Spatially clustered neuronal assemblies comprise the microstructure of synchrony in chronically epileptic networks

Epilepsy insights revealed by intravital functional optical imaging

Despite an abundance of pharmacologic and surgical epilepsy treatments, there remain millions of patients suffering from poorly controlled seizures. One approach to closing this treatment gap may be found through a deeper mechanistic understanding of the network alterations that underly this aberrant activity. Functional optical imaging in vertebrate models provides powerful advantages to this end, enabling the spatiotemporal acquisition of individual neuron activity patterns across multiple seizures. This coupled with the advent of genetically encoded indicators, be them for specific ions, neurotransmitters or voltage, grants researchers unparalleled access to the intact nervous system. Here, we will review how in vivo functional optical imaging in various vertebrate seizure models has advanced our knowledge of seizure dynamics, principally seizure initiation, propagation and termination.

Seizure event detection using intravital two-photon calcium imaging data

Significance

Intravital cellular calcium imaging has emerged as a powerful tool to investigate how different types of neurons interact at the microcircuit level to produce seizure activity, with newfound potential to understand epilepsy. Although many methods exist to measure seizure-related activity in traditional electrophysiology, few yet exist for calcium imaging.

Aim

To demonstrate an automated algorithmic framework to detect seizure-related events using calcium imaging-including the detection of pre-ictal spike events, propagation of the seizure wavefront, and terminal spreading waves for both population-level activity and that of individual cells.

Approach

We developed an algorithm for precise recruitment detection of population and individual cells during seizure-associated events, which broadly leverages averaged population activity and high-magnitude slope features to detect single-cell pre-ictal spike and seizure recruitment. We applied this method to data recorded using awake in vivo two-photon calcium imaging during pentylenetetrazol-induced seizures in mice.

Results

We demonstrate that our detected recruitment times are concordant with visually identified labels provided by an expert reviewer and are sufficiently accurate to model the spatiotemporal progression of seizure-associated traveling waves.

Conclusions

Our algorithm enables accurate cell recruitment detection and will serve as a useful tool for researchers investigating seizure dynamics using calcium imaging.

Neuronal ensembles: Building blocks of neural circuits

Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.

Barrel cortex development lacks a key stage of hyperconnectivity from deep to superficial layers in a rat model of Absence Epilepsy

During development of the sensory cortex, the ascending innervation from deep to upper layers provides a temporary scaffold for the construction of other circuits that remain at adulthood. Whether an alteration in this sequence leads to brain dysfunction in neuro-developmental diseases remains unknown. Using functional approaches in a genetic model of Absence Epilepsy (GAERS), we investigated in barrel cortex, the site of seizure initiation, the maturation of excitatory and inhibitory innervations onto layer 2/3 pyramidal neurons and cell organization into neuronal assemblies. We found that cortical development in GAERS lacks the early surge of connections originating from deep layers observed at the end of the second postnatal week in normal rats and the concomitant structuring into multiple assemblies. Later on, at seizure onset (1 month old), excitatory neurons are hyper-excitable in GAERS when compared to Wistar rats. These findings suggest that early defects in the development of connectivity could promote this typical epileptic feature and/or its comorbidities.

A computational model to uncover the biophysical underpinnings of neural firing heterogeneity in dissociated hippocampal cultures

Calcium (Ca2+ ) imaging reveals a variety of correlated firing in cultures of dissociated hippocampal neurons, pinpointing the non-synaptic paracrine release of glutamate as a possible mediator for such firing patterns, although the biophysical underpinnings remain unknown. An intriguing possibility is that extracellular glutamate could bind metabotropic receptors linked with inositol trisphosphate (IP3 ) mediated release of Ca2+ from the endoplasmic reticulum of individual neurons, thereby modulating neural activity in combination with sarco/endoplasmic reticulum Ca2+ transport ATPase (SERCA) and voltage-gated Ca2+ channels (VGCC). However, the possibility that such release may occur in different neuronal compartments and can be inherently stochastic poses challenges in the characterization of such interplay between various Ca2+ channels. Here we deploy biophysical modeling in association with Monte Carlo parameter sampling to characterize such interplay and successfully predict experimentally observed Ca2+ patterns. The results show that the neurotransmitter level at the plasma membrane is the extrinsic source of heterogeneity in somatic Ca2+ transients. Our analysis, in particular, identifies the origin of such heterogeneity to an intrinsic differentiation of hippocampal neurons in terms of multiple cellular properties pertaining to intracellular Ca2+ signaling, such as VGCC, IP3 receptor, and SERCA expression. In the future, the biophysical model and parameter estimation approach used in this study can be upgraded to predict the response of a system of interconnected neurons.

