Automatic and Reliable Quantification of Tonic Dopamine Concentrations In Vivo Using a Novel Probabilistic Inference Methodopen access
- Authors
- Kim, Jaekyung; Barath, Abhijeet S.; Rusheen, Aaron E.; Cabrera, Juan M. Rojas; Price, J. Blair; Shin, Hojin; Goyal, Abhinav; Yuen, Jason W.; Jondal, Danielle E.; Blaha, Charles D.; Lee, Kendall H.; Jang, Dong Pyo; Oh, Yoonbae
- Issue Date
- Mar-2021
- Publisher
- AMER CHEMICAL SOC
- Citation
- ACS OMEGA, v.6, no.10, pp.6607 - 6613
- Indexed
- SCIE
SCOPUS
- Journal Title
- ACS OMEGA
- Volume
- 6
- Number
- 10
- Start Page
- 6607
- End Page
- 6613
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/142177
- DOI
- 10.1021/acsomega.0c05217
- ISSN
- 2470-1343
- Abstract
- Dysregulation of the neurotransmitter dopamine (DA) is implicated in several neuropsychiatric conditions. Multiple-cyclic square-wave voltammetry (MCSWV) is a state-of-the-art technique for measuring tonic DA levels with high sensitivity (<5 nM), selectivity, and spatiotemporal resolution. Currently, however, analysis of MCSWV data requires manual, qualitative adjustments of analysis parameters, which can inadvertently introduce bias. Here, we demonstrate the development of a computational technique using a statistical model for standardized, unbiased analysis of experimental MCSWV data for unbiased quantification of tonic DA. The oxidation current in the MCSWV signal was predicted to follow a lognormal distribution. The DA-related oxidation signal was inferred to be present in the top 5% of this analytical distribution and was used to predict a tonic DA level. The performance of this technique was compared against the previously used peak-based method on paired in vivo and post-calibration in vitro datasets. Analytical inference of DA signals derived from the predicted statistical model enabled high-fidelity conversion of the in vivo current signal to a concentration value via in vitro post-calibration. As a result, this technique demonstrated reliable and improved estimation of tonic DA levels in vivo compared to the conventional manual post-processing technique using the peak current signals. These results show that probabilistic inference-based voltammetry signal processing techniques can standardize the determination of tonic DA concentrations, enabling progress toward the development of MCSWV as a robust research and clinical tool.
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