Reward predictions not only promote reward pursuit, they also shape how reward is pursed. Such predictions are supported by environmental cues that signal reward availability and probability. Such cues trigger dopamine release in the nucleus accumbens core (NAc). Thus, here we used dopamine sensor fiber photometry, cell-type and pathway-specific optogenetic inhibition, Pavlovian cue-reward conditioning, and test of cue-induced reward-pursuit strategy in male and female rats, to ask whether cue-evoked phasic dopamine release is shaped by reward prediction to support reward pursuit. We found that cue-evoked NAc core dopamine is positively shaped by reward prediction and inversely relates to and predicts instrumental reward seeking. Cues that predicted imminent reward with high probability triggered a large NAc dopamine response and this was associated checking for the expected reward in the delivery location, rather than instrumental reward seeking. Cues that predicted reward with low probability elicited less dopamine and this was associated with a bias towards seeking, rather than check for reward. Correspondingly, inhibition of cue-evoked NAc dopamine increased instrumental reward-seeking and decreased reward-checking behavior. Thus, transient, cue-evoked NAc core dopamine release supports reward prediction to shape reward-pursuit strategy. Cues that signal reward availability promote reward pursuit. To ensure this is adaptive, we use the predictions these cues enable to select how to pursue reward. When reward prediction is low, we'll seek out new reward opportunities. When it is high, we'll check for the reward it in its usual location. Here we discovered that cue-evoked nucleus accumbens dopamine supports reward predictions to shape how reward is pursued. The data show that dopamine can actually constrain reward seeking and promote reward checking when reward is predicted strongly and imminently. These results provide new information on how dopamine shapes behavior in the moment and help understand the link between motivational and dopamine disruptions in psychiatric conditions such as addictions and depression.

Striatal dopamine (DA) release regulates reward-related learning and motivation and is believed to consist of a short-lived and continuous component. Here, we build a large-scale three-dimensional model of extracellular DA dynamics in dorsal (DS) and ventral striatum (VS). The model predicts rapid dynamics in DS with little to no basal DA and slower dynamics in the VS enabling build-up of DA levels. These regional differences do not reflect release-related phenomena but rather differential dopamine transporter (DAT) activity. Interestingly, our simulations posit DAT nanoclustering as a possible regulator of this activity. Receptor binding simulations show that D1 receptor occupancy follows extracellular DA concentration with milliseconds delay, while D2 receptors do not respond to brief pauses in firing but rather integrate DA signal over seconds. Summarised, our model distills recent experimental observations into a computational framework that challenges prevailing paradigms of striatal DA signalling.

Chronic pain frequently co-occurs with depression, forming a vicious cycle that mutually exacerbates both. Although the medial shell of nucleus accumbens (NAcMed) is known to modulate both pain and affective states, the distinct roles of D1- and D2-dopamine receptor-expressing medium spiny neurons (D1- and D2-MSNs) within the NAcMed, as well as their respective circuits, in chronic pain and comorbid depression remain poorly defined. We observed decreased activity in both MSN subtypes during chronic pain and comorbid depression. Notably, activation of D1-MSNs alleviated depressive-like behaviors, whereas activation of D2-MSNs produced analgesic effects. Furthermore, we identified two parallel neural circuits: the NAcMed→mediodorsal thalamus pathway, which preferentially modulates depressive-like behaviors, and the NAcMed→lateral hypothalamus pathway, which selectively relieves pain. These findings delineate a circuit-specific dichotomy in which NAcMed and NAcMed govern distinct affective and sensory dimensions of chronic pain-depression comorbidity, providing circuit-specific targets for potential treatment.

