TY - JOUR
T1 - Neurochemical Concentration Prediction Using Deep Learning vs Principal Component Regression in Fast Scan Cyclic Voltammetry
T2 - A Comparison Study
AU - Choi, Hoseok
AU - Shin, Hojin
AU - Cho, Hyun U.
AU - Blaha, Charles D.
AU - Heien, Michael L.
AU - Oh, Yoonbae
AU - Lee, Kendall H.
AU - Jang, Dong Pyo
N1 - Publisher Copyright:
© 2022 American Chemical Society.
PY - 2022/8/3
Y1 - 2022/8/3
N2 - Neurotransmitters, such as dopamine and serotonin, are responsible for mediating a wide array of neurologic functions, from memory to motivation. From measurements using fast scan cyclic voltammetry (FSCV), one of the main tools used to detect synaptic efflux of neurochemicals in vivo, principal component regression (PCR), has been commonly used to predict the identity and concentrations of neurotransmitters. However, the sensitivity and discrimination performance of PCR have room for improvement, especially for analyzing mixtures of similar oxidizable neurochemicals. Deep learning may be able to address these challenges. To date, there have been a few studies to apply machine learning to FSCV, but no attempt to apply deep learning to neurotransmitter mixture discrimination and no comparative study have been performed between PCR and deep learning methods to demonstrate which is more accurate for FSCV analysis so far. In this study, we compared the neurochemical identification and concentration estimation performance of PCR and deep learning in an analysis of FSCV recordings of catecholamine and indolamine neurotransmitters. Both analysis methods were tested on in vitro FSCV data with a single or mixture of neurotransmitters at the desired concentration. In addition, the estimation performance of PCR and deep learning was compared in incorporation with in vivo experiments to evaluate the practical usage. Pharmacological tests were also conducted to see whether deep learning would track the increased amount of catecholamine levels in the brain. Using conventional FSCV, we used five electrodes and recorded in vitro background-subtracted cyclic voltammograms from four neurotransmitters, dopamine, epinephrine, norepinephrine, and serotonin, with five concentrations of each substance, as well as various mixtures of the four analytes. The results showed that the identification accuracy errors were reduced 5-20% by using deep learning compared to using PCR for mixture analysis, and the two methods were comparable for single analyte analysis. The applied deep-learning-based method demonstrated not only higher identification accuracy but also better discrimination performance than PCR for mixtures of neurochemicals and even for in vivo testing. Therefore, we suggest that deep learning should be chosen as a more reliable tool to analyze FSCV data compared to conventional PCR methods although further work is still needed on developing complete validation procedures prior to widespread use.
AB - Neurotransmitters, such as dopamine and serotonin, are responsible for mediating a wide array of neurologic functions, from memory to motivation. From measurements using fast scan cyclic voltammetry (FSCV), one of the main tools used to detect synaptic efflux of neurochemicals in vivo, principal component regression (PCR), has been commonly used to predict the identity and concentrations of neurotransmitters. However, the sensitivity and discrimination performance of PCR have room for improvement, especially for analyzing mixtures of similar oxidizable neurochemicals. Deep learning may be able to address these challenges. To date, there have been a few studies to apply machine learning to FSCV, but no attempt to apply deep learning to neurotransmitter mixture discrimination and no comparative study have been performed between PCR and deep learning methods to demonstrate which is more accurate for FSCV analysis so far. In this study, we compared the neurochemical identification and concentration estimation performance of PCR and deep learning in an analysis of FSCV recordings of catecholamine and indolamine neurotransmitters. Both analysis methods were tested on in vitro FSCV data with a single or mixture of neurotransmitters at the desired concentration. In addition, the estimation performance of PCR and deep learning was compared in incorporation with in vivo experiments to evaluate the practical usage. Pharmacological tests were also conducted to see whether deep learning would track the increased amount of catecholamine levels in the brain. Using conventional FSCV, we used five electrodes and recorded in vitro background-subtracted cyclic voltammograms from four neurotransmitters, dopamine, epinephrine, norepinephrine, and serotonin, with five concentrations of each substance, as well as various mixtures of the four analytes. The results showed that the identification accuracy errors were reduced 5-20% by using deep learning compared to using PCR for mixture analysis, and the two methods were comparable for single analyte analysis. The applied deep-learning-based method demonstrated not only higher identification accuracy but also better discrimination performance than PCR for mixtures of neurochemicals and even for in vivo testing. Therefore, we suggest that deep learning should be chosen as a more reliable tool to analyze FSCV data compared to conventional PCR methods although further work is still needed on developing complete validation procedures prior to widespread use.
KW - Deep learning
KW - concentration estimation
KW - discrimination
KW - fast scan cyclic voltammetry
KW - neurochemical
KW - principal component regression
UR - http://www.scopus.com/inward/record.url?scp=85135597745&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135597745&partnerID=8YFLogxK
U2 - 10.1021/acschemneuro.2c00069
DO - 10.1021/acschemneuro.2c00069
M3 - Article
C2 - 35876751
AN - SCOPUS:85135597745
SN - 1948-7193
VL - 13
SP - 2288
EP - 2297
JO - ACS Chemical Neuroscience
JF - ACS Chemical Neuroscience
IS - 15
ER -