Dopamine release, diffusion and uptake: A computational model for synaptic and volume transmission

PLoS Comput Biol. 2020 Nov 30;16(11):e1008410. doi: 10.1371/journal.pcbi.1008410. eCollection 2020 Nov.

Abstract

Computational modeling of dopamine transmission is challenged by complex underlying mechanisms. Here we present a new computational model that (I) simultaneously regards release, diffusion and uptake of dopamine, (II) considers multiple terminal release events and (III) comprises both synaptic and volume transmission by incorporating the geometry of the synaptic cleft. We were able to validate our model in that it simulates concentration values comparable to physiological values observed in empirical studies. Further, although synaptic dopamine diffuses into extra-synaptic space, our model reflects a very localized signal occurring on the synaptic level, i.e. synaptic dopamine release is negligibly recognized by neighboring synapses. Moreover, increasing evidence suggests that cognitive performance can be predicted by signal variability of neuroimaging data (e.g. BOLD). Signal variability in target areas of dopaminergic neurons (striatum, cortex) may arise from dopamine concentration variability. On that account we compared spatio-temporal variability in a simulation mimicking normal dopamine transmission in striatum to scenarios of enhanced dopamine release and dopamine uptake inhibition. We found different variability characteristics between the three settings, which may in part account for differences in empirical observations. From a clinical perspective, differences in striatal dopaminergic signaling contribute to differential learning and reward processing, with relevant implications for addictive- and compulsive-like behavior. Specifically, dopaminergic tone is assumed to impact on phasic dopamine and hence on the integration of reward-related signals. However, in humans DA tone is classically assessed using PET, which is an indirect measure of endogenous DA availability and suffers from temporal and spatial resolution issues. We discuss how this can lead to discrepancies with observations from other methods such as microdialysis and show how computational modeling can help to refine our understanding of DA transmission.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biological Transport
  • Computational Biology*
  • Dopamine / metabolism*
  • Dopaminergic Neurons / metabolism
  • Humans
  • Receptors, Dopamine / metabolism
  • Signal Transduction
  • Synapses / metabolism*
  • Synaptic Transmission

Substances

  • Receptors, Dopamine
  • Dopamine

Grants and funding

This work was supported by IFB Adiposity Diseases, German Federal Ministry of Education and Research (Grant Number FKZ: 01EO1501, www.bmbf.de) to KW, AH, AV, JN, and the German Research Foundation (CRC-1052 Obesity Mechanisms, Subproject A5, Project number: 209933838, www.dfg.de) to KW, AH, AV, JN. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.