Freshly frozen E18 rat cortical cells can generate functional neural networks after standard cryopreservation and thawing procedures

Primary dissociated brain tissue from rodents is widely used in a variety of different scientific methods to investigate cellular processes in vitro. Often, for this purpose cell cultures need to be generated just on time, requiring extensive animal lab infrastructure. We show here that cryopreservation and thawing of dissociated tissue from rat cerebral cortex at embryonic day 18 is feasible without affecting its ability to form functional neuronal networks in vitro. Vitality of fresh and re-thawed cortical cells was comparable, assessed by CellTiter-Blue-assay, CytoTox-ONE assay, immunocytochemical characterization and in vitro neuronal network activity recordings on microelectrode arrays. These findings suggest that planning and execution of experiments might be considerably facilitated by using cryo-preserved neurons instead of acutely dissociated neural cultures due to fewer logistical issues with regard to animal breeding and pregnancy timed preparations.     

Response differences of intersegmental auditory neurons recorded close to or far away from the presumed spike-generating zone

Intracellular recordings may give valuable information about processing of a neuron and possibly its input from the network. Impalement with an electrode causes injury to the cell and depolarization from intrusion of extracellular fluid. Thus, penetration artefacts may contaminate recordings and conceal or even alter relevant information. These penetration artefacts may have the strongest impact close to the spike-generating zone near the dendrites. Recordings in axonal portions might therefore be less vulnerable while providing insufficient information about the synaptic input. In this study, we present data of five previously identified intersegmental auditory neurons of a bushcricket independently recorded in their dendrites (prothorax) and axon (brain). Generally, responses to acoustic pulses of the same parameter combination were similar within a neuronal class at the two recording sites. However, all neuronal classes showed significantly higher response variability and a tendency for higher spike activity when recorded in the dendrites. Unexpectedly, the combined activity of two neurons (Ascending Neurons 1 and 2) recorded in the brain provides a better fit to song recognition than when recorded in the thorax. Axonal recordings of T-shaped Neuron 1 revealed graded potentials originating in the brain and modulating its output in a potentially behaviourally relevant manner.     

Physiological and pathological oscillatory networks in the human motor system.

Human brain functions are heavily contingent on neural interactions both at the single neuron and the neural population or system level. Accumulating evidence from neurophysiological studies strongly suggests that coupling of oscillatory neural activity provides an important mechanism to establish neural interactions. With the availability of whole-head magnetoencephalography (MEG) macroscopic oscillatory activity can be measured non-invasively from the human brain with high temporal and spatial resolution. To localise, quantify and map oscillatory activity and interactions onto individual brain anatomy we have developed the 'dynamic imaging of coherent sources' (DICS) method which allows to identify and analyse cerebral oscillatory networks from MEG recordings. Using this approach we have characterized physiological and pathological oscillatory networks in the human sensorimotor system. Coherent 8 Hz oscillations emerge from a cerebello-thalamo-premotor-motor cortical network and exert an 8 Hz oscillatory drive on the spinal motor neurons which can be observed as a physiological tremulousness of the movement termed movement discontinuities. This network represents the neurophysiological substrate of a discrete mode of motor control. In parkinsonian resting tremor we have identified an extensive cerebral network consisting of primary motor and lateral premotor cortex, supplementary motor cortex, thalamus/basal ganglia, posterior parietal cortex and secondary somatosensory cortex, which are entrained in the tremor or twice the tremor rhythm. This low frequency entrapment of motor areas likely plays an important role in the pathophysiology of parkinsonian motor symptoms. Finally, studies on patients with postural tremor in hepatic encephalopathy revealed that this type of tremor results from a pathologically slow thalamocortical and cortico-muscular coupling during isometric hold tasks. In conclusion, the analysis of oscillatory cerebral networks provides new insights into physiological mechanisms of motor control and pathophysiological mechanisms of tremor disorders.     

Transient coordinated activity within the developing brain��s default network

The concept of a brain default network postulates that specific brain regions are more active when a person is engaged in introspective mental activity. Transient functional coordination between groups of neurons is thought to be necessary for information processing. Since children develop introspection as they mature, regions of the default network may establish increasing functional coordination with age, resulting in fewer fluctuations in synchronization patterns. We investigated the transient coordinated activity in regions of the default network in seventeen children aged 11 months to 17 years of age using EEG recordings while subjects were resting quietly with eyes closed. The temporal and spatial fluctuations in the phase synchrony patterns were estimated across sites associated with the default network pattern and compared to other regions. Lower variability of the spatio-temporal patterns of phase synchronization associated with the default network was observed in the older group as compared to the younger group. This indicates that functional coordination increases among regions of the default network as children develop.     

