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Dopamine Effect

Dopamine Effect: The powerful influence of the neurotransmitter dopamine on brain function and behavior.
Dopamine plays a crucial role in regulating mood, motivation, reward processing, and motor control.
Alterations in dopamine signaling have been implicated in various neurological and psychiatric disorders, such as Parkinson's disease, addiction, and schizophrenia.
Understanding the dopamine effect is essential for developing effective treatments and unlocking the potential of this fascinating neuromodulator.

Most cited protocols related to «Dopamine Effect»

1
Probabilistic Selection Task: Dopamine and Learning
The probabilistic selection task (PST) is an instrumental-learning task that has been used to describe the effect of do-pamine on learning in both clinical and normal populations (Frank, Santamaria, O’Reilly, & Willcutt, 2007 (link); Frank, Seeberger, & O’Reilly, 2004 (link)), in which increases in dopamine boost relative learning from positive as compared to negative feedback. On the basis of a detailed neural-network model of the basal ganglia, these effects are thought to be due to the selective modulation of striatal D1 and D2 receptors through dopamine (Frank et al., 2004 (link)). The task has been used to investigate the effects of dopamine on learning and decision making in ADHD (Frank, Santamaria, et al., 2007 (link)), autism spectrum disorder (Solomon, Frank, & Ragland, 2015 (link)), Parkinson’s disease (Frank et al., 2004 (link)), and schizophrenia (Doll et al., 2014 (link)), among others.
The PST consists of a learning phase and a test phase. During the learning phase, decision makers are presented with three different stimulus pairs (AB, CD, EF), represented as Japanese hiragana letters, and learn to choose one of the two stimuli in each pair on the basis of reward feedback. Reward probabilities differ between the stimulus pairs. In AB trials, choosing A is rewarded with a probability of .8, whereas B is rewarded with a probability of .2. In the CD pair, C is rewarded with a probability of .7, and D .3, and in the EF pair, E is rewarded with a probability of .6, and F .4. Because stimulus pairs are presented in random order, the reward probabilities for all six stimuli have to be maintained throughout the task. Success in the learning phase is to learn to maximize rewards by choosing the optimal (A, C, E) over the suboptimal (B, D, F) option in each stimulus pair (AB, CD, EF). Subjects perform as many blocks (of 60 trials each) as required until their running accuracy at the end of a block is above 65% for AB pairs, 60% for CD pairs, and 50% for EF pairs, or until they complete six blocks (360 trials) if the criteria are not met. The PSTalso includes a test phase, which we will not examine in the present research because it does not involve trial-to-trial learning and exploration. Instead, we will focus on the learning phase of the PST, which can be described as a probabilistic instrumental-learning task.
The data from the learning phase of the PST in Frank, Santamaria, O’Reilly, and Willcutt (2007) (link) were used to assess the RLDD models’ abilities to account for data from human subjects. We also used the task to simulate data from synthetic subjects in order to test the best-fitting model’s ability to recover the parameters. In the original article, the effects of stimulant medication were tested in ADHD patients with a within-subjects medication manipulation, and 17 ADHD subjects were also compared to 21 healthy controls. In the present study, we focused on the results from ADHD patients to understand the causes of the appreciable effects of medication on this group. Subjects were tested twice in a within-subjects design. The order of medication administration was randomized between the ADHD subjects. The results showed that medication improved learning performance, and the subsequent test phase showed that this change was accompanied by a selective boost in reward learning rather than in learning from negative outcomes, consistent with the predictions of the basal ganglia model related to dopaminergic signaling in striatum (Frank, Santamaria, et al., 2007 (link)).
Pedersen M.L., Frank M.J, & Biele G. (2017). The drift diffusion model as the choice rule in reinforcement learning. Psychonomic bulletin & review, 24(4), 1234-1251.