Seizure Event Detection Using Intravital Two-Photon Calcium Imaging Data

Significance

Genetic cellular calcium imaging has emerged as a powerful tool to investigate how different types of neurons interact at the microcircuit level to produce seizure activity, with newfound potential to understand epilepsy. Although many methods exist to measure seizure-related activity in traditional electrophysiology, few yet exist for calcium imaging.

Aim

To demonstrate an automated algorithmic framework to detect seizure-related events using calcium imaging - including the detection of pre-ictal spike events, propagation of the seizure wavefront, and terminal spreading waves for both population-level activity and that of individual cells.

Approach

We developed an algorithm for precise recruitment detection of population and individual cells during seizure-associated events, which broadly leverages averaged population activity and high-magnitude slope features to detect single-cell pre-ictal spike and seizure recruitment. We applied this method to data recorded using awake in vivo two-photon calcium imaging during pentylenetetrazol induced seizures in mice.

Results

We demonstrate that our detected recruitment times are concordant with visually identified labels provided by an expert reviewer and are sufficiently accurate to model the spatiotemporal progression of seizure-associated traveling waves.

Conclusions

Our algorithm enables accurate cell recruitment detection and will serve as a useful tool for researchers investigating seizure dynamics using calcium imaging.

Kainate receptors modulate the microstructure of synchrony during dentate gyrus epileptiform activity

Temporal Lobe Epilepsy (TLE) is the most common form of epilepsy in adults. In TLE, recurrent mossy fiber (rMF) sprouting from dentate gyrus granule cells (DGCs) forms an aberrant epileptogenic network between dentate granule cells (DGCs) that operates via ectopically expressed kainate receptors (KARs). It was previously shown that KARs expressed at the rMF-DGC synapses play a prominent role in epileptiform network events in TLE. However, it is not well understood how KARs influence neuronal network dynamics and contribute to the generation of epileptiform network activity in the dentate gyrus. To address this question, we monitored the activity of DGCs using single-cell resolution calcium imaging performed in a reliable in vitro model of TLE. Under our experimental conditions, the most prominent DGC activity patterns were interictal-like epileptiform network events, which were correlated with high levels of neuronal synchronization. The pharmacological blockade of KARs reduced the frequency as well as the number of neurons involved in these events, without altering their spatiotemporal dynamics. Analysis of the microstructure of synchrony showed that blockade of KARs diminished the fraction of neurons forming the main functional cluster. Therefore, we propose that KARs act as modulators in the epileptic network by facilitating the recruitment of neurons into coactive cell assemblies, thereby contributing to the occurrence of epileptiform network events.

From single-neuron dynamics to higher-order circuit motifs in control and pathological brain networks

The convergence of advanced single-cell in vivo functional imaging techniques, computational modelling tools and graph-based network analytics has heralded new opportunities to study single-cell dynamics across large-scale networks, providing novel insights into principles of brain communication and pointing towards potential new strategies for treating neurological disorders. A major recent finding has been the identification of unusually richly connected hub cells that have capacity to synchronize networks and may also be critical in network dysfunction. While hub neurons are traditionally defined by measures that consider solely the number and strength of connections, novel higher-order graph analytics now enables the mining of massive networks for repeating subgraph patterns called motifs. As an illustration of the power offered by higher-order analysis of neuronal networks, we highlight how recent methodological advances uncovered a new functional cell type, the superhub, that is predicted to play a major role in regulating network dynamics. Finally, we discuss open questions that will be critical for assessing the importance of higher-order cellular-scale network analytics in understanding brain function in health and disease.

Microscale Neuronal Activity Collectively Drives Chaotic and Inflexible Dynamics at the Macroscale in Seizures

Neuronal activity propagates through the network during seizures, engaging brain dynamics at multiple scales. Such propagating events can be described through the avalanches framework, which can relate spatiotemporal activity at the microscale with global network properties. Interestingly, propagating avalanches in healthy networks are indicative of critical dynamics, where the network is organized to a phase transition, which optimizes certain computational properties. Some have hypothesized that the pathologic brain dynamics of epileptic seizures are an emergent property of microscale neuronal networks collectively driving the brain away from criticality. Demonstrating this would provide a unifying mechanism linking microscale spatiotemporal activity with emergent brain dysfunction during seizures. Here, we investigated the effect of drug-induced seizures on critical avalanche dynamics, using in vivo whole-brain two-photon imaging of GCaMP6s larval zebrafish (males and females) at single neuron resolution. We demonstrate that single neuron activity across the whole brain exhibits a loss of critical statistics during seizures, suggesting that microscale activity collectively drives macroscale dynamics away from criticality. We also construct spiking network models at the scale of the larval zebrafish brain, to demonstrate that only densely connected networks can drive brain-wide seizure dynamics away from criticality. Importantly, such dense networks also disrupt the optimal computational capacities of critical networks, leading to chaotic dynamics, impaired network response properties and sticky states, thus helping to explain functional impairments during seizures. This study bridges the gap between microscale neuronal activity and emergent macroscale dynamics and cognitive dysfunction during seizures.SIGNIFICANCE STATEMENT Epileptic seizures are debilitating and impair normal brain function. It is unclear how the coordinated behavior of neurons collectively impairs brain function during seizures. To investigate this we perform fluorescence microscopy in larval zebrafish, which allows for the recording of whole-brain activity at single-neuron resolution. Using techniques from physics, we show that neuronal activity during seizures drives the brain away from criticality, a regime that enables both high and low activity states, into an inflexible regime that drives high activity states. Importantly, this change is caused by more connections in the network, which we show disrupts the ability of the brain to respond appropriately to its environment. Therefore, we identify key neuronal network mechanisms driving seizures and concurrent cognitive dysfunction.