In vitro CNS tissue analogues formed by self-organisation of reaggregated post-natal brain tissue

In this paper, we report the characterization of 'Hi-Spot' cultures formed by the re-aggregation of dissociated postnatal CNS tissue grown at an air-liquid interface. This produces a self-organised, dense, organotypic cellular network. Western blot, immunohistochemical, viral transfection and electron microscopy analyses reveal neuronal and glial populations, and the development of a synaptic network. Multi-electrode array recordings show synaptically driven network activity that develops through time from single unit spiking activity to global network bursting events. This activity is blocked by tetanus toxin and modified by antagonists of glutamatergic and GABAergic receptors suggesting tonic activity of excitatory and inhibitory synaptic signaling. The tissue-like properties of these cultures has been further demonstrated by their relative insensitivity to glutamate toxicity. Exposure to millimolar concentrations of glutamate for hours is necessary to produce significant excitotoxic neuronal death, as in vivo. We conclude that 'Hi-Spots' are biological analogues of CNS tissue at a level of complexity that allows for detailed functional analyses of emergent neuronal network properties.     

Invasive recordings from the human brain: clinical insights and beyond

Although non-invasive methods such as functional magnetic resonance imaging, electroencephalograms and magnetoencephalograms provide most of the current data about the human brain, their resolution is insufficient to show physiological processes at the cellular level. Clinical approaches sometimes allow invasive recordings to be taken from the human brain, mainly in patients with epilepsy or with movement disorders, and such recordings can sample neural activity at spatial scales ranging from single cells to distributed cell assemblies. In addition to their clinical relevance, these recordings can provide unique insights into brain functions such as movement control, perception, memory, language and even consciousness.     

Big words, halved brains and small worlds: complex brain networks of figurative language comprehension

Language comprehension is a complex task that involves a wide network of brain regions. We used topological measures to qualify and quantify the functional connectivity of the networks used under various comprehension conditions. To that aim we developed a technique to represent functional networks based on EEG recordings, taking advantage of their excellent time resolution in order to capture the fast processes that occur during language comprehension. Networks were created by searching for a specific causal relation between areas, the negative feedback loop, which is ubiquitous in many systems. This method is a simple way to construct directed graphs using event-related activity, which can then be analyzed topologically. Brain activity was recorded while subjects read expressions of various types and indicated whether they found them meaningful. Slightly different functional networks were obtained for event-related activity evoked by each expression type. The differences reflect the special contribution of specific regions in each condition and the balance of hemispheric activity involved in comprehending different types of expressions and are consistent with the literature in the field. Our results indicate that representing event-related brain activity as a network using a simple temporal relation, such as the negative feedback loop, to indicate directional connectivity is a viable option for investigation which also derives new information about aspects not reflected in the classical methods for investigating brain activity.     

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.     

Modeling shifts in the rate and pattern of subthalamopallidal network activity during deep brain stimulation

Deep brain stimulation (DBS) of the subthlamic nucleus (STN) represents an effective treatment for medically refractory Parkinson’s disease; however, understanding of its effects on basal ganglia network activity remains limited. We constructed a computational model of the subthalamopallidal network, trained it to fit in vivo recordings from parkinsonian monkeys, and evaluated its response to STN DBS. The network model was created with synaptically connected single compartment biophysical models of STN and pallidal neurons, and stochastically defined inputs driven by cortical beta rhythms. A least mean square error training algorithm was developed to parameterize network connections and minimize error when compared to experimental spike and burst rates in the parkinsonian condition. The output of the trained network was then compared to experimental data not used in the training process. We found that reducing the influence of the cortical beta input on the model generated activity that agreed well with recordings from normal monkeys. Further, during STN DBS in the parkinsonian condition the simulations reproduced the reduction in GPi bursting found in existing experimental data. The model also provided the opportunity to greatly expand analysis of GPi bursting activity, generating three major predictions. First, its reduction was proportional to the volume of STN activated by DBS. Second, GPi bursting decreased in a stimulation frequency dependent manner, saturating at values consistent with clinically therapeutic DBS. And third, ablating STN neurons, reported to generate similar therapeutic outcomes as STN DBS, also reduced GPi bursting. Our theoretical analysis of stimulation induced network activity suggests that regularization of GPi firing is dependent on the volume of STN tissue activated and a threshold level of burst reduction may be necessary for therapeutic effect.     