The substantia nigra pars reticulata (SNr) is a primary output for basal ganglia signaling. It plays an important role in the control of movement, integrating inputs from upstream structures in the basal ganglia, before sending organized projections to a range of targets in the midbrain, brainstem, and thalamus. Here, we present a detailed in silico model of the mouse SNr, including its major afferent inputs. The electrophysiological and morphological properties of SNr neurons are characterized in acute brain slices via whole cell patch-clamp recordings and morphological reconstruction. Using reconstructed morphologies, multicompartmental models of single neurons are instantiated within the NEURON simulation environment and populated with relevant modeled ion channels. Model parameters are optimized via an evolutionary algorithm, such that simulated neurons faithfully reproduce recorded electrophysiological behavior. Using the simulation infrastructure software , single neuron models are incorporated into a circuit-level model, where the sparse connectivity within the SNr is recreated. We simulate the mouse SNr at scale, featuring realistic volumes and neuronal density. The unique synaptic properties and activity patterns of different afferent sources are captured in silico. Born out of ex vivo data, our model reproduces in vivo firing patterns. Our simulations suggest that paradoxical activity increases in response to experimental inhibition can be explained by lateral connectivity. In addition, our model predicts the functional implications of characteristic short-term synaptic plasticity in the indirect pathway of the basal ganglia. The model can be extended to include additional inputs and be connected with existing models of upstream basal ganglia nuclei to further explore circuit dynamics.

Striatal output is dynamically modulated by cholinergic interneurons (CINs), the primary source of acetylcholine in the striatum. CINs have been classically viewed as a random and homogeneous population, but recent evidence suggests heterogeneity in their anatomical and functional organization. Here, using systematic mapping and quantitative spatial analyses, we found that-contrary to current dogma-CINs exhibited striking enrichment and nonrandom clustering in the striosome compartment, particularly in the lateral striatum. Similar analyses carried out for parvalbumin- and somatostatin-expressing interneurons revealed that compartmental organization is interneuron specific. The strong "striosome preference" exhibited by CINs was confined within striosome borders, not extending to the surrounding matrix. We further found that striosome and matrix CINs differed in their expression levels of phospho-S6 ribosomal protein-Ser240/244 and choline acetyltransferase, suggesting functional differences, and clustered CINs differed from unclustered CINs in their intrinsic membrane properties. Finally, CINs expressing Lhx6, which defines a distinct γ-aminobutyric acid (GABA) coreleasing population, were notably absent from regions where highly clustered striosomal CINs appeared. Collectively, our findings uncover important dimensions of CIN organization, suggesting that modulation of regional and compartmental striatal output may depend upon the spatial-functional heterogeneity of CINs.

Precisely neuromodulating deep brain regions could bring transformative advancements in both neuroscience and treatment. We demonstrate that non-invasive transcranial ultrasound stimulation (TUS) can selectively modulate deep brain activity and affect learning and decision making, comparable to deep brain stimulation (DBS). We tested whether TUS could causally influence neural and behavioural responses by targeting the nucleus accumbens (NAcc) using a reinforcement learning task. Twenty-six healthy adults completed a within-subject TUS-fMRI experiment with three conditions: TUS to the NAcc, dorsal anterior cingulate cortex (dACC), or Sham. After TUS, participants performed a probabilistic learning task during fMRI. TUS-NAcc altered BOLD responses to reward expectation in the NAcc and surrounding areas. It also affected reward-related behaviours, including win-stay strategy use, learning rate following rewards, learning curves, and repetition rates of rewarded choices. DBS-NAcc perturbed the same features, confirming target engagement. These findings establish TUS as a viable approach for non-invasive deep-brain neuromodulation.

Interval timing is an evolutionarily well-preserved function that presents similar behavioral signatures across different species. However, the neural basis of interval timing remains an open question. For instance, although dopamine has been implicated as a vital component of the internal clock, its precise role is debated due to equivocal findings from various methodologies and their interpretations. We tested this question by optogenetically exciting versus inhibiting tyrosine hydroxylase-positive (TH+) neurons of the substantia nigra pars compacta while male mice produced at least a 3-second-long interval by depressing a lever for reward. Excitation of TH+ neurons shifted their timing behavior to the right, while inhibition led to a shift to the left. Our drift-diffusion-timing model-based analysis of the behavioral data clearly showed that TH+ neuron excitation and inhibition heightened and lowered the timing threshold, respectively, without affecting the rate of temporal integration (i.e., clock speed). Our work attributes a clear mechanistic role (i.e., threshold setting) to nigrostriatal dopaminergic function as part of the internal clock. Despite the ubiquity of time experience, how the brain perceives time is unresolved. Dopamine is a key neuromodulator system involved in subjective time experience. For instance, the time sense is disrupted in conditions characterized by dopaminergic dysfunction (e.g., Parkinson's disease, schizophrenia). However, the mechanistic role of dopamine in the operation of the internal clock is debated. We resolve this debate by optogenetically upregulating and downregulating the nigrostriatal dopamine in mice and evaluating the behavioral outcomes under a computational framework that assumes that the brain times by accumulating brain signals up to a threshold. Our results showed that modulating the nigrostriatal dopamine system alters the level to which the brain integrates clock signals (temporal caution) without altering the clock speed.