Publication 2017
Autism Spectrum Disorders Basal Ganglia Disorder, Attention Deficit-Hyperactivity Dopamine Dopamine D2 Receptor Dopamine Effect Ganglia Hydrochloride, Dopamine Japanese Methobromide, Hyoscine Patients Pharmaceutical Preparations Schizophrenia Striatum, Corpus
2
Optogenetic Modulation of Dopamine Neurons

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Publication 2017
Discrimination, Psychology Dopamine Effect Dopaminergic Neurons Light Mus Operative Surgical Procedures Optogenetics Psychological Inhibition Psychometrics Pulse Rate
3
Modeling Dopaminergic Control of Exploration-Exploitation
We sought to link the role of striatal dopamine in affecting the basal ganglia output to potentially measurable effects in subjects’ choice behavior. To do so, we simulated a probabilistic two-choice forced selection task, using a reinforcement learning model in which the basal ganglia model performed the action selection step. By testing this combined model under different levels of striatal dopamine we sought to detect the signature of dopaminergic control over the exploration-exploitation trade-off in the behavioral performance. Moreover, we would be able to address how the exploration-exploitation trade-off interacts with ongoing learning.
The conceptual form of the task was taken from Frank et al. (2004 (link), 2007 (link)), as this has proved an excellent probe for the effects of altered dopamine on human choice behavior. Three stimuli pairs (A,B), (C,D), and (E,F) are presented in random sequence, each stimulus of the pair corresponding to some semantically meaningless symbol. Subjects are probabilistically rewarded for choosing one of each pair, with probabilities: A (0.8), B (0.2); C (0.7), D (0.3); E (0.6), F (0.4). Thus the subjects are expected to learn to choose stimuli A, C, and E over B, D, and F when each pair is presented.
We simulated learning of this task with a trial-by-trial Q-learning model. On each trial t, a pair of stimuli was presented, stimulus s chosen, and reward rt ∈ [0, 1] obtained with the probabilities given above. The value of that stimulus was then updated by Q(s) ← Q(s) + α[rt − Q(s)], with learning rate α. All models had α = 0.1, from the fits to subject behavior in Frank et al. (2007 (link)). Every simulated subject had a specified level of tonic dopamine λ, and had Q-values all initialized to zero; following (Frank et al., 2007 (link)), each simulated subject was run for 360 trials, seeing each stimulus pair 120 times in random order. We simulated 40 subjects per dopamine level.
When choosing the stimulus, we considered the Q-values corresponding to the presented pair as the inputs (c1, c2) to a two-channel basal ganglia model. Conceptually, this simulates either that action-values are learnt in orbitofrontal or medial prefrontal cortex (Schultz et al., 2000 (link); Sul et al., 2010 (link)) and transmitted to striatum, or that action-values are computed directly in the striatum from converging cortical inputs (Samejima et al., 2005 (link)). We then ran this basal ganglia model to equilibrium, and converted its output into a probability distribution function for action selection (see Results and Figure 3 for details). The chosen response to the stimulus pair was then randomly selected from this probability distribution.
Humphries M.D., Khamassi M, & Gurney K. (2012). Dopaminergic Control of the Exploration-Exploitation Trade-Off via the Basal Ganglia. Frontiers in Neuroscience, 6, 9.
Publication 2012
Basal Ganglia Cortex, Cerebral Dopamine Dopamine Effect Homo sapiens Hydrochloride, Dopamine Prefrontal Cortex Reinforcement, Psychological Seizures Simulate composite resin Striatum, Corpus
4
Neurotransmitter Effects on Information Gathering
To assess the effects of neurotransmitters dopamine and noradrenaline on information gathering, we used three different drug conditions. The noradrenaline group received 40 mg of propranolol (β-adrenoceptor antagonist). The dopamine group received 400 mg of the D2/3 antagonist amisulpride. We selected these drugs because they have an affinity for the targeted neurotransmitter with a high specificity. The dopamine group received the active drug 120 min before the task and an additional placebo 30 min after the first drug. The noradrenaline group first received a placebo and, after 30 min, the active drug. A third placebo group received placebo at both time points. This schedule aligned with procedures used in previous studies investigating the effects of dopamine or noradrenaline on cognition (Silver et al., 2004 (link); Gibbs et al., 2007 (link); De Martino et al., 2008 (link); Hauser et al., 2017a (link); Kahnt and Tobler, 2017 (link)).
