Neuronal Ensemble Activity Research Articles

Presynaptic GCaMP expression decreases vesicle release probability at the calyx of Held.

Synaptic vesicle (SV) exocytosis is intimately dependent on free local Ca2+ near active zones. Genetically encoded calcium indicators (GECIs) have become an indispensable tool to monitor calcium dynamics during physiological responses, and they are widely used as a proxy to monitor activity in neuronal ensembles and at synaptic terminals. However, GECIs’ ability to bind Ca2+ at physiologically relevant concentration makes them strong candidates to affect calcium homeostasis and alter synaptic transmission by exogenously increasing Ca2+ buffering. In the present study, we show that genetically expressed GCaMP6m modulates SV release probability at the mouse calyx of Held synapse. GCaMP6m expression for approximately three weeks decreased initial SV release for both low‐frequency stimulation and high‐frequency stimulation trains, and slowed presynaptic short‐term depression. However, GCaMP6m does not affect quantal events during spontaneous activity at this synapse. This study emphasizes the careful use of GECIs as monitors of neuronal activity and inspects the role of these transgenic indicators which may alter calcium‐dependent physiological responses.

Parameter Estimation in Synaptic Coupling Model Using a Point Process Modeling Framework.

Biophysical models are widely used to characterize temporal dynamics of the brain networks on different topological and spatial scales. In parallel, the state-space modeling framework with point process observations has been successfully applied in characterizing spiking activity of neuronal ensembles in response to different dynamical covariates. Parameter estimation in biophysical models is generally done heuristically, which hampers their applicability and interpretability. Heuristic parameter estimation becomes an intractable problem when the number of model parameters grows. Here, we propose an algorithm for estimating biophysical model parameters using point-process models and a state-space framework. The framework provides methods for parameter estimation as well as model validation. We demonstrate the application of this methodology to the problem of estimating the parameters of a dynamic synapse model. We generate simulation data for the dynamic synapse across a range of parameters values and assess the estimation accuracy of our method using a combination of goodness-of-fit measures. The proposed methodology can be applied broadly to parameter estimation problems across a broad range of biophysical models, including Hodgkin-Huxley models and network models.

A Dorsal Hippocampus-Accumbens Circuit Motif to Guide Appetitive Memory in Space

Retrieving and acting upon memories of food-predicting environments are essential for survival. Pyramidal cells (PYRs) in dorsal CA1 hippocampus (dCA1) of the mammalian brain provide mnemonic representations of space. While dCA1 PYRs cannot directly access motor centers, the brain substrates by which these internal representations guide appetitive behavior are unknown. Here, we uncover a circuit motif embedded in the nucleus accumbens (NAc) that enables the behavioral readout of reward-place memories. By monitoring neuronal ensemble activity in mouse dCA1-NAc pathway, combined with cell-type-selective optogenetic manipulations of dCA1-input-defined postsynaptic neurons, we show that PYRs innervate and engage NAc parvalbumin-expressing fast-spiking interneurons (PV+ FSIs) to influence medium spiny neuron (MSN) firing and thus, NAc output. This motif is specialized for memory-guided appetitive behavior, being dispensable for spatial novelty detection and hedonic motivation. Our findings demonstrate that this PYR-PV+ FSI-MSN circuit motif instantiates a limbic-motor interface for hippocampal representations of space to promote behaviorally-effective appetitive memory.

Network oscillation rules imposed by species-specific electrical coupling.

Electrical junctions are widespread within the mammalian CNS. Yet, their role in organizing neuronal ensemble activity remains incompletely understood. Here, in a functionally well-characterized system - neuroendocrine tuberoinfundibular dopamine (TIDA) neurons - we demonstrate a striking species difference in network behavior: rat TIDA cells discharge in highly stereotyped, robust, synchronized slow oscillations, whereas mouse oscillations are faster, flexible and show substantial cell-to-cell variability. We show that these distinct operational modes are explained by the presence of strong TIDA-TIDA gap junction coupling in the rat, and its complete absence in the mouse. Both species, however, encompass a similar heterogeneous range of intrinsic resonance frequencies, suggesting similar network building blocks. We demonstrate that gap junctions select and impose the slow network rhythm. These data identify a role for electrical junctions in determining oscillation frequency and show how related species can rely on distinct network strategies to accomplish adaptive control of hormone release.

