Influence of phasic and tonic dopamine release on receptor activation

Functional PET/MRI reveals active inhibition of neuronal activity during optogenetic activation of the nigrostriatal pathway

The dopaminergic system is a central component of the brain's neurobiological framework, governing motor control and reward responses and playing an essential role in various brain disorders. Within this complex network, the nigrostriatal pathway represents a critical circuit for dopamine neurotransmission from the substantia nigra to the striatum. However, stand-alone functional magnetic resonance imaging is unable to study the intricate interplay between brain activation and its molecular underpinnings. In our study, the use of a functional [fluorine-18]2-fluor-2-deoxy-d-glucose positron emission tomography approach, simultaneously with blood oxygen level-dependent functional magnetic resonance imaging, provided an important insight that demonstrates an active suppression of the nigrostriatal activity during optogenetic stimulation. This result increases our understanding of the molecular mechanisms of brain function and provides an important perspective on how dopamine influences hemodynamic responses in the brain.

Midbrain KCC2 downregulation: Implications for stress-related and substance use behaviors

Stress-related and substance use disorders are both characterized by disruptions in reward-related behaviors, and these disorders are often comorbid with one another. Recent investigations have identified a novel mechanism of inhibitory plasticity induced by both stress and substance use within the ventral tegmental area (VTA), a key region in reward processing. This mechanism involves the neuron-specific potassium chloride cotransporter isoform 2 (KCC2), which is essential in modulating inhibitory signaling through the regulation of intracellular chloride (Cl-) in VTA GABA neurons. Experiences, such as exposure to stress or substance use, diminish KCC2 expression in VTA GABA neurons, leading to abnormal reward-related behaviors. Here, we review literature suggesting that KCC2 downregulation contributes to irregular dopamine (DA) transmission, impacting multiple reward circuits and promoting maladaptive behaviors. Activating KCC2 restores canonical GABA functioning and reduces behavioral deficits in preclinical models, leading us to advocate for KCC2 as a target for therapies aimed at alleviating and mitigating various stress-related and substance use disorders.

Aversion-induced drug taking and escape behavior involve similar nucleus accumbens core dopamine signaling signatures

Dopamine release in the nucleus accumbens core (NAcC) has long been associated with the promotion of motivated behavior. However, inhibited dopamine signaling can increase behavior in certain settings, such as during drug self-administration. While aversive environmental stimuli can reduce dopamine, it is unclear whether such stimuli reliably engage this mechanism in different contexts. Here we compared the physiological and behavioral responses to the same aversive stimulus in different designs to determine if there is uniformity in the manner that aversive stimuli are encoded and promote behavior. NAcC dopamine was measured using fiber photometry in male and female rats during cocaine self-administration sessions in which an acutely aversive 90 dB white noise was intermittently presented. In a separate group of rats, aversion-induced changes in dopamine were measured in an escape design in which operant responses terminated aversive white noise. Aversive white noise significantly reduced NAcC dopamine and increased cocaine self-administration in both male and female rats. The same relationship was observed in the escape design, in which white noise reduced dopamine and promoted escape attempts. In both designs, the magnitude of the dopamine reduction predicted behavioral performance. While prior research demonstrated that pharmacologically reduced dopamine signaling can promote intake, this report demonstrates that this physiological mechanism is naturally engaged by aversive environmental stimuli and generalizable to non-drug contexts. These findings illustrate a common physiological signature in response to aversion that may promote both adaptive and maladaptive behavior.

Dynamics of striatal action selection and reinforcement learning

Spiny projection neurons (SPNs) in dorsal striatum are often proposed as a locus of reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules. Direct-pathway (dSPN) and indirect-pathway (iSPN) neurons, which promote and suppress actions, respectively, exhibit synaptic plasticity that reinforces activity associated with elevated or suppressed dopamine release. We show that iSPN plasticity prevents successful learning, as it reinforces activity patterns associated with negative outcomes. However, this pathological behavior is reversed if functionally opponent dSPNs and iSPNs, which promote and suppress the current behavior, are simultaneously activated by efferent input following action selection. This prediction is supported by striatal recordings and contrasts with prior models of SPN representations. In our model, learning and action selection signals can be multiplexed without interference, enabling learning algorithms beyond those of standard temporal difference models.

Dopamine Release Dynamics in the Nucleus Accumbens Are Modulated by the Timing of Electrical Stimulation Pulses When Applied to the Medial Forebrain Bundle and Medial Prefrontal Cortex

Electrical brain stimulation has been used in vivo and in vitro to investigate neural circuitry. Historically, stimulation parameters such as amplitude, frequency, and pulse width were varied to investigate their effects on neurotransmitter release and behavior. These experiments have traditionally employed fixed-frequency stimulation patterns, but it has previously been found that neurons are more precisely tuned to variable input. Introducing variability into the interpulse interval of stimulation pulses will inform on how dopaminergic release can be modulated by variability in pulse timing. Here, dopaminergic release in rats is monitored in the nucleus accumbens (NAc), a key dopaminergic center which plays a role in learning and motivation, by fast-scan cyclic voltammetry. Dopaminergic release in the NAc could also be modulated by stimulation region due to differences in connectivity. We targeted two regions for stimulation─the medial forebrain bundle (MFB) and the medial prefrontal cortex (mPFC)─due to their involvement in reward processing and projections to the NAc. Our goal is to investigate how variable interpulse interval stimulation patterns delivered to these regions affect the time course of dopamine release in the NAc. We found that stimulating the MFB with these variable stimulation patterns saw a highly responsive, frequency-driven dopaminergic response. In contrast, variable stimulation patterns applied to the mPFC were not as sensitive to the variable frequency changes. This work will help inform on how stimulation patterns can be tuned specifically to the stimulation region to improve the efficiency of electrical stimulation and control dopamine release.

A chemogenetic approach for dopamine imaging with tunable sensitivity

Genetically-encoded dopamine (DA) sensors enable high-resolution imaging of DA release, but their ability to detect a wide range of extracellular DA levels, especially tonic versus phasic DA release, is limited by their intrinsic affinity. Here we show that a human-selective dopamine receptor positive allosteric modulator (PAM) can be used to boost sensor affinity on-demand. The PAM enhances DA detection sensitivity across experimental preparations (in vitro, ex vivo and in vivo) via one-photon or two-photon imaging. In vivo photometry-based detection of optogenetically-evoked DA release revealed that DETQ administration produces a stable 31 minutes window of potentiation without effects on animal behavior. The use of the PAM revealed region-specific and metabolic state-dependent differences in tonic DA levels and enhanced single-trial detection of behavior-evoked phasic DA release in cortex and striatum. Our chemogenetic strategy can potently and flexibly tune DA imaging sensitivity and reveal multi-modal (tonic/phasic) DA signaling across preparations and imaging approaches.

Striatal Dopamine Contributions to Skilled Motor Learning

Coordinated multijoint limb and digit movements-"manual dexterity"-underlie both specialized skills (e.g., playing the piano) and more mundane tasks (e.g., tying shoelaces). Impairments in dexterous skill cause significant disability, as occurs with motor cortical injury, Parkinson's disease, and a range of other pathologies. Clinical observations, as well as basic investigations, suggest that corticostriatal circuits play a critical role in learning and performing dexterous skills. Furthermore, dopaminergic signaling in these regions is implicated in synaptic plasticity and motor learning. Nonetheless, the role of striatal dopamine signaling in skilled motor learning remains poorly understood. Here, we use fiber photometry paired with a genetically encoded dopamine sensor to investigate striatal dopamine release in both male and female mice as they learn and perform a skilled reaching task. Dopamine rapidly increases during a skilled reach and peaks near pellet consumption. In the dorsolateral striatum, dopamine dynamics are faster than in the dorsomedial and ventral striatum. Across training, as reaching performance improves, dopamine signaling shifts from pellet consumption to cues that predict pellet availability, particularly in medial and ventral areas of the striatum. Furthermore, performance prediction errors are present across the striatum, with reduced dopamine release after an unsuccessful reach. These findings show that dopamine dynamics during skilled motor behaviors change with learning and are differentially regulated across striatal subregions.

