English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  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.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files
hide Files
:
Wiencke_2020.pdf (Publisher version), 7MB
Name:
Wiencke_2020.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Wiencke, Kathleen1, 2, Author              
Horstmann, Annette1, 2, 3, Author              
Mathar, David4, Author              
Villringer, Arno1, 2, 5, 6, Author              
Neumann, Jane1, 2, 7, Author              
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              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s): eng - English
 Dates: 2019-04-082020-09-302020-11-30
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1371/journal.pcbi.1008410
Other: online ahead of print
PMID: 33253315
 Degree: -

Event

show

Legal Case

show

Project information

show hide
Project name : -
Grant ID : 01EO1501
Funding program : -
Funding organization : German Federal Ministry of Education and Research

Source 1

show
hide
Title: PLoS Computational Biology
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 16 (11) Sequence Number: e1008410 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1