Publications

Background The gold standard anesthesia for deep brain stimulation (DBS) surgery is the "awake" approach, using local anesthesia alone. Although it offers high-quality microelectrode recordings and therapeutic-window assessment, it potentially causes patients extreme stress and might result in suboptimal surgical outcomes. General anesthesia or deep sedation is an alternative, but may reduce physiological testing reliability and lead localization accuracy. Objectives The aim is to investigate a novel anesthesia regimen of ketamine-induced conscious sedation for the physiological testing phase of DBS surgery. Methods Parkinson's patients undergoing subthalamic DBS surgery were randomly divided into experimental and control groups. During physiological testing, the groups received 0.25mg/kg/h ketamine infusion and normal saline, respectively. Both groups had moderate propofol sedation before and after physiological testing. The primary outcome was recording quality. Secondary outcomes included hemodynamic stability, lead accuracy, motor and cognitive outcome, patient satisfaction, and adverse events. Results Thirty patients, 15 from each group, were included. Intraoperatively, the electrophysiological signature and lead localization were similar under ketamine and saline. Tremor amplitude was slightly lower under ketamine. Postoperatively, patients in the ketamine group reported significantly higher satisfaction with anesthesia. The improvement in Unified Parkinson's disease rating scale part-III was similar between the groups. No negative effects of ketamine on hemodynamic stability or cognition were reported perioperatively. Conclusions Ketamine-induced conscious sedation provided high quality microelectrode recordings comparable with awake conditions. Additionally, it seems to allow superior patient satisfaction and hemodynamic stability, while maintaining similar post-operative outcomes. Therefore, it holds promise as a novel alternative anesthetic regimen for DBS.

Outcomes and feedbacks on performance may influence behavior beyond the context in which it was received, yet it remains unclear what neurobehavioral mechanisms may account for such lingering influences on behavior. The average reward rate (ARR) has been suggested to regulate motivated behavior, and was found to interact with dopamine-sensitive cognitive processes, such as vigilance and associative memory encoding. The ARR could therefore provide a bridge between independent tasks when these are performed in temporal proximity, such that the reward rate obtained in one task could influence performance in a second subsequent task. Reinforcement learning depends on the coding of prediction error signals by dopamine neurons and their downstream targets, in particular the nucleus accumbens. Because these brain regions also respond to changes in ARR, reinforcement learning may be vulnerable to changes in ARR. To test this hypothesis, we designed a novel paradigm in which participants (n = 245) performed two probabilistic reinforcement learning tasks presented in interleaved trials. The ARR was controlled by an \u201cinduction\u201d task which provided feedback with a low (p = 0.58), a medium (p = 0.75), or a high probability of reward (p = 0.92), while the impact of ARR on reinforcement learning was tested by a second \u201creference\u201d task with a constant reward probability (p = 0.75). We find that performance was significantly lower in the reference task when the induction task provided low reward probabilities (i.e., during low levels of ARR), as compared to the medium and high ARR conditions. Behavioral modeling further revealed that the influence of ARR is best described by models which accumulates average rewards (rather than average prediction errors), and where the ARR directly modulates the prediction error signal (rather than affecting learning rates or exploration). Our results demonstrate how affective information in one domain may transfer and affect motivated behavior in other domains. These findings are particularly relevant for understanding mood disorders, but may also inform abnormal behaviors attributed to dopamine dysfunction.

Stimuli that evoke the same feelings can nevertheless look different and have different semantic meanings. Although we know much about the neural representation of emotion, the neural underpinnings of emotional similarity are unknown. One possibility is that the same brain regions represent similarity between emotional and neutral stimuli, perhaps with different strengths. Alternatively, emotional similarity could be coded in separate regions, possibly those sensitive to emotional valence and arousal. In behavior, the extent to which people consider similarity along emotional dimensions when they evaluate the overall similarity between stimuli has never been investigated. Although the emotional features of stimuli may dominate explicit ratings of similarity, it is also possible that people neglect emotional dimensions as irrelevant to that judgment. We contrasted these hypotheses in (male and female) healthy controls using two measures of similarity and two picture databases of complex negative and neutral scenes, the second of which provided exquisite control over semantic and visual attributes. The similarity between emotional stimuli was greater than between neutral stimuli in the inferior temporal cortex, the fusiform face area, and the precuneus. Additionally, only the similarity between emotional stimuli was significantly represented in early visual cortex, anterior insula and dorsal anterior cingulate cortex. Intriguingly, despite the stronger neural similarity between emotional stimuli, the same participants did not rate them as more similar to each other than neutral stimuli. These results contribute to our understanding of how emotion is represented within a general conceptual workspace and of the overgeneralization bias in anxiety disorders.

