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520 results for “Dopamine”
Cholinergic interneurons and dopamine in the nucleus accumbens
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Dopamine axon population Ca signals in the striatum during odor cue- and reward-based choice tasks in mice
<p>Dopamine axon activity in the ventral, dorsomedial, and dorsolateral striatum was recorded, while mice performed a perceptual and value-based decision-making task. In one experiment, thirsty mice performed a perceptual decision-making task using mixtures of odor A and B (100/0, 90/10, 65/35, 35/65, 10/90, 0/100), in which identity of a dominant odor determined an available water port, and odor C which signaled no outcome. A fixed amount of water was always delivered with a correct choice. In the next experiment, mice performed a perceptual and value-based decision-making task using mixtures of odor A and B (100/0, 65/35, 35/65, 0/100), in which identity of a dominant odor determined an available water port, with probabilistic water reward. In this task, a fixed amount of water was delivered in block 1, and then in block 2, one water port delivered big or medium size of water in a pseudo-random order, and another water port delivered medium or small size of water in a pseudo-random order. In both tasks, odor-water port (left or right) rule was held constant throughout training and recording in each animal.</p>
Data from: Dopamine disruption increases cleanerfish cooperative investment in novel client partners
Social familiarization is a process of gaining knowledge that results from direct or indirect participation in social events. Cooperative exchanges are thought to be conditional upon familiarity with others. Indeed, individuals seem to prefer to engage with those that have previously interacted with them, which are more accurate predictors of reward than novel partners. On the other hand, highly social animals do seek novelty. Truth is that the physiological bases underlying how familiarity and novelty may affect cooperative decision-making are still rather obscure. Here, we provide the first experimental evidence that the level of the dopaminergic influence in cooperative exchanges is constrained to mechanisms of social familiarization and novelty in a cleanerfish, Labroides dimidiatus. Cleaners were tested against familiar and novel Ctenochaetus striatus surgeonfish (a common client species) in laboratorial conditions, and were found to spend more time providing physical contact (also referred to as tactile stimulation) to familiar fish clients. Cleaners use tactile stimulation as a way to reduce the risk of a non-rewarding outcome, a behavioural response that is even more pronounced when blocking dopamine (DA) D1 receptors. We discovered that the influence of DA disruption on cleaners' provision of physical contact was dependent on the level of familiarity with its partner, being highly exacerbated whenever the client is novel, and unnoticed when dealing with a familiar one. Our findings demonstrate that DA mediation influences the valuation of partner stimuli and the enhancing investment in novel partners, mechanisms that are similar to other vertebrates, including humans.
Data from: Genetic polymorphism in dopamine receptor D4 is associated with early body condition in a large population of greater flamingos, Phoenicopterus roseus
Body condition is an important determinant of fitness in many natural populations. However, as for many fitness traits, the underlying genes that regulate body condition remain elusive. The dopamine receptor D4 gene (DRD4) is a promising candidate as dopamine is known to play an important role in the regulation of food intake and the metabolism of both glucose and lipids in vertebrates. In this study we take advantage of a large dataset of greater flamingos, Phoenicopterus roseus, to test whether DRD4 polymorphism predicts early body condition (EBC) while controlling for whole genome effects of inbreeding and outbreeding using microsatellite multi-locus heterozygosity (MLH). We typed 670 of these individuals for exon 3 of the homologue of the human DRD4 gene and 10 microsatellite markers. When controlling for effects of yearly environmental variations and differences between sexes, we found strong evidence of an association between exon 3 DRD4 polymorphisms and EBC, with 2.2-2.3% of the variation being explained by DRD4 polymorphism, whereas there was only weak evidence that MLH predicts EBC. Because EBC is most likely a polygenic trait, this is a considerable amount of variation explained by a single gene. This is to our knowledge the first study to show an association between exon 3 DRD4 polymorphism and body condition in nonhuman animals. We anticipate that the DRD4 gene as well as other genes coding for neurotransmitters and their receptors may play an important role in explaining variation in traits that affect fitness.
