Primate Prefrontal Cortex Research Articles

Local organization of spatial and shape information in the primate prefrontal cortex.

The current understanding of sensory and motor cortical areas has been defined by the existence of topographical maps across the brain surface, however, higher cortical areas, such as the prefrontal cortex, seem to lack an equivalent organization, and only limited evidence of functional clustering of neurons with similar stimulus properties is evident in them. We thus sought to examine whether neurons that represent similar spatial and object information are clustered in the monkey prefrontal cortex and whether such an organization only emerges as a result of training. To this end, we analyzed neurophysiological recordings from male macaque monkeys before and after training in spatial and shape working memory tasks. Neurons with similar spatial or shape selectivity were more likely than chance to be encountered at short distances from each other. Some aspects of organization were present even in naïve animals, however other changes appeared after cognitive training. Our results reveal that prefrontal microstructure automatically supports orderly representations of spatial and object information.

Timescales of learning in prefrontal cortex.

The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.

Closed-loop microstimulations of the orbitofrontal cortex during real-life gaze interaction enhance dynamic social attention

Neurons from multiple prefrontal areas encode several key variables of social gaze interaction. To explore the causal roles of the primate prefrontal cortex in real-life gaze interaction, we applied weak closed-loop microstimulations that were precisely triggered by specific social gaze events. Microstimulations of the orbitofrontal cortex, but not the dorsomedial prefrontal cortex or the anterior cingulate cortex, enhanced momentary dynamic social attention in the spatial dimension by decreasing the distance of fixations relative to a partner's eyes and in the temporal dimension by reducing the inter-looking interval and the latency to reciprocate the other's directed gaze. By contrast, on a longer timescale, microstimulations of the dorsomedial prefrontal cortex modulated inter-individual gaze dynamics relative to one's own gaze positions. These findings demonstrate that multiple regions in the primate prefrontal cortex may serve as functionally accessible nodes in controlling different aspects of dynamic social attention and suggest their potential for a therapeutic brain interface.

Sequential neuronal processing of number values, abstract decision, and action in the primate prefrontal cortex.

Decision-making requires processing of sensory information, comparing the gathered evidence to make a judgment, and performing the action to communicate it. How neuronal representations transform during this cascade of representations remains a matter of debate. Here, we studied the succession of neuronal representations in the primate prefrontal cortex (PFC). We trained monkeys to judge whether a pair of sequentially presented displays had the same number of items. We used a combination of single neuron and population-level analyses and discovered a sequential transformation of represented information with trial progression. While numerical values were initially represented with high precision and in conjunction with detailed information such as order, the decision was encoded in a low-dimensional subspace of neural activity. This decision encoding was invariant to both retrospective numerical values and prospective motor plans, representing only the binary judgment of "same number" versus "different number," thus facilitating the generalization of decisions to novel number pairs. We conclude that this transformation of neuronal codes within the prefrontal cortex supports cognitive flexibility and generalizability of decisions to new conditions.

The neuronal implementation of representational geometry in primate prefrontal cortex

Modern neuroscience has seen the rise of a population-doctrine that represents cognitive variables using geometrical structures in activity space. Representational geometry does not, however, account for how individual neurons implement these representations. Leveraging the principle of sparse coding, we present a framework to dissect representational geometry into biologically interpretable components that retain links to single neurons. Applied to extracellular recordings from the primate prefrontal cortex in a working memory task with interference, the identified components revealed disentangled and sequential memory representations including the recovery of memory content after distraction, signals hidden to conventional analyses. Each component was contributed by small subpopulations of neurons with distinct spiking properties and response dynamics. Modeling showed that such sparse implementations are supported by recurrently connected circuits as in prefrontal cortex. The perspective of neuronal implementation links representational geometries to their cellular constituents, providing mechanistic insights into how neural systems encode and process information.

Convergent Circuit Computation for Categorization in the Brains of Primates and Songbirds.

Categorization is crucial for behavioral flexibility because it enables animals to group stimuli into meaningful classes that can easily be generalized to new circumstances. A most abstract quantitative category is set size, the number of elements in a set. This review explores how categorical number representations are realized by the operations of excitatory and inhibitory neurons in associative telencephalic microcircuits in primates and songbirds. Despite the independent evolution of the primate prefrontal cortex and the avian nidopallium caudolaterale, the neuronal computations of these associative pallial circuits show surprising correspondence. Comparing cellular functions in distantly related taxa can inform about the evolutionary principles of circuit computations for cognition in distinctly but convergently realized brain structures.