Superior colliculus neuronal ensemble activity signals optimal rather than subjective confidence

Recent studies suggest that neurons in sensorimotor circuits involved in perceptual decision-making also play a role in decision confidence. In these studies, confidence is often considered to be an optimal readout of the probability that a decision is correct. However, the information leading to decision accuracy and the report of confidence often covaried, leaving open the possibility that there are actually two dissociable signal types in the brain: signals that correlate with decision accuracy (optimal confidence) and signals that correlate with subjects' behavioral reports of confidence (subjective confidence). We recorded neuronal activity from a sensorimotor decision area, the superior colliculus (SC) of monkeys, while they performed two different tasks. In our first task, decision accuracy and confidence covaried, as in previous studies. In our second task, we implemented a motion discrimination task with stimuli that were matched for decision accuracy but produced different levels of confidence, as reflected by behavioral reports. We used a multivariate decoder to predict monkeys' choices from neuronal population activity. As in previous studies on perceptual decision-making mechanisms, we found that neuronal decoding performance increased as decision accuracy increased. However, when decision accuracy was matched, performance of the decoder was similar between high and low subjective confidence conditions. These results show that the SC likely signals optimal decision confidence similar to previously reported cortical mechanisms, but is unlikely to play a critical role in subjective confidence. The results also motivate future investigations to determine where in the brain signals related to subjective confidence reside.

Fear Learning Regulates Cortical Sensory Representations by Suppressing Habituation.

Projections from auditory cortex to the amygdala are thought to contribute to the induction of auditory fear learning. In addition, fear conditioning has been found to enhance cortical responses to conditioned tones, suggesting that cortical plasticity contributes to fear learning. However, the functional role of auditory cortex in the retrieval of fear memories is unclear and how fear learning regulates cortical sensory representations is not well understood. To address these questions, we use acute optogenetic silencing and chronic two-photon calcium imaging in mouse auditory cortex during fear learning. Longitudinal imaging of neuronal ensemble activity reveals that discriminative fear learning modulates cortical sensory representations via the suppression of cortical habituation.

Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity.

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

Theta Oscillations in the Human Medial Temporal Lobe during Real-World Ambulatory Movement

The theta rhythm-a slow (6-12 Hz) oscillatory component of the local field potential-plays a critical role in spatial navigation and memory by coordinating the activity of neuronal ensembles within the medial temporal lobe (MTL). Although theta has been extensively studied in freely moving rodents, its presence in humans has been elusive and primarily investigated in stationary subjects. Here we used a unique clinical opportunity to examine theta within the human MTL during untethered, real-world ambulatory movement. We recorded intracranial electroencephalographic activity from participants chronically implanted with the wireless NeuroPace responsive neurostimulator (RNS) and tracked their motion with sub-millimeter precision. Our data revealed that movement-related theta oscillations indeed exist in humans, such that theta power is significantly higher during movement than immobility. Unlike in rodents, however, theta occurs in short bouts, with average durations of ∼400ms, which are more prevalent during fast versus slow movements. In a rare opportunity to study a congenitally blind participant, we found that both the prevalence and duration of theta bouts were increased relative to the sighted participants. These results provide critical support for conserved neurobiological characteristics of theta oscillations during ambulatory spatial navigation, while highlighting some fundamental differences across species in these oscillations between humans and rodents.

Cortical Phase-Amplitude Coupling in a Progressive Model of Parkinsonism in Nonhuman Primates.

Parkinson's disease is associated with abnormal oscillatory electrical activities of neurons and neuronal ensembles throughout the basal ganglia-thalamocortical network. It has recently been documented in patients with advanced parkinsonism that the amplitude of gamma-band oscillations (50-200 Hz) in electrocorticogram recordings from the primary motor cortex is abnormally coupled to the phase of beta band oscillations within the same signals. It is not known when in the course of the disease the abnormal phase-amplitude coupling (PAC) arises, and whether it is influenced by arousal or prior exposure to dopaminergic medications. To address these issues, we analyzed the relationship between the severity of parkinsonian motor signs and the extent of PAC in a progressive model of parkinsonism, using primates that were not exposed to levodopa prior to testing. PAC was measured in electrocorticogram signals from the primary motor cortex and the supplementary motor area in 3 monkeys that underwent weekly injections of small doses of the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, rendering them progressively parkinsonian. We found that parkinsonism was associated with increased coupling between the phase of low-frequency (4-10 Hz) oscillations and the amplitude of oscillations in the high gamma band (50-150 Hz). These changes only reached significance when the animals became fully parkinsonian. The increased PAC was normalized after levodopa treatment. We also found a similar increase in PAC during sleep, even in normal animals. The identified PAC was independent of concomitant changes in spectral power in the 2.9-9.8Hz or 49.8-150.4 Hz ranges. We conclude that PAC is predominately a sign of advanced parkinsonism, and is, thus, not essential for the development of parkinsonism. However, increased PAC appears to correlate with the severity of fully developed parkinsonism.