Characterizing the aggregated encoding method utilizing bursts activated by a VCSEL-neuron with a feedback structure

The rapid advancement of photonic technologies has facilitated the development of photonic neurons that emulate neuronal functionalities akin to those observed in the human brain. Neuronal bursts frequently occur in behaviors where information is encoded and transmitted. Here, we present the demonstration of the bursting response activated by an artificial photonic neuron. This neuron utilizes a single vertical-cavity surface-emitting laser (VCSEL) and encodes multiple stimuli effectively by varying the spike count during a burst based on the polarization competition in the VCSEL. By virtue of the modulated optical injection in the VCSEL employed to trigger the spiking response, we activate bursts output in the VCSEL with a feedback structure in this scheme. The bursting response activated by the VCSEL-neuron exhibits neural signal characteristics, promising an excitation threshold and the refractory period. Significantly, this marks the inaugural implementation of a controllable integrated encoding scheme predicated on bursts within photonic neurons. There are two remarkable merits; on the one hand, the interspike interval of bursts is distinctly diminished, amounting to merely one twenty-fourth compared to that observed in optoelectronic oscillators. Moreover, the interspike period of bursts is about 70.8% shorter than the period of spikes activated by a VCSEL neuron without optical feedback. Our results may shed light on the analogy between optical and biological neurons and open the door to fast burst encoding-based optical systems with a speed several orders of magnitude faster than their biological counterparts.

Ultrastructural analysis of nigrostriatal dopaminergic terminals in a knockin mouse model of DYT1 dystonia

DYT1 dystonia is associated with decreased striatal dopamine release. In this study, we examined the possibility that ultrastructural changes of nigrostriatal dopamine terminals could contribute to this neurochemical imbalance using a serial block face/scanning electron microscope (SBF/SEM) and three-dimensional reconstruction to analyse striatal tyrosine hydroxylase-immunoreactive (TH-IR) terminals and their synapses in a DYT1(ΔE) knockin (DYT1-KI) mouse model of DYT1 dystonia. Furthermore, to study possible changes in vesicle packaging capacity of dopamine, we used transmission electron microscopy to assess the synaptic vesicle size in striatal dopamine terminals. Quantitative comparative analysis of 80 fully reconstructed TH-IR terminals in the WT and DYT1-KI mice indicate (1) no significant difference in the volume of TH-IR terminals; (2) no major change in the proportion of axo-spinous versus axo-dendritic synapses; (3) no significant change in the post-synaptic density (PSD) area of axo-dendritic synapses, while the PSDs of axo-spinous synapses were significantly smaller in DYT1-KI mice; (4) no significant change in the contact area between TH-IR terminals and dendritic shafts or spines, while the ratio of PSD area/contact area decreased significantly for both axo-dendritic and axo-spinous synapses in DYT1-KI mice; (5) no significant difference in the mitochondria volume; and (6) no significant difference in the synaptic vesicle area between the two groups. Altogether, these findings suggest that abnormal morphometric changes of nigrostriatal dopamine terminals and their post-synaptic targets are unlikely to be a major source of reduced striatal dopamine release in DYT1 dystonia.

Activation of Multiple G Protein Pathways to Characterize the Five Dopamine Receptor Subtypes Using Bioluminescence Technology

G protein-coupled receptors show preference for G protein subtypes but can recruit multiple G proteins with various downstream signaling cascades. This functional selection can guide drug design. Dopamine receptors are both stimulatory (D1-like) and inhibitory (D2-like) with diffuse expression across the central nervous system. Functional selectivity of G protein subunits may help with dopamine receptor targeting and their downstream effects. Three bioluminescence-based assays were used to characterize G protein coupling and function with the five dopamine receptors. Most proximal to ligand binding was the miniG protein assay with split luciferase technology used to measure recruitment. For endogenous and selective ligands, the G-CASE bioluminescence resonance energy transfer (BRET) assay measured G protein activation and receptor selectivity. Downstream, the BRET-based CAMYEN assay quantified cyclic adenosine monophosphate (cAMP) changes. Several dopamine receptor agonists and antagonists were characterized for their G protein recruitment and cAMP effects. G protein selectivity with dopamine revealed potential Gq coupling at all five receptors, as well as the ability to activate subtypes with the "opposite" effects to canonical signaling. D1-like receptor agonist (+)-SKF-81297 and D2-like receptor agonist pramipexole showed selectivity at all receptors toward Gs or Gi/o/z activation, respectively. The five dopamine receptors show a wide range of potentials for G protein coupling and activation, reflected in their downstream cAMP signaling. Targeting these interactions can be achieved through drug design. This opens the door to pharmacological treatment with more selectivity options for inducing the correct physiological events.

Coupled action potential and calcium dynamics underlie robust spontaneous firing in dopaminergic neurons

Dopaminergic neurons are specialized cells in the substantia nigra, tasked with dopamine secretion. This secretion relies on intracellular calcium signaling coupled to neuronal electrical activity. These neurons are known to display spontaneous calcium oscillationsin-vitroandin-vivo, even in synaptic isolation, controlling the basal dopamine levels. Here we outline a kinetic model for the ion exchange across the neuronal plasma membrane. Crucially, we relax the assumption of constant, cytoplasmic sodium and potassium concentration. We show that sodium-potassium dynamics are strongly coupled to calcium dynamics and are essential for the robustness of spontaneous firing frequency. The model predicts several regimes of electrical activity, including tonic and 'burst' oscillations, and predicts the switch between those in response to perturbations. 'Bursting' correlates with increased calcium amplitudes, while maintaining constant average, allowing for a vast change in the calcium signal responsible for dopamine secretion. All the above traits provide the flexibility to create rich action potential dynamics that are crucial for cellular function.

Role of dopamine neuron activity in Parkinson's disease pathophysiology

Neural activity is finely tuned to produce normal behaviors, and disruptions in activity likely occur early in the course of many neurodegenerative diseases. However, how neural activity is altered, and how these changes influence neurodegeneration is poorly understood. Here, we focus on evidence that the activity of dopamine neurons is altered in Parkinson's disease (PD), either as a compensatory response to degeneration or as a result of circuit dynamics or pathologic proteins, based on available human data and studies in animal models of PD. We then discuss how this abnormal activity may augment other neurotoxic phenomena in PD, including mitochondrial deficits, protein aggregation and spread, dopamine toxicity, and excitotoxicity. A more complete picture of how activity is altered and the resulting effects on dopaminergic neuron health and function may inform future therapeutic interventions to target and protect dopamine neurons from degeneration.

Striatal dopamine contributions to skilled motor learning

Coordinated multi-joint limb and digit movements - "manual dexterity" - underlie both specialized skills (e.g., playing the piano) and more mundane tasks (e.g., tying shoelaces). Impairments in dexterous skill cause significant disability, as occurs with motor cortical injury, Parkinson's Disease, and a range of other pathologies. Clinical observations, as well as basic investigations, suggest that cortico-striatal circuits play a critical role in learning and performing dexterous skills. Furthermore, dopaminergic signaling in these regions is implicated in synaptic plasticity and motor learning. Nonetheless, the role of striatal dopamine signaling in skilled motor learning remains poorly understood. Here, we use fiber photometry paired with a genetically encoded dopamine sensor to investigate striatal dopamine release as mice learn and perform a skilled reaching task. Dopamine rapidly increases during a skilled reach and peaks near pellet consumption. In dorsolateral striatum, dopamine dynamics are faster than in dorsomedial and ventral striatum. Across training, as reaching performance improves, dopamine signaling shifts from pellet consumption to cues that predict pellet availability, particularly in medial and ventral areas of striatum. Furthermore, performance prediction errors are present across the striatum, with reduced dopamine release after an unsuccessful reach. These findings show that dopamine dynamics during skilled motor behaviors change with learning and are differentially regulated across striatal subregions.