The importance of the excitatory-inhibitory (E/I) balance in a wide range of cognitive and behavioral processes has prompted a commensurate interest in methods for reliably quantifying it. Proton Magnetic Resonance Spectroscopy (1H-MRS) remains the only method capable of safely and non-invasively measuring the concentrations of the brain's major excitatory (glutamate) and inhibitory (γ-aminobutyric-acid, GABA) neurotransmitters in-vivo. MRS relies on spectral Mescher-Garwood (MEGA) editing techniques at 3T to distinguish GABA from its overlapping resonances. However, with the increased spectral resolution at ultrahigh field strengths of 7T and above, non-edited spectroscopic techniques become potential viable alternatives to MEGA based approaches, and also address some of their shortcomings, such as signal loss, sensitivity to transmitter inhomogeneities and temporal resolution. We present a comprehensive comparison of both edited and non-edited strategies at 7T for simultaneously quantifying glutamate and GABA from the dorsal anterior cingulate cortex (dACC), and evaluate their reproducibility and relative bias. The combined root-mean-square test-retest reproducibility of Glu and GABA (CVE/I) was as low as 13.3% for unedited MRS at TE=80 ms using SemiLASER localization, while edited MRS at TE=80 ms yielded CVE/I=20% and 21% for asymmetric and symmetric MEGA editing, respectively. An unedited SemiLASER acquisition using a shorter echo time of TE=42 ms yielded CVE/I as low as 24.9%. Our results show that non-edited sequences at an echo time of 80 ms provide better reproducibility than either edited sequences at the same TE, or non-edited sequences at a shorter TE of 42 ms. This is supported by numerical simulations and is driven in part by a pseudo-singlet appearance of the GABA multiplets at TE=80 ms, and the excellent spectral resolution at 7T. Our results uphold a transition to non-edited MRS for monitoring the E/I balance at ultrahigh fields, and stress the importance of using a properly-optimized echo time.

Βeta oscillatory activity (human: 1335Hz; primate: 824Hz) is pervasive within the cortex and basal ganglia. Studies in Parkinsons disease patients and animal models suggest that beta-power increases with dopamine depletion. However, the exact relationship between oscillatory power, frequency and dopamine tone remains unclear. We recorded neural activity in the cortex and basal ganglia of healthy non-human primates while acutely and chronically up- and down-modulating dopamine levels. We assessed changes in beta oscillations in patients with Parkinsons following acute and chronic changes in dopamine tone. Here we show beta oscillation frequency is strongly coupled with dopamine tone in both monkeys and humans. Power, coherence between single-units and local field potentials (LFP), spike-LFP phase-locking, and phase-amplitude coupling are not systematically regulated by dopamine levels. These results demonstrate that beta frequency is a key property of pathological oscillations in cortical and basal ganglia networks.

Background: Anesthetics aim to prevent memory of unpleasant experiences. The amygdala and dorsal anterior cingulate cortex participate in forging emotional and valence-driven memory formation. It was hypothesized that this circuitry maintains its role under sedation. Methods: Two nonhuman primates underwent aversive tone-odor conditioning under sedative states induced by ketamine or midazolam (1 to 8 and 0.1 to 0.8 mg/kg, respectively). The primary outcome was behavioral and neural evidence suggesting memory formation. This study simultaneously measured conditioned inspiratory changes and changes in firing rate of single neurons in the amygdala and the dorsal anterior cingulate cortex in response to an expected aversive olfactory stimulus appearing during acquisition and tested their retention after recovery. Results: Aversive memory formation occurred in 26 of 59 sessions under anesthetics (16 of 29 and 10 of 30, 5 of 30 and 21 of 29 for midazolam and ketamine at low and high doses, respectively). Single-neuron responses in the amygdala and dorsal anterior cingulate cortex were positively correlated between acquisition and retention (amygdala, n = 101, r = 0.51, P

