Single neurons in the thalamus and subthalamic nucleus process cardiac and respiratory signals in humans.

Visceral signals are constantly processed by our central nervous system, enable homeostatic regulation, and influence perception, emotion, and cognition. While visceral processes at the cortical level have been extensively studied using non-invasive imaging techniques, very few studies have investigated how this information is processed at the single neuron level, both in humans and animals. Subcortical regions, relaying signals from peripheral interoceptors to cortical structures, are particularly understudied and how visceral information is processed in thalamic and subthalamic structures remains largely unknown. Here, we took advantage of intraoperative microelectrode recordings in patients undergoing surgery for deep brain stimulation (DBS) to investigate the activity of single neurons related to cardiac and respiratory functions in three subcortical regions: ventral intermedius nucleus (Vim) and ventral caudalis nucleus (Vc) of the thalamus, and subthalamic nucleus (STN). We report that the activity of a large portion of the recorded neurons (about 70%) was modulated by either the heartbeat, the cardiac inter-beat interval, or the respiration. These cardiac and respiratory response patterns varied largely across neurons both in terms of timing and their kind of modulation. A substantial proportion of these visceral neurons (30%) was responsive to more than one of the tested signals, underlining specialization and integration of cardiac and respiratory signals in STN and thalamic neurons. By extensively describing single unit activity related to cardiorespiratory function in thalamic and subthalamic neurons, our results highlight the major role of these subcortical regions in the processing of visceral signals.

Dynamic spatial representation of self and others' actions in the macaque frontal cortex.

Modulation of neuronal firing rates by the spatial locations of physical objects is a widespread phenomenon in the brain. However, little is known about how neuronal responses to the actions of biological entities are spatially tuned and whether such spatially tuned responses are affected by social contexts. These issues are of key importance for understanding the neural basis of embodied social cognition, such as imitation and perspective-taking. Here, we show that spatial representation of actions can be dynamically changed depending on others' social relevance and agents of action. Monkeys performed a turn-taking choice task with a real monkey partner sitting face-to-face or a filmed partner in prerecorded videos. Three rectangular buttons (left, center, and right) were positioned in front of the subject and partner as their choice targets. We recorded from single neurons in two frontal nodes in the social brain, the ventral premotor cortex (PMv) and the medial prefrontal cortex (MPFC). When the partner was filmed rather than real, spatial preference for partner-actions was markedly diminished in MPFC, but not PMv, neurons. This social context-dependent modulation in the MPFC was also evident for self-actions. Strikingly, a subset of neurons in both areas switched their spatial preference between self-actions and partner-actions in a diametrically opposite manner. This observation suggests that these cortical areas are associated with coordinate transformation in ways consistent with an actor-centered perspective-taking coding scheme. The PMv may subserve such functions in context-independent manners, whereas the MPFC may do so primarily in social contexts.

Encoding of Predictive Associations in Human Prefrontal and Medial Temporal Neurons During Pavlovian Appetitive Conditioning.

Pavlovian conditioning is thought to involve the formation of learned associations between stimuli and values, and between stimuli and specific features of outcomes. Here, we leveraged human single neuron recordings in ventromedial prefrontal, dorsomedial frontal, hippocampus, and amygdala while patients of both sexes performed an appetitive Pavlovian conditioning task probing both stimulus-value and stimulus-stimulus associations. Ventromedial prefrontal cortex encoded predictive value along with the amygdala, and also encoded predictions about the identity of stimuli that would subsequently be presented, suggesting a role for neurons in this region in encoding predictive information beyond value. Unsigned error signals were found in dorsomedial frontal areas and hippocampus, potentially supporting learning of non-value related outcome features. Our findings implicate distinct human prefrontal and medial temporal neuronal populations in mediating predictive associations which could partially support model-based mechanisms during Pavlovian conditioning.

Single-neuron bursts encode pathological oscillations in subcortical nuclei of patients with Parkinson's disease and essential tremor.