Pre-surgical features of intrinsic brain networks predict single and joint epilepsy surgery outcomes

Despite the effectiveness of surgical interventions for the treatment of intractable focal temporal lobe epilepsy (TLE), the substrates that support good outcomes are poorly understood. While algorithms have been developed for the prediction of either seizure or cognitive/psychiatric outcomes alone, no study has reported on the functional and structural architecture that supports joint outcomes. We measured key aspects of pre-surgical whole brain functional/structural network architecture and evaluated their ability to predict post-operative seizure control in combination with cognitive/psychiatric outcomes. Pre-surgically, we identified the intrinsic connectivity networks (ICNs) unique to each person through independent component analysis (ICA), and computed: (1) the spatial-temporal match between each person's ICA components and established, canonical ICNs, (2) the connectivity strength within each identified person-specific ICN, (3) the gray matter (GM) volume underlying the person-specific ICNs, and (4) the amount of variance not explained by the canonical ICNs for each person. Post-surgical seizure control and reliable change indices of change (for language [naming, phonemic fluency], verbal episodic memory, and depression) served as binary outcome responses in random forest (RF) models. The above functional and structural measures served as input predictors. Our empirically derived ICN-based measures customized to the individual showed that good joint seizure and cognitive/psychiatric outcomes depended upon higher levels of brain reserve (GM volume) in specific networks. In contrast, singular outcomes relied on systematic, idiosyncratic variance in the case of seizure control, and the weakened pre-surgical presence of functional ICNs that encompassed the ictal temporal lobe in the case of cognitive/psychiatric outcomes. Our data made clear that the ICNs differed in their propensity to provide reserve for adaptive outcomes, with some providing structural (brain), and others functional (cognitive) reserve. Our customized methodology demonstrated that when substantial unique, patient-specific ICNs are present prior to surgery there is a reliable association with poor post-surgical seizure control. These ICNs are idiosyncratic in that they did not match the canonical, normative ICNs and, therefore, could not be defined functionally, with their location likely varying by patient. This important finding suggested the level of highly individualized ICN's in the epileptic brain may signal the emergence of epileptogenic activity after surgery.

Cortical neuronal assemblies coordinate with EEG microstate dynamics during resting wakefulness

The disruption of cortical assembly activity has been associated with anesthesia-induced loss of consciousness. However, the relationship between cortical assembly activity and the variations in consciousness associated with natural vigilance states remains unclear. Here, we address this by performing vigilance state-specific clustering analysis on 2-photon calcium imaging data from the sensorimotor cortex in combination with global electroencephalogram (EEG) microstate analysis derived from multi-EEG signals obtained over widespread cortical locations. We report no difference in the structure of assembly activity during quiet wakefulness (QW), non-rapid eye movement sleep (NREMs), or REMs, despite the latter two vigilance states being associated with significantly reduced levels of consciousness relative to QW. However, we describe a significant coordination between global EEG microstate dynamics and general local cortical assembly activity during periods of QW, but not sleep. These results suggest that the coordination of cortical assembly activity with global brain dynamics could be a key factor of sustained conscious experience.

Chloride ion dysregulation in epileptogenic neuronal networks

GABA is the major inhibitory neurotransmitter in the mature CNS. When GABA_A receptors are activated the membrane potential is driven towards hyperpolarization due to chloride entry into the neuron. However, chloride ion dysregulation that alters the ionic gradient can result in depolarizing GABAergic post-synaptic potentials instead. In this review, we highlight that GABAergic inhibition prevents and restrains focal seizures but then reexamine this notion in the context of evidence that a static and/or a dynamic chloride ion dysregulation, that increases intracellular chloride ion concentrations, promotes epileptiform activity and seizures. To reconcile these findings, we hypothesize that epileptogenic pathologically interconnected neuron (PIN) microcircuits, representing a small minority of neurons, exhibit static chloride dysregulation and should exhibit depolarizing inhibitory post-synaptic potentials (IPSPs). We speculate that chloride ion dysregulation and PIN cluster activation may generate fast ripples and epileptiform spikes as well as initiate the hypersynchronous seizure onset pattern and microseizures. Also, we discuss the genetic, molecular, and cellular players important in chloride dysregulation which regulate epileptogenesis and initiate the low-voltage fast seizure onset pattern. We conclude that chloride dysregulation in neuronal networks appears to be critical for epileptogenesis and seizure genesis, but feed-back and feed-forward inhibitory GABAergic neurotransmission plays an important role in preventing and restraining seizures as well.