The direction of the eye gaze of others is a prominent social cue in primates and is important for communication(1-11). Although gaze can signal threat and elicit anxiety(6,12,13), it remains unclear whether it shares neural circuitry with stimulus value. Notably, gaze not only has valence, but can also serve as a predictor of the outcome of a social encounter, which can be either negative or positive(2,8,12,13). Here we show that the neural codes for gaze and valence overlap in primates and that they involve two different mechanisms: one for the outcome and another for its expectation. Monkeys participated in the human intruder test(13,14), in which a human participant had either a direct or averted gaze, interleaved with blocks of aversive and appetitive conditioning. We find that single neurons in the amygdala encode gaze(15), whereas neurons in the anterior cingulate cortex encode the social context(16), but not gaze. We identify a shared population in the amygdala for which the neural responses to direct and averted gaze parallel the responses to aversive and appetitive stimulus, respectively. Furthermore, we distinguish between two neural mechanisms-an overall-activity scheme that is used for gaze and the unconditioned stimulus, and a correlated-selectivity scheme that is used for gaze and the conditioned stimulus. These findings provide insights into the origins of the neural mechanisms that underlie the computations of both social interactions and valence, and could help to shed light on mechanisms that underlie social anxiety and the comorbidity between anxiety and impaired social interactions.

Memory consolidation can be promoted via targeted memory reactivation (TMR) that re-presents training cues or context during sleep. Whether TMR acts locally or globally on cortical sleep oscillations remains unknown. Here, we exploit the unique functional neuroanatomy of olfaction with its ipsilateral stimulus processing to perform local TMR in one brain hemisphere. Participants learned associations between words and locations in left or right visual fields with contextual odor throughout. We found lateralized event-related potentials during task training that indicate unihemispheric memory processes. During post-learning naps, odors were presented to one nostril in non-rapid eye movement (NREM) sleep. Memory for specific words processed in the cued hemisphere (ipsilateral to stimulated nostril) was improved after local TMR during sleep. Unilateral odor cues locally modulated slow-wave (SW) power such that regional SW power increase was lower in the cued hemisphere relative to the uncued hemisphere and negatively correlated with select memories for cued words. Moreover, local TMR improved phase-amplitude coupling (PAC) between slow oscillations and sleep spindles specifically in the cued hemisphere. The effects on memory performance and cortical sleep oscillations were not observed when unilateral olfactory stimulation during sleep followed learning without contextual odor. Thus, TMR in human sleep transcends global action by selectively promoting specific memories associated with local sleep oscillations.

Affective learning and memory are essential for daily behavior, with both adaptive and maladaptive learning depending on stimulus-evoked activity in the amygdala circuitry. Behavioral studies further suggest that post-association offline processing contributes to memory formation. Here we investigated spike sequences across simultaneously recorded neurons while monkeys learned to discriminate between aversive and pleasant tone-odor associations. We show that triplets of neurons exhibit consistent temporal sequences of spiking activity that differed from firing patterns of individual neurons and pairwise correlations. These sequences occurred throughout the long post-trial period, contained valence-related information, declined as learning progressed and were selectively present in activity evoked by the recent pairing of a conditioned stimulus with an unconditioned stimulus. Our findings reveal that temporal sequences across neurons in the primate amygdala serve as a coding mechanism and might aid memory formation through the rehearsal of the recently experienced association.

Deciding when to exploit what is already known and when to explore new possibilities is crucial for adapting to novel and dynamic environments. Using reinforcement-based decision making, Costa et al. (2019) in this issue of Neuron find that neurons in the amygdala and ventral-striatum differentially signal the benefit from exploring new options and exploiting familiar ones.

Ultrasound (US) can potentially be the first non-invasive neuromodulation modality that can selectively target small areas deep in the brain. Understanding the mechanism of US neuromodulation is an important step to establishing its viability and safety.Hippocampal neural cultures were exposed to single continuous wave US pulses while being imaged via fluorescent calcium imaging.Connectivity in neural cultures was blocked using GABA and Glutamate receptor antagonists. This made it possible to examine effects at the level of the single independent neuron, without the confounding recurrent activity present in the fully connected culture. Blockers of mechanosensitive ion channels were also applied.Single US pulses were effective in generating responses in the intact cultures as well as in pharmacologically disconnected neurons. These responses were abolished by Tetrodotoxin (TTX), but were resistant to Ruthenium Red (RR).Successful stimulation while synaptic inputs are blocked indicates that a post-synaptic mechanism is involved. This, however, may not rule out a separate presynaptic mechanism. The abolition by TTX affirms that the responses measured are the result of neuronal activity and that the main mechanism is not a lasting poration of the plasma membrane, nor is it a large direct effect of US on calcium influx. The resistance to RR suggests that neither TRPV nor Piezo type mechanosensitive channels mediate this effect.Propedium Iodide, a membrane impassable fluorescent indicator, was used to investigate membrane integrity. It showed that only a small percentage of the responding cells had become permeable during stimulation. This undercuts membrane poration as a major mechanism, although very small pores (