Training-Dependent Gradients of Timescales of Neural Dynamics in the Primate Prefrontal Cortex and Their Contributions to Working Memory.

Cortical neurons exhibit multiple timescales related to dynamics of spontaneous fluctuations (intrinsic timescales) and response to task events (seasonal timescales) in addition to selectivity to task-relevant signals. These timescales increase systematically across the cortical hierarchy, for example, from parietal to prefrontal and cingulate cortex, pointing to their role in cortical computations. It is currently unknown whether these timescales are inherent properties of neurons and/or depend on training in a specific task and if the latter, how their modulations contribute to task performance. To address these questions, we analyzed single-cell recordings within five subregions of the prefrontal cortex (PFC) of male macaques before and after training on a working-memory task. We found fine-grained but opposite gradients of intrinsic and seasonal timescales that mainly appeared after training. Intrinsic timescales decreased whereas seasonal timescales increased from posterior to anterior subregions within both dorsal and ventral PFC. Moreover, training was accompanied by increases in proportions of neurons that exhibited intrinsic and seasonal timescales. These effects were comparable to the emergence of response selectivity due to training. Finally, task selectivity accompanied opposite neural dynamics such that neurons with task-relevant selectivity exhibited longer intrinsic and shorter seasonal timescales. Notably, neurons with longer intrinsic and shorter seasonal timescales exhibited superior population-level coding, but these advantages extended to the delay period mainly after training. Together, our results provide evidence for plastic, fine-grained gradients of timescales within PFC that can influence both single-cell and population coding, pointing to the importance of these timescales in understanding cognition.

Side-by-side regions in dorsolateral prefrontal cortex estimated within the individual respond differentially to domain-specific and domain-flexible processes.

A recurring debate concerns whether regions of primate prefrontal cortex (PFC) support domain-flexible or domain-specific processes. Here we tested the hypothesis with functional MRI (fMRI) that side-by-side PFC regions, within distinct parallel association networks, differentially support domain-flexible and domain-specialized processing. Individuals (N = 9) were intensively sampled, and all effects were estimated within their own idiosyncratic anatomy. Within each individual, we identified PFC regions linked to distinct networks, including a dorsolateral PFC (DLPFC) region coupled to the medial temporal lobe (MTL) and an extended region associated with the canonical multiple-demand network. We further identified an inferior PFC region coupled to the language network. Exploration in separate task data, collected within the same individuals, revealed a robust functional triple dissociation. The DLPFC region linked to the MTL was recruited during remembering and imagining the future, distinct from juxtaposed regions that were modulated in a domain-flexible manner during working memory. The inferior PFC region linked to the language network was recruited during sentence processing. Detailed analysis of the trial-level responses further revealed that the DLPFC region linked to the MTL specifically tracked processes associated with scene construction. These results suggest that the DLPFC possesses a domain-specialized region that is small and easily confused with nearby (larger) regions associated with cognitive control. The newly described region is domain specialized for functions traditionally associated with the MTL. We discuss the implications of these findings in relation to convergent anatomical analysis in the monkey.NEW & NOTEWORTHY Competing hypotheses link regions of prefrontal cortex (PFC) to domain-flexible or domain-specific processes. Here, using a precision neuroimaging approach, we identify a domain-specialized region in dorsolateral PFC, coupled to the medial temporal lobe and recruited for scene construction. This region is juxtaposed to, but distinct from, broader PFC regions recruited flexibly for cognitive control. Region distinctions align with broader network differences, suggesting that PFC regions gain dissociable processing properties via segregated anatomical projections.

Decoding working memory information from neurons with and without persistent activity in the primate prefrontal cortex.

Persistent activity of neurons in the prefrontal cortex has been thought to represent the information maintained in working memory, though alternative models have challenged this idea. Theories that depend on the dynamic representation of information posit that stimulus information may be maintained by the activity pattern of neurons whose firing rate is not significantly elevated above their baseline during the delay period of working memory tasks. We thus tested the ability of neurons that do and do not generate persistent activity in the prefrontal cortex of monkeys to represent spatial and object information in working memory. Neurons that generated persistent activity represented more information about the stimuli in both spatial and object working memory tasks. The amount of information that could be decoded from neural activity depended on the choice of decoder and parameters used but neurons with persistent activity outperformed non-persistent neurons consistently. Averaged across all neurons and stimuli, the firing rate did not appear clearly elevated above baseline during the maintenance of neural activity particularly for object working memory; however, this grand average masked neurons that generated persistent activity selective for their preferred stimuli, which carried the majority of stimulus information. These results reveal that prefrontal neurons that generate persistent activity maintain information more reliably during working memory.NEW & NOTEWORTHY Competing theories suggest that neurons that generate persistent activity or do not are primarily responsible for the maintenance of information, particularly regarding object working memory. Although the two models have been debated on theoretical terms, direct comparison of empirical results has been lacking. Analysis of neural activity in a large database of prefrontal recordings revealed that neurons that generate persistent activity were primarily responsible for the maintenance of both spatial and object working memory.