Deep brain stimulation procedures offer an invaluable opportunity to study disease through intracranial recordings from awake patients. Here, we address the relationship between single-neuron and aggregate-level (local field potential; LFP) activities in the subthalamic nucleus (STN) and thalamic ventral intermediate nucleus (Vim) of patients with Parkinson's disease (n = 19) and essential tremor (n = 16), respectively. Both disorders have been characterized by pathologically elevated LFP oscillations, as well as an increased tendency for neuronal bursting. Our findings suggest that periodic single-neuron bursts encode both pathophysiological beta (13 to 33 Hz; STN) and tremor (4 to 10 Hz; Vim) LFP oscillations, evidenced by strong time-frequency and phase-coupling relationships between the bursting and LFP signals. Spiking activity occurring outside of bursts had no relationship to the LFP. In STN, bursting activity most commonly preceded the LFP oscillation, suggesting that neuronal bursting generated within STN may give rise to an aggregate-level LFP oscillation. In Vim, LFP oscillations most commonly preceded bursting activity, suggesting that neuronal firing may be entrained by periodic afferent inputs. In both STN and Vim, the phase-coupling relationship between LFP and high-frequency oscillation (HFO) signals closely resembled the relationships between the LFP and single-neuron bursting. This suggests that periodic single-neuron bursting is likely representative of a higher spatial and temporal resolution readout of periodic increases in the amplitude of HFOs, which themselves may be a higher resolution readout of aggregate-level LFP oscillations. Overall, our results may reconcile "rate" and "oscillation" models of Parkinson's disease and shed light on the single-neuron basis and origin of pathophysiological oscillations in movement disorders.

CREBA and CREBB in two identified neurons gate long-term memory formation in Drosophila.

Episodic events are frequently consolidated into labile memory but are not necessarily transferred to persistent long-term memory (LTM). Regulatory mechanisms leading to LTM formation are poorly understood, however, especially at the resolution of identified neurons. Here, we demonstrate enhanced LTM following aversive olfactory conditioning in Drosophila when the transcription factor cyclic AMP response element binding protein A (CREBA) is induced in just two dorsal-anterior-lateral (DAL) neurons. Our experiments show that this process is regulated by protein-gene interactions in DAL neurons: (1) crebA transcription is induced by training and repressed by crebB overexpression, (2) CREBA bidirectionally modulates LTM formation, (3) crebA overexpression enhances training-induced gene transcription, and (4) increasing membrane excitability enhances LTM formation and gene expression. These findings suggest that activity-dependent gene expression in DAL neurons during LTM formation is regulated by CREB proteins.

External Stimuli on Neural Networks: Analytical and Numerical Approaches.

Based on the behavior of living beings, which react mostly to external stimuli, we introduce a neural-network model that uses external patterns as a fundamental tool for the process of recognition. In this proposal, external stimuli appear as an additional field, and basins of attraction, representing memories, arise in accordance with this new field. This is in contrast to the more-common attractor neural networks, where memories are attractors inside well-defined basins of attraction. We show that this procedure considerably increases the storage capabilities of the neural network; this property is illustrated by the standard Hopfield model, which reveals that the recognition capacity of our model may be enlarged, typically, by a factor 102. The primary challenge here consists in calibrating the influence of the external stimulus, in order to attenuate the noise generated by memories that are not correlated with the external pattern. The system is analyzed primarily through numerical simulations. However, since there is the possibility of performing analytical calculations for the Hopfield model, the agreement between these two approaches can be tested-matching results are indicated in some cases. We also show that the present proposal exhibits a crucial attribute of living beings, which concerns their ability to react promptly to changes in the external environment. Additionally, we illustrate that this new approach may significantly enlarge the recognition capacity of neural networks in various situations; with correlated and non-correlated memories, as well as diluted, symmetric, or asymmetric interactions (synapses). This demonstrates that it can be implemented easily on a wide diversity of models.

The Caudal Part of Putamen Represents the Historical Object Value Information.

