Activation of VTA GABA Neurons Disrupts Reward Consumption

Zessen, Ruud van; Phillips, Jana L.; Budygin, Evgeny A.; Stuber, Garret D.

doi:10.1016/j.neuron.2012.02.016

Cited by 523 publications

(526 citation statements)

References 64 publications

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“…Some of these inputs are activated by aversive stimuli (10,102,582). Optogenetic activation of pars reticulata GABAergic neurons reduces dopamine impulse activity (613). Inhibition from GABAergic pars re-ticulata neurons showing sustained activations during reward prediction may also result in cancellation of reward activations in dopamine neurons by predicted rewards (102).…”

Section: Origin Of Reward Prediction Error Responsementioning

confidence: 99%

“…The forebrain bundle stimulation likely serves as immediate reward, whereas the cortical stimulation provides the necessary spatial information. Corresponding to the action-inducing effects of dopamine stimulation, indirect inhibition of VTA dopamine neurons via optogenetic activation of local, presynaptic GABA neurons reduces reward consumption (613). At the postsynaptic level, optogenetic stimulation of dopamine D1 receptor expressing striatal neurons increases behavioral choices toward contralateral nose poke targets, whereas stimulation of D2 receptor expressing striatal neurons increases ipsilateral choices (or contralateral dispreferences) (FIGURE 46C) (576), suggesting an immediate, differential effect on reward value for the two actions.…”

Section: Immediate Dopamine Influencesmentioning

confidence: 99%

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Neuronal Reward and Decision Signals: From Theories to Data

Schultz

2015

Physiological Reviews

870

765

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LSchultz W. Neuronal Reward and Decision Signals: From Theories to Data. Physiol Rev 95: 853-951, 2015. Published June 24, 2015 doi:10.1152/physrev.00023.2014.-Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act.

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Section: Origin Of Reward Prediction Error Responsementioning

confidence: 99%

Section: Immediate Dopamine Influencesmentioning

confidence: 99%

Neuronal Reward and Decision Signals: From Theories to Data

Schultz

2015

Physiological Reviews

870

765

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“…in intracranial self-stimulation tests [53] . In contrast, optogenetic activation of VTA GABAergic neurons induces an inhibitory response [54,55] . to opioid, which plays an important role in addiction [31] .…”

Section: Ventral Tegmental Area (Vta)mentioning

confidence: 96%

Optogenetics in neuroscience: what we gain from studies in mammals

Chen

Zeng

2012

Neurosci. Bull.

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Abstract:Optogenetics is a newly-introduced technology in the life sciences and is gaining increasing attention. It refers to the combination of optical technologies and genetic methods to control the activity of specific cell groups in living tissue, during which high-resolution spatial and temporal manipulation of cells is achieved. Optogenetics has been applied to numerous regions, including cerebral cortex, hippocampus, ventral tegmental area, nucleus accumbens, striatum, spinal cord, and retina, and has revealed new directions of research in neuroscience and the treatment of related diseases. Since optogenetic tools are controllable at high spatial and temporal resolution, we discuss its applications in these regions in detail and the recent understanding of higher brain functions, such as reward-seeking, learning and memory, and sleep.Further, the possibilities of improved utility of this newly-emerging technology are discussed. We intend to provide a paradigm of the latest advances in neuroscience using optogenetics.

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“…To choose this number, we note first that the percentage of GABAergic neurons varies between 12 and 45% in different sub regions of the ventral tegmental area (VTA) [96]. Since the GABA neurons powerfully modulate DA neuron activity via direct, monosynaptic inhibitory connections [55,[97][98][99][100], one can expect multiple GABA neurons to make connections with a single DA neuron. Second, we found that our results are valid for a wide range of the number of GABA neurons and start to change only if the number becomes small (10 or lower) [91].…”

Section: Synaptic Inputsmentioning

confidence: 99%

Dopamine neurons change the type of excitability in response to stimuli

Morozova

Zakharov²,

Gutkin

et al. 2016

Preprint

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The dynamics of neuronal excitability determine the neuron's response to stimuli, its synchronization and resonance properties and, ultimately, the computations it performs in the brain. We investigated the dynamical mechanisms underlying the excitability type of dopamine (DA) neurons, using a conductance-based biophysical model, and its regulation by intrinsic and synaptic currents. Calibrating the model to reproduce low frequency tonic firing results in N-methyl-D-aspartate (NMDA) excitation balanced by γ-Aminobutyric acid (GABA)-mediated inhibition and leads to type I excitable behavior characterized by a continuous decrease in firing frequency in response to hyperpolarizing currents. Furthermore, we analyzed how excitability type of the DA neuron model is influenced by changes in the intrinsic current composition. A subthreshold sodium current is necessary for a continuous frequency decrease during application of a negative current, and the low-frequency "balanced" state during simultaneous activation of NMDA and GABA receptors. Blocking this current switches the neuron to type II characterized by the abrupt onset of repetitive firing. Enhancing the anomalous rectifier Ih current also switches the excitability to type II. Key characteristics of synaptic conductances that may be observed in vivo also change the type of excitability: a depolarized γ-Aminobutyric acid receptor (GABAR) reversal potential or coactivation of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) leads to an abrupt frequency drop to zero, which is typical for type II excitability. Coactivation of N-methyl-D-aspartate receptors (NMDARs) together with AMPARs and GABARs shifts the type I/II boundary toward more hyperpolarized GABAR reversal potentials. To better understand how altering each of the aforementioned currents leads to changes in excitability profile of DA neuron, we provide a thorough dynamical analysis. Collectively, these results imply that type I excitability in dopamine neurons might be important for low firing rates and fine-tuning basal dopamine levels, while switching excitability to type II during NMDAR and AMPAR activation may facilitate a transient increase in dopamine concentration, as type II neurons are more amenable to synchronization by mutual excitation. Author SummaryDopamine neurons play a central role in guiding motivated behaviors. However, complete understanding of computations these neurons perform to encode rewarding and salient stimuli is still forthcoming. Network connectivity influences neural responses to stimuli, but so do intrinsic excitability properties of individual neurons, as such properties define synchronization qualities and neural coding strategy. We investigated the excitability type of the DA neuron and found that, depending on the synaptic and intrinsic current composition, DA neurons can switch from type I to type II excitability. In short, without synaptic inputs or under balanced excitatory and inhibitory inputs DA neurons exhibits type I excitability, w...

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Activation of VTA GABA Neurons Disrupts Reward Consumption

Cited by 523 publications

References 64 publications

Neuronal Reward and Decision Signals: From Theories to Data

Neuronal Reward and Decision Signals: From Theories to Data

Optogenetics in neuroscience: what we gain from studies in mammals

Dopamine neurons change the type of excitability in response to stimuli

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