Kappa Opioid Receptors Negatively Regulate Real Time Spontaneous Dopamine Signals by Reducing Release and Increasing Uptake

The role of the dynorphin/kappa opioid receptor (KOR) system in dopamine (DA) regulation has been extensively investigated. KOR activation reduces extracellular DA concentrations and increases DA transporter (DAT) activity and trafficking to the membrane. To explore KOR influences on real-time DA fluctuations, we used the photosensor dLight1.2 with fiber photometry in the nucleus accumbens (NAc) core of freely moving male and female C57BL/6 mice. First, we established that the rise and fall of spontaneous DA signals were due to DA release and reuptake, respectively. Then mice were systemically administered the KOR agonist U50,488H (U50), with or without pretreatment with the KOR antagonist aticaprant (ATIC). U50 reduced both the amplitude and width of spontaneous signals in males, but only reduced width in females. Further, the slope of the correlation between amplitude and width was increased in both sexes, suggesting that DA uptake rates were increased. U50 also reduced the frequency of signals in both males and females. All effects of KOR activation were stronger in males. Overall, KORs exerted significant inhibitory control over spontaneous DA signaling, acting through at least three mechanisms - inhibiting DA release, promoting DAT-mediated uptake, and reducing the frequency of signals.

Computational insights on asymmetrical D1 and D2 receptor-mediated chunking: implications for OCD and Schizophrenia

Repetitive thoughts and motor programs including perseveration are bridge symptoms characteristic of obsessive compulsive disorder (OCD), schizophrenia and in the co-morbid overlap of these conditions. The above pathologies are sensitive to altered activation and kinetics of dopamine D 1 and D 2 receptors that differently influence sequence learning and recall. Recognizing start and stop elements of motor and cognitive behaviors has crucial importance. During chunking, frequent components of temporal strings are concatenated into single units. We extended a published computational model (Asabuki et al. 2018), where two populations of neurons are connected and simulated in a reservoir computing framework. These neural pools were adopted to represent D1 and D2 striatal neuronal populations. We investigated how specific neural and striatal circuit parameters can influence start/stop signaling and found that asymmetric intra-network connection probabilities, synaptic weights and differential time constants may contribute to signaling of start/stop elements within learned sequences. Asymmetric coupling between the striatal D 1 and D 2 neural populations was also demonstrated to be beneficial. Our modeling results predict that dynamical differences between the two dopaminergic striatal populations and the interaction between them may play complementary roles in chunk boundary signaling. Start and stop dichotomies can arise from the larger circuit dynamics as well, since neural and intra-striatal connections only partially support a clear division of labor.

An opponent striatal circuit for distributional reinforcement learning

Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards - an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2,3, but little is known about whether, where, and how neurons in this circuit encode information about higher-order moments of reward distributions4. To fill this gap, we used high-density probes (Neuropixels) to acutely record striatal activity from well-trained, water-restricted mice performing a classical conditioning task in which reward mean, reward variance, and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Remarkably, chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons - D1 and D2 MSNs - contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 MSNs5-15 to reap the computational benefits of distributional RL.

Substantial decline of phasic dopamine signaling in senescent male rats does not impact dopamine-dependent Pavlovian conditioning

Normal aging is associated with cognitive decline which impacts financial decision making. One of the underlying features of decision making is probability estimation, in which nucleus accumbens dopamine signaling has been implicated. Here we used fast-scan cyclic voltammetry to probe for age differences in dopamine signaling, and pharmacological manipulation to test for age differences in the dopamine dependence of Pavlovian conditioning. We found differences in phasic dopamine signaling to reward delivery, and unconditioned and conditioned stimuli, but no difference in conditioned approach between adult and senescent groups. In addition, we found that dopamine receptor antagonism with flupenthixol (225 μg/kg, i.p.) partially inhibited conditioned approach in the adult group, whereas it completely blocked conditioned approach in the senescent group. Further increase in concentration to 300 μg/kg, i.p. resulted in complete inhibition of conditioned approach behavior in both age groups. Therefore, while phasic dopamine signaling in the nucleus accumbens of senescent animals is greatly diminished in concentration, these animals maintain dopamine dependent Pavlovian conditioning.

It's all about making new contacts: How being metabotropic and phasicity help D1-like receptors promote LTP in the PFC

D1-like receptors have two important qualities, they are all metabotropic and they activate with phasic dopamine. After analyzing the molecular implications of each of these qualities separately and then combining them for the specific case of the prefrontal cortex, we propose a model that explains why long term potentiation in this cortical area depends on the amount of contact between D1-like receptors and dopamine. This simple model also explains why in order to promote long term potentiation, dopamine transporters should be scarce in the prefrontal cortex. Additionally, it explains why stimulants like methamphetamine could have such detrimental cognitive effects on regular substance consumers.

Slow-rising and fast-falling dopaminergic dynamics jointly adjust negative prediction error in the ventral striatum

The greater the reward expectations are, the more different the brain's physiological response will be. Although it is well-documented that better-than-expected outcomes are encoded quantitatively via midbrain dopaminergic (DA) activity, it has been less addressed experimentally whether worse-than-expected outcomes are expressed quantitatively as well. We show that larger reward expectations upon unexpected reward omissions are associated with the preceding slower rise and following larger decrease (DA dip) in the DA concentration at the ventral striatum of mice. We set up a lever press task on a fixed ratio (FR) schedule requiring five lever presses as an effort for a food reward (FR5). The mice occasionally checked the food magazine without a reward before completing the task. The percentage of this premature magazine entry (PME) increased as the number of lever presses approached five, showing rising expectations with increasing proximity to task completion, and hence greater reward expectations. Fibre photometry of extracellular DA dynamics in the ventral striatum using a fluorescent protein (genetically encoded GPCR activation-based DA sensor: GRABDA2m ) revealed that the slow increase and fast decrease in DA levels around PMEs were correlated with the PME percentage, demonstrating a monotonic relationship between the DA dip amplitude and degree of expectations. Computational modelling of the lever press task implementing temporal difference errors and state transitions replicated the observed correlation between the PME frequency and DA dip amplitude in the FR5 task. Taken together, these findings indicate that the DA dip amplitude represents the degree of reward expectations monotonically, which may guide behavioural adjustment.

Tonic dopamine and biases in value learning linked through a biologically inspired reinforcement learning model

A hallmark of various psychiatric disorders is biased future predictions. Here we examined the mechanisms for biased value learning using reinforcement learning models incorporating recent findings on synaptic plasticity and opponent circuit mechanisms in the basal ganglia. We show that variations in tonic dopamine can alter the balance between learning from positive and negative reward prediction errors, leading to biased value predictions. This bias arises from the sigmoidal shapes of the dose-occupancy curves and distinct affinities of D1- and D2-type dopamine receptors: changes in tonic dopamine differentially alters the slope of the dose-occupancy curves of these receptors, thus sensitivities, at baseline dopamine concentrations. We show that this mechanism can explain biased value learning in both mice and humans and may also contribute to symptoms observed in psychiatric disorders. Our model provides a foundation for understanding the basal ganglia circuit and underscores the significance of tonic dopamine in modulating learning processes.