Dataset from 'Billino,J., Hennig, J., & Gegenfurtner K.R. (2016). Association between COMT genotype and the control of memory guided saccades: Individual differences in healthy adults reveal a detrimental role of dopamine. Vision Research. doi: 10.1016/j.visres.2016.10.001'
<p>Dataset associated with the following publication:</p> <p>Billino,J., Hennig, J., & Gegenfurtner K.R. (2016). Association between COMT genotype and the control of memory guided saccades: Individual differences in healthy adults reveal a detrimental role of dopamine. Vision Research. doi: 10.1016/j.visres.2016.10.001 <span><a></a></span></p> <p>--------------------------------------------------------------</p> <p>The folder contains 4 data files and 1 description file providing column labels.<br> The data file 'maindata.txt' contains the complete dataset including all analyzed parameters.<br> The data file 'accuracybydelay.txt' contains accuracy data across delay conditions, providing the dataset for Figure 3A.<br> The data file 'accuracybyamplitude.txt' contains accuracy data across target amplitude conditions, providing the dataset for Figure 3B.<br> The data file 'singlesublanding.txt' contains landing positions of primary memory guided saccades from three subjects of different genotype groups, providing the dataset for Figure 3C.</p> <p>-------------------------------------------------------------</p> <p>For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de</p>
Dataset from 'Billino,J., Hennig, J., & Gegenfurtner K.R. (2016). The role of dopamine in anticipatory pursuit eye movements: Insights from genetic polymorphisms in healthy adults. eNeuro, 3(6). doi: 10.1523/ENEURO.0190-16.2016'
<p>Dataset associated with the following publication:</p> <p>Billino,J., Hennig, J., & Gegenfurtner K.R. (2016). The role of dopamine in anticipatory pursuit eye movements: Insights from genetic polymorphisms in healthy adults. eNeuro, 3(6). doi: 10.1523/ENEURO.0190-16.2016 <span><a></a></span></p> <p>--------------------------------------------------------------</p> <p>The folder contains 2 data files and 1 description file providing column labels.<br> The data file 'anticippursuit.txt' contains the dataset for the anticipatory pursuit task.<br> The data file 'visualpursuit.txt' contains the dataset for the visually pursuit task.</p> <p>-------------------------------------------------------------</p> <p>For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de</p> <p> </p>
Double dissociation of dopamine and subthalamic nucleus stimulation on effortful cost/benefit decision making
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Subregions specific dynamics of striatal dopamine
<div> <div> <div> <p>Transient increases in dopamine within the striatum can encode reward prediction errors, critical signals for updating predictions of future rewards. However, it is unclear how this mechanism can provide suitable feedback for predictions across a wide range of time horizons: from seconds or less (if singing a song) to potentially hours or more (if hunting for food). Here we report that dopamine transients in distinct striatal subregions convey prediction errors over distinct time scales. Dopamine dynamics systematically accelerated from ventral to dorsal- medial to dorsal-lateral striatum, in the tempo of their spontaneous fluctuations, their temporal integration of prior rewards, and their discounting of future rewards. This spectrum of time scales for evaluative computations can help achieve efficient learning and adaptive motivation for a wide range of behaviors.</p> </div> </div> </div>
An action potential initiation mechanism in distal axons for the control of dopamine release
<p>Data table for An action potential initiation mechanism in distal axons for the control of dopamine release</p>
Data table for an action potential initiation mechanism in distal axons for the control of dopamine release
<p>Data table for the article: An action potential initiation mechanism in distal axons for the control of dopamine release</p>
Raw data for: Structure of the human dopamine transporter and mechanisms of allosteric inhibition