The basal ganglia, especially the circuits originating from the putamen, are essential for controlling normal body movements. Notably, the putamen receives inputs not only from motor cortical areas but also from multiple sensory cortices. However, how these sensory signals are processed in the putamen remains unclear. We recorded the activity of tentative medium spiny neurons in the caudal part of the putamen when the monkey viewed many fractal objects. We found many neurons that responded to these objects, mostly in the ventral region. We called this region "putamen tail" (PUTt), as it is dorsally adjacent to "caudate tail" (CDt). Although PUTt and CDt are mostly separated by a thin layer of white matter, their neurons shared several features. Almost all of them had receptive fields in the contralateral hemifield. Moreover, their responses were object selective (i.e., variable across objects). The object selectivity was higher in the ventral region (i.e., CDt > PUTt). Some neurons above PUTt, which we called the caudal-dorsal putamen (cdPUT), also responded to objects, but less selectively than PUTt. Next, we examined whether these visual neurons changed their responses based on the reward outcome. We found that many neurons encoded the values of many objects based on long-term memory, but not based on short-term memory. Such stable value responses were stronger in PUTt and CDt than in cdPUT. These results suggest that PUTt, together with CDt, controls saccade/attention among objects with different historical values, and may control other motor actions as well.SIGNIFICANCE STATEMENT Although the putamen receives inputs not only from motor cortical areas but also from sensory cortical areas, how these sensory signals are processed remains unclear. Here we found that neurons in the caudal-ventral part of the putamen (putamen tail) process visual information including spatial and object features. These neurons discriminate many objects, first by their visual features and later by their reward values as well. Importantly, the value discrimination was based on long-term memory, but not on short-term memory. These results suggest that the putamen tail controls saccade/attention among objects with different historical values and might control other motor actions as well.

Deviant Processing in the Primary Somatosensory Cortex.

Stimulus-specific adaptation (SSA) to repetitive stimulation has been proposed to separate behaviorally relevant features from a stream of continuous sensory information. However, the exact mechanisms giving rise to SSA and cortical deviance detection are not well understood. We therefore used an oddball paradigm and multicontact electrodes to characterize single-neuron and local field potential responses to various deviant stimuli across the rat somatosensory cortex. Changing different single-whisker stimulus features evoked robust SSA in individual cortical neurons over a wide range of stimulus repetition rates (0.25-80 Hz). Notably, SSA was weakest in the granular input layer and significantly stronger in the supra- and infragranular layers, suggesting that a major part of SSA is generated within cortex. Moreover, we found a small subset of neurons in the granular layer with a deviant-specific late response, occurring roughly 200 ms after stimulus offset. This late deviant response exhibited true-deviance detection properties that were not explained by depression of sensory inputs. Our results show that deviant responses are actively amplified within cortex and contain an additional late component that is sensitive for context-specific sensory deviations. This strongly implicates deviance detection as a feature of intracortical stimulus processing beyond simple sensory input depression.

Implications of Lateral Cerebellum in Proactive Control of Saccades.

UNLABELLED: Although several lines of evidence establish the involvement of the medial and vestibular parts of the cerebellum in the adaptive control of eye movements, the role of the lateral hemisphere of the cerebellum in eye movements remains unclear. Ascending projections from the lateral cerebellum to the frontal and parietal association cortices via the thalamus are consistent with a role of these pathways in higher-order oculomotor control. In support of this, previous functional imaging studies and recent analyses in subjects with cerebellar lesions have indicated a role for the lateral cerebellum in volitional eye movements such as anti-saccades. To elucidate the underlying mechanisms, we recorded from single neurons in the dentate nucleus of the cerebellum in monkeys performing anti-saccade/pro-saccade tasks. We found that neurons in the posterior part of the dentate nucleus showed higher firing rates during the preparation of anti-saccades compared with pro-saccades. When the animals made erroneous saccades to the visual stimuli in the anti-saccade trials, the firing rate during the preparatory period decreased. Furthermore, local inactivation of the recording sites with muscimol moderately increased the proportion of error trials, while successful anti-saccades were more variable and often had shorter latency during inactivation. Thus, our results show that neuronal activity in the cerebellar dentate nucleus causally regulates anti-saccade performance. Neuronal signals from the lateral cerebellum to the frontal cortex might modulate the proactive control signals in the corticobasal ganglia circuitry that inhibit early reactive responses and possibly optimize the speed and accuracy of anti-saccades. SIGNIFICANCE STATEMENT: Although the lateral cerebellum is interconnected with the cortical eye fields via the thalamus and the pons, its role in eye movements remains unclear. We found that neurons in the caudal part of the lateral (dentate) nucleus of the cerebellum showed the increased firing rate during the preparation of anti-saccades. Inactivation of the recording sites modestly elevated the rate of erroneous saccades to the visual stimuli in the anti-saccade trials, while successful anti-saccades during inactivation tended to have a shorter latency. Our data indicate that neuronal signals in the lateral cerebellum may proactively regulate anti-saccade generation through the pathways to the frontal cortex, and may inhibit early reactive responses and regulate the accuracy of anti-saccades.