Dopamine and Glutamate Crosstalk Worsen the Seizure Outcome in TLE-HS Patients

Temporal lobe epilepsy (TLE), accompanied by hippocampal sclerosis (HS), is the most common form of drug-resistant epilepsy (DRE). Nearly 20% of the patients showed seizure recurrence even after surgery, and the reasons are yet to be understood. Dysregulation of neurotransmitters is evident during seizures, which can induce excitotoxicity. The present study focused on understanding the molecular changes associated with Dopamine (DA) and glutamate signaling and their possible impact on the persistence of excitotoxicity and seizure recurrence in patients with drug-resistant TLE-HS who underwent surgery. According to the International League against Epilepsy (ILAE) suggested classification for seizure outcomes, the patients (n = 26) were classified as class 1 (no seizures) and class 2 (persistent seizures) using the latest post-surgery follow-up data to understand the prevalent molecular changes in seizure-free and seizure-recurrence patient groups. Our study uses thioflavin T assay, western blot analysis, immunofluorescence assays, and fluorescence resonance energy transfer (FRET) assays. We have observed a substantial increase in the DA and glutamate receptors that promote excitotoxicity. Patients who had seizure recurrence showed a significant increase in (pNR2B, p < 0.009; and pGluR1, p < 0.01), protein phosphatase1γ (PP1γ; p < 0.009), protein kinase A (PKAc; p < 0.001) and dopamine-cAMP regulated phospho protein32 (pDARPP32T34; p < 0.009) which are critical for long-term potentiation (LTP), excitotoxicity compared to seizure-free patients and controls. A significant increase in D1R downstream kinases like PKA (p < 0.001), pCAMKII (p < 0.009), and Fyn (p < 0.001) was observed in patient samples compared to controls. Anti-epileptic DA receptor D2R was found to be decreased in ILAE class 2 (p < 0.02) compared to class 1. Since upregulation of DA and glutamate signaling supports LTP and excitotoxicity, we believe it could impact seizure recurrence. Further studies about the impact of DA and glutamate signaling on the distribution of PP1γ at postsynaptic density and synaptic strength could help us understand the seizure microenvironment in patients. Dopamine, Glutamate signal crosstalk. Diagram representing the PP1γ regulation by NMDAR negative feedback inhibition signaling (green circle-left) and D1R signal (red circle-middle) domination over PP1γ though increased PKA, pDARPP32T34, and supports pGluR1, pNR2B in seizure recurrent patients. D1R-D2R hetero dimer activation (red circle-right) increases cellular Ca2+ and pCAMKIIα activation. All these events lead to calcium overload in HS patients and excitotoxicity, particularly in patients experiencing recurrent seizures.

Profiling transcriptomic responses of human stem cell-derived medium spiny neuron-like cells to exogenous phasic and tonic neurotransmitters

Transcriptomic responses to neurotransmitters contribute to the complex processes driving memory and addiction. Advances in both measurement methods and experimental models continue to improve our understanding of this regulatory layer. Here we focus on the experimental potential of stem cell derived neurons, currently the only ethical model that can be used in reductionist and experimentally perturbable studies of human cells. Prior work has focused on generating distinct cell types from human stem cells, and has also shown their utility in modeling development and cellular phenotypes related to neurodegeneration. Here we seek an understanding of how stem cell derived neural cultures respond to perturbations experienced during development and disease progression. This work profiles transcriptomic responses of human medium spiny neuron-like cells with three specific goals. We first characterize transcriptomic responses to dopamine and dopamine receptor agonists and antagonists presented in dosing patterns mimicking acute, chronic, and withdrawal regimens. We also assess transcriptomic responses to low and persistent tonic levels of dopamine, acetylcholine, and glutamate to better mimic the in vivo environment. Finally, we identify similar and distinct responses between hMSN-like cells derived from H9 and H1 stem cell lines, providing some context for the extent of variability these types of systems will likely pose for experimentalists. The results here suggest future optimizations of human stem cell derived neurons to increase their in vivo relevance and the biological insights that can be garnered from these models.

Enhancing reinforcement learning models by including direct and indirect pathways improves performance on striatal dependent tasks

A major advance in understanding learning behavior stems from experiments showing that reward learning requires dopamine inputs to striatal neurons and arises from synaptic plasticity of cortico-striatal synapses. Numerous reinforcement learning models mimic this dopamine-dependent synaptic plasticity by using the reward prediction error, which resembles dopamine neuron firing, to learn the best action in response to a set of cues. Though these models can explain many facets of behavior, reproducing some types of goal-directed behavior, such as renewal and reversal, require additional model components. Here we present a reinforcement learning model, TD2Q, which better corresponds to the basal ganglia with two Q matrices, one representing direct pathway neurons (G) and another representing indirect pathway neurons (N). Unlike previous two-Q architectures, a novel and critical aspect of TD2Q is to update the G and N matrices utilizing the temporal difference reward prediction error. A best action is selected for N and G using a softmax with a reward-dependent adaptive exploration parameter, and then differences are resolved using a second selection step applied to the two action probabilities. The model is tested on a range of multi-step tasks including extinction, renewal, discrimination; switching reward probability learning; and sequence learning. Simulations show that TD2Q produces behaviors similar to rodents in choice and sequence learning tasks, and that use of the temporal difference reward prediction error is required to learn multi-step tasks. Blocking the update rule on the N matrix blocks discrimination learning, as observed experimentally. Performance in the sequence learning task is dramatically improved with two matrices. These results suggest that including additional aspects of basal ganglia physiology can improve the performance of reinforcement learning models, better reproduce animal behaviors, and provide insight as to the role of direct- and indirect-pathway striatal neurons.

Dietary fat restriction affects brain reward regions in a randomized crossover trial

BACKGROUNDWeight-loss diets often target dietary fat or carbohydrates, macronutrients that are sensed via distinct gut-brain pathways and differentially affect peripheral hormones and metabolism. However, the effects of such diet changes on the human brain are unclear. METHODSWe investigated whether selective isocaloric reductions in dietary fat or carbohydrates altered dopamine D2/3 receptor binding potential (D2BP) and neural activity in brain-reward regions in response to visual food cues in 17 inpatient adults with obesity as compared with a eucaloric baseline diet using a randomized crossover design. RESULTSOn the fifth day of dietary fat restriction, but not carbohydrate restriction, both D2BP and neural activity to food cues were decreased in brain-reward regions. After the reduced-fat diet, ad libitum intake shifted toward foods high in both fat and carbohydrates. CONCLUSIONThese results suggest that dietary fat restriction increases tonic dopamine in brain-reward regions and affects food choice in ways that may hamper diet adherence. TRIAL REGISTRATIONClinicalTrials.gov NCT00846040 FUNDING. NIDDK 1ZIADK013037.

Exploring the underlying mechanisms of drug-induced impulse control disorders: a pharmacovigilance-pharmacodynamic study

INTRODUCTION

Impulse control disorders (e.g. pathological gambling, hypersexuality) may develop as adverse reactions to drugs. Pathogenetic hypotheses have mainly focused on D3-receptor agonism, and switching to alternatives with different pharmacologic mechanisms represents a common management strategy. Nonetheless, treatment failure is common and gaining pathophysiological insights is needed.

AIM

We aimed to identify targets potentially contributing to pathologic impulsivity.

METHOD

We performed a pharmacovigilance-pharmacodynamic study on dopamine agonists and antipsychotics using the Food and Drug Administration Adverse Event Reporting System (January 2004-December 2021). We estimated disproportionate reporting using the Bayesian information component. Using online public databases (IUPHAR, ChEMBL, PDSP, DrugBank), we calculated drug occupancies. To identify the targets potentially contributing to impulsivity, we fitted univariate regression models interpolating information components and occupancies within dopamine agonists and antipsychotics. Sensitivity analyses were performed to check for the robustness of the results.