The dorsal anterior cingulate cortex (dACC) is crucial for motivation, reward- and error-guided decision-making, yet its excitatory and inhibitory mechanisms remain poorly explored in humans. In particular, the balance between excitation and inhibition (E/I), demonstrated to play a role in animal studies, is difficult to measure in behaving humans. Here, we used functional magnetic-resonance-spectroscopy (1H-fMRS) to measure the brain's major inhibitory (GABA) and excitatory (Glutamate) neurotransmitters during reinforcement learning with three different conditions: high cognitive load (uncertainty); probabilistic discrimination learning; and a control null-condition. Participants learned to prefer the gain option in the discrimination phase and had no preference in the other conditions. We found increased GABA levels during the uncertainty condition, potentially reflecting recruitment of inhibitory systems during high cognitive load when trying to learn. Further, higher GABA levels during the null (baseline) condition correlated with improved discrimination learning. Finally, glutamate and GABA levels were correlated during high cognitive load. These results suggest that availability of dACC inhibitory resources enables successful learning. Our approach helps elucidate the potential contribution of the balance between excitation and inhibition to learning and motivation in behaving humans.GABA and Glutamate were measured in the dACC during learning.Learning included: cognitive-load, discrimination-learning and control conditions.Increased GABA levels were observed during high cognitive load.GABA levels during the control condition were correlated with better learning.Availability of dACC inhibitory resources enabled successful learning.

Explicit memory after anaesthesia has gained considerable attention because of its negative implications, while implicit memory, which is more elusive and lacks patients' explicit recall, has received less attention and dedicated research. This is despite the likely impact of implicit memory on postoperative long-term well-being and behaviour. Given the scarcity of human data, fear conditioning in animals offers a reliable model of implicit learning, and importantly, one where we already have a good understanding of the underlying neural circuitry in awake conditions. Animal studies provide evidence that fear conditioning occurs under anaesthesia. The effects of different anaesthetics on memory are complex, with different drugs interacting at different stages of learning. Modulatory suppressive effects can be because of context, specific drugs, and dose dependency. In some cases, low doses of general anaesthetics can actually lead to a paradoxical opposite effect. The underlying mechanisms involve several neurotransmitter systems, acting mainly in the amygdala, hippocampus, and neocortex. Here, we review animal studies of aversive conditioning under anaesthesia, discuss the complex picture that arises, identify the gaps in knowledge that require further investigation, and highlight the potential translational relevance of the models.

The contribution of oscillatory synchrony in the primate amygdala-prefrontal pathway to aversive learning remains largely unknown. We found increased power and phase synchrony in the theta range during aversive conditioning. The synchrony was linked to single-unit spiking and exhibited specific directionality between input and output measures in each region. Although it was correlated with the magnitude of conditioned responses, it declined once the association stabilized. The results suggest that amygdala spikes help to synchronize ACC activity and transfer error signal information to support memory formation. Taub et al. report increased synchrony between amygdala's output (spikes) and prefrontal's input (local field potentials) that is locked to theta band and declines once memory is formed. Therefore, the primate amygdala→prefrontal pathway uses theta to transfer error-like information during fear learning.

To learn from others' experience, one must link environmental conditions with social cues. A specific amygdala circuit underlies social learning of fear, and targeted activation normalizes behavior in a rodent model of autism.

Autonomic hyperarousal and avoidance in post-traumatic stress disorder (PTSD) can be triggered by a host of stimuli or situations that bear some similarity or association to the trauma event. As these triggers are often encountered in safe environments removed from the original trauma, this overgeneralization of fear and anxiety is a burden that can interfere with daily life. Recent efforts to understand the neurobiology of PTSD have relied on laboratory models of Pavlovian fear conditioning and extinction. This chapter reviews studies of fear generalization in animals and humans, which provide a valuable model to conceptualize the excessive fear generalization characteristic of PTSD.