Being able to switch from established choices to new alternatives when conditions change - behavioral flexibility - is essential for survival. Cholinergic signaling in the striatum contributes to such flexible behavior, yet the timing and spatial organization of acetylcholine release during contingency changes remain unclear, limiting conceptual understanding of its role in behavioral flexibility. Using a genetically encoded acetylcholine sensor and 2-photon imaging in the dorsal striatum of behaving mice, we visualized acetylcholine dynamics during acquisition and reversal learning in a virtual reality Y-maze. Rewarded outcomes evoked phasic decreases in acetylcholine, whereas unexpected non-reward following reversal triggered widespread increases that predicted lose-shift behavior. Targeted inhibition of cholinergic interneurons reduced this adaptive response. Spatial analysis revealed heterogeneous, temporally distinct signals forming functionally diverse microdomains. These findings suggest that widespread and focal acetylcholine release during unexpected outcomes promotes adaptive response shifts, offering a mechanistic framework for understanding disorders such as addiction and obsessive-compulsive rituals.

The basal ganglia (BG) are a collection of subcortical nuclei involved in motor control, sensorimotor integration, and procedural learning. They play a key role in the acquisition and adaptation of movements, a process driven by dopamine-dependent plasticity at cortico-striatal projections, which serve as BG input. However, BG output is not necessary for executing many well-learned movements. This raises a fundamental question: How can plasticity at BG input contribute to the acquisition and adaptation of movements which execution does not require BG output? Existing models of BG function often neglect the feedback dynamics within cortico-BG-thalamo-cortical circuitry and do not capture the interaction between the cortex and BG in movement generation and adaptation. In this work, we address the above question in a theoretical model of the BG-thalamo-cortical multiregional network, incorporating anatomical, physiological, and behavioral evidence. We examine how its dynamics influence the execution and reward-based adaptation of reaching movements. We demonstrate how the BG-thalamo-cortical network can shape cortical motor output through the combination of three mechanisms: i) the diverse dynamics emerging from its closed-loop architecture, ii) attractor dynamics driven by recurrent cortical connections, and iii) reinforcement learning via dopamine-dependent cortico-striatal plasticity. Our study highlights the role of the cortico-BG-thalamo-cortical feedback in efficient visuomotor adaptation. It also suggests a mechanism for early-stage acquisition of reaching movements through motor babbling. More generally, our model explains how the BG-cortical network refines motor output through its intricate closed-loop dynamics and dopamine-dependent plasticity at cortico-striatal synapses.

Neural activity encoding recent experiences is replayed during sleep and rest to promote consolidation of memories. However, precisely which features of experience influence replay prioritisation to optimise adaptive behaviour remains unclear. Here, we trained adult male rats on a novel maze-based reinforcement learning task designed to dissociate reward outcomes from reward-prediction errors. Four variations of a reinforcement learning model were fitted to the rats' behaviour over multiple days. Behaviour was best predicted by a model incorporating replay biased by reward-prediction error, compared to the same model with no replay, random replay or reward-biased replay. Neural population recordings from the hippocampus and ventral striatum of rats trained on the task evidenced preferential reactivation of reward-prediction and reward-prediction error signals during post-task rest. These insights disentangle the influences of salience on replay, suggesting that reinforcement learning is tuned by post-learning replay biased by reward-prediction error, not by reward per se. This work therefore provides a behavioural and theoretical toolkit with which to measure and interpret the neural mechanisms linking replay and reinforcement learning.

Axons of dopaminergic neurons express gamma-aminobutyric acid type-A receptors (GABARs) and nicotinic acetylcholine receptors (nAChRs), which are positioned to shape striatal dopamine release. We examine how interactions between GABARs and nAChRs influence dopaminergic axon excitability. Axonal patch-clamp recordings reveal that potentiation of GABARs by benzodiazepines suppress dopaminergic axon responses to cholinergic interneuron transmission. In imaging experiments, we use the first temporal derivative of axonal calcium signals to distinguish between direct stimulation of dopaminergic axons and nAChR-evoked activity. Inhibition of GABARs with gabazine selectively enhance nAChR-evoked axonal calcium signals but does not alter the strength or dynamics of acetylcholine release, suggesting that the enhancement is mediated primarily by GABARs on dopaminergic axons. Unexpectedly, we find that a widely used GABAR antagonist, picrotoxin, inhibits axonal nAChRs and should be used cautiously for striatal circuit analysis. Overall, we demonstrate that GABARs on dopaminergic axons regulate integration of nicotinic input to shape axonal excitability.