<p>This repository contains raw data related to "Structure of the human dopamine transporter and mechanisms of allosteric inhibition" by Srivastava et al.</p> <p>Included are molecular dynamics input parameter files, amber prmtop, production trajectories and analysis scripts. Trajectories are subsampled with one frame every 10 ns. </p> <p> </p> <p>Contact information:</p> <p>Name: Md Fulbabu Sk</p> <p>Institution: Theoretical and Computational Biophysics Group (TCBG), Beckman Institute, University of Illinois Urbana Champaign</p> <p>Address: 405 N. Mathews Avenue, Urbana, Illinois 61801</p> <p>Email: mfsk@illinois.edu</p>
Excessive Firing of Dyskinesia-Associated Striatal Direct Pathway Neurons is Gated By Dopamine and Excitatory Synaptic Input
<p>This folder contains data associated with a submitted manuscript: Ryan, Girasole et al, which was first posted on BioRxiv in 2022. https://www.biorxiv.org/content/10.1101/2022.10.31.514594v1.</p> <p><strong>Files associated with each figure: </strong></p> <p><strong>Figure 1 & Supp Figure 1: </strong></p> <ul> <li>Summary.mat</li> <li>Behavior.mat</li> <li>Data.mat</li> <li>TRAP_InVivo_Analysis.m</li> <li>TRAP_Remove_INs.m</li> </ul> <p><strong>Figure 2 & Supp Figure 2: </strong></p> <ul> <li>See BioStudies for rabies dataset: <a href="https://www.ebi.ac.uk/biostudies/studies/S-BSST1374?key=9a1b2103-25a6-49ed-b257-0269d3ea19a6" target="_blank" rel="noopener">https://www.ebi.ac.uk/biostudies/studies/S-BSST1374?key=9a1b2103-25a6-49ed-b257-0269d3ea19a6</a></li> </ul> <p><strong>Figure 3 & Supp Figure 3 & Supp Figure 4: </strong></p> <ul> <li> <p>Fig3_FigS3_FigS4_Data.xlsx</p> </li> </ul> <p><strong>Figure 4 & Supp Figure 5: </strong></p> <ul> <li>Fig4Data.xlsx</li> <li>RNAscopeAnalysis_D1_D2.m</li> <li>RNAdata.mat</li> <li>RNAscopeAnalysis_D1_pDyn.m</li> <li>RNAScopeAnalysis.m</li> <li>pDynRNAdata.mat</li> <li>pDynSummary.mat</li> </ul> <p><strong>Contributions</strong>: </p> <p>Behavioral data was collected and analyzed by Ally Girasole, Michael Ryan, and Emily Twedell.</p> <p>In vivo electrophysiological data was collected and analyzed by Michael Ryan and Ally Girasole.</p> <p>Ex vivo electrophysiological data was collected and analyzed by Ally Girasole, Michael Ryan, Emily Twedell, and Alexandra Nelson.</p> <p>Anatomical tracing and histology data was collected and analyzed by Ron Paletzki, Chip Gerfen, Michael Ryan, Ally Girasole and Rea Brakaj.</p> <p>RNA Scope data was collected and analyzed by Andrew Flores, Mike Ryan, and Tom Hnasko.</p>
Raw and Preprocessed Data for the Paper "A chemogenetic approach for dopamine imaging with tunable sensitivity"
<p>This Dataset includes matlab variables as well as Excel tables containing all raw and preprocessed data</p>
JM-20 administration to animals with lesion of the nigrostriatal dopamine pathway induced by 6-hydroxydopamine, partially reverses motor damage and oxidative stress
<p><strong><span>Abstract</span></strong></p> <p><span>Previous studies have shown that JM-20, a new chemical hybrid molecule, protects against rotenone and 6-hydroxydopamine <span>(6-OHDA)</span> neurotoxicity. Also, we demonstrated that JM-20 blocks the formation of toxic alpha-synuclein aggregated species and <em><span>aminochrome </span></em>cytotoxicity. The present study sought to determine the neuroprotective property of JM-20 in animals with a partial <span>lesion of the nigrostriatal dopamine pathway induced by 6-OHDA </span>. For <em><span>in vivo</span></em> studies, adult male Wistar rats were lesioned in the right <em><span>substantia nigra pars compacta</span></em> (SNpc) upon the administration of 6-OHDA. Fifteen days after surgery, the animal’s asymmetry levels were assessed. Those with asymmetry values higher than 50% were divided into two groups: animals that did not receive any treatment and those that were administered with JM-20 (40 mg/kg, intragastric via gavage) for 27 days. Every seven days, the asymmetry values of the animals were analyzed until day 42 after the surgery. At the end of the experiment, the animals were euthanized and the SNpc and striatum were taken out for the analysis of oxidative stress. Our results reveal a behavioral function progressively recovered in the JM-20-treated animals, diminishing the percentage of motor asymmetry. Also, it ameliorates the oxidative stress in the SNpc and the striatal tissue of these animals. Our study provides the preclinical evidence to support the long term neuroprotective potential of JM-20 in 6-OHDA hemiparkinson's rat model, pointing out to its possible use as a disease-modifying agent in PD.</span></p>
Dataset: Dopamine neurons projecting to medial shell of the nucleus accumbens drive heroin reinforcement.