Abstract Fear can be an adaptive emotion that helps defend against potential danger. Classical conditioning models elegantly describe how animals learn which stimuli in the environment signal danger, but understanding how this learning is generalized to other stimuli that resemble aspects of a learned threat remains a challenge. Critically, the overgeneralization of fear to harmless stimuli or situations is a burden to daily life and characteristic of posttraumatic stress disorder and other anxiety disorders. Here, we review emerging evidence on behavioral and neural mechanisms of generalization of emotional learning with the goal of encouraging further research on generalization in anxiety disorders. We begin by placing research on fear generalization in a rich historical context of stimulus generalization dating back to Pavlov, which lays the foundation for theoretical and experimental approaches used today. We then transition to contemporary behavioral and neurobiological research on generalization of emotional learning in humans and nonhuman animals and discuss the factors that promote generalization on the one hand from discrimination on the other hand.

The study of neurobiological mechanisms underlying anxiety disorders has been shaped by learning models that frame anxiety as maladaptive learning. Pavlovian conditioning and extinction are particularly influential in defining learning stages that can account for symptoms of anxiety disorders. Recently, dynamic and task related communication between the basolateral complex of the amygdala (BLA) and the medial prefrontal cortex (mPFC) has emerged as a crucial aspect of successful evaluation of threat and safety. Ongoing patterns of neural signaling within the mPFC-BLA circuit during encoding, expression and extinction of adaptive learning are reviewed. The mechanisms whereby deficient mPFC-BLA interactions can lead to generalized fear and anxiety are discussed in learned and innate anxiety. Findings with cross-species validity are emphasized.

The ability to switch flexibly between aversive and neutral behaviors based on predictive cues relies on learning driven by surprise or errors in outcome prediction. Surprise can occur as absolute value of the error (unsigned error) or its direction (signed errors; positive when something unexpected is delivered and negative when something expected is omitted). Signed and unsigned errors coexist in the brain and were associated with different systems, but how they interact and form across large networks remains vague. We recorded simultaneously in the amygdala and dorsal anterior cingulate cortex (dACC) of monkeys performing a reversal aversive-conditioning paradigm and quantified changes in interregional correlations when contingencies shift. We report that errors exist in different magnitudes and that they differentially develop at millisecond resolution. Our results support a model where unsigned errors first develop in the amygdala during successful learning and then propagate into the dACC, where signed errors develop and are distributed back to the amygdala.

Neural responses are commonly studied in terms of "tuning curves," characterizing changes in neuronal response as a function of a continuous stimulus parameter. In the motor system, neural responses to movement direction often follow a bell-shaped tuning curve for which the exact shape determines the properties of neuronal movement coding. Estimating the shape of that tuning curve robustly is hard, especially when directions are sampled unevenly and at a coarse resolution. Here, we describe a Bayesian estimation procedure that improves the accuracy of curve-shape estimation even when the curve is sampled unevenly and at a very coarse resolution. Using this approach, we characterize the movement direction tuning curves in the supplementary motor area (SMA) of behaving monkeys. We compare the SMA tuning curves to tuning curves of neurons from the primary motor cortex (M1) of the same monkeys, showing that the tuning curves of the SMA neurons tend to be narrower and shallower. We also show that these characteristics do not depend on the specific location in each region.

Keeping track of self-executed facial expressions is essential for the ability to correctly interpret and reciprocate social expressions. However, little is known about neural mechanisms that participate in self-monitoring of facial expression. We designed a natural paradigm for social interactions where a monkey is seated in front of a peer monkey that is concealed by an opaque liquid crystal display shutter positioned between them. Opening the shutter for short durations allowed the monkeys to see each other and encouraged facial communication. To explore neural mechanisms that participate in self-monitoring of facial expression, we simultaneously recorded the elicited natural facial interactions and the neural activity of single neurons in the amygdala and the dorsal anterior cingulate cortex (dACC), two regions that are implicated with decoding of others' gestures. Neural activity in both regions was temporally locked to distinctive facial gestures and close inspection of time lags revealed activity that either preceded (production) or lagged (monitor) initiation of facial expressions. This result indicates that single neurons in the dACC and the amygdala hold information about self-executed facial expressions and demonstrates an intimate overlap between the neural networks that participate in decoding and production of socially informative facial information.

Time perception is compromised in emotional situations, yet our ability to remember these events is enhanced. Here we suggest how the two phenomena might be functionally linked and describe the neural networks that underlie this association. We found that participants perceived an emotionally aversive stimulus longer than it was, compared to an immediately following neutral stimulus. These time estimation errors were in the same trials associated with better recognition memory for the emotionally aversive stimuli and poorer memory for the neutral stimuli. Functional imaging revealed that the superior frontal gyrus was activated during time perception with aversive stimuli, and the amygdala, putamen and insula showed activations that are specific to time estimation errors in this aversive context. We further found that activity in the insula and putamen was correlated with memory performance but only during over-estimation of time with the aversive stimuli. We suggest that processing is accelerated during the experience of emotionally aversive events, presumably in the service of memory-related operations, resulting in better encoding but at the expense of time perception accuracy.

