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

Wiencke, K., Horstmann, A., Mathar, D., Villringer, A., & Neumann, J. (2020). Dopamine release, diffusion and uptake: A computational model for synaptic and volume transmission. PLoS Computational Biology, 16(11): e1008410. doi:10.1371/journal.pcbi.1008410.

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 Urheber:
Wiencke, Kathleen1, 2, Autor           
Horstmann, Annette1, 2, 3, Autor           
Mathar, David4, Autor           
Villringer, Arno1, 2, 5, 6, Autor           
Neumann, Jane1, 2, 7, Autor           
Affiliations:
1Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany, ou_persistent22              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
3Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland, ou_persistent22              
4Department of Psychology, University of Cologne, Germany, ou_persistent22              
5Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
6Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
7Institute for Biomedical Engineering and Informatics, TU Ilmenau, Germany, ou_persistent22              

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 Zusammenfassung: 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.

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Sprache(n): eng - English
 Datum: 2019-04-082020-09-302020-11-30
 Publikationsstatus: Online veröffentlicht
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1371/journal.pcbi.1008410
Anderer: eCollection 2020
PMID: 33253315
 Art des Abschluß: -

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Projektname : -
Grant ID : 01EO1501
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Förderorganisation : German Federal Ministry of Education and Research (BMBF)
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Förderprogramm : (CRC-1052)
Förderorganisation : German Research Foundation (DFG)

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Titel: PLoS Computational Biology
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: San Francisco, CA : Public Library of Science
Seiten: - Band / Heft: 16 (11) Artikelnummer: e1008410 Start- / Endseite: - Identifikator: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1