Lack of Spike-Count and Spike-Time Correlations in the Substantia Nigra Reticulata Despite Overlap of Neural Responses

Multidimensional encoding of movement and contextual variables by rat globus pallidus neurons during a novel environment exposure task

The basal ganglia (BG) play a critical role in a variety of functions that are essential for animal survival. Information from different cortical areas propagates through the BG in anatomically segregated circuits along the parallel direct and indirect pathways. We examined how the globus pallidus (GP), a nucleus within the indirect pathway, encodes input from the motor and cognitive domains. We chronically recorded and analyzed neuronal activity in the GP of male rats engaged in a novel environment exposure task. GP neurons displayed multidimensional responses to movement and contextual information. A model predicting single unit activity required many task-related behavioral variables, thus confirming the multidimensionality of GP neurons. In addition, populations of GP neurons, but not single units, reliably encoded the animals’ locomotion speed and the environmental novelty. We posit that the GP independently processes information from different domains, effectively compresses it and collectively conveys it to successive nuclei.

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Single GP neurons encode independently many behavioral and contextual variables

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Many behavioral variables contribute to the prediction of single neuron firing rate

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Single neurons fail to approximate the rat’s locomotion and the environment novelty

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Populations of GP neurons encode the rats’ locomotion and the environment novelty

Bridging neuronal correlations and dimensionality reduction

Two commonly used approaches to study interactions among neurons are spike count correlation, which describes pairs of neurons, and dimensionality reduction, applied to a population of neurons. Although both approaches have been used to study trial-to-trial neuronal variability correlated among neurons, they are often used in isolation and have not been directly related. We first established concrete mathematical and empirical relationships between pairwise correlation and metrics of population-wide covariability based on dimensionality reduction. Applying these insights to macaque V4 population recordings, we found that the previously reported decrease in mean pairwise correlation associated with attention stemmed from three distinct changes in population-wide covariability. Overall, our work builds the intuition and formalism to bridge between pairwise correlation and population-wide covariability and presents a cautionary tale about the inferences one can make about population activity by using a single statistic, whether it be mean pairwise correlation or dimensionality.

Neural activity during a simple reaching task in macaques is counter to gating and rebound in basal ganglia-thalamic communication

Task-related activity in the ventral thalamus, a major target of basal ganglia output, is often assumed to be permitted or triggered by changes in basal ganglia activity through gating- or rebound-like mechanisms. To test those hypotheses, we sampled single-unit activity from connected basal ganglia output and thalamic nuclei (globus pallidus-internus [GPi] and ventrolateral anterior nucleus [VLa]) in monkeys performing a reaching task. Rate increases were the most common peri-movement change in both nuclei. Moreover, peri-movement changes generally began earlier in VLa than in GPi. Simultaneously recorded GPi-VLa pairs rarely showed short-time-scale spike-to-spike correlations or slow across-trials covariations, and both were equally positive and negative. Finally, spontaneous GPi bursts and pauses were both followed by small, slow reductions in VLa rate. These results appear incompatible with standard gating and rebound models. Still, gating or rebound may be possible in other physiological situations: simulations show how GPi-VLa communication can scale with GPi synchrony and GPi-to-VLa convergence, illuminating how synchrony of basal ganglia output during motor learning or in pathological conditions may render this pathway effective. Thus, in the healthy state, basal ganglia-thalamic communication during learned movement is more subtle than expected, with changes in firing rates possibly being dominated by a common external source.

Bilateral responses of rat ventral striatum tonically active neurons to unilateral medial forebrain bundle stimulation

