Automatic and Reliable Quantification of Tonic Dopamine Concentrations In Vivo Using a Novel Probabilistic Inference...

Area of Science:

• Neuroscience
• Analytical Chemistry
• Computational Biology

Background:

• Dopamine (DA) dysregulation is linked to neuropsychiatric disorders.
• Multiple-cyclic square-wave voltammetry (MCSWV) measures DA with high sensitivity but requires manual analysis, risking bias.
• Standardized, unbiased methods are needed for accurate DA quantification.

Purpose of the Study:

• To develop a computational technique for unbiased analysis of MCSWV data.
• To standardize the quantification of tonic dopamine levels using MCSWV.
• To improve the reliability of MCSWV as a research and clinical tool.

Main Methods:

• Developed a statistical model to analyze MCSWV signals, predicting oxidation current using a lognormal distribution.
• Inferred dopamine signals from the top 5% of the analytical distribution.
• Compared the statistical method against the conventional peak-based method using in vivo and in vitro data.

Main Results:

• The statistical model accurately predicted dopamine-related oxidation signals.
• High-fidelity conversion of in vivo current signals to concentration values was achieved via in vitro post-calibration.
• The new technique demonstrated improved and reliable estimation of in vivo tonic DA levels compared to manual methods.

Conclusions:

• Probabilistic inference-based signal processing can standardize tonic dopamine concentration determination.
• The developed technique offers an unbiased approach to MCSWV data analysis.
• This advancement supports the development of MCSWV as a robust tool for neuroscience research and clinical diagnostics.