RESULTS

Among 19 887 reports of impulsivity, 5898 recorded an antipsychotic, and 3100 a dopamine agonist. The more robust signals concerned aripiprazole (N = 3091; median information component [95% confidence interval] = 4.51[4.45-4.55]) and brexpiprazole (229; 4.00[3.78-4.16]) for antipsychotics, pergolide (105; 5.82[5.50-6.06]) and pramipexole (2009; 5.43[5.36-5.48]) for dopamine agonists. Robust, significant positive associations between drug occupancy and impulsivity reporting were found for D3 within dopamine agonists (beta = 1.52; P-value = 0.047) and 5-HT1a within antipsychotics (1.92, 0.029).

CONCLUSION

Our results supported the role of D3-receptor agonism in inducing impulsivity in dopamine receptor agonists and identified a potential role of 5-HT1a receptor agonism in antipsychotics. Investigating these receptors may drive towards a better management of drug-induced impulsivity.

Neuromodulation of Neural Oscillations in Health and Disease

Using EEG and local field potentials (LFPs) as an index of large-scale neural activities, research has been able to associate neural oscillations in different frequency bands with markers of cognitive functions, goal-directed behavior, and various neurological disorders. While this gives us a glimpse into how neurons communicate throughout the brain, the causality of these synchronized network activities remains poorly understood. Moreover, the effect of the major neuromodulatory systems (e.g., noradrenergic, cholinergic, and dopaminergic) on brain oscillations has drawn much attention. More recent studies have suggested that cross-frequency coupling (CFC) is heavily responsible for mediating network-wide communication across subcortical and cortical brain structures, implicating the importance of neurotransmitters in shaping coordinated actions. By bringing to light the role each neuromodulatory system plays in regulating brain-wide neural oscillations, we hope to paint a clearer picture of the pivotal role neural oscillations play in a variety of cognitive functions and neurological disorders, and how neuromodulation techniques can be optimized as a means of controlling neural network dynamics. The aim of this review is to showcase the important role that neuromodulatory systems play in large-scale neural network dynamics, informing future studies to pay close attention to their involvement in specific features of neural oscillations and associated behaviors.

Behavioral encoding across timescales by region-specific dopamine dynamics

The dorsal (DS) and ventral striatum (VS) receive dopaminergic projections that control motor functions and reward-related behavior. It remains poorly understood how dopamine release dynamics across different temporal scales in these regions are coupled to behavioral outcomes. Here, we employ the dopamine sensor dLight1.3b together with multiregion fiber photometry and machine learning-based analysis to decode dopamine dynamics across the striatum during self-paced exploratory behavior in mice. Our data show a striking coordination of rapidly fluctuating signal in the DS, carrying information across dopamine levels, with a slower signal in the VS, consisting mainly of slow-paced transients. Importantly, these release dynamics correlated with discrete behavioral motifs, such as turns, running, and grooming on a subsecond-to-minute time scale. Disruption of dopamine dynamics with cocaine caused randomization of action selection sequencing and disturbance of DS-VS coordination. The data suggest that distinct dopamine dynamics of DS and VS jointly encode behavioral sequences during unconstrained activity with DS modulating the stringing together of actions and VS the signal to initiate and sustain the selected action.

Involvement of dopamine D2 -like receptors in the antiepileptogenic effects of deep brain stimulation during kindling in rats

AIMS

Deep brain electrical stimulation (DBS), as a potential therapy for drug resistive epileptic patients, has inhibitory action on epileptogenesis. In the present investigation, the role of dopamine D2 -like receptors in the antiepileptogenic action of DBS was studied.

METHODS

Seizures were induced in adult rats by stimulating the perforant path in a semi-rapid kindling method. Five minutes after the last kindling stimulation, daily DBS was applied to the perforant path at the pattern of low frequency stimulation (LFS; 1 Hz; pulse duration: 0.1 ms; intensity: 50-150 μA; 4 trains of 200 pulses at 5 min intervals). Sulpiride (10 μg/1 μl, i.c.v.), a selective dopamine D2 -like receptor antagonist, was administered prior to the daily LFS application.

RESULTS

Kindling stimulations increased cumulative daily behavioral seizure stages, daily afterdischarge duration (dADD), and population spike amplitude (PS) in dentate gyrus following perforant path stimulation, while applying LFS decreased the kindled seizures' parameters. In addition, kindling potentiated the early (at 10-50 ms inter-pulse interval) and late (at 150-1000 ms inter-pulse interval) paired-pulse inhibition and decreased the paired-pulse facilitation (at 70-100 ms inter-pulse interval). These effects were also inhibited by applying LFS. All inhibitory effects of LFS on kindling procedure were prevented by sulpiride administration.

CONCLUSION

These data may suggest that LFS exerts its preventive effect on kindling development, at least partly, through the receptors on which sulpiride acts which are mainly dopamine D2 -like (including D2 , D3 , and D4 ) receptors.

Elevated prefrontal dopamine interferes with the stress-buffering properties of behavioral control in female rats

Stress-linked disorders are more prevalent in women than in men and differ in their clinical presentation. Thus, investigating sex differences in factors that promote susceptibility or resilience to stress outcomes, and the circuit elements that mediate their effects, is important. In male rats, instrumental control over stressors engages a corticostriatal system involving the prelimbic cortex (PL) and dorsomedial striatum (DMS) that prevent many of the sequelae of stress exposure. Interestingly, control does not buffer against stress outcomes in females, and here, we provide evidence that the instrumental controlling response in females is supported instead by the dorsolateral striatum (DLS). Additionally, we used in vivo microdialysis, fluorescent in situ hybridization, and receptor subtype pharmacology to examine the contribution of prefrontal dopamine (DA) to the differential impact of behavioral control. Although both sexes preferentially expressed D1 receptor mRNA in PL GABAergic neurons, there were robust sex differences in the dynamic properties of prefrontal DA during controllable stress. Behavioral control potently attenuated stress-induced DA efflux in males, but not females, who showed a sustained DA increase throughout the entire stress session. Importantly, PL D1 receptor blockade (SCH 23390) shifted the proportion of striatal activity from the DLS to the DMS in females and produced the protective effects of behavioral control. These findings suggest a sex-selective mechanism in which elevated DA in the PL biases instrumental responding towards prefrontal-independent striatal circuitry, thereby eliminating the protective impact of coping with stress.

Tonic and phasic stimulations of ventral tegmental area have opposite effects on pentylenetetrazol kindled seizures in mice

Dopamine may be involved in the anticonvulsant action of deep brain stimulation (DBS). Therefore, ventral tegmental area (VTA), as a brain dopaminergic nucleus, may be a suitable target for DBS anticonvulsant action. This study investigated the effect of tonic and phasic stimulations of the VTA on seizure parameters. Seizures were induced in adult mice by sequential injections of a sub-convulsive dose of 35 mg/kg pentylenetetrazole (PTZ) every 48 h to develop the chemical kindling until the mice reached full kindled state (showing three consecutive seizure stages 4 or 5). Fully kindled mice received DBS once a day as tonic (square waves at 1 Hz; pulse duration: 200 μs; intensity: 300 μA; 600 pulses in 10 min) or phasic (square waves at 100 Hz; pulse duration: 200 μs; intensity: 300 μA; 8 trains of 10 pulses at 1 min interval; 800 pulses in 10 min) stimulations applied into their VTA for 4 days. A single dose of PTZ was injected after each DBS. Simultaneously electrocorticography and video recordings were performed during the seizure for accuracy in seizure severity parameters detection. Tonic but not phasic stimulation significantly decreased the epileptiform discharge duration and the seizure behavioral parameters such as maximum seizure stage, stage 5 duration, seizure duration. In addition, focal to generalized seizure latency increased following VTA tonic stimulation. These data suggest that tonic (but not phasic) stimulation of VTA before PTZ injection on 4 test days had anticonvulsant effects on PTZ-kindled seizures.