Hauser T.U., Moutoussis M., Purg N., Dayan P, & Dolan R.J. (2018). Beta-Blocker Propranolol Modulates Decision Urgency During Sequential Information Gathering. The Journal of Neuroscience, 38(32), 7170-7178.
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Publication 2018
Adrenergic Antagonists Amisulpride Cognition Dopamine Dopamine Effect Neurotransmitters Norepinephrine Pharmaceutical Preparations Placebos Propranolol Silver
5
First-Episode Psychosis Cohort Study
Patients were recruited from first-episode psychosis services in London. Inclusion criteria were diagnosis of a psychotic disorder according to ICD-10 criteria (28) , fulfilling criteria for having a first episode of psychosis (29) (link), requiring treatment with antipsychotic medication, and being antipsychotic naïve or antipsychotic free for at least 6 weeks [other clinical studies in similar populations require being antipsychotic free for a minimum of 3 weeks 30 (link), 31 (link)].
For comparison, a matched sample of healthy control subjects was included. Inclusion criteria included no psychiatric morbidity, as assessed by the Mini-International Neuropsychiatric Interview (32) , and no contraindications to PET scanning, as per the patient sample.
Exclusion criteria for all subjects were history of significant head trauma, dependence on illicit substances or alcohol, medical comorbidity (other than minor illnesses), use of sodium valproate [owing to effects on dopamine synthesis capacity (33) (link)], and contraindications to scanning (such as pregnancy).
Tobacco smoking was not an exclusion criterion.
Jauhar S., Veronese M., Nour M.M., Rogdaki M., Hathway P., Natesan S., Turkheimer F., Stone J., Egerton A., McGuire P., Kapur S, & Howes O.D. (2018). The Effects of Antipsychotic Treatment on Presynaptic Dopamine Synthesis Capacity in First-Episode Psychosis: A Positron Emission Tomography Study. Biological Psychiatry, 85(1), 79-87.
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Publication 2018
Anabolism Antipsychotic Agents Craniocerebral Trauma Diagnosis Dopamine Effect Ethanol Healthy Volunteers Patients Pharmacotherapy Population Group Pregnancy Psychotic Disorders Sodium Valproate Substance Dependence

Most recents protocols related to «Dopamine Effect»

1
Restless Legs Syndrome and Dementia Risk
Chi-square for categorical variables and Mann–Whitney U test for continuous variables due to non-normality were used to compare the baseline characteristics between patients with RLS and RLS-free controls. Cox proportional hazards regression models were applied to explore the association between RLS and the risk of dementia after adjusting for age, sex, income, residence, CCI, and history of other comorbidities. Among the Cox regression models, we used the Fine–Gray subdistribution hazard model with mortality as a competing risk given the old age of the study population. The proportional hazard assumption was satisfied in our Cox model (Schoenfeld individual test p-value > 0.05).
Sensitivity analyses were performed using four different models. In model 1, dementia was defined as the prescription of anti-dementia medications (donepezil, rivastigmine, galantamine, and memantine) at least twice and a diagnosis of the ICD-code of dementia. Although these medications were approved for only AD (rivastigmine additionally for Parkinson’s disease dementia), they can be used for cognitive symptoms in other types of dementia based on recommendations from multiple guidelines [31 (link)–33 (link)]. The previous study revealed that the definition of all-cause dementia by ICD-10 code plus anti-dementia medications had a positive predictive value of 94.7% when reviewing the medical records of 972 patients in two hospitals [34 (link)]. In model 2, medication history was added to the ICD code to define RLS. Patients with RLS ICD-code (G25.8) who had taken dopamine agonists (ropinirole or pramipexole) twice or more were regarded as patients with RLS (n = 1458). In this sensitivity model, we excluded patients with Parkinson’s disease because they could also take dopamine agonists. In model 3, patients taking antipsychotic agents were excluded because the antidopaminergic property of antipsychotic agents could lead to a misdiagnosis of RLS (n = 2482). The following antipsychotic agents approved in South Korea were used in this study: haloperidol, sulpiride, chlorpromazine, perphenazine, pimozide, risperidone, olanzapine, quetiapine, paliperidone, amisulpride, aripiprazole, ziprasidone, clozapine, blonanserin, and zotepine. In model 4, patients with RLS only diagnosed by psychiatrists or neurologists were included (n = 1154) to preclude the possible misdiagnosis by non-expert physicians.