Parvalbumin interneurons in the dorsal horn: it's not all about GABA.

Parvalbumin containing inhibitory interneurons (PV + INs) are a key element controlling co-ordinated activity of neuronal ensembles in the mammalian central nervous system (CNS). This article is protected by copyright. All rights reserved

Skin suturing and cortical surface viral infusion improves imaging of neuronal ensemble activity with head-mounted miniature microscopes

BackgroundIn vivo optical imaging of neural activity provides important insights into brain functions at the single-cell level. Cranial windows and virally delivered calcium indicators are commonly used for imaging cortical activity through two-photon microscopes in head-fixed animals. Recently, head-mounted one-photon microscopes have been developed for freely behaving animals. However, minimizing tissue damage from the virus injection procedure and maintaining window clarity for imaging can be technically challenging. New methodWe used a wide-diameter glass pipette at the cortical surface for infusing the viral calcium reporter AAV-GCaMP6 into the cortex. After infusion, the scalp skin over the implanted optical window was sutured to facilitate postoperative recovery. The sutured scalp was removed approximately two weeks later and a miniature microscope was attached above the window to image neuronal activity in freely moving mice. ResultsWe found that cortical surface virus infusion efficiently labeled neurons in superficial layers, and scalp skin suturing helped to maintain the long-term clarity of optical windows. As a result, several hundred neurons could be recorded in freely moving animals. Comparison with existing methodsCompared to intracortical virus injection and open-scalp postoperative recovery, our methods minimized tissue damage and dura overgrowth underneath the optical window, and significantly increased the experimental success rate and the yield of identified neurons. ConclusionOur improved cranial surgery technique allows for high-yield calcium imaging of cortical neurons with head-mounted microscopes in freely behaving animals. This technique may be beneficial for other optical applications such as two-photon microscopy, multi-site imaging, and optogenetic modulation.

Markovian Analysis of the Sequential Behavior of the Spontaneous Spinal Cord Dorsum Potentials Induced by Acute Nociceptive Stimulation in the Anesthetized Cat.

In a previous study we developed a Machine Learning procedure for the automatic identification and classification of spontaneous cord dorsum potentials (CDPs). This study further supported the proposal that in the anesthetized cat, the spontaneous CDPs recorded from different lumbar spinal segments are generated by a distributed network of dorsal horn neurons with structured (non-random) patterns of functional connectivity and that these configurations can be changed to other non-random and stable configurations after the noceptive stimulation produced by the intradermic injection of capsaicin in the anesthetized cat. Here we present a study showing that the sequence of identified forms of the spontaneous CDPs follows a Markov chain of at least order one. That is, the system has memory in the sense that the spontaneous activation of dorsal horn neuronal ensembles producing the CDPs is not independent of the most recent activity. We used this markovian property to build a procedure to identify portions of signals as belonging to a specific functional state of connectivity among the neuronal networks involved in the generation of the CDPs. We have tested this procedure during acute nociceptive stimulation produced by the intradermic injection of capsaicin in intact as well as spinalized preparations. Altogether, our results indicate that CDP sequences cannot be generated by a renewal stochastic process. Moreover, it is possible to describe some functional features of activity in the cord dorsum by modeling the CDP sequences as generated by a Markov order one stochastic process. Finally, these Markov models make possible to determine the functional state which produced a CDP sequence. The proposed identification procedures appear to be useful for the analysis of the sequential behavior of the ongoing CDPs recorded from different spinal segments in response to a variety of experimental procedures including the changes produced by acute nociceptive stimulation. They are envisaged as a useful tool to examine alterations of the patterns of functional connectivity between dorsal horn neurons under normal and different pathological conditions, an issue of potential clinical concern.

Deciphering neuronal population codes for acute thermal pain

Objective. Pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage. Current pain research mostly focuses on molecular and synaptic changes at the spinal and peripheral levels. However, a complete understanding of pain mechanisms requires the physiological study of the neocortex. Our goal is to apply a neural decoding approach to read out the onset of acute thermal pain signals, which can be used for brain–machine interface. Approach. We used micro wire arrays to record ensemble neuronal activities from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) in freely behaving rats. We further investigated neural codes for acute thermal pain at both single-cell and population levels. To detect the onset of acute thermal pain signals, we developed a novel latent state-space framework to decipher the sorted or unsorted S1 and ACC ensemble spike activities, which reveal information about the onset of pain signals. Main results. The state space analysis allows us to uncover a latent state process that drives the observed ensemble spike activity, and to further detect the ‘neuronal threshold’ for acute thermal pain on a single-trial basis. Our method achieved good detection performance in sensitivity and specificity. In addition, our results suggested that an optimal strategy for detecting the onset of acute thermal pain signals may be based on combined evidence from S1 and ACC population codes. Significance. Our study is the first to detect the onset of acute pain signals based on neuronal ensemble spike activity. It is important from a mechanistic viewpoint as it relates to the significance of S1 and ACC activities in the regulation of the acute pain onset.

