신호진 (Mayo Clinic)

한빛사논문

조회258

신호진Hojin Shin

Mayo Clinic

CV File 168 kb

Mol. Psychiatr., Apr 25 2024, | https://doi.org/10.1038/s41380-024-02537-1 Resolution of tonic concentrations of highly similar neurotransmitters using voltammetry and deep learning

Abhinav Goyal ^1,2, Jason Yuen ³, Stephen Sinicrope ⁴, Bailey Winter ¹, Lindsey Randall ¹, Aaron E. Rusheen ⁵, Charles D. Blaha ², Kevin E. Bennet ^2,6, Kendall H. Lee ^2,7, Hojin Shin^2,7 and Yoonbae Oh ^2,7

¹Medical Scientist Training Program, Mayo Clinic, Rochester, MN 55905, USA.

²Department of Neurologic Surgery, Mayo Clinic, Rochester, MN 55905, USA.

³Department of Neurosurgery, Southmead Hospital, Bristol BS10 5NB, UK.

⁴Department of Neuroscience, University of Chicago, Chicago, IL 60637, USA.

⁵Department of Neurosurgery, Johns Hopkins University, Baltimore, MD 21287, USA.

⁶Division of Engineering, Mayo Clinic, Rochester, MN 55905, USA.

⁷Department of Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA.

Corresponding authors : Correspondence to Hojin Shin or Yoonbae Oh.

Abstract

With advances in our understanding regarding the neurochemical underpinnings of neurological and psychiatric diseases, there is an increased demand for advanced computational methods for neurochemical analysis. Despite having a variety of techniques for measuring tonic extracellular concentrations of neurotransmitters, including voltammetry, enzyme-based sensors, amperometry, and in vivo microdialysis, there is currently no means to resolve concentrations of structurally similar neurotransmitters from mixtures in the in vivo environment with high spatiotemporal resolution and limited tissue damage. Since a variety of research and clinical investigations involve brain regions containing electrochemically similar monoamines, such as dopamine and norepinephrine, developing a model to resolve the respective contributions of these neurotransmitters is of vital importance. Here we have developed a deep learning network, DiscrimNet, a convolutional autoencoder capable of accurately predicting individual tonic concentrations of dopamine, norepinephrine, and serotonin from both in vitro mixtures and the in vivo environment in anesthetized rats, measured using voltammetry. The architecture of DiscrimNet is described, and its ability to accurately predict in vitro and unseen in vivo concentrations is shown to vastly outperform a variety of shallow learning algorithms previously used for neurotransmitter discrimination. DiscrimNet is shown to generalize well to data captured from electrodes unseen during model training, eliminating the need to retrain the model for each new electrode. DiscrimNet is also shown to accurately predict the expected changes in dopamine and serotonin after cocaine and oxycodone administration in anesthetized rats in vivo. DiscrimNet therefore offers an exciting new method for real-time resolution of in vivo voltammetric signals into component neurotransmitters.

논문정보

형식Research article
게재일2024년 04월 (BRIC 등록일 2024-04-30)
연구진국외 연구진
분야 바이오·의료융합 > 바이오센싱 및 나노바이오물질

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