapping signals by using training sets (i.e., signals obtained from electrodes, recording sessions, and/or subjects other than those used for experimental data collection) as calibration models ( Johnson et al, 2016 , 2017 ; Kishida et al, 2016 ; Lohrenz et al, 2016 ; Rodeberg et al, 2017 ; Meunier et al, 2018 ). The effectiveness of these analysis techniques depends on the existence of well-characterized relationships between the potential at the working electrode and the measured redox currents for the neurochemical species of interest.…”

“…This process oxidizes and/or reduces neurotransmitters at specific potentials and results in electrical currents with amplitude proportional to the concentration of the neurotransmitter in the extracellular space ( Clark et al, 2010 ; Bucher and Wightman, 2015 ; Figure 1 ). Under the right conditions, this enables the estimation of changes in neurotransmitter levels with high spatial and temporal resolution, sensitivity, and chemical selectivity ( Borland and Michael, 2007 ; Johnson et al, 2017 ).…”

Electrochemical Selectivity Achieved Using a Double Voltammetric Waveform and Partial Least Squares Regression: Differentiating Endogenous Hydrogen Peroxide Fluctuations from Shifts in pH

“…26 Alternatively, voltammograms for individual chemical contributors to complex FSCV signals can be resolved using multivariate data analysis paradigms, such as principal component regression (PCR), elastic net regression, 27 or multivariate curve resolution. 28 One example that has been generally accepted by the field is PCR, a combination of principal component analysis with inverse least squares regression. 29–37 PCR utilizes information collected across the entire potential window for a training set comprised of cyclic voltammograms (CVs) of known analytes (and concentrations) to determine principal components (PCs), or basis eigenvectors, that describe the variance in the training data.…”