Unilateral medial forebrain bundle (MFB) stimulation is an extremely effective promoter of reinforcement learning irrespective of the conditioned cue's laterality. The effectiveness of unilateral MFB stimulation, which activates the mesolimbic pathway connecting the ventral tegmental area to the ventral striatum (vStr), is surprising considering that these fibers rarely cross to the contralateral hemisphere. Specifically, this type of biased fiber distribution entails the activation of brain structures that are primarily ipsilateral to the stimulated MFB, along with weak to negligible activation of the contralateral structures, thus impeding the formation of a cue-outcome association. To better understand the spread of activation of MFB stimulation across hemispheres, we studied whether unilateral MFB stimulation primarily activates the ipsilateral vStr or the vStr of both hemispheres. We simultaneously recorded neuronal activity in the vStr of both hemispheres in response to several sets of unilateral MFB stimulation in anesthetized and freely moving rats. Unilateral MFB stimulation evoked strong stimulus-dependent activation of vStr tonically active neurons (TANs), presumably the cholinergic interneurons, in both hemispheres. However, the TANs' activation patterns and responsiveness depended on whether the stimulus was delivered ipsilaterally or contralaterally to the recorded neuron. These findings indicate that unilateral MFB stimulation effectively activates the vStr in both hemispheres in a stimulus-dependent manner which may serve as neuronal substrate for the formation of cue-outcome associations during reinforcement learning.

Weakly correlated activity of pallidal neurons in behaving monkeys

The basal ganglia play a crucial role in the control of voluntary movements. Neurons in both the external and internal segments of the globus pallidus, the connecting and output nuclei of the basal ganglia, respectively, change their firing rates in relation to movements. Firing rate changes of movement-related neurons seem to convey signals for motor control. On the other hand, coincident spikes among neurons, that is, correlated activity, may also contribute to motor control. To address this issue, we first identified multiple pallidal neurons receiving inputs from the forelimb regions of the primary motor cortex and supplementary motor area, recorded neuronal activity of these neurons simultaneously, and analyzed their spike correlations while monkeys performed a hand-reaching task. Most (79%) pallidal neurons exhibited task-related firing rate changes, whereas only a small fraction (20%) showed significant but small and short correlated activity during the task performance. These results suggest that motor control signals are conveyed primarily by firing rate changes in the external and internal segments of the globus pallidus and that the contribution of correlated activity may play only a minor role in the healthy state.

The asynchronous state's relation to large-scale potentials in cortex

Understanding the relation between large-scale potentials (M/EEG) and their underlying neural activity can improve the precision of research and clinical diagnosis. Recent insights into cortical dynamics highlighted a state of strongly reduced spike count correlations, termed the asynchronous state (AS). The AS has received considerable attention from experimenters and theorists alike, regarding its implications for cortical dynamics and coding of information. However, how reconcilable are these vanishing correlations in the AS with large-scale potentials such as M/EEG observed in most experiments? Typically the latter are assumed to be based on underlying correlations in activity, in particular between subthreshold potentials. We survey the occurrence of the AS across brain states, regions, and layers and argue for a reconciliation of this seeming disparity: large-scale potentials are either observed, first, at transitions between cortical activity states, which entail transient changes in population firing rate, as well as during the AS, and, second, on the basis of sufficiently large, asynchronous populations that only need to exhibit weak correlations in activity. Cells with no or little spiking activity can contribute to large-scale potentials via their subthreshold currents, while they do not contribute to the estimation of spiking correlations, defining the AS. Furthermore, third, the AS occurs only within particular cortical regions and layers associated with the currently selected modality, allowing for correlations at other times and between other areas and layers.

Midbrain activity can explain perceptual decisions during an attention task

We introduce a decision model that interprets the relative levels of moment-by-moment spiking activity from the right and left superior colliculus to distinguish relevant from irrelevant stimulus events. The model explains detection performance in a covert attention task, both in intact animals and when performance is perturbed by causal manipulations. This provides a specific example of how midbrain activity could support perceptual judgments during attention tasks.

Learning the meaning of new stimuli increases the cross-correlated activity of prefrontal neurons

The prefrontal cortex (PF) has a key role in learning rules and generating associations between stimuli and responses also called conditional motor learning. Previous studies in PF have examined conditional motor learning at the single cell level but not the correlation of discharges between neurons at the ensemble level. In the present study, we recorded from two rhesus monkeys in the dorsolateral and the mediolateral parts of the prefrontal cortex to address the role of correlated firing of simultaneously recorded pairs during conditional motor learning. We trained two rhesus monkeys to associate three stimuli with three response targets, such that each stimulus was mapped to only one response. We recorded the neuronal activity of the same neuron pairs during learning of new associations and with already learned associations. In these tasks after a period of fixation, a visual instruction stimulus appeared centrally and three potential response targets appeared in three positions: right, left, and up from center. We found a higher number of neuron pairs significantly correlated and higher cross-correlation coefficients during stimulus presentation in the new than in the familiar mapping task. These results demonstrate that learning affects the PF neural correlation structure.