Single-cell transcriptional and functional analysis of dopaminergic neurons in organoid-like cultures derived from human fetal midbrain

Significant efforts are ongoing to develop refined differentiation protocols to generate midbrain dopamine (DA) neurons from pluripotent stem cells for application in disease modeling, diagnostics, drug screening and cell-based therapies for Parkinson's disease. An increased understanding of the timing and molecular mechanisms that promote the generation of distinct subtypes of human midbrain DA during development will be essential for guiding future efforts to generate molecularly defined and subtype-specific DA neurons from pluripotent stem cells. Here, we use droplet-based single-cell RNA sequencing to transcriptionally profile the developing human ventral midbrain (VM) when the DA neurons are generated (6-11 weeks post-conception) and their subsequent differentiation into functional mature DA neurons in primary fetal 3D organoid-like cultures. This approach reveals that 3D cultures are superior to monolayer conditions for their ability to generate and maintain mature DA neurons; hence, they have the potential to be used for studying human VM development. These results provide a unique transcriptional profile of the developing human fetal VM and functionally mature human DA neurons that can be used to guide stem cell-based therapies and disease modeling approaches in Parkinson's disease.

Mechanistic modeling as an explanatory tool for clinical treatment of chronic catatonia

Mathematical modeling of neural systems is an effective means to integrate complex information about the brain into a numerical tool that can help explain observations. However, the use of neural models to inform clinical decisions has been limited. In this study, we use a simple model of brain circuitry, the Wilson-Cowan model, to predict changes in a clinical measure for catatonia, the Bush-Francis Catatonia Rating Scale, for use in clinical treatment of schizophrenia. This computational tool can then be used to better understand mechanisms of action for pharmaceutical treatments, and to fine-tune dosage in individual cases. We present the conditions of clinical care for a residential patient cohort, and describe methods for synthesizing data to demonstrated the functioning of the model. We then show that the model can be used to explain effect sizes of treatments and estimate outcomes for combinations of medications. We conclude with a demonstration of how this model could be personalized for individual patients to inform ongoing treatment protocols.

Biological Predictors of Treatment Response in Adult Attention Deficit Hyperactivity Disorder (ADHD): A Systematic Review

Background: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent condition with onset in childhood and in many cases persisting into adulthood. Even though an increasing number of studies have investigated the efficacy of pharmacotherapy in the management of adult ADHD, few authors have tried to identify the biological predictors of treatment response. Objectives: To summarize the available data about the biological markers of treatment response in adults affected by ADHD. Methods: A search on the main biomedical and psychological archives (PubMed, Embase, Scopus, and PsycINFO) was performed. Manuscripts in English, published up to May 2022 and having the biological predictors of treatment response in adults with ADHD as their main topic, were included. Results: A total of 3855 articles was screened. Twenty-two articles were finally included. Most of the manuscripts studied neuroimaging and electrophysiological factors as potential predictors of treatment response in adult ADHD patients. No reliable markers were identified until now. Promising findings on this topic regard genetic polymorphisms in snap receptor (SNARE) proteins and default mode network-striatum connectivity. Conclusions: Even though some biological markers seem promising for the prediction of treatment response in adults affected by ADHD, further studies are needed to confirm the available data in the context of precision medicine.

Second-Derivative-Based Background Drift Removal for a Tonic Dopamine Measurement in Fast-Scan Cyclic Voltammetry

The dysregulation of dopamine, a neuromodulator, is associated with a broad spectrum of brain disorders, including Parkinson's disease, addiction, and schizophrenia. Quantitative measurements of dopamine are essential for understanding dopamine functional dynamics. Fast-scan cyclic voltammetry (FSCV) is the most popular electrochemical technique for measuring real-time in vivo dopamine level changes. Standard FSCV has only analyzed "phasic dopamine" (changes in seconds) because the gradual generation of background charging current is inevitable and is the primary noise source in the low-frequency band. Although "tonic dopamine" (changes in minutes to hours) is critical for understanding the dopamine system, an electrochemical technique capable of simultaneously measuring phasic and tonic dopamine in an in vivo environment has not been established. Several modified voltammetric techniques have been developed for measuring tonic dopamine; however, the sampling rates (0.1-0.05 Hz) are too low to be useful. Further investigation of the in vivo applicability of previously developed background drift removal methods for measuring tonic dopamine levels is required. We developed a second-derivative-based background removal (SDBR) method for simultaneously measuring phasic and tonic neurotransmitter levels in real-time. The performance of this technique was tested via in silico and in vitro tonic dopamine experiments. Furthermore, its applicability was tested in vivo. SDBR is a simple, robust, postprocessing technique that can extract tonic neurotransmitter levels from all FSCV data. As SDBR is calculated in individual-scan voltammogram units, it can be applied to any real-time closed-loop system that uses a neurotransmitter as a biomarker.

A mechanistic model of ADHD as resulting from dopamine phasic/tonic imbalance during reinforcement learning

Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children. Although the involvement of dopamine in this disorder seems to be established, the nature of dopaminergic dysfunction remains controversial. The purpose of this study was to test whether the key response characteristics of ADHD could be simulated by a mechanistic model that combines a decrease in tonic dopaminergic activity with an increase in phasic responses in cortical-striatal loops during learning reinforcement. To this end, we combined a dynamic model of dopamine with a neurocomputational model of the basal ganglia with multiple action channels. We also included a dynamic model of tonic and phasic dopamine release and control, and a learning procedure driven by tonic and phasic dopamine levels. In the model, the dopamine imbalance is the result of impaired presynaptic regulation of dopamine at the terminal level. Using this model, virtual individuals from a dopamine imbalance group and a control group were trained to associate four stimuli with four actions with fully informative reinforcement feedback. In a second phase, they were tested without feedback. Subjects in the dopamine imbalance group showed poorer performance with more variable reaction times due to the presence of fast and very slow responses, difficulty in choosing between stimuli even when they were of high intensity, and greater sensitivity to noise. Learning history was also significantly more variable in the dopamine imbalance group, explaining 75% of the variability in reaction time using quadratic regression. The response profile of the virtual subjects varied as a function of the learning history variability index to produce increasingly severe impairment, beginning with an increase in response variability alone, then accumulating a decrease in performance and finally a learning deficit. Although ADHD is certainly a heterogeneous disorder, these results suggest that typical features of ADHD can be explained by a phasic/tonic imbalance in dopaminergic activity alone.

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

Racing and Pacing in the Reward System: A Multi-Clock Circadian Control Over Dopaminergic Signalling

Level of motivation, responsiveness to rewards and punishment, invigoration of exploratory behaviours, and motor performance are subject to daily fluctuations that emerge from circadian rhythms in neuronal activity of the midbrain’s dopaminergic system. While endogenous circadian rhythms are weak in the ventral tegmental area and substantia nigra pars compacta, daily changes in expression of core clock genes, ion channels, neurotransmitter receptors, dopamine-synthesising enzymes, and dopamine transporters, accompanied by changes in electrical activity, are readily observed in these nuclei. These processes cause dopamine levels released in structures innervated by midbrain dopaminergic neurons (e.g., the striatum) to oscillate in a circadian fashion. Additionally, growing evidence show that the master circadian clock located in the suprachiasmatic nucleus of the hypothalamus (SCN) rhythmically influences the activity of the dopaminergic system through various intermediate targets. Thus, circadian changes in the activity of the dopaminergic system and concomitant dopamine release observed on a daily scale are likely to be generated both intrinsically and entrained by the master clock. Previous studies have shown that the information about the value and salience of stimuli perceived by the animal is encoded in the neuronal activity of brain structures innervating midbrain dopaminergic centres. Some of these structures themselves are relatively autonomous oscillators, while others exhibit a weak endogenous circadian rhythm synchronised by the SCN. Here, we place the dopaminergic system as a hub in the extensive network of extra-SCN circadian oscillators and discuss the possible consequences of its daily entrainment for animal physiology and behaviour.

