Neurochemical Concentration Prediction Using Deep Learning vs Principal Component Regression in Fast Scan Cyclic Voltammetry: A Comparison Study.
Neurochemical Concentration Prediction Using Deep Learning vs Principal Component Regression in Fast Scan Cyclic Voltammetry: A Comparison Study.
复制标题
使用深度学习与快速扫描循环伏安法中的主成分回归进行神经化学浓度预测：比较研究。
DOI：
10.1021/acschemneuro.2c00069
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发表时间：
2022
影响因子：
5
通讯作者：
Jang,DongPyo
中科院分区：
文献类型：
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作者：
Choi,Hoseok;Shin,Hojin;Cho,HyunU;Blaha,CharlesD;Heien,MichaelL;Oh,Yoonbae;Lee,KendallH;Jang,DongPyo
神经递质，如多巴胺和血清素，负责调节从记忆到动机的各种神经功能。快速扫描循环伏安法（FSCV）是检测体内神经化学物质突触外排的主要工具之一，主成分回归（PCR）是一种常用的预测神经递质种类和浓度的方法。然而，PCR的灵敏度和区分性能有改进的空间，特别是用于分析类似的可氧化神经化学物质的混合物。深度学习可以解决这些挑战。到目前为止，已经有一些研究将机器学习应用于FSCV，但还没有尝试将深度学习应用于神经递质混合物的辨别，也没有在PCR和深度学习方法之间进行比较研究，以证明哪种方法对FSCV分析更准确。在这项研究中，我们比较了PCR和深度学习在分析儿茶酚胺和吲哚胺神经递质的FSCV记录中的神经化学识别和浓度估计性能。这两种分析方法都是用所需浓度的单一或混合神经递质在体外FSCV数据上进行测试的。此外，在体内实验中比较了PCR和深度学习的估计性能，以评估其实际用途。还进行了药理学测试，以了解深度学习是否会跟踪大脑中儿茶酚胺水平的增加。使用传统的FSCV，我们使用五个电极和记录在vitrobackground-subtracted循环伏安图从四种神经递质，多巴胺，肾上腺素，去甲肾上腺素，和血清素，与五种浓度的每种物质，以及各种混合物的四种分析物。结果表明，与使用PCR进行混合物分析相比，使用深度学习的识别准确性误差降低了5-20%，并且这两种方法在单一分析物分析方面具有可比性。所应用的基于深度学习的方法不仅表现出更高的识别准确性，而且对于神经化学物质的混合物，甚至对于体内测试，也表现出比PCR更好的区分性能。因此，我们建议，与传统的PCR方法相比，深度学习应该被选为分析FSCV数据的更可靠的工具，尽管在广泛使用之前，仍需要进一步的工作来开发完整的验证程序。
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 neurochemicalsin 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 onin vitroFSCV 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 within vivoexperiments 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 recordedin vitrobackground-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 forin vivotesting. 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.