Functional abnormalities in the dorsal-anterior-cingulate-cortex (dACC) underlie anxiety disorders and specifically post-traumatic stress disorder (PTSD). Promising and common behavioral approaches have limited effectiveness and many subjects exhibit spontaneous recovery of fear, as also evident in animal models following extinction training. Here, we use low-frequency stimulation (LFS), a protocol shown to induce long-term depression, with the aim of affecting synaptic plasticity induced by fear acquisition and extinction. We use aversive conditioning of either tone or visual stimuli paired with an aversive air-puff to the eye in a trace-conditioning paradigm. We find that LFS in the nonhuman primate (Macaca fascicularis) dACC, when combined with extinction training, was successful in preventing spontaneous recovery of the memory 24-72 h following extinction. We simultaneously record single-units and local-field-potentials across the dACC, and show that LFS gradually depressed evoked responses. Moreover, this decrease in neural excitability predicted the successful reduction of overnight spontaneous recovery on a day-by-day basis. Finally, we show that this effect occurs when using either visual or auditory modality as the conditioned stimulus, and that the reduction was specific to the conditioned modality. Our results suggest that the primate dACC is actively involved in maintaining the original aversive memory, and propose that a combination of LFS with behavioral therapy might significantly improve treatment in severe cases.

The influence of monetary loss on decision making and choice behavior is extensively studied. However, the effect of loss on sensory perception is less explored. Here, we use conditioning in human subjects to explore how monetary loss associated with a pure tone can affect changesinperceptualthresholdsforthepreviouslyneutralstimulus.Wefoundthatloss conditioning, whencompared withneutral exposure, decreases sensitivity and increases perceptual thresholds (i.e., a relative increase inthe just-noticeable-difference). This was so even when compared with gain conditioning of comparable intensity, suggesting that the finding is related to valence. We further show that these perceptual changes are related to future decisions about stimuli that are farther away from the conditioned one (wider generalization), resulting in overall increased and irrational monetary loss for the subjects. We use functional imaging to identify the neural network whose activity correlates with the deterioration in sensitivity on an individual basis. In addition, we show that activity in theamygdala was tightly correlated with the wider behavioralgeneralization, namely, whenwrongdecisions were made.Wesuggestthat, in principle, less discrimination can be beneficial in loss scenarios, because it assures an accurate and fast response to stimuli that resemble the original stimulus and hence haveahigh likelihoodofentailingthesameoutcome.Butwhereas this canbeuseful forprimary reinforcers that can impact survival, it can also underlie wrong and costly behaviors in scenarios of contemporary life that involve secondary reinforcers.

Animal studies of discriminative fear conditioning traditionally use stimuli that are distant in physical features and thus easily distinguished perceptually. Independently, human studies have shown that training mostly improves discrimination thresholds. We found that aversive learning actually induced an increase in discrimination thresholds in humans and that subjective aversion during conditioning predicted the individual threshold change. This counterintuitive performance deterioration occurred when using odors or sounds as aversive reinforcers and was not a result of attentional distraction or decision bias. In contrast, positive reinforcement or mere exposure induced the typically reported decrease in thresholds. Our findings indicate that aversive outcomes induce wider stimulus generalization by modulating perceptual thresholds, suggesting the engagement of low-level mechanisms. We suggest that for risk-or loss-related stimuli, less specificity could be a benefit, as it invokes the same mechanisms that respond quickly and efficiently in the face of danger.

We have little understanding of how odorants are processed in neural networks of the primate brain. Because chemo-stimuli are harder to control than physical stimuli (e.g. vision, audition), such research was limited by the temporal resolution, accuracy, and reliability of olfactometers (odor producing machines). Recent advances were able to create olfactometers that overcome these limitations, allowing their use together with neuroimaging techniques in humans. From the behavioral point of view, olfaction research requires a behavioral measure that can be used to quantify olfactory performance. This becomes a real problem when working with animals, where, unlike humans, explicit measures are harder to obtain. Furthermore, because odorants are powerful primitive reinforcers, such implicit measures can be beneficial to use in learning paradigms. Here we describe an olfactometer suitable for use in non-human primates, and an end-port design that allows the accurate measure of real-time respiratory modulations that are elicited in response to odor presentation. We demonstrate that this implicit measure is differentially modulated when experiencing pleasant or aversive odors. We then present an experimental paradigm in which monkeys learn to associate tones with odors, and show that the time delay from the conditioned stimuli to the next breath can be used to measure learning and memory expression in this paradigm. Using this construct, we reveal olfactory performance during acquisition and extinction of odor conditioning. These techniques can be used in electrophysiological recordings from relevant brain areas to shed light on neural networks involved in odor processing and reinforcement-learning.