The Ventral Tegmental Area and Nucleus Accumbens as Circadian Oscillators: Implications for Drug Abuse and Substance Use Disorders

Circadian rhythms convergently evolved to allow for optimal synchronization of individuals’ physiological and behavioral processes with the Earth’s 24-h periodic cycling of environmental light and temperature. Whereas the suprachiasmatic nucleus (SCN) is considered the primary pacemaker of the mammalian circadian system, many extra-SCN oscillatory brain regions have been identified to not only exhibit sustainable rhythms in circadian molecular clock function, but also rhythms in overall region activity/function and mediated behaviors. In this review, we present the most recent evidence for the ventral tegmental area (VTA) and nucleus accumbens (NAc) to serve as extra-SCN oscillators and highlight studies that illustrate the functional significance of the VTA’s and NAc’s inherent circadian properties as they relate to reward-processing, drug abuse, and vulnerability to develop substance use disorders (SUDs).

Uncertainty-guided learning with scaled prediction errors in the basal ganglia

To accurately predict rewards associated with states or actions, the variability of observations has to be taken into account. In particular, when the observations are noisy, the individual rewards should have less influence on tracking of average reward, and the estimate of the mean reward should be updated to a smaller extent after each observation. However, it is not known how the magnitude of the observation noise might be tracked and used to control prediction updates in the brain reward system. Here, we introduce a new model that uses simple, tractable learning rules that track the mean and standard deviation of reward, and leverages prediction errors scaled by uncertainty as the central feedback signal. We show that the new model has an advantage over conventional reinforcement learning models in a value tracking task, and approaches a theoretic limit of performance provided by the Kalman filter. Further, we propose a possible biological implementation of the model in the basal ganglia circuit. In the proposed network, dopaminergic neurons encode reward prediction errors scaled by standard deviation of rewards. We show that such scaling may arise if the striatal neurons learn the standard deviation of rewards and modulate the activity of dopaminergic neurons. The model is consistent with experimental findings concerning dopamine prediction error scaling relative to reward magnitude, and with many features of striatal plasticity. Our results span across the levels of implementation, algorithm, and computation, and might have important implications for understanding the dopaminergic prediction error signal and its relation to adaptive and effective learning.

Biophysical Modeling of Dopaminergic Denervation Landscapes in the Striatum Reveals New Therapeutic Strategy

Parkinson’s disease (PD) results from a loss of dopaminergic neurons. What triggers the break-down of neuronal signaling, and how this might be compensated, is not understood. The age of onset, progression and symptoms vary between patients, and our understanding of the clinical variability remains incomplete. In this study, we investigate this, by characterizing the dopaminergic landscape in healthy and denervated striatum, using biophysical modeling. Based on currently proposed mechanisms, we model three distinct denervation patterns, and show how this affect the dopaminergic network. Depending on the denervation pattern, we show how local and global differences arise in the activity of striatal neurons. Finally, we use the mathematical formalism to suggest a cellular strategy for maintaining normal dopamine (DA) signaling following neuronal denervation. This strategy is characterized by dual enhancement of both the release and uptake capacity of DA in the remaining neurons. Overall, our results derive a new conceptual framework for the impaired dopaminergic signaling related to PD and offers testable predictions for future research directions.

Time Course of Neurobehavioral Disruptions and Regional Brain Metabolism Changes in the Rotenone Mice Model of Parkinson’s Disease

Parkinson’s disease (PD) is characterized by slow progression with a long prodromal stage and the gradual evolution of both neuropsychological symptoms and subtle motor changes, preceding motor dysfunction. Thus, in order for animal models of PD to be valid, they should reproduce these characteristics of the disease. One of such models, in which neuropathology is induced by chronic injections of low doses of mitochondrial toxin rotenone, is well established in rats. However, data on this model adapted to mice remain controversial. We have designed the study to describe the timecourse of motor and non-motor symptoms during chronic subcutaneous administration of rotenone (4 mg/kg daily for 35 days) in C57BL/6 mice. We characterize the underlying neuropathological processes (dopaminergic neuron degeneration, regional brain metabolism, monoamine neurotransmitter and lipid peroxidation changes) at different timepoints: 1 day, 2 weeks and 5 weeks of daily rotenone exposure. Based on the behavioral data, we can describe three stages of pathology: cognitive changes from week 2 of rotenone exposure, subtle motor changes in week 3–4 and motor dysfunction starting roughly from week 4. Neuropathological changes in this model include a general decrease in COX activity in different areas of the brain (acute effect of rotenone) and a more specific decrease in midbrain (chronic effect), followed by significant neurodegeneration in SNpc but not VTA by the 5th week of rotenone exposure. However, we were unable to find changes in the level of monoamine neurotransmitters neither in the striatum nor in the cortex, nor in the level of lipid peroxidation in the brainstem. Thus, the gradual progression of pathology in this model is linked with metabolic changes, rather than with oxidative stress or tonic neurotransmitter release levels. Overall, this study supports the idea that a low-dose rotenone mouse model can also reproduce different stages of PD as well as rats.

The Aged Striatum: Evidence of Molecular and Structural Changes Using a Longitudinal Multimodal Approach in Mice

To study the aging human brain requires significant resources and time. Thus, mice models of aging can provide insight into changes in brain biological functions at a fraction of the time when compared to humans. This study aims to explore changes in dopamine D₁ and D₂ receptor availability and of gray matter density in striatum during aging in mice and to evaluate whether longitudinal imaging in mice may serve as a model for normal brain aging to complement cross-sectional research in humans. Mice underwent repeated structural magnetic resonance imaging (sMRI), and [¹¹C]Raclopride and [¹¹C]SCH23390 positron emission tomography (PET) was performed on a subset of aging mice. PET and sMRI data were analyzed by binding potential (BP_ND), voxel- and tensor-based morphometry (VBM and TBM, respectively). Longitudinal PET revealed a significant reduction in striatal BP_ND for D₂ receptors over time, whereas no significant change was found for D₁ receptors. sMRI indicated a significant increase in modulated gray matter density (mGMD) over time in striatum, with limited clusters showing decreased mGMD. Mouse [¹¹C]Raclopride data is compatible with previous reports in human cross-sectional studies, suggesting that a natural loss of dopaminergic D₂ receptors in striatum can be assessed in mice, reflecting estimates from humans. No changes in D₁ were found, which may be attributed to altered [¹¹C]SCH23390 kinetics in anesthetized mice, suggesting that this tracer is not yet able to replicate human findings. sMRI revealed a significant increase in mGMD. Although contrary to expectations, this increase in modulated GM density may be attributed to an age-related increase in non-neuronal cells.