To evaluate the effect of dopamine agonists (pramipexole and ropinirole) on the development of dementia, the risk of dementia was compared after dividing RLS patients by dopamine agonist use. Patients with RLS who were prescribed pramipexole or ropinirole at least once were considered dopamine agonist users. All missing data were addressed using listwise deletion. Data processing and statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA). Statistical significance was set at a two-tailed p-value of < 0.05.
Kim K.Y., Kim E.H., Lee M., Ha J., Jung I, & Kim E. (2023). Restless leg syndrome and risk of all-cause dementia: a nationwide retrospective cohort study. Alzheimer's Research & Therapy, 15, 46.
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Publication 2023
Age Groups agonists Amisulpride Antipsychotic Agents Aripiprazole blonanserin Chlorpromazine Clozapine Deletion Mutation Donepezil Dopamine Agonists Dopamine Effect Galantamine Haloperidol Hypersensitivity Memantine Neurobehavioral Manifestations Neurologists Olanzapine Paliperidone Parkinson Disease Patients Perphenazine Pharmaceutical Preparations Physicians Pimozide Pramipexole Prescription Drugs Presenile Dementia Psychiatrist Quetiapine Risperidone Rivastigmine ropinirole Sulpiride ziprasidone zotepine
2
Dopamine Depletion and Terazosin Effects on Interval Timing
For experiments with VTA dopamine depletion and chronic terazosin administration, we analyzed effects of dopamine depletion and terazosin using two-sided non-parametric Wilcoxon tests. Four-to-five sessions of interval timing behavior were collected and analyzed per mouse, both before surgery and approximately 16 days post-surgery. Post-surgery switch time coefficients of variability (CV) and mean switch times were normalized to each mouse’s pre-surgical baseline to account for animal-specific variability in timing behavior. For acute pharmacological administration of terazosin and tamsulosin, each drug treatment was compared to injections of saline one day prior using two-sided non-parametric Wilcoxon tests. All data was analyzed with custom routines written in MATLAB and R, and all statistics was reviewed by the Biomedical Epidemiology Research and Design core in the Institute for Clinical and Translational Sciences at the University of Iowa.
Weber M.A., Sivakumar K., Tabakovic E.E., Oya M., Aldridge G.M., Zhang Q., Simmering J.E, & Narayanan N.S. (2023). Glycolysis-enhancing α1-adrenergic antagonists modify cognitive symptoms related to Parkinson’s disease. NPJ Parkinson's Disease, 9, 32.
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Publication 2023
Animals Dopamine Dopamine Effect Mice, House Operative Surgical Procedures Pharmaceutical Preparations Pharmacotherapy Saline Solution Tamsulosin Terazosin
3
Dopaminergic Neuron Characterization
Dopamine-specific elements relevant for the characterization are listed in Table 1 (genetic markers) and Table 2 (protein markers). Tyrosine Hydroxylase (TH/TH) is a marker for catecholaminergic neurons, which is a precursor for dopaminergic neurons. There are several transcription factors that are crucial along the different stages of dopaminergic differentiation, such as LIM homeobox transcription factor 1 beta (LMXB1), Paired Like homeodomain 3 (PITX3), and Nuclear receptor subfamily 4 group A member 2 (NR4A2). For functional characterization, transporters in dopaminergic neurons are also included. Vesicular monoamine transporter 2 (SLC18A2/VMAT2) is a transporter responsible for the packaging of monoaminergic neurotransmitters such as dopamine into synaptic vesicles, and the dopamine transporter (SLC6A3/DAT) is responsible for the reuptake of dopamine from the synaptic cleft into the presynapse. G-protein-regulated inward-rectifier potassium channel 2 (KCNJ6) is implicated in excitability, neurotransmission, and modulating the effects of dopaminergic neurons.
