Fast spiking interneuron activity in primate striatum tracks learning of attention cues

Contributions of Basal Ganglia Circuits to Perception, Attention, and Consciousness

Research into ascending sensory pathways and cortical networks has generated detailed models of perception. These same cortical regions are strongly connected to subcortical structures, such as the basal ganglia (BG), which have been conceptualized as playing key roles in reinforcement learning and action selection. However, because the BG amasses experiential evidence from higher and lower levels of cortical hierarchies, as well as higher-order thalamus, it is well positioned to dynamically influence perception. Here, we review anatomical, functional, and clinical evidence to demonstrate how the BG can influence perceptual processing and conscious states. This depends on the integrative relationship between cortex, BG, and thalamus, which allows contributions to sensory gating, predictive processing, selective attention, and representation of the temporal structure of events.

How cortico-basal ganglia-thalamic subnetworks can shift decision policies to maximize reward rate

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 translates into experience-dependent shifts of decision policies represents a longstanding challenge in neuroscience. Here we used a computational approach to address this problem. Specifically, we simulated decisions driven by CBGT circuits under baseline, unrewarded conditions using a spiking neural network, and fit the resulting behavior to an evidence accumulation model. Using canonical correlation analysis, we then replicated the existence of three recently identified control ensembles (responsiveness, pliancy and choice) within CBGT circuits, with each ensemble 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. We find that value-based learning, via dopaminergic signals acting on cortico-striatal synapses, effectively manages the speed-accuracy tradeoff so as to increase reward rate over time. Within this process, learning-related changes in decision policy can be decomposed in terms of the contributions of each control ensemble, and these changes are driven by sequential reward prediction errors on individual trials. Our results provide a clear and simple mechanism for how dopaminergic plasticity shifts specific subnetworks within CBGT circuits so as to strategically modulate decision policies in order to maximize effective reward rate.

Neural evidence for attentional capture by salient distractors

Salient objects often capture our attention, serving as distractors and hindering our current goals. It remains unclear when and how salient distractors interact with our goals, and our knowledge on the neural mechanisms responsible for attentional capture is limited to a few brain regions recorded from non-human primates. Here we conducted a multivariate analysis on human intracranial signals covering most brain regions and successfully dissociated distractor-specific representations from target-arousal signals in the high-frequency (60-100 Hz) activity. We found that salient distractors were processed rapidly around 220 ms, while target-tuning attention was attenuated simultaneously, supporting initial capture by distractors. Notably, neuronal activity specific to the distractor representation was strongest in the superior and middle temporal gyrus, amygdala and anterior cingulate cortex, while there were smaller contributions from the parietal and frontal cortices. These results provide neural evidence for attentional capture by salient distractors engaging a much larger network than previously appreciated.

Bridging the Gap: Local Field Potentials Offer a Peek Into the Brain of a Person With Parkinson Disease

Clinically available deep brain recordings in patients with Parkinson disease (PD) offer insights into disease mechanisms and create a pathway for personalized treatment strategies. This case illustrates the transformative potential of recordings of neuronal firing in the form of local field potentials (LFPs) by detailing a patient's clinical trajectory for 6 months after deep brain stimulation (DBS) surgery to treat their PD symptoms. LFPs, obtained easily in clinic with a tablet interface to measure and track brain rhythms across the disease course, enriched the patient's clinical picture. Specifically, strong beta peaks were captured at initial programming, and, as the beta peaks diminished over the course of optimizing settings, symptoms improved. These signals may also reveal insights into the neural dynamics of PD such as hypersynchrony in basal ganglia circuitry. Furthermore, the ability to record chronically may unlock new understanding of neuronal dysfunction in PD, possibly enabling future adaptive DBS. In conclusion, identification, tracking, and modulation of LFPs correlated with subjective and objective clinical improvement in the case presented. The use of neurophysiologic signals in the future may lead to therapeutic innovations for our patients with PD.

Noradrenergic alpha-2a Receptor Stimulation Enhances Prediction Error Signaling in Anterior Cingulate Cortex and Striatum

The noradrenergic system is implicated to support behavioral flexibility by increasing exploration during periods of uncertainty and by enhancing working memory for goal-relevant stimuli. Possible sources mediating these pro-cognitive effects are α2A adrenoceptors (α2AR) in prefrontal cortex or the anterior cingulate cortex facilitating fronto-striatal learning processes. We tested this hypothesis by selectively stimulating α2ARs using Guanfacine during feature-based attentional set shifting in nonhuman primates. We found that α2A stimulation improved learning from errors and facilitates updating the target feature of an attentional set. Neural recordings in the anterior cingulate cortex (ACC), the dorsolateral prefrontal cortex (dlPFC), and the striatum showed that α2A stimulation selectively enhanced the neural representation of negative reward prediction errors in neurons of the ACC and of positive prediction errors in the striatum, but not in dlPFC. This modulation was accompanied by enhanced encoding of the feature and location of the attended target across the fronto-striatal network. Enhanced learning was paralleled by enhanced encoding of outcomes in putative fast-spiking interneurons in the ACC, dlPFC, and striatum but not in broad spiking cells, pointing to an interneuron mediated mechanism of α2AR action. These results illustrate that α2A receptors causally support the noradrenergic enhancement of updating attention sets through an enhancement of prediction error signaling in the ACC and the striatum.