Quantitative analysis of axon collaterals of single pyramidal cells of the anterior piriform cortex of the guinea pig.

BACKGROUND: The role of the piriform cortex (PC) in olfactory information processing remains largely unknown. The anterior part of the piriform cortex (APC) has been the focus of cortical-level studies of olfactory coding, and associative processes have attracted considerable attention as an important part in odor discrimination and olfactory information processing. Associational connections of pyramidal cells in the guinea pig APC were studied by direct visualization of axons stained and quantitatively analyzed by intracellular biocytin injection in vivo. RESULTS: The observations illustrated that axon collaterals of the individual cells were widely and spatially distributed within the PC, and sometimes also showed a long associational projection to the olfactory bulb (OB). The data showed that long associational axons were both rostrally and caudally directed throughout the PC, and the intrinsic associational fibers of pyramidal cells in the APC are omnidirectional connections in the PC. Within the PC, associational axons typically followed rather linear trajectories and irregular bouton distributions. Quantitative data of the axon collaterals of two pyramidal cells in the APC showed that the average length of axonal collaterals was 101 mm, out of which 79 mm (78% of total length) were distributed in the PC. The average number of boutons was 8926 and 7101, respectively, with 79% of the total number of boutons being distributed in the PC. The percentage of the total area of the APC and the posterior piriform cortex occupied by the average distribution region of the axon collaterals of two superficial pyramidal (SP) cells was about 18 and 5%, respectively. CONCLUSION: Our results demonstrate that omnidirectional connection of pyramidal cells in the APC provides a substrate for recurrent processes. These findings indicate that the axon collaterals of SP cells in the PC could make synaptic contacts with all granule cells in the OB. This study provides the morphological evidence for understanding the mechanisms of information processing and associative memory in the APC.

A neuronal reward inequity signal in primate striatum.

Primates are social animals, and their survival depends on social interactions with others. Especially important for social interactions and welfare is the observation of rewards obtained by other individuals and the comparison with own reward. The fundamental social decision variable for the comparison process is reward inequity, defined by an asymmetric reward distribution among individuals. An important brain structure for coding reward inequity may be the striatum, a component of the basal ganglia involved in goal-directed behavior. Two rhesus monkeys were seated opposite each other and contacted a touch-sensitive table placed between them to obtain specific magnitudes of reward that were equally or unequally distributed among them. Response times in one of the animals demonstrated differential behavioral sensitivity to reward inequity. A group of neurons in the striatum showed distinct signals reflecting disadvantageous and advantageous reward inequity. These neuronal signals occurred irrespective of, or in conjunction with, own reward coding. These data demonstrate that striatal neurons of macaque monkeys sense the differences between other's and own reward. The neuronal activities are likely to contribute crucial reward information to neuronal mechanisms involved in social interactions.

Exploring human epileptic activity at the single-neuron level.

Today, localization of the seizure focus heavily relies on EEG monitoring (scalp or intracranial). However, current technology enables much finer resolutions. The activity of hundreds of single neurons in the human brain can now be simultaneously explored before, during, and after a seizure or in association with an interictal discharge. This technology opens up new horizons to understanding epilepsy at a completely new level. This review therefore begins with a brief description of the basis of the technology, the microelectrodes, and the setup for their implantation in patients with epilepsy. Using these electrodes, recent studies provide novel insights into both the time domain and firing patterns of epileptic activity of single neurons. In the time domain, seizure-related activity may occur even minutes before seizure onset (in its current, EEG-based definition). Seizure-related neuronal interactions exhibit complex heterogeneous dynamics. In the seizure-onset zone, changes in firing patterns correlate with cell loss; in the penumbra, neurons maintain their spike stereotypy during a seizure. Hence, investigation of the extracellular electrical activity is expected to provide a better understanding of the mechanisms underlying the disease; it may, in the future, serve for a more accurate localization of the seizure focus; and it may also be employed to predict the occurrence of seizures prior to their behavioral manifestation in order to administer automatic therapeutic interventions.