Diversity in Excitation-Inhibition Mismatch Underlies Local Functional Heterogeneity in the Rat Auditory Cortex

Cortical neurons are heterogeneous in their functional properties. This heterogeneity is fundamental for the processing of different features of sensory information. However, functional diversity within a local group of neurons is poorly understood. Here, we demonstrate that neighboring cortical neurons in layer 5 but not those of layer 4 of the rat anterior auditory field (AAF) exhibited a surprisingly high level of diversity in tonal receptive fields. Invivo whole-cell voltage-clamp recordings revealed that the diversity of frequency representation was due to a spectral mismatch between synaptic excitation and inhibition to varying degrees. The spectral distribution of excitation was skewed at different levels, whereas inhibition was homogeneous and non-skewed, similar to the summed spiking activity of local neuronal ensembles, which further enhanced diversity. Our results indicate that AAF in the auditory cortex is involved in processing auditory information in a highly refined manner that is important for complex pattern recognition.

Bidirectional Shifting Effects of the Sound Intensity on the Best Frequency in the Rat Auditory Cortex

Frequency and intensity are two independent attributes of sound stimuli. Psychoacoustic studies have found that the sound intensity can affect the perception of frequency; however, the underlying neuronal mechanism remains largely unknown. To investigate if and how the sound level affects the frequency coding for auditory cortical neurons, we recorded the activities of neuronal ensembles and single neurons, as well as the synaptic input evoked by pure tones of different frequency and intensity combinations, in layer 4 of the rat primary auditory cortex. We found that the best frequency (BF) shifted bidirectionally with the increases in intensity. Specifically, the BF of neurons with a low characteristic frequency (CF) shifted lower, whereas the BF of neurons with a higher CF shifted higher. Meanwhile, we found that these shifts in the BF can lead to the expansion of high- and low-frequency areas in the tonotopic map, increasing the evenness of the BF distribution at high intensities. Our results revealed that the frequency tuning can bidirectionally shift with an increase in the sound intensity at both the cellular and population level. This finding is consistent with the perceptual illusions observed in humans and could provide a potential mechanism for this psychoacoustic effect.

Pumilio2-deficient mice show a predisposition for epilepsy.

ABSTRACTEpilepsy is a neurological disease that is caused by abnormal hypersynchronous activities of neuronal ensembles leading to recurrent and spontaneous seizures in human patients. Enhanced neuronal excitability and a high level of synchrony between neurons seem to trigger these spontaneous seizures. The molecular mechanisms, however, regarding the development of neuronal hyperexcitability and maintenance of epilepsy are still poorly understood. Here, we show that pumilio RNA-binding family member 2 (Pumilio2; Pum2) plays a role in the regulation of excitability in hippocampal neurons of weaned and 5-month-old male mice. Almost complete deficiency of Pum2 in adult Pum2 gene-trap mice (Pum2 GT) causes misregulation of genes involved in neuronal excitability control. Interestingly, this finding is accompanied by the development of spontaneous epileptic seizures in Pum2 GT mice. Furthermore, we detect an age-dependent increase in Scn1a (Nav1.1) and Scn8a (Nav1.6) mRNA levels together with a decrease in Scn2a (Nav1.2) transcript levels in weaned Pum2 GT that is absent in older mice. Moreover, field recordings of CA1 pyramidal neurons show a tendency towards a reduced paired-pulse inhibition after stimulation of the Schaffer-collateral-commissural pathway in Pum2 GT mice, indicating a predisposition to the development of spontaneous seizures at later stages. With the onset of spontaneous seizures at the age of 5 months, we detect increased protein levels of Nav1.1 and Nav1.2 as well as decreased protein levels of Nav1.6 in those mice. In addition, GABA receptor subunit alpha-2 (Gabra2) mRNA levels are increased in weaned and adult mice. Furthermore, we observe an enhanced GABRA2 protein level in the dendritic field of the CA1 subregion in the Pum2 GT hippocampus. We conclude that altered expression levels of known epileptic risk factors such as Nav1.1, Nav1.2, Nav1.6 and GABRA2 result in enhanced seizure susceptibility and manifestation of epilepsy in the hippocampus. Thus, our results argue for a role of Pum2 in epileptogenesis and the maintenance of epilepsy.