Routing states transition during oscillatory bursts and attentional selection

Brain-wide information routing relies on the spatio-temporal dynamics of neural activity, but it remains unclear how routing states emerge at fast spiking timescales and relate to slower activity dynamics during cognitive processes. Here, we show that localized spiking events participate in directional routing states with spiking activity in distant brain areas that dynamically switch or amplify states during oscillatory bursts, attentional selection, and decision-making. Modeling and neural recordings from lateral prefrontal cortex (LPFC), anterior cingulate cortex (ACC), and striatum of nonhuman primates revealed that cross-regional routing states arise within 20 ms following individual neuron spikes, with LPFC spikes leading the activity in ACC and striatum. The baseline routing state amplified during LPFC beta bursts between LPFC and striatum and switched direction during ACC theta/alpha bursts between ACC and LPFC. Selective attention amplified theta-/alpha-band-specific lead ensembles in ACC, while decision-making increased the lead of ACC and LPFC spikes to the striatum.

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.

Beta Oscillations in Monkey Striatum Encode Reward Prediction Error Signals

Reward prediction error (RPE) signals are crucial for reinforcement learning and decision-making as they quantify the mismatch between predicted and obtained rewards. RPE signals are encoded in the neural activity of multiple brain areas, such as midbrain dopaminergic neurons, prefrontal cortex, and striatum. However, it remains unclear how these signals are expressed through anatomically and functionally distinct subregions of the striatum. In the current study, we examined to which extent RPE signals are represented across different striatal regions. To do so, we recorded local field potentials (LFPs) in sensorimotor, associative, and limbic striatal territories of two male rhesus monkeys performing a free-choice probabilistic learning task. The trial-by-trial evolution of RPE during task performance was estimated using a reinforcement learning model fitted on monkeys' choice behavior. Overall, we found that changes in beta band oscillations (15-35 Hz), after the outcome of the animal's choice, are consistent with RPE encoding. Moreover, we provide evidence that the signals related to RPE are more strongly represented in the ventral (limbic) than dorsal (sensorimotor and associative) part of the striatum. To conclude, our results suggest a relationship between striatal beta oscillations and the evaluation of outcomes based on RPE signals and highlight a major contribution of the ventral striatum to the updating of learning processes.SIGNIFICANCE STATEMENT Reward prediction error (RPE) signals are crucial for reinforcement learning and decision-making as they quantify the mismatch between predicted and obtained rewards. Current models suggest that RPE signals are encoded in the neural activity of multiple brain areas, including the midbrain dopaminergic neurons, prefrontal cortex and striatum. However, it remains elusive whether RPEs recruit anatomically and functionally distinct subregions of the striatum. Our study provides evidence that RPE-related modulations in local field potential (LFP) power are dominant in the striatum. In particular, they are stronger in the rostro-ventral rather than the caudo-dorsal striatum. Our findings contribute to a better understanding of the role of striatal territories in reward-based learning and may be relevant for neuropsychiatric and neurologic diseases that affect striatal circuits.

Anterior cingulate cortex causally supports flexible learning under motivationally challenging and cognitively demanding conditions

Anterior cingulate cortex (ACC) and striatum (STR) contain neurons encoding not only the expected values of actions, but also the value of stimulus features irrespective of actions. Values about stimulus features in ACC or STR might contribute to adaptive behavior by guiding fixational information sampling and biasing choices toward relevant objects, but they might also have indirect motivational functions by enabling subjects to estimate the value of putting effort into choosing objects. Here, we tested these possibilities by modulating neuronal activity in ACC and STR of nonhuman primates using transcranial ultrasound stimulation while subjects learned the relevance of objects in situations with varying motivational and cognitive demands. Motivational demand was indexed by varying gains and losses during learning, while cognitive demand was varied by increasing the uncertainty about which object features could be relevant during learning. We found that ultrasound stimulation of the ACC, but not the STR, reduced learning efficiency and prolonged information sampling when the task required averting losses and motivational demands were high. Reduced learning efficiency was particularly evident at higher cognitive demands and when subjects experienced loss of already attained tokens. These results suggest that the ACC supports flexible learning of feature values when loss experiences impose a motivational challenge and when uncertainty about the relevance of objects is high. Taken together, these findings provide causal evidence that the ACC facilitates resource allocation and improves visual information sampling during adaptive behavior.

Keeping the Breath in Mind: Respiration, Neural Oscillations, and the Free Energy Principle

Scientific interest in the brain and body interactions has been surging in recent years. One fundamental yet underexplored aspect of brain and body interactions is the link between the respiratory and the nervous systems. In this article, we give an overview of the emerging literature on how respiration modulates neural, cognitive and emotional processes. Moreover, we present a perspective linking respiration to the free-energy principle. We frame volitional modulation of the breath as an active inference mechanism in which sensory evidence is recontextualized to alter interoceptive models. We further propose that respiration-entrained gamma oscillations may reflect the propagation of prediction errors from the sensory level up to cortical regions in order to alter higher level predictions. Accordingly, controlled breathing emerges as an easily accessible tool for emotional, cognitive, and physiological regulation.