Characterisation of methylphenidate-induced excitation in midbrain dopamine neurons, an electrophysiological study in the rat brain

Methylphenidate (MPH) is a drug routinely used for patients with attention deficit and hyperactivity disorder (ADHD). Concerns arise about psychostimulant use, with dramatic increases in prescriptions. Besides, antipsychotic drugs are often administered in combination with MPH. In this study, we examine the consequences of MPH exposure in combination with dopamine D₂ receptor antagonism (eticlopride) on midbrain dopaminergic neurons in anaesthetised rodents, using in vivo extracellular single-cell electrophysiology. As expected, we show that methylphenidate (2 mg/kg, i.v.) decreases the firing and bursting activities of ventral tegmental area (VTA) dopamine neurons, an effect that is reversed with eticlopride (0.2 mg/kg, i.v.). However, using such a paradigm, we observed higher firing and bursting activities than under baseline conditions. Furthermore, we demonstrate that such an effect is dependent on dual alpha-1 and dopamine D₁ receptors, as well as glutamatergic transmission, through glutamate N-Methyl-D-aspartate (NMDA) receptor activation. Chronic MPH treatment during adolescence greatly dampens MPH-induced excitatory effects measured at adulthood. To conclude, we demonstrated here that a combination of methylphenidate and a dopamine D₂ receptor antagonist produced long-lasting consequences on midbrain dopamine neurons, via glutamatergic-dependent mechanisms.

Effects of methamphetamine-induced neurotoxicity on striatal long-term potentiation

RATIONALE

Methamphetamine (METH) exposure is associated with damage to central monoamine systems, particularly dopamine signaling. Rodent models of such damage have revealed a decrease in the amplitude of phasic dopamine signals and significant striatal dysfunction, including changes in the molecular, system, and behavioral functions of the striatum. Dopamine signaling through D1 receptors promotes corticostriatal long-term potentiation (LTP), a critical substrate of these striatal functions.

OBJECTIVES

Therefore, the purpose of this study was to determine if METH-induced dopamine neurotoxicity would impair D1 receptor-dependent striatal LTP in mice.

METHODS

Mice were treated with a METH binge regimen (4 × 10 mg/kg d,l-methamphetamine, s.c.) that recapitulates all of the known METH-induced neurotoxic effects observed in humans, including dopamine toxicity. Three weeks later, acute brain slices containing either the dorsomedial striatum (DMS) or dorsolateral striatum (DLS) were prepared, and plasticity was assessed using white matter, high-frequency stimulation (HFS), and striatal extracellular electrophysiology.

RESULTS

Under these conditions, LTP was induced in brain slices containing the DMS from saline-pretreated mice, but not mice with METH-induced neurotoxicity. Furthermore, the LTP observed in DMS slices from saline-pretreated mice was blocked by the dopamine D1 receptor antagonist SCH23390, indicating that this LTP is dopamine D1 receptor-dependent. Finally, acute in vivo treatment of METH-pretreated mice with bupropion (50 mg/kg, i.p.) promoted LTP in DMS slices.

CONCLUSIONS

Together, these studies demonstrate that METH-induced neurotoxicity impairs dopamine D1 receptor-dependent LTP within the DMS and that the FDA-approved drug bupropion restores induction of striatal LTP in mice with METH-induced dopamine neurotoxicity.

Impulsivity and risk-seeking as Bayesian inference under dopaminergic control

Bayesian models successfully account for several of dopamine (DA)'s effects on contextual calibration in interval timing and reward estimation. In these models, tonic levels of DA control the precision of stimulus encoding, which is weighed against contextual information when making decisions. When DA levels are high, the animal relies more heavily on the (highly precise) stimulus encoding, whereas when DA levels are low, the context affects decisions more strongly. Here, we extend this idea to intertemporal choice and probability discounting tasks. In intertemporal choice tasks, agents must choose between a small reward delivered soon and a large reward delivered later, whereas in probability discounting tasks, agents must choose between a small reward that is always delivered and a large reward that may be omitted with some probability. Beginning with the principle that animals will seek to maximize their reward rates, we show that the Bayesian model predicts a number of curious empirical findings in both tasks. First, the model predicts that higher DA levels should normally promote selection of the larger/later option, which is often taken to imply that DA decreases 'impulsivity,' and promote selection of the large/risky option, often taken to imply that DA increases 'risk-seeking.' However, if the temporal precision is sufficiently decreased, higher DA levels should have the opposite effect-promoting selection of the smaller/sooner option (higher impulsivity) and the small/safe option (lower risk-seeking). Second, high enough levels of DA can result in preference reversals. Third, selectively decreasing the temporal precision, without manipulating DA, should promote selection of the larger/later and large/risky options. Fourth, when a different post-reward delay is associated with each option, animals will not learn the option-delay contingencies, but this learning can be salvaged when the post-reward delays are made more salient. Finally, the Bayesian model predicts correlations among behavioral phenotypes: Animals that are better timers will also appear less impulsive.

The Role of Dopaminergic Genes in Probabilistic Reinforcement Learning in Schizophrenia Spectrum Disorders

Schizophrenia spectrum disorders (SZ) are characterized by impairments in probabilistic reinforcement learning (RL), which is associated with dopaminergic circuitry encompassing the prefrontal cortex and basal ganglia. However, there are no studies examining dopaminergic genes with respect to probabilistic RL in SZ. Thus, the aim of our study was to examine the impact of dopaminergic genes on performance assessed by the Probabilistic Selection Task (PST) in patients with SZ in comparison to healthy control (HC) subjects. In our study, we included 138 SZ patients and 188 HC participants. Genetic analysis was performed with respect to the following genetic polymorphisms: rs4680 in COMT, rs907094 in DARP-32, rs2734839, rs936461, rs1800497, and rs6277 in DRD2, rs747302 and rs1800955 in DRD4 and rs28363170 and rs2975226 in DAT1 genes. The probabilistic RL task was completed by 59 SZ patients and 95 HC subjects. SZ patients performed significantly worse in acquiring reinforcement contingencies during the task in comparison to HCs. We found no significant association between genetic polymorphisms and RL among SZ patients; however, among HC participants with respect to the DAT1 rs28363170 polymorphism, individuals with 10-allele repeat genotypes performed better in comparison to 9-allele repeat carriers. The present study indicates the relevance of the DAT1 rs28363170 polymorphism in RL in HC participants.

The Dopamine System and Automatization of Movement Sequences: A Review With Relevance for Speech and Stuttering

The last decades of research have gradually elucidated the complex functions of the dopamine system in the vertebrate brain. The multiple roles of dopamine in motor function, learning, attention, motivation, and the emotions have been difficult to reconcile. A broad and detailed understanding of the physiology of cerebral dopamine is of importance in understanding a range of human disorders. One of the core functions of dopamine involves the basal ganglia and the learning and execution of automatized sequences of movements. Speech is one of the most complex and highly automatized sequential motor behaviors, though the exact roles that the basal ganglia and dopamine play in speech have been difficult to determine. Stuttering is a speech disorder that has been hypothesized to be related to the functions of the basal ganglia and dopamine. The aim of this review was to provide an overview of the current understanding of the cerebral dopamine system, in particular the mechanisms related to motor learning and the execution of movement sequences. The primary aim was not to review research on speech and stuttering, but to provide a platform of neurophysiological mechanisms, which may be utilized for further research and theoretical development on speech, speech disorders, and other behavioral disorders. Stuttering and speech are discussed here only briefly. The review indicates that a primary mechanism for the automatization of movement sequences is the merging of isolated movements into chunks that can be executed as units. In turn, chunks can be utilized hierarchically, as building blocks of longer chunks. It is likely that these mechanisms apply also to speech, so that frequent syllables and words are produced as motor chunks. It is further indicated that the main learning principle for sequence learning is reinforcement learning, with the phasic release of dopamine as the primary teaching signal indicating successful sequences. It is proposed that the dynamics of the dopamine system constitute the main neural basis underlying the situational variability of stuttering.