Intrinsic uncertainty in the reward environment requires the brain to run multiple models simultaneously to predict outcomes from preceding cues or actions. For example, reward outcomes may be linked to specific stimuli and actions, corresponding to stimulus- and action-based learning. But how does the brain arbitrate between such models? Here, we combined multiple computational approaches to quantify concurrent learning in male monkeys performing tasks with different levels of uncertainty about the model of the environment. By comparing behavior in control monkeys and monkeys with bilateral lesions to the amygdala or ventral striatum, we found evidence for a dynamic, competitive interaction between stimulus-based and action-based learning, and for a distinct role of the amygdala in model arbitration. We demonstrated that the amygdala adjusts the initial balance between the two learning systems and is essential for updating arbitration according to the correct model, which in turn alters the interaction between arbitration and learning that governs the time course of learning and choice behavior. In contrast, VS lesions lead to an overall reduction in stimulus-value signals. This role of the amygdala reconciles existing contradictory observations and provides testable predictions for future studies into circuit-level mechanisms of flexible learning and choice under uncertainty.

Flexible control of motor timing is crucial for behaviour. Before volitional movement begins, the frontal cortex and striatum exhibit ramping spiking activity, with variable ramp slopes anticipating movement onsets. This activity in the cortico-basal ganglia loop may function as an adjustable 'timer,' triggering actions at the desired timing. However, because the frontal cortex and striatum share similar ramping dynamics and are both necessary for timing behaviours, distinguishing their individual roles in this timer function remains challenging. Here, to address this, we conducted perturbation experiments combined with multi-regional electrophysiology in mice performing a flexible lick-timing task. Following transient silencing of the frontal cortex, cortical and striatal activity swiftly returned to pre-silencing levels and resumed ramping, leading to a shift in lick timing close to the silencing duration. Conversely, briefly inhibiting the striatum caused a gradual decrease in ramping activity in both regions, with ramping resuming from post-inhibition levels, shifting lick timing beyond the inhibition duration. Thus, inhibiting the frontal cortex and striatum effectively paused and rewound the timer, respectively. These findings are consistent with a model in which the striatum is part of a network that temporally integrates input from the frontal cortex and generates ramping activity that regulates motor timing.

All mammals exhibit flexible decision policies that depend, at least in part, on the cortico-basal ganglia-thalamic (CBGT) pathways. Yet understanding how the complex connectivity, dynamics, and plasticity of CBGT circuits translate into experience-dependent shifts of decision policies represents a longstanding challenge in neuroscience. Here we present the results of a computational approach to address this problem. Specifically, we simulated decisions during the early learning process driven by CBGT circuits under baseline, unrewarded conditions using a spiking neural network, and fit an evidence accumulation model to the resulting behavior. Using canonical correlation analysis, we then replicated the identification of three control ensembles (responsiveness, pliancy and choice) within CBGT circuits, with each of these subnetworks mapping to a specific configuration of the evidence accumulation process. We subsequently simulated learning in a simple two-choice task with one optimal (i.e., rewarded) target and found that, during early stages of learning, feedback-driven dopaminergic plasticity on cortico-striatal synapses effectively increases reward rate over time. The learning-related changes in the decision policy can be decomposed in terms of the contributions of each control ensemble, whose influence is driven by sequential reward prediction errors on individual trials. Our results provide a clear and simple mechanism for how dopaminergic plasticity shifts subnetworks within CBGT circuits so as to increase reward rate by strategically modulating how evidence is used to drive decisions.

Dopamine (DA) is essential for the production of vigorous actions, but how DA modifies the gain of motor commands remains unclear. Here we show that subsecond DA transients in the striatum of mice are neither required nor sufficient for specifying the vigor of ongoing forelimb movements. Our findings have important implications for our understanding of how DA contributes to motor control under physiological conditions and in Parkinson's disease.