Activation of neuronal ensembles via controlled synchronization

In this contribution we present the activation of neuronal ensembles of Hindmarsh-Rose neurons by controlled synchronization. The main problem consists in to impose a particular spiking-bursting behavior in all the neurons of the network. We consider a network where the neurons are in its resting state, it is desired that the neurons change their resting state to a particular behavior of activation, dictated by a neuron called the reference neuron. The goal is reached by controlling some neurons in the network controlling only the membrane potential (electrical synapse). The key feature of the present contribution is that by controlling a small number of neurons in the network a desired behavior is induced in all the neurons in the network despite its network topology. The important parameters are the control gain and the coupling strength, thus the activation of the network lays down on a compromise between the control gain and the coupling strength.

Genetic Feedback Regulation of Frontal Cortical Neuronal Ensembles Through Activity-Dependent Arc Expression and Dopaminergic Input.

Mental functions involve coordinated activities of specific neuronal ensembles that are embedded in complex brain circuits. Aberrant neuronal ensemble dynamics is thought to form the neurobiological basis of mental disorders. A major challenge in mental health research is to identify these cellular ensembles and determine what molecular mechanisms constrain their emergence and consolidation during development and learning. Here, we provide a perspective based on recent studies that use activity-dependent gene Arc/Arg3.1 as a cellular marker to identify neuronal ensembles and a molecular probe to modulate circuit functions. These studies have demonstrated that the transcription of Arc is activated in selective groups of frontal cortical neurons in response to specific behavioral tasks. Arc expression regulates the persistent firing of individual neurons and predicts the consolidation of neuronal ensembles during repeated learning. Therefore, the Arc pathway represents a prototypical example of activity-dependent genetic feedback regulation of neuronal ensembles. The activation of this pathway in the frontal cortex starts during early postnatal development and requires dopaminergic (DA) input. Conversely, genetic disruption of Arc leads to a hypoactive mesofrontal dopamine circuit and its related cognitive deficit. This mutual interaction suggests an auto-regulatory mechanism to amplify the impact of neuromodulators and activity-regulated genes during postnatal development. Such a mechanism may contribute to the association of mutations in dopamine and Arc pathways with neurodevelopmental psychiatric disorders. As the mesofrontal dopamine circuit shows extensive activity-dependent developmental plasticity, activity-guided modulation of DA projections or Arc ensembles during development may help to repair circuit deficits related to neuropsychiatric disorders.

Early Cortical Changes in Gamma Oscillations in Alzheimer's Disease.

The entorhinal cortices in the temporal lobe of the brain are key structures relaying memory related information between the neocortex and the hippocampus. The medial entorhinal cortex (MEC) routes spatial information, whereas the lateral entorhinal cortex (LEC) routes predominantly olfactory information to the hippocampus. Gamma oscillations are known to coordinate information transfer between brain regions by precisely timing population activity of neuronal ensembles. Here, we studied the organization of in vitro gamma oscillations in the MEC and LEC of the transgenic (tg) amyloid precursor protein (APP)-presenilin 1 (PS1) mouse model of Alzheimer’s Disease (AD) at 4–5 months of age. In vitro gamma oscillations using the kainate model peaked between 30–50 Hz and therefore we analyzed the oscillatory properties in the 20–60 Hz range. Our results indicate that the LEC shows clear alterations in frequency and power of gamma oscillations at an early stage of AD as compared to the MEC. The gamma-frequency oscillation slows down in the LEC and also the gamma power in dorsal LEC is decreased as early as 4–5 months in the tg APP-PS1 mice. The results of this study suggest that the timing of olfactory inputs from LEC to the hippocampus might be affected at an early stage of AD, resulting in a possible erroneous integration of the information carried by the two input pathways to the hippocampal subfields.

BRAIN-COMPUTER INTERFACE FOR THE AUGMENTATION OF BRAIN FUNCTIONS

Brain-computer interface (BCI) connects the nervous system departments with external devices for the purpose of recovery of motor and sensory functions of patients with neurological lesions. Over the past half-century BCI have gone from initial ideas to the high-tech modern incarnations. This development contributed significantly to the invasive techniques of multichannel registration activity of neuronal ensembles. Modern BCI are able to manage mechanical prosthetic arms and legs. Furthermore, BCI can provide sensory feedback, allowing the user to feel the movement of the prosthesis and its interaction with external objects. Latest BCI connect multiple users to the brain network. In this review, these achievements are dealt with a focus on invasive BCI.