Single-neuron representation of nonsymbolic and symbolic number zero in the human medial temporal lobe.

The number zero holds a special status among numbers, indispensable for developing a comprehensive number theory.1,2,3,4 Despite its importance in mathematics, the neuronal foundation of zero in the human brain is unknown. We conducted single-neuron recordings in neurosurgical patients5,6,7 while they made judgments involving nonsymbolic number representations (dot numerosity), including the empty set, and symbolic numbers (Arabic numerals), including numeral zero. Neurons showed responsiveness to either the empty set or numeral zero, but not both. Neuronal activity to zero in both nonsymbolic and symbolic formats exhibited a numerical distance effect, indicating that zero representations are integrated together with countable numerosities and positive integers at the low end of the number line.8,9 A boundary in neuronal coding existed between the nonsymbolic empty set and small numerosities, correlating with the relative difficulty in discriminating numerosity zero behaviorally. Conversely, no such boundary was found for symbolic zero activity, suggesting that symbolic representations integrate zero with other numerals along the number line, reconciling its outlier role. The status of zero as a special nonsymbolic numerical quantity is reflected in the activity of neurons in the human brain, which seems to serve as a scaffold for more advanced representations of zero as a symbolic number.

Single neurons on microelectrode array chip: manipulation and analyses.

Chips-based platforms intended for single-cell manipulation are considered powerful tools to analyze intercellular interactions and cellular functions. Although the conventional cell co-culture models could investigate cell communication to some extent, the role of a single cell requires further analysis. In this study, a precise intercellular interaction model was built using a microelectrode array [microelectrode array (MEA)]-based and dielectrophoresis-driven single-cell manipulation chip. The integrated platform enabled precise manipulation of single cells, which were either trapped on or transferred between electrodes. Each electrode was controlled independently to record the corresponding cellular electrophysiology. Multiple parameters were explored to investigate their effects on cell manipulation including the diameter and depth of microwells, the geometry of cells, and the voltage amplitude of the control signal. Under the optimized microenvironment, the chip was further evaluated using 293T and neural cells to investigate the influence of electric field on cells. An examination of the inappropriate use of electric fields on cells revealed the occurrence of oncosis. In the end of the study, electrophysiology of single neurons and network of neurons, both differentiated from human induced pluripotent stem cells (iPSC), was recorded and compared to demonstrate the functionality of the chip. The obtained preliminary results extended the nature growing model to the controllable level, satisfying the expectation of introducing more elaborated intercellular interaction models.

Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner.

The spiking activity of populations of cortical neurons is well described by the dynamics of a small number of population-wide covariance patterns, whose activation we refer to as 'latent dynamics'. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFPs). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable throughout the behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.

Java application for cytoskeleton filament characterization (JACFC).

JACFC is a Java web application (http://neuronanobiophysics.utsa.edu/) that provides both experts and non-experts in the field suitable tools for elucidating the molecular mechanisms modulating the electrical signal propagation, stability, and bundle formation of microtubules and actin filaments under different molecular (wild type, isoforms, mutants) and environmental (physiological and pathological) conditions. This acknowledgment might reveal the potential role of cytoskeleton filaments in neuronal activities, including molecular-level processing of information and neural regeneration. Molecular understanding of the polyelectrolyte properties of bionanowires, is also crucial for development of reliability, highly functioning small devices with biotechnological applications such as bionanosensors and computing bionanoprocessors.

Distinct neural networks for the volitional control of vocal and manual actions in the monkey homologue of Broca's area.