de Leeuw V.C., van Oostrom C.T., Zwart E.P., Heusinkveld H.J, & Hessel E.V. (2023). Prolonged Differentiation of Neuron-Astrocyte Co-Cultures Results in Emergence of Dopaminergic Neurons. International Journal of Molecular Sciences, 24(4), 3608.
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Publication 2023
Dopamine Dopamine Effect Dopaminergic Neurons Genetic Markers GTP-Binding Proteins Hydrochloride, Dopamine inward rectifier potassium channel 2 LIM homeobox transcription factor 1 beta Membrane Transport Proteins Neurons Neurotransmitters Nuclear Receptor Subfamily 4, Group A, Member 2 Proteins Synaptic Transmission Synaptic Vesicles Transcription Factor Tyrosine 3-Monooxygenase Vesicular Monoamine Transporter 2
4
Plasma Dopamine Excursion and Metabolic Outcomes
Assessment of plasma dopamine excursion upon nutrient feeding through time one-way repeated measures ANOVA with Dunn’s post-hoc corrections for multiple comparisons were performed, while between groups a comparison one-way ANOVA with Tukey’s post-hoc corrections for multiple comparisons was performed. To assess sleeve surgery effect on plasma dopamine excursion, one-way repeated measures ANOVA with Dunn’s post-hoc corrections for multiple comparisons were performed to assess differences in plasma dopamine concentration throughout time after a mixed meal feeding. Comparison between groups was assessed by one-way ANOVA with Tukey’s post-hoc corrections for multiple comparisons. A one way-ANOVA test with Tukey’s post-hoc corrections for multiple comparisons was performed to analyze all protein levels in sleeve gastrectomy surgery, bromocriptine, and liraglutide-treated animal models in all tissues. TH level on ilium and explants data were analyzed using the non-parametric Kruskal-Wallis with multiple comparisons test with Dunn’s post-hoc corrections. Data from adipose tissue ex vivo incubations were analyzed by one-way ANOVA with no corrections for multiple comparisons (Fisher’s LSD test). Results were presented as mean ± SEM. Regarding human data analysis, non-parametric tests were performed (sample size < 30/group), and results were presented as median and interquartile range. The Kruskal-Wallis test was applied to compare GLP-1R gene expression between groups. The Spearman correlation test was performed to assess the correlation between GLP-1 and dopamine receptors gene expression. Differences were considered significant at p < 0.05, and all computation analysis was performed using Graphpad Prism (6.0 version, GraphPad Software, Inc., San Diego, CA, USA).
Tavares G., Rosendo-Silva D., Simões F., Eickhoff H., Marques D., Sacramento J.F., Capucho A.M., Seiça R., Conde S.V, & Matafome P. (2023). Circulating Dopamine Is Regulated by Dietary Glucose and Controls Glucagon-like 1 Peptide Action in White Adipose Tissue. International Journal of Molecular Sciences, 24(3), 2464.