Mechanisms regulating the properties of inhibition-based gamma oscillations in primate prefrontal and parietal cortices.

In primates, the dorsolateral prefrontal (DLPFC) and posterior parietal (PPC) cortices are key nodes in the working memory network. The working memory-related gamma oscillations induced in these areas, predominantly in layer 3, exhibit higher frequency in DLPFC. Although these regional differences in oscillation frequency are likely essential for information transfer between DLPFC and PPC, the mechanisms underlying these differences remain poorly understood. We investigated, in rhesus monkey, the DLPFC and PPC layer 3 pyramidal neuron (L3PN) properties that might regulate oscillation frequency and assessed the effects of these properties simulating oscillations in computational models. We found that GABAAR-mediated synaptic inhibition synchronizes L3PNs in both areas, but analysis of GABAAR mRNA levels and inhibitory synaptic currents suggested similar mechanisms of inhibition-mediated synchrony in DLPFC and PPC. Basal dendrite spine density and AMPAR/NMDAR mRNA levels were higher in DLPFC L3PNs, whereas excitatory synaptic currents were similar between areas. Therefore, synaptically evoked excitation might be stronger in DLPFC L3PNs due to a greater quantity of synapses in basal dendrites, a main target of recurrent excitation. Simulations in computational networks showed that oscillation frequency and power increased with increasing recurrent excitation, suggesting a mechanism by which the DLPFC-PPC differences in oscillation properties are generated.

Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.

Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep neural networks. Here, we propose novel computational constraints for artificial neural networks, inspired by earlier work on gating in the primate prefrontal cortex, that capture the cost of interleaved training and allow the network to learn two tasks in sequence without forgetting. We augment standard stochastic gradient descent with two algorithmic motifs, so-called "sluggish" task units and a Hebbian training step that strengthens connections between task units and hidden units that encode task-relevant information. We found that the "sluggish" units introduce a switch-cost during training, which biases representations under interleaved training towards a joint representation that ignores the contextual cue, while the Hebbian step promotes the formation of a gating scheme from task units to the hidden layer that produces orthogonal representations which are perfectly guarded against interference. Validating the model on previously published human behavioural data revealed that it matches performance of participants who had been trained on blocked or interleaved curricula, and that these performance differences were driven by misestimation of the true category boundary.

Temporal dynamics of neural ensemble coding of remembered target location in the primate prefrontal cortex

Temporal dynamics of neural ensemble coding of remembered target location in the primate prefrontal cortex

Category learning in a recurrent neural network with reinforcement learning.

It is known that humans and animals can learn and utilize category information quickly and efficiently to adapt to changing environments, and several brain areas are involved in learning and encoding category information. However, it is unclear that how the brain system learns and forms categorical representations from the view of neural circuits. In order to investigate this issue from the network level, we combine a recurrent neural network with reinforcement learning to construct a deep reinforcement learning model to demonstrate how the category is learned and represented in the network. The model consists of a policy network and a value network. The policy network is responsible for updating the policy to choose actions, while the value network is responsible for evaluating the action to predict rewards. The agent learns dynamically through the information interaction between the policy network and the value network. This model was trained to learn six stimulus-stimulus associative chains in a sequential paired-association task that was learned by the monkey. The simulated results demonstrated that our model was able to learn the stimulus-stimulus associative chains, and successfully reproduced the similar behavior of the monkey performing the same task. Two types of neurons were found in this model: one type primarily encoded identity information about individual stimuli; the other type mainly encoded category information of associated stimuli in one chain. The two types of activity-patterns were also observed in the primate prefrontal cortex after the monkey learned the same task. Furthermore, the ability of these two types of neurons to encode stimulus or category information was enhanced during this model was learning the task. Our results suggest that the neurons in the recurrent neural network have the ability to form categorical representations through deep reinforcement learning during learning stimulus-stimulus associations. It might provide a new approach for understanding neuronal mechanisms underlying how the prefrontal cortex learns and encodes category information.

Comparative analysis of astrocytes in the prefrontal cortex of primates: Insights into the evolution of human brain energetics.