Multimodal functional network connectivity: an EEG-fMRI fusion in network space

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.     

Noise during rest enables the exploration of the brain's dynamic repertoire

Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1–100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space–time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.     

Neural network mechanisms underlying stimulus driven variability reduction

It is well established that the variability of the neural activity across trials, as measured by the Fano factor, is elevated. This fact poses limits on information encoding by the neural activity. However, a series of recent neurophysiological experiments have changed this traditional view. Single cell recordings across a variety of species, brain areas, brain states and stimulus conditions demonstrate a remarkable reduction of the neural variability when an external stimulation is applied and when attention is allocated towards a stimulus within a neuron's receptive field, suggesting an enhancement of information encoding. Using an heterogeneously connected neural network model whose dynamics exhibits multiple attractors, we demonstrate here how this variability reduction can arise from a network effect. In the spontaneous state, we show that the high degree of neural variability is mainly due to fluctuation-driven excursions from attractor to attractor. This occurs when, in the parameter space, the network working point is around the bifurcation allowing multistable attractors. The application of an external excitatory drive by stimulation or attention stabilizes one specific attractor, eliminating in this way the transitions between the different attractors and resulting in a net decrease in neural variability over trials. Importantly, non-responsive neurons also exhibit a reduction of variability. Finally, this reduced variability is found to arise from an increased regularity of the neural spike trains. In conclusion, these results suggest that the variability reduction under stimulation and attention is a property of neural circuits.     

Measuring Information-Transfer Delays

In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener’s principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics.     

Prolonged Intracellular Na+ Dynamics Govern Electrical Activity in Accessory Olfactory Bulb Mitral Cells

Persistent activity has been reported in many brain areas and is hypothesized to mediate working memory and emotional brain states and to rely upon network or biophysical feedback. Here, we demonstrate a novel mechanism by which persistent neuronal activity can be generated without feedback, relying instead on the slow removal of Na⁺ from neurons following bursts of activity. We show that mitral cells in the accessory olfactory bulb (AOB), which plays a major role in mammalian social behavior, may respond to a brief sensory stimulation with persistent firing. By combining electrical recordings, Ca²⁺ and Na⁺ imaging, and realistic computational modeling, we explored the mechanisms underlying the persistent activity in AOB mitral cells. We found that the exceptionally slow inward current that underlies this activity is governed by prolonged dynamics of intracellular Na⁺ ([Na⁺]_i), which affects neuronal electrical activity via several pathways. Specifically, elevated dendritic [Na⁺]_i reverses the Na⁺-Ca²⁺ exchanger activity, thus modifying the [Ca²⁺]_i set-point. This process, which relies on ubiquitous membrane mechanisms, is likely to play a role in other neuronal types in various brain regions.     

Studying brain functions with mesoscopic measurements: Advances in electrocorticography for non-human primates

Our brain is organized in a modular structure. Information in different modalities is processed within distinct cortical areas. However, individual cortical areas cannot enable complex cognitive functions without interacting with other cortical areas. Electrocorticography (ECoG) has recently become an important tool for studying global network activity across cortical areas in animal models. With stable recordings of electrical field potentials from multiple cortical areas, ECoG provides an opportunity to systematically study large-scale cortical activity at a mesoscopic spatiotemporal resolution under various experimental conditions. Recent developments in thin, flexible ECoG electrodes permit recording field potentials from not only gyral but intrasulcal cortical surfaces. Our review here focuses on the recent advances of ECoG applications to non-human primates.     

Characterization of synchronized bursts in cultured hippocampal neuronal networks with learning training on microelectrode arrays

Spontaneous synchronized bursts seem to play a key role in brain functions such as learning and memory. Still controversial is the characterization of spontaneous synchronized bursts in neuronal networks after learning training, whether depression or promotion. By taking advantages of the main features of the microelectrode array (MEA) technology (i.e. multisite recordings, stable and long-term coupling with the biological preparation), we analyzed changes of spontaneous synchronized bursts in cultured hippocampal neuronal networks after learning training. And for this purpose, a learning model at networking level on MEA system was constructed, and analysis of spontaneous synchronized burst activity modulation was presented. Preliminary results show that, the number of burst was increased by 154%, burst duration was increased by 35%, and the number of spikes per burst was increased by 124%, while interburst interval decreased by 44% with learning. In particular, correlation and synchrony of neuronal activities in networks were enhanced by 51% and 36%, respectively, with learning. In contrast, dynamic properties of neuronal networks were not changed much when the network was under “non-learning” condition. These results indicate that firing, association and synchrony of spontaneous bursts in neuronal networks were promoted by learning. Furthermore, from these observations, we are encouraged to think of a more engineered system based on in vitro hippocampal neurons, as a novel sensitive system for electrophysiological evaluations.     