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Publication 2023
Animal Model Bromocriptine Dopamine Dopamine Effect Dopamine Receptor Gastrectomy Gene Expression Glucagon-Like Peptide 1 Homo sapiens Ilium Liraglutide neuro-oncological ventral antigen 2, human Nutrients Operative Surgical Procedures Plasma prisma Proteins Tissue, Adipose Tissues
5
Reinforcement Learning Model of Dopamine-Guided Behavior
RL models have four key components: a reward signal, a state, a state-dependent set of available actions, and a policy (which governs how actions are chosen). Here, a simple Q-learning agent with a softmax policy was designed to model mouse behaviour in the open field as an RL process over endogenous dopamine levels44 . Our model was recast (specifically a Q-learning agent with a softmax policy) to use endogenous dopamine (that is, syllable-associated dLight) as a reward signal, behavioural syllables as states, and transitions between behavioural syllables as actions. Given a syllable at time t + 1, the dLight peak occurring during the syllable at time t is considered the ‘reward’. The Q-table for the model was initialized with a uniform matrix with the diagonal set to 0, since by definition there are no self-transitions in our data. For every step of each simulation, given the currently expressed syllable (that is, the state), the model samples possible future syllables (actions) based on the behavioural policy and the expected dLight transient magnitude (expected reward, specified by the Q-table) associated with each syllable transition. Then, the model selected actions according to the softmax equation p(a|s)=eQs(a)/τ∑b=1neQs(b)/τ where τ is the temperature. The model is fed 30-min experiments of actual data. Data was formatted as a sequence of states and syllable-associated dopamine. Given the current state, the model selects an action according to the softmax equation. To update the Q-table and simulate the effect of endogenous dopamine as reward, the syllable-associated dopamine is presented to the model as reward in a standard Q-learning equation. Specifically, the Q-table was then updated according to Q(st,at)←Q(st,at)+α[rt+1+γmaxaQ(st+1,a)−Q(st,at)] where Q is the Q-table that defines the probability of action a while in state s, α is the learning rate, r is the reward associated with action a and state s (the dLight peak value at the transition between syllable a and syllable s), and γ is the discount factor. Performance was assessed by taking the Pearson correlation between the model’s resulting Q-table at the end of the simulation and the empirical transition matrix observed in the experimental data. Here, each row of the empirical transition matrix and the Q-table were separately z-scored prior to computing the Pearson correlation. Note that the learned Q-table is functionally equivalent to a transition matrix in this formulation. To avoid degradation in performance due to syllable sparsity, the top 10 syllables were used.
Markowitz J.E., Gillis W.F., Jay M., Wood J., Harris R.W., Cieszkowski R., Scott R., Brann D., Koveal D., Kula T., Weinreb C., Osman M.A., Pinto S.R., Uchida N., Linderman S.W., Sabatini B.L, & Datta S.R. (2023). Spontaneous behaviour is structured by reinforcement without explicit reward. Nature, 614(7946), 108-117.
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Publication 2023
Dopamine Dopamine Effect Mice, Laboratory Transients

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More about "Dopamine Effect"

The Dopamine Effect: Unlocking the Power of the Brain's Rewarding Neurotransmitter.
Dopamine, a crucial neurotransmitter, plays a pivotal role in regulating mood, motivation, reward processing, and motor control.
Alterations in dopamine signaling have been implicated in various neurological and psychiatric disorders, such as Parkinson's disease, addiction, and schizophrenia.
Understanding the Dopamine Effect is essential for developing effective treatments and unlocking the potential of this fascinating neuromodulator.
Synonyms and related terms include Dopaminergic, DA, Reward Pathway, Motivation Circuit, Corpus Striatum, Ventral Tegmental Area (VTA), and Substantia Nigra.
Subtopics to explore include Dopamine Receptors (D1-D5), Dopamine Transporter (DAT), Dopamine Metabolism (MAO, COMT), and Dopamine-related Disorders (Parkinson's, Addiction, ADHD, Schizophrenia).
Leverage cutting-edge tools like PubCompare.ai, M1000 Genius Pro, Sulpiride, Envison, Prism 6, SKF-81297, EasySep magnet, L-dopa, Atropine sulfate monohydrate, and Fenoldopam to uncover the latest research, optimize protocols, and advance your understanding of the Dopamine Effect.
Empower your research and unlock new discoveries with the power of dopamine!