Astrocytes are the main homeostatic cell of the brain involved in many processes related to cognition, immune response, and energy expenditure. It has been suggested that the distribution of astrocytes is associated with brain size, and that they are specialized in humans. To evaluate these, we quantified astrocyte density, soma volume, and total glia density in layer I and white matter in Brodmann's area 9 of humans, chimpanzees, baboons, and macaques. We found that layer I astrocyte density, soma volume, and ratio of astrocytes to total glia cells were highest in humans and increased with brain size. Overall glia density in layer I and white matter were relatively invariant across brain sizes, potentially due to their important metabolic functions on a per volume basis. We also quantified two transporters involved in metabolism through the astrocyte-neuron lactate shuttle, excitatory amino acid transporter 2 (EAAT2) and glucose transporter 1 (GLUT1). We expected these transporters would be increased in human brains due to their high rate of metabolic consumption and associated gene activity. While humans have higher EAAT2 cell density, GLUT1 vessel volume, and GLUT1 area fraction compared to baboons and chimpanzees, they did not differ from macaques. Therefore, EAAT2 and GLUT1 are not related to increased energetic demands of the human brain. Taken together, these data provide evidence that astrocytes play a unique role in both brain expansion and evolution among primates, with an emphasis on layer I astrocytes having a potentially significant role in human-specific metabolic processing and cognition.

Systemic D1-R and D2-R antagonists in non-human primates differentially impact learning and memory while impairing motivation and motor performance.

Dopamine (DA) modulates cognition in part via differential activation of D1 and D2 receptors within the striatum and prefrontal cortex, yet evidence for cognitive impairments stemming from DA blockade or deficiency is inconsistent. Given the predominance of D1 over D2 receptors (R) in the prefrontal cortex of primates, D1-R blockade should more strongly influence frontal executive function (including working memory), while D2-R blockade should impair processes more strongly associated with the dorsal striatum (including cognitive flexibility, and learning). To test how systemic DA blockade disrupts cognition, we administered D1-R and D2-R like antagonists to healthy monkeys while they performed a series of cognitive tasks. Two selective DA receptor antagonist drugs (SCH-23390 hydrochloride: D1/D5-R antagonist; or Eticlopride hydrochloride: D2/D3-R antagonist) or placebo (0.9% saline) were systemically administered. Four tasks were used: (1) 'visually guided reaching', to test response time and accuracy, (2) 'reversal learning', to test association learning and attention, (3) 'self-ordered sequential search' to test spatial working memory, and (4) 'delayed match to sample' to test object working memory. Increased reach response times and decreased motivation to work for liquid reward was observed with both the D1/D5-R and D2/D3-R antagonists at the maximum dosages that still enabled task performance. The D2/D3-R antagonist impaired performance in the reversal learning task, while object and spatial working memory performance was not consistently affected in the tested tasks for either drug. These results are consistent with the theory that systemic D2/D3-R antagonists preferentially influence striatum processes (cognitive flexibility) while systemic D1/D5-R administration is less detrimental to frontal executive function.

Identification of Prefrontal Cortex and Amygdala Expressed Genes Associated With Sevoflurane Anesthesia on Non-human Primate.

Clinical trials and animal studies have indicated that long-term use or multiple administrations of anesthesia may lead to fine motor impairment in the developing brain. Most studies on anesthesia-induced neurotoxicity have focused on the hippocampus and prefrontal cortex (PFC); however, the role of other vital encephalic regions, such as the amygdala, is still unclear. Herein, we focused on sevoflurane, the most commonly used volatile anesthetic in infants, and performed a transcriptional analysis of the PFC and amygdala of macaques after multiple exposures to the anesthetic by RNA sequencing. The overall, overlapping, and encephalic region-specific transcriptional patterns were separately analyzed to reveal their functions and differentially expressed gene sets that were influenced by sevoflurane. Specifically, functional, protein–protein interaction, neighbor gene network, and gene set enrichment analyses were performed. Further, we built the basic molecular feature of the amygdala by comparing it to the PFC. In comparison with the amygdala’s changing pattern following sevoflurane exposure, functional annotations of the PFC were more enriched in glial cell-related biological functions than in neuron and synapsis development. Taken together, transcriptional studies and bioinformatics analyses allow for an improved understanding of the primate PFC and amygdala.

