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Low amplitude burst detection of catecholamines

Eltahir, A. M.; White, J.; Lohrenz, T. M.; Montague, R.

2021-08-04 neuroscience
10.1101/2021.08.02.454747 bioRxiv
Show abstract

Machine learning advances in electrochemical detection have recently produced sub-second and concurrent detection of dopamine and serotonin during perception and action tasks in conscious humans. Here, we present a new machine learning approach to sub-second, concurrent separation of dopamine, norepinephrine, and serotonin. The method exploits a low amplitude burst protocol for the controlled voltage waveform and we demonstrate its efficacy by showing how it separates dopamine-induced signals from norepinephrine induced signals. Previous efforts to deploy electrochemical detection of dopamine in vivo have not separated the dopamine-dependent signal from a norepinephrine-dependent signal. Consequently, this new method can provide new insights into concurrent signaling by these two important neuromodulators.

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