Interneuron-specific gamma synchronization indexes cue uncertainty and prediction errors in lateral prefrontal and anterior cingulate cortex

Inhibitory interneurons are believed to realize critical gating functions in cortical circuits, but it has been difficult to ascertain the content of gated information for well-characterized interneurons in primate cortex. Here, we address this question by characterizing putative interneurons in primate prefrontal and anterior cingulate cortex while monkeys engaged in attention demanding reversal learning. We find that subclasses of narrow spiking neurons have a relative suppressive effect on the local circuit indicating they are inhibitory interneurons. One of these interneuron subclasses showed prominent firing rate modulations and (35–45 Hz) gamma synchronous spiking during periods of uncertainty in both, lateral prefrontal cortex (LPFC) and anterior cingulate cortex (ACC). In LPFC, this interneuron subclass activated when the uncertainty of attention cues was resolved during flexible learning, whereas in ACC it fired and gamma-synchronized when outcomes were uncertain and prediction errors were high during learning. Computational modeling of this interneuron-specific gamma band activity in simple circuit motifs suggests it could reflect a soft winner-take-all gating of information having high degree of uncertainty. Together, these findings elucidate an electrophysiologically characterized interneuron subclass in the primate, that forms gamma synchronous networks in two different areas when resolving uncertainty during adaptive goal-directed behavior.

Cannabinoid Modulation of Dopamine Release During Motivation, Periodic Reinforcement, Exploratory Behavior, Habit Formation, and Attention

Motivational and attentional processes energize action sequences to facilitate evolutionary competition and promote behavioral fitness. Decades of neuropharmacology, electrophysiology and electrochemistry research indicate that the mesocorticolimbic DA pathway modulates both motivation and attention. More recently, it was realized that mesocorticolimbic DA function is tightly regulated by the brain's endocannabinoid system and greatly influenced by exogenous cannabinoids-which have been harnessed by humanity for medicinal, ritualistic, and recreational uses for 12,000 years. Exogenous cannabinoids, like the primary psychoactive component of cannabis, delta-9-tetrahydrocannabinol, produce their effects by acting at binding sites for naturally occurring endocannabinoids. The brain's endocannabinoid system consists of two G-protein coupled receptors, endogenous lipid ligands for these receptor targets, and several synthetic and metabolic enzymes involved in their production and degradation. Emerging evidence indicates that the endocannabinoid 2-arachidonoylglycerol is necessary to observe concurrent increases in DA release and motivated behavior. And the historical pharmacology literature indicates a role for cannabinoid signaling in both motivational and attentional processes. While both types of behaviors have been scrutinized under manipulation by either DA or cannabinoid agents, there is considerably less insight into prospective interactions between these two important signaling systems. This review attempts to summate the relevance of cannabinoid modulation of DA release during operant tasks designed to investigate either motivational or attentional control of behavior. We first describe how cannabinoids influence DA release and goal-directed action under a variety of reinforcement contingencies. Then we consider the role that endocannabinoids might play in switching an animal's motivation from a goal-directed action to the search for an alternative outcome, in addition to the formation of long-term habits. Finally, dissociable features of attentional behavior using both the 5-choice serial reaction time task and the attentional set-shifting task are discussed along with their distinct influences by DA and cannabinoids. We end with discussing potential targets for further research regarding DA-cannabinoid interactions within key substrates involved in motivation and attention.

Activity of fast-spiking interneurons in the monkey striatum during reaching movements guided by external cues or by a free choice

Parvalbumin-containing GABAergic interneurons in the striatum, electrophysiologically identified as fast-spiking interneurons (FSIs), exert inhibitory control over striatal output to drive appropriate behavior. While a number of studies have emphasized their importance in motor control, it is unknown how these putative interneurons adapt their functional properties to different modes of movement selection. Here, we tested whether FSIs are sensitive to externally versus internally selected movements by recording their activity while two male rhesus monkeys performed reaching movements to visual targets. Two variants were used: an external condition, in which movements were instructed via external cues, and an internal condition, in which movements were guided by an internal representation of the target location. These conditions allowed to contrast the FSI activity associated with either externally cued or internally driven movement selection. After extensive training, reaching performance was only marginally affected by the type of movement, albeit with some differences between the monkeys. Over two-thirds of the FSIs were modulated around movement onset, regardless of the condition, and consisting mostly of increased activity. We found that a subset of FSIs showed stronger activation related to the initiation of movements in the external condition than in the internal condition, suggesting a dependence on movement selection mode. Moreover, this difference in the strength of FSI activation was predominant in the motor striatum. These data indicate that changes in FSI activity carry information that is scaled by constraints on action selection reflecting the involvement of local striatal inhibitory circuits in adaptation of behavior according to task demands.