Prefrontal Neuronal Activities During Active Retrieval of Information From Long-Term Memory

Abstract. Single-neuron studies performed in the primate prefrontal cortex (PFC) revealed that retaining information in working memory (WM) is associated with sustained firing during the delay period in a match-to-sample task. On the other hand, single-neuron studies using a pair-association task have shown that retrieving information from long-term memory (LTM) is related to two kinds of neural activities: decreasing activity representing information linked to the sample stimulus and increasing activity predicting information for the forthcoming matching stimulus. To further examine neuronal behavioral patterns during LTM retrieval, we used a partial correlation coefficient analysis to analyze single-neuron activities in the PFC while monkeys performed the visual pair-association task. Results showed that, for most of the task-related neurons, firing activity depicted information from the sample stimulus. Nevertheless, some neurons showed an opposite pattern, this is, increasing activity during the delay period, possibly indicating a prospective memory coding from LTM. Interestingly, both activities seem to be present at different degrees as the delay period progresses. Together, these results unveil a new aspect of PFC neurons when retrieving unseen information from LTM.

Cell type specific cannabinoid CB1 receptor distribution across the human and non-human primate cortex

Alterations in cannabinoid CB1 receptor (CB1R) are implicated in various psychiatric disorders. CB1R participates in both depolarization induced suppression of inhibition (DSI) and depolarization induced suppression of excitation (DSE), suggesting its involvement in regulating excitatory and inhibitory (E/I) balance. Prior studies examining neuronal cell type specific CB1R distribution have been conducted near exclusively within rodents. Identification of these distribution patterns within the human and non-human primate cortex is essential to increase our insight into its function. Using co-labeling immunohistochemistry and fluorescent microscopy, we examined CB1R protein levels within excitatory and inhibitory boutons of male human and non-human primate prefrontal cortex and auditory cortices, regions involved in the behavioral effects of exogenous cannabinoid exposures. We found that CB1R was present in both bouton populations within all brain regions examined in both species. Significantly higher CB1R levels were found within inhibitory than within excitatory boutons across all regions in both species, although the cell type by brain region interactions differed between the two species. Our results support the importance of conducting more in-depth CB1R examinations to understand how cell type and brain region dependent differences contribute to regional E/I balance regulation, and how aberrations in CB1R distribution may contribute to pathology.

P.161 Spatiotemporal dynamics of neuronal ensembles in the primate prefrontal cortex during virtual reality navigation tasks

Background: Brain-machine-interface research has utilized multichannel single neuron recordings to decode movement intention. However, the prefrontal cortex (PFC) contains mental representations of more abstract task and goal elements which may be utilized as important signals in a brain-machine-interface. We therefore utilized virtual reality to simulate a real-world task while recording from ensembles of primate PFC neurons. Methods: Two male rhesus macaques (macaca mulatta) were trained to navigate a virtual reality environment using a joystick and learn a context-object association rule. We implanted each monkey with two 96-channel Utah arrays (Blackrock Microsystems) in the lateral PFC (areas 9/46 and 8a) and simultaneously recorded from multiple single neurons. Results: A linear support-vector-machine decoded task elements (context, target location and chosen direction of movement) with significantly greater than chance accuracy. This information was decoded in a sequential manner as the primates made a rule-based decision, with context information appearing first, followed by target location, and chosen side. Conclusions: We found that different neuronal ensembles encode the elements needed for implementing the context rule, and that such ensembles are activated sequentially. Brain-machine-interface systems may benefit by integrating neural data from the PFC, providing salient goal-related information such as the content of the goal and its spatial location.

Mapping brain-wide excitatory projectome of primate prefrontal cortex at submicron resolution and comparison with diffusion tractography.

Resolving trajectories of axonal pathways in the primate prefrontal cortex remains crucial to gain insights into higher-order processes of cognition and emotion, which requires a comprehensive map of axonal projections linking demarcated subdivisions of prefrontal cortex and the rest of brain. Here, we report a mesoscale excitatory projectome issued from the ventrolateral prefrontal cortex (vlPFC) to the entire macaque brain by using viral-based genetic axonal tracing in tandem with high-throughput serial two-photon tomography, which demonstrated prominent monosynaptic projections to other prefrontal areas, temporal, limbic, and subcortical areas, relatively weak projections to parietal and insular regions but no projections directly to the occipital lobe. In a common 3D space, we quantitatively validated an atlas of diffusion tractography-derived vlPFC connections with correlative green fluorescent protein-labeled axonal tracing, and observed generally good agreement except a major difference in the posterior projections of inferior fronto-occipital fasciculus. These findings raise an intriguing question as to how neural information passes along long-range association fiber bundles in macaque brains, and call for the caution of using diffusion tractography to map the wiring diagram of brain circuits.