posted on 2021-03-03, 17:37authored byJaekyung 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
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.