Automatic and Reliable Quantification of Tonic Dopamine Concentrations in Vivo Using a Novel Probabilistic Inference Method

Jaekyung Kim, Abhijeet S. Barath, Aaron E. Rusheen, Juan M. Rojas Cabrera, J. Blair Price, Hojin Shin, Abhinav Goyal, Jason W. Yuen, Danielle E. Jondal, Charles D. Blaha, Kendall H. Lee, Dong Pyo Jang, Yoonbae Oh

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Pages (from-to)6607-6613
Number of pages7
JournalACS Omega
Volume6
Issue number10
DOIs
StatePublished - 2021 Mar 16

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