The inhibitory microcircuit of the substantia nigra provides feedback gain control of the basal ganglia output

Dysfunction of the basal ganglia produces severe deficits in the timing, initiation, and vigor of movement. These diverse impairments suggest a control system gone awry. In engineered systems, feedback is critical for control. By contrast, models of the basal ganglia highlight feedforward circuitry and ignore intrinsic feedback circuits. In this study, we show that feedback via axon collaterals of substantia nigra projection neurons control the gain of the basal ganglia output. Through a combination of physiology, optogenetics, anatomy, and circuit mapping, we elaborate a general circuit mechanism for gain control in a microcircuit lacking interneurons. Our data suggest that diverse tonic firing rates, weak unitary connections and a spatially diffuse collateral circuit with distinct topography and kinetics from feedforward input is sufficient to implement divisive feedback inhibition. The importance of feedback for engineered systems implies that the intranigral microcircuit, despite its absence from canonical models, could be essential to basal ganglia function. DOI: http://dx.doi.org/10.7554/eLife.02397.001.

Quantification of pairwise neuronal interactions: going beyond the significance lines

BACKGROUND

Normal brain function depends on intact interactions between multiple neuronal ensembles. Interactions within and between local networks comprising multiple neuronal types may occur on a range of time scales thus affecting the estimation of interaction strength. A common technique to investigate functional interactions within neuronal ensembles is pairwise cross-correlation analysis. However, conventional cross-correlation methods address the question of whether an observed peak in the cross-correlation is statistically significant relative to the null hypothesis which assumes a lack of correlation. Ultimately, these methods were not designed to evaluate the strength of the observed interactions.

NEW METHOD

We devised four complementary measures - Triplets, Bin crossing, Bin height and Entropy - for assessing the strength of neuronal interactions; each is sensitive to different features of the cross-correlogram peak such as height, width and smoothness.

RESULTS

First, a comparison of five prevalent methods for evaluating whether an observed peak in neuronal cross-correlogram is significant allowed their ranking from the most conservative to the more sensitive for purposes of selecting the appropriate method based on the data structure and preferred strategy. Second, the performance of the four measures we derived improved with interaction strength and the number of spikes in the cross-correlogram. The four measures also enabled the reconstruction of interaction parameters of simulated networks including the detection of time-dependent alterations.

CONCLUSIONS

We suggest that the combination of several measures of peak characteristics helps rectify the individual shortcomings of specific measures and can yield a broad coverage of interaction strengths and widths.

Motor thalamus integration of cortical, cerebellar and basal ganglia information: implications for normal and parkinsonian conditions

Motor thalamus (Mthal) is implicated in the control of movement because it is strategically located between motor areas of the cerebral cortex and motor-related subcortical structures, such as the cerebellum and basal ganglia (BG). The role of BG and cerebellum in motor control has been extensively studied but how Mthal processes inputs from these two networks is unclear. Specifically, there is considerable debate about the role of BG inputs on Mthal activity. This review summarizes anatomical and physiological knowledge of the Mthal and its afferents and reviews current theories of Mthal function by discussing the impact of cortical, BG and cerebellar inputs on Mthal activity. One view is that Mthal activity in BG and cerebellar-receiving territories is primarily "driven" by glutamatergic inputs from the cortex or cerebellum, respectively, whereas BG inputs are modulatory and do not strongly determine Mthal activity. This theory is steeped in the assumption that the Mthal processes information in the same way as sensory thalamus, through interactions of modulatory inputs with a single driver input. Another view, from BG models, is that BG exert primary control on the BG-receiving Mthal so it effectively relays information from BG to cortex. We propose a new "super-integrator" theory where each Mthal territory processes multiple driver or driver-like inputs (cortex and BG, cortex and cerebellum), which are the result of considerable integrative processing. Thus, BG and cerebellar Mthal territories assimilate motivational and proprioceptive motor information previously integrated in cortico-BG and cortico-cerebellar networks, respectively, to develop sophisticated motor signals that are transmitted in parallel pathways to cortical areas for optimal generation of motor programmes. Finally, we briefly review the pathophysiological changes that occur in the BG in parkinsonism and generate testable hypotheses about how these may affect processing of inputs in the Mthal.

Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network

The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior.

Measuring and interpreting neuronal correlations

Mounting evidence suggests that understanding how the brain encodes information and performs computations will require studying the correlations between neurons. The recent advent of recording techniques such as multielectrode arrays and two-photon imaging has made it easier to measure correlations, opening the door for detailed exploration of their properties and contributions to cortical processing. However, studies have reported discrepant findings, providing a confusing picture. Here we briefly review these studies and conduct simulations to explore the influence of several experimental and physiological factors on correlation measurements. Differences in response strength, the time window over which spikes are counted, spike sorting conventions and internal states can all markedly affect measured correlations and systematically bias estimates. Given these complicating factors, we offer guidelines for interpreting correlation data and a discussion of how best to evaluate the effect of correlations on cortical processing.

Loss of specificity in Basal Ganglia related movement disorders

The basal ganglia (BG) are a group of interconnected nuclei which play a pivotal part in limbic, associative, and motor functions. This role is mirrored by the wide range of motor and behavioral abnormalities directly resulting from dysfunction of the BG. Studies of normal behavior have found that BG neurons tend to phasically modulate their activity in relation to different behavioral events. In the normal BG, this modulation is highly specific, with each neuron related only to a small subset of behavioral events depending on specific combinations of movement parameters and context. In many pathological conditions involving BG dysfunction and motor abnormalities, this neuronal specificity is lost. Loss of specificity (LOS) manifests in neuronal activity related to a larger spectrum of events and consequently a large overlap of movement-related activation patterns between different neurons. We review the existing evidence for LOS in BG-related movement disorders, the possible neural mechanisms underlying LOS, its effects on frequently used measures of neuronal activity and its relation to theoretical models of the BG. The prevalence of LOS in a many BG-related disorders suggests that neuronal specificity may represent a key feature of normal information processing in the BG system. Thus, the concept of neuronal specificity may underlie a unifying conceptual framework for the BG role in normal and abnormal motor control.

CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains

Recent developments in electrophysiological and optical recording techniques enable the simultaneous observation of large numbers of neurons. A meaningful interpretation of the resulting multivariate data, however, presents a serious challenge. In particular, the estimation of higher-order correlations that characterize the cooperative dynamics of groups of neurons is impeded by the combinatorial explosion of the parameter space. The resulting requirements with respect to sample size and recording time has rendered the detection of coordinated neuronal groups exceedingly difficult. Here we describe a novel approach to infer higher-order correlations in massively parallel spike trains that is less susceptible to these problems. Based on the superimposed activity of all recorded neurons, the cumulant-based inference of higher-order correlations (CuBIC) presented here exploits the fact that the absence of higher-order correlations imposes also strong constraints on correlations of lower order. Thus, estimates of only few lower-order cumulant suffice to infer higher-order correlations in the population. As a consequence, CuBIC is much better compatible with the constraints of in vivo recordings than previous approaches, which is shown by a systematic analysis of its parameter dependence.

Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

Anatomy of a songbird basal ganglia circuit essential for vocal learning and plasticity

Vocal learning in songbirds requires an anatomically discrete and functionally dedicated circuit called the anterior forebrain pathway (AFP). The AFP is homologous to cortico-basal ganglia-thalamo-cortical loops in mammals. The basal ganglia portion of this pathway, Area X, shares many features characteristic of the mammalian striatum and pallidum, including cell types and connectivity. The AFP also deviates from mammalian basal ganglia circuits in fundamental ways. In addition, the microcircuitry, role of neuromodulators, and function of Area X are still unclear. Elucidating the mechanisms by which both mammalian-like and unique features of the AFP contribute to vocal learning may help lead to a broad understanding of the sensorimotor functions of basal ganglia circuits.