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise

During slow-wave sleep, brain electrical activity is dominated by the slow (< 1 Hz) electroencephalogram (EEG) oscillations characterized by the periodic transitions between active (or Up) and silent (or Down) states in the membrane voltage of the cortical and thalamic neurons. Sleep slow oscillation is believed to play critical role in consolidation of recent memories. Past computational studies, based on the Hodgkin-Huxley type neuronal models, revealed possible intracellular and network mechanisms of the neuronal activity during sleep, however, they failed to explore the large-scale cortical network dynamics depending on collective behavior in the large populations of neurons. In this new study, we developed a novel class of reduced discrete time spiking neuron models for large-scale network simulations of wake and sleep dynamics. In addition to the spiking mechanism, the new model implemented nonlinearities capturing effects of the leak current, the Ca²⁺ dependent K⁺ current and the persistent Na⁺ current that were found to be critical for transitions between Up and Down states of the slow oscillation. We applied the new model to study large-scale two-dimensional cortical network activity during slow-wave sleep. Our study explained traveling wave dynamics and characteristic synchronization properties of transitions between Up and Down states of the slow oscillation as observed in vivo in recordings from cats. We further predict a critical role of synaptic noise and slow adaptive currents for spike sequence replay as found during sleep related memory consolidation.     

Brain rhythms reveal a hierarchical network organization

Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or "virtual brains", whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs) and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic), in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states display lower complexity than virtual brains modeling normal neural function. We finally discuss the implications of our results for the neurobiology of health and disease.     

Incremental mutual information: a new method for characterizing the strength and dynamics of connections in neuronal circuits

Understanding the computations performed by neuronal circuits requires characterizing the strength and dynamics of the connections between individual neurons. This characterization is typically achieved by measuring the correlation in the activity of two neurons. We have developed a new measure for studying connectivity in neuronal circuits based on information theory, the incremental mutual information (IMI). By conditioning out the temporal dependencies in the responses of individual neurons before measuring the dependency between them, IMI improves on standard correlation-based measures in several important ways: 1) it has the potential to disambiguate statistical dependencies that reflect the connection between neurons from those caused by other sources (e.g. shared inputs or intrinsic cellular or network mechanisms) provided that the dependencies have appropriate timescales, 2) for the study of early sensory systems, it does not require responses to repeated trials of identical stimulation, and 3) it does not assume that the connection between neurons is linear. We describe the theory and implementation of IMI in detail and demonstrate its utility on experimental recordings from the primate visual system.     

Large-Scale,High-Resolution Multielectrode-Array Recording Depicts Functional Network Differences of Cortical and Hippocampal Cultures

Understanding the detailed circuitry of functioning neuronal networks is one of the major goals of neuroscience. Recent improvements in neuronal recording techniques have made it possible to record the spiking activity from hundreds of neurons simultaneously with sub-millisecond temporal resolution. Here we used a 512-channel multielectrode array system to record the activity from hundreds of neurons in organotypic cultures of cortico-hippocampal brain slices from mice. To probe the network structure, we employed a wavelet transform of the cross-correlogram to categorize the functional connectivity in different frequency ranges. With this method we directly compare, for the first time, in any preparation, the neuronal network structures of cortex and hippocampus, on the scale of hundreds of neurons, with sub-millisecond time resolution. Among the three frequency ranges that we investigated, the lower two frequency ranges (gamma (30–80 Hz) and beta (12–30 Hz) range) showed similar network structure between cortex and hippocampus, but there were many significant differences between these structures in the high frequency range (100–1000 Hz). The high frequency networks in cortex showed short tailed degree-distributions, shorter decay length of connectivity density, smaller clustering coefficients, and positive assortativity. Our results suggest that our method can characterize frequency dependent differences of network architecture from different brain regions. Crucially, because these differences between brain regions require millisecond temporal scales to be observed and characterized, these results underscore the importance of high temporal resolution recordings for the understanding of functional networks in neuronal systems.