Memory formation requires the placement of experienced events in the same order in which they appeared. A large body of evidence from human studies indicates that structures in the medial temporal lobe are critically involved in forming and maintaining such memories, and complementing evidence from lesion and electrophysiological work in animals support these findings. However, it remains unclearhowsingle cells and networks of cells can signal this temporal relationship between events. Here we used recordings from single cells in the human brain obtained while subjects viewed repeated presentations of cinematic episodes. We found that neuronal activity in successive time segments became gradually correlated, and, as a result, activity in a given timewindowbecamea faithful predictor of the activity to follow. This correlation emerged rapidly, within two to three presentations of an episode and exceeded both context-independent and pure stimulus-driven correlations. The correlation was specific for hippocampal neurons, did not occur in theamygdala andanterior cingulate cortex,andwas found for single cells, cell pairs, and triplets of cells, supporting the notion that cell assemblies code for the temporal relationships between sensory events. Importantly, this neuronal measure of temporal binding successfully predicted subjects' ability to recall and verbally report the viewed episodes later. Our findings suggest a neuronal substrate for the formation of memory of the temporal order of events.

Paz R, Bauer EP, Pare D. Measuring correlations and interactions among four simultaneously recorded brain regions during learning. J Neurophysiol 101: 2507-2515, 2009. First published February 25, 2009; doi:10.1152/jn.91259.2008. Brain function depends on coordinated interactions in spatially distributed neuronal populations. Thanks to recent technological advances, it is now possible to monitor the activity of large groups of neurons. Although significant progress has been made in analyzing neuronal interactions across large samples of simultaneously recorded cells, most of the available approaches do not allow direct visualization of multidimensional correlations in the time domain. This study describes a novel analysis technique, termed four-dimensional spike-triggered joint histogram (4-d STJH) that permits the study of co-modulations of unit activity across four simultaneously recorded brain regions while preserving the time domain. To illustrate how this technique works, we recorded simultaneously from basolateral amygdala (BLA) and medial prefrontal (mPFC), as well as perirhinal and entorhinal neurons in animals learning an appetitive trace-conditioning task. Using the 4d-STJH, we show that coincident activity in the BLA and mPFC modulates the interactions between perirhinal and entorhinal neurons in a manner that cannot be explained by a linear combination of the individual BLA and mPFC-related modulations. We conclude with a discussion of the strengths and limitations of 4-d STJH and offer recommendations regarding optimal conditions for its use.

Memory consolidation is thought to involve the gradual transfer of transient hippocampal-dependent traces to distributed neocortical sites via the rhinal cortices. Recently, medial prefrontal (mPFC) neurons were shown to facilitate this process when their activity becomes synchronized. However, the mechanisms underlying this enhanced synchrony remain unclear. Because the hippocampus projects to the mPFC., we tested whether theta oscillations contribute to synchronize mPFC neurons during learning. Thus, we obtained field (LFP) and unit recordings from multiple mPFC sites during the acquisition of a trace-conditioning task, where a visual conditioned stimulus (CS) predicted reward delivery. In quiet waking, the activity of mPFC neurons was modulated by theta oscillations. During conditioning, CS presentation caused an increase in mPFC theta power that augmented as the CS gained predictive value for reward delivery. This increased theta power coincided with a transient theta phase locking at distributed mPFC sites, an effect that was also manifest in the timing of mPFC unit activity. Overall, these results show that theta oscillations contribute to synchronize neuronal activity at distributed mPFC sites, suggesting that the hippocampus, by generating a stronger theta source during learning, can synchronize mPFC activity, in turn facilitating rhinal transfer of its activity to the neocortex.