The ventrolateral frontal lobe (Broca's area) of the human brain is crucial in speech production. In macaques, neurons in the ventrolateral prefrontal cortex, the suggested monkey homologue of Broca's area, signal the volitional initiation of vocalizations. We explored whether this brain area became specialized for vocal initiation during primate evolution and trained macaques to alternate between a vocal and manual action in response to arbitrary cues. During task performance, single neurons recorded from the ventrolateral prefrontal cortex and the rostroventral premotor cortex of the inferior frontal cortex predominantly signaled the impending vocal or, to a lesser extent, manual action, but not both. Neuronal activity was specific for volitional action plans and differed during spontaneous movement preparations. This implies that the primate inferior frontal cortex controls the initiation of volitional utterances via a dedicated network of vocal selective neurons that might have been exploited during the evolution of Broca's area.

Single Neuron Coding of Identity in the Human Hippocampal Formation.

Experimental findings show the ubiquitous presence of graded responses and tuning curves in the neocortex, particularly in visual areas [1-15]. Among these, inferotemporal-cortex (IT) neurons respond to complex visual stimuli, but differences in the neurons' responses can be used to distinguish the stimuli eliciting the responses [8, 9, 16-18]. The IT projects directly to the medial temporal lobe (MTL) [19], where neurons respond selectively to different pictures of specific persons and even to their written and spoken names [20-22]. However, it is not clear whether this is done through a graded coding, as in the neocortex, or a truly invariant code, in which the response-eliciting stimuli cannot be distinguished from each other. To address this issue, we recorded single neurons during the repeated presentation of different stimuli (pictures and written and spoken names) corresponding to the same persons. Using statistical tests and a decoding approach, we found that only in a minority of cases can the different pictures of a given person be distinguished from the neurons' responses and that in a larger proportion of cases, the responses to the pictures were different to the ones to the written and spoken names. We argue that MTL neurons tend to lack a representation of sensory features (particularly within a sensory modality), which can be advantageous for the memory function attributed to this area [23-25], and that a full representation of memories is given by a combination of mostly invariant coding in the MTL with a representation of sensory features in the neocortex.

Dopamine and Cognitive Control in Prefrontal Cortex.

Cognitive control, the ability to orchestrate behavior in accord with our goals, depends on the prefrontal cortex. These cognitive functions are heavily influenced by the neuromodulator dopamine. We review here recent insights exploring the influence of dopamine on neuronal response properties in prefrontal cortex (PFC) during ongoing behaviors in primates. This review suggests three major computational roles of dopamine in cognitive control: (i) gating sensory input, (ii) maintaining and manipulating working memory contents, and (iii) relaying motor commands. For each of these roles, we propose a neuronal microcircuit based on known mechanisms of action of dopamine in PFC, which are corroborated by computational network models. This conceptual approach accounts for the various roles of dopamine in prefrontal executive functioning.

Neural Networks for Modeling Neural Spiking in S1 Cortex.

Somatosensation is composed of two distinct modalities: touch, arising from sensors in the skin, and proprioception, resulting primarily from sensors in the muscles, combined with these same cutaneous sensors. In contrast to the wealth of information about touch, we know quite less about the nature of the signals giving rise to proprioception at the cortical level. Likewise, while there is considerable interest in developing encoding models of touch-related neurons for application to brain machine interfaces, much less emphasis has been placed on an analogous proprioceptive interface. Here we investigate the use of Artificial Neural Networks (ANNs) to model the relationship between the firing rates of single neurons in area 2, a largely proprioceptive region of somatosensory cortex (S1) and several types of kinematic variables related to arm movement. To gain a better understanding of how these kinematic variables interact to create the proprioceptive responses recorded in our datasets, we train ANNs under different conditions, each involving a different set of input and output variables. We explore the kinematic variables that provide the best network performance, and find that the addition of information about joint angles and/or muscle lengths significantly improves the prediction of neural firing rates. Our results thus provide new insight regarding the complex representations of the limb motion in S1: that the firing rates of neurons in area 2 may be more closely related to the activity of peripheral sensors than it is to extrinsic hand position. In addition, we conduct numerical experiments to determine the sensitivity of ANN models to various choices of training design and hyper-parameters. Our results provide a baseline and new tools for future research that utilizes machine learning to better describe and understand the activity of neurons in S1.