Encoding of probabilistic rewarding and aversive events by pallidal and nigral neurons

Previous studies have rarely tested whether the activity of high-frequency discharge (HFD) neurons of the basal ganglia (BG) is modulated by expectation, delivery, and omission of aversive events. Therefore the full value domain encoded by the BG network is still unknown. We studied the activity of HFD neurons of the globus pallidus external segment (GPe, n=310), internal segment (GPi, n=149), and substantia nigra pars reticulata (SNr, n=145) in two monkeys during a classical conditioning task with cues predicting the probability of food, neutral, or airpuff outcomes. The responses of BG HFD neurons were long-lasting and diverse with coincident increases and decreases in discharge rate. The population responses to reward-related events were larger than the responses to aversive and neutral-related events. The latter responses were similar, except for the responses to actual airpuff delivery. The fraction of responding cells was larger for reward-related events, with better discrimination between rewarding and aversive trials in the responses with an increase rather than a decrease in discharge rate. GPe and GPi single units were more strongly modulated and better reflected the probability of reward- than aversive-related events. SNr neurons were less biased toward the encoding of the rewarding events, especially during the outcome epoch. Finally, the latency of SNr responses to all predictive cues was shorter than the latency of pallidal responses. These results suggest preferential activation of the BG HFD neurons by rewarding compared with aversive events.

Balance of increases and decreases in firing rate of the spontaneous activity of basal ganglia high-frequency discharge neurons

Most neurons in the external and internal segments of the globus pallidus and the substantia nigra pars reticulata (GPe, GPi, and SNr) are characterized by a high-frequency discharge (HFD) rate (50-80 Hz) that, in most GPe neurons, is also interrupted by pauses. Almost all (approximately 90%) of the synaptic inputs to these HFD neurons are GABAergic and inhibitory. Nevertheless, their responses to behavioral events are usually dominated by increases in discharge rate. Additionally, there are no reports of prolonged bursts in the spontaneous activity of these cells that could reflect their disinhibition by GPe pauses. We recorded the spontaneous activity of 385 GPe, GPi, and SNr HFD neurons during a quiet-wakeful state from two monkeys. We developed three complementary methods to quantify the balance of increases and decreases in the spontaneous discharge of HFD neurons and validated them by simulations. Unlike the behavioral evoked responses, the spontaneous activity of pallidal and SNr neurons is not dominated by increases. Moreover, the activity of basal ganglia neurons does not include bursts that could reflect disinhibition by the spontaneous pauses of GPe neurons. These findings suggest that the discharge increase/decrease balance during a quiet-wakeful state better reflects the inhibitory input of the HFD basal ganglia neurons than during responses to behavioral events; however, the GPe pauses are not echoed by comparable bursts either in the GPe or in the output nuclei. Changes in the excitatory drive of these structures (e.g., during behavioral activity) thus may lead to a remarkable change in this balance.

Structural correlates of efficient GABAergic transmission in the basal ganglia-thalamus pathway

Giant inhibitory terminals with multiple synapses, the counterparts of excitatory "detonator" or "driver" terminals, have not been described in the forebrain. Using three-dimensional reconstructions of electron microscopic images, we quantitatively characterize a GABAergic pathway that establishes synaptic contacts exclusively via multiple synapses. Axon terminals of the nigrothalamic pathway formed, on average, 8.5 synapses on large-diameter dendrites and somata of relay cells in the ventromedial nucleus of the rat thalamus. All synapses of a given terminal converged on a single postsynaptic element. The vast majority of the synapses established by a single terminal were not separated by astrocytic processes. Nigrothalamic terminals in the macaque monkey showed the same ultrastructural features both in qualitative and quantitative terms (the median number of synapse per target was also 8.5). The individual synapses were closely spaced in both species. The nearest-neighbor synaptic distances were 169 nm in the rat and 178 nm in the monkey. The average number of synapses within 0.75 microm from any given synapse was 3.8 in the rat and 3.5 in the monkey. The arrangement of synapses described in this study creates favorable conditions for intersynaptic spillover of GABA among the multiple synapses of a single bouton, which can result in larger charge transfer. This could explain faithful and efficient GABAergic signal transmission in the nigrothalamic pathway in the healthy condition and during Parkinson's disease. In addition, our structural data suggest that the rodent nigrothalamic pathway can be a valid model of the primate condition, when the mechanism of GABAergic transmission is studied.