Much data suggests that hippocampal-medial prefrontal cortex (mPFC) interactions support memory consolidation. This process is thought to involve the gradual transfer of transient hippocampal-dependent memories to distributed neocortical sites for long-term storage. However, hippocampal projections to the neocortex involve a multisynaptic pathway that sequentially progresses through the entorhinal and perirhinal regions before reaching the neocortex. Similarly, the mPFC influences the hippocampus via the rhinal cortices, suggesting that the rhinal cortices occupy a strategic position in this network. The present study thus tested the idea that the mPFC supports memory by facilitating the transfer of hippocampal activity to the neocortex via an enhancement of entorhinal to perirhinal communication. To this end, we simultaneously recorded mPFC, perirhinal, and entorhinal neurons during the acquisition of a trace-conditioning task in which a visual conditioned stimulus ( CS) was followed by a delay period after which a liquid reward was administered. At learning onset, correlated perirhinal-entorhinal firing increased in relation to mPFC activity, but with no preferential directionality, and only after reward delivery. However, as learning progressed across days, mPFC activity gradually enhanced rhinal correlations in relation to the CS as well, and did so in a specific direction: from entorhinal to perirhinal neurons. This suggests that, at late stages of learning, mPFC activity facilitates entorhinal to perirhinal communication. Because this connection is a necessary step for the transfer of hippocampal activity to the neocortex, our results suggest that the mPFC is involved in the slow iterative process supporting the integration of hippocampal-dependent memories into neocortical networks.

Acquisition and retention of sensorimotor skills have been extensively investigated psychophysically, but little is known about the underlying neuronal mechanisms. Here we examine the evolution of neural activity associated with adaptation to new kinematic tasks in two cortical areas: the caudal supplementary motor area( SMA proper), and the primary motor cortex ( MI). We investigate the hypothesis that adaptation starts at premotor areas, i.e., higher in the hierarchy of computation, until a stable representation is formed in primary areas. In accordance with previous studies, we found that adaptation can be characterized by two phases: an early phase that is accompanied by fast and substantial reduction of errors, followed by a late phase with slower and more moderate improvements in behavior. We used unsupervised clustering to separate the activity of the single cells into groups of cells with similar response patterns, under the assumption that each such subpopulation forms a functional unit. We specifically observed the number of clusters in each cortical area during early and late phases of the adaptation and found that the number of clusters is higher in the SMA during early phases of adaptation. In contrast, a higher number of clusters was observed in MI only during late phases. Our results suggest a new approach to analyze responses of large populations of neurons and use it to show a hierarchy of dynamic reorganization of functional groups during adaptation.

Inner-product operators, often referred to as kernels in statistical learning, define a mapping from some input space into a feature space. The focus of this letter is the construction of biologically motivated kernels for cortical activities. The kernels we derive, termed Spikernels, map spike count sequences into an abstract vector space in which we can perform various prediction tasks. We discuss in detail the derivation of Spikernels and describe an efficient algorithm for computing their value on any two sequences of neural population spike counts. We demonstrate the merits of our modeling approach by comparing the Spikernel to various standard kernels in the task of predicting hand movement velocities from cortical recordings. All of the kernels that we tested in our experiments outperform the standard scalar product used in linear regression, with the Spikernel consistently achieving the best performance.

Historically, different groups of researchers have investigated the mechanisms of perceptual learning and motor learning. For sensory cortex, neurophysiological and psychophysical findings have linked changes in perception with altered neuronal tuning properties. However, less information has been forthcoming from motor cortex. This review compares recent findings on perceptual and motor learning, and suggests that similar mechanisms govern both. These mechanisms involve changes in both the center of neuronal tuning functions and their width or slope. The former reflects the values of the sensory or motor parameters that a neuron encodes, and the latter adjusts the encoding sensitivity. These similarities suggest that specific unifying principles for neural coding and computation exist across sensory and motor domains.

Many recent studies describe learning-related changes in sensory and motor areas, but few have directly probed for improvement in neuronal coding after learning. We used information theory to analyze single-cell activity from the primary motor cortex of monkeys, before and after learning a local rotational visuomotor task. We show that after learning, neurons in the primary motor cortex conveyed more information about the direction of movement and did so with relation to their directional sensitivity. Similar to recent findings in sensory systems, this specific improvement in encoding is correlated with an increase in the slope of the neurons' tuning curve. We further demonstrate that the improved information after learning enables a more accurate reconstruction of movement direction from neuronal populations. Our results suggest that similar mechanisms govern learning in sensory and motor areas and provide further evidence for a tight relationship between the locality of learning and the properties of neurons; namely, cells only show plasticity if their preferred direction is near the training one. The results also suggest that simple learning tasks can enhance the performance of brain-machine interfaces.