<p>This dataset contains the data presented in the paper Dopamine neurons projecting to medial shell of the nucleus accumbens drive heroin reinforcement. (doi: 10.7554/eLife.39945 )</p>
Phasic Dopamine Release
<p>Fast-scan cyclic voltammograms (FSCV) raw data were obtained from the Laboratory of Central Nervous System of the Federal University of Parana (UFPR) at Curitiba, Brazil, and from D. Robinson’s Laboratory of the University of North Carolina (UNC) at Chapel Hill, United States of America. The images were generated from 30 different experimental records with a total of 1005 electrically evoked dopamine release. Each record has dopamine release evoked with different magnitude of electrical stimulation, resulting in different pattern of form and intensity. All experiments were performed in accordance with the NIH Guide for the Care and Use of Laboratory Animals with procedures approved by the Institutional Animal Care and Use Committee of the University of North Carolina, and the Institutional Ethics Committee for Animal Experimentation of the Federal University of Parana (Protocol 638).</p> <p>This new dataset version is composed of images generated from all these experimental records, which include those in the version 1 – 2018. Unlike the first version, the new one has not only phasic dopamine release images, but also entire images with no DA release. In total there are 2010 images, 1005 of each of these classes, with resolution of 875×656 pixels representing a 20 seconds recording. During the generation of these FSCV images, a background subtraction is commonly used before applying a fake color palette. Normally for each image, one column or more columns are selected to subtract the values from the others. In the case of the first version of the dataset, this process was done manually during its generation. In this new one, the process is done automatically, choosing 3 different background positions: the Background A was selected from a column at the beginning of each image (0.5 seconds), the Background B from the middle of each image (10 seconds), and the Background C from the end of each image (19.5 seconds).</p> <p>These images with different background end up generating different results. Thus it is possible to explore different approaches of training and testing, since for each DA release 3 images were generated. Each image containing DA was manually labeled with the approximated information of each release interval and peak. All images were divided into 3 folds with same amount of samples from each of the two classes: (1) phasic DA release images and (2) non-release images. </p> <p>The full details are in our papers [ <a href="https://doi.org/10.1016/j.compbiomed.2019.103466">https://doi.org/10.1016/j.compbiomed.2019.103466</a> ] and [ <a href="http://dx.doi.org/10.1109/IWSSIP.2018.8439339">http://dx.doi.org/10.1109/IWSSIP.2018.8439339</a> ]. </p> <p> </p>
Supplementary data for article: Central insulin modulates dopamine signaling in the human striatum
<p><b>Objective:</b> Activity in the dopaminergic pathways of the brain is highly sensitive to body weight and metabolic states. Animal studies show that dopamine neurons are important targets for the metabolic hormone insulin with abolished effects in the insulin resistant state, leading to increases in body weight and food intake. In humans, the influence of central acting insulin on dopamine and effects of their interplay are still elusive.</p> <p><b>Research Design and Methods:</b> We investigated whether central administered insulin influences dopaminergic activity in striatal regions and whole-brain neural activity. Using a PET/MRI hybrid scanner, we simultaneously performed [<sup>11</sup>C]-raclopride-PET and resting state fMRI in 10 healthy normal weight men after application of intranasal insulin or placebo on two separate days in a randomized, placebo-controlled, blinded, crossover trial.</p> <p><b>Results:</b> In response to central insulin compared to placebo administration, we observed greater [<sup>11</sup>C]-raclopride binding potential (BP<sub>nd</sub>) in the bilateral ventral and dorsal striatum. This suggests an insulin-induced reduction in synaptic dopamine levels. Resting-state striatal activity was lower 15 and 30 min after nasal insulin compared to placebo. Functional connectivity of the mesocorticolimbic circuitry associated with differences in dopamine levels: individuals with a stronger insulin-induced effect on dopamine levels showed a stronger increase in functional connectivity 45 min after intranasal insulin.</p> <p><b>Conclusions</b>: This study indicates that central insulin modulates dopaminergic tone in the striatum, which may affect regional brain activity and connectivity. Our results deepen the understanding of the insulin-dopamine interaction and the complex network that underlies the regulation of whole-body metabolism.</p>
Nigrostriatal dopamine signals sequence-specific action-outcome prediction errors
<p>Data for Hollon et al. (2021) Nigrostriatal dopamine signals sequence-specific action-outcome prediction errors. <em>Current Biology</em></p>
Dopamine in the Dorsal Bed Nucleus of Stria Terminalis signals Pavlovian sign-tracking and reward violations
<p>Raw data associated with Gyawali et al., 2022</p>
Human Embryonic Stem C ells-derived Immature Midbrain Dopaminergic Neurons Transplanted in Parkinsonian Mon-keys Recover Dopamine Levels and Motor Behavior
<p>Supplementary videos of the article "Human Embryonic Stem Cells-derived Immature Midbrain Dopaminergic Neurons Transplanted in Parkinsonian Mon-keys Recover Dopamine Levels and Motor Behavior"</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.