SCN1A channelopathies: Navigating from genotype to neural circuit dysfunction

The SCN1A gene is strongly associated with epilepsy and plays a central role for supporting cortical excitation-inhibition balance through the expression of Na_V1.1 within inhibitory interneurons. The phenotype of SCN1A disorders has been conceptualized as driven primarily by impaired interneuron function that predisposes to disinhibition and cortical hyperexcitability. However, recent studies have identified SCN1A gain-of-function variants associated with epilepsy, and the presence of cellular and synaptic changes in mouse models that point toward homeostatic adaptations and complex network remodeling. These findings highlight the need to understand microcircuit-scale dysfunction in SCN1A disorders to contextualize genetic and cellular disease mechanisms. Targeting the restoration of microcircuit properties may be a fruitful strategy for the development of novel therapies.

Mapping of structural arrangement of cells and collective calcium transients: an integrated framework combining live cell imaging using confocal microscopy and UMAP-assisted HDBSCAN-based approach

Live cell calcium (Ca2+) imaging is one of the important tools to record cellular activity during in vitro and in vivo preclinical studies. Specially, high-resolution microscopy can provide valuable dynamic information at the single cell level. One of the major challenges in the implementation of such imaging schemes is to extract quantitative information in the presence of significant heterogeneity in Ca2+ responses attained due to variation in structural arrangement and drug distribution. To fill this gap, we propose time-lapse imaging using spinning disk confocal microscopy and machine learning-enabled framework for automated grouping of Ca2+ spiking patterns. Time series analysis is performed to correlate the drug induced cellular responses to self-assembly pattern present in multicellular systems. The framework is designed to reduce the large-scale dynamic responses using uniform manifold approximation and projection (UMAP). In particular, we propose the suitability of hierarchical DBSCAN (HDBSCAN) in view of reduced number of hyperparameters. We find UMAP-assisted HDBSCAN outperforms existing approaches in terms of clustering accuracy in segregation of Ca2+ spiking patterns. One of the novelties includes the application of non-linear dimension reduction in segregation of the Ca2+ transients with statistical similarity. The proposed pipeline for automation was also proved to be a reproducible and fast method with minimal user input. The algorithm was used to quantify the effect of cellular arrangement and stimulus level on collective Ca2+ responses induced by GPCR targeting drug. The analysis revealed a significant increase in subpopulation containing sustained oscillation corresponding to higher packing density. In contrast to traditional measurement of rise time and decay ratio from Ca2+ transients, the proposed pipeline was used to classify the complex patterns with longer duration and cluster-wise model fitting. The two-step process has a potential implication in deciphering biophysical mechanisms underlying the Ca2+ oscillations in context of structural arrangement between cells.

Dynamic Fluorescence Imaging in Experimental Epilepsy

The ability to develop effective new treatments for epilepsy may depend on improved understanding of seizure pathophysiology, about which many questions remain. Dynamic fluorescence imaging of activity at single-neuron resolution with fluorescent indicators in experimental model systems in vivo has revolutionized basic neuroscience and has the potential to do so for epilepsy research as well. Here, we review salient issues as they pertain to experimental imaging in basic epilepsy research, including commonly used imaging technologies, data processing and analysis, interpretation of results, and selected examples of how imaging-based approaches have revealed new insight into mechanisms of seizures and epilepsy.

Characterisation of NLRP3 pathway-related neuroinflammation in temporal lobe epilepsy

Objective

Inflammation of brain structures, in particular the hippocampal formation, can induce neuronal degeneration and be associated with increased excitability manifesting as propensity for repetitive seizures. An increase in the abundance of individual proinflammatory molecules including interleukin 1 beta has been observed in brain tissue samples of patients with pharmacoresistant temporal lobe epilepsy (TLE) and corresponding animal models. The NLRP3-inflammasome, a cytosolic protein complex, acts as a key regulator in proinflammatory innate immune signalling. Upon activation, it leads to the release of interleukin 1 beta and inflammation-mediated neurodegeneration. Transient brain insults, like status epilepticus (SE), can render hippocampi chronically hyperexcitable and induce segmental neurodegeneration. The underlying mechanisms are referred to as epileptogenesis. Here, we have tested the hypothesis that distinct NLRP3-dependent transcript and protein signalling dynamics are induced by SE and whether they differ between two classical SE models. We further correlated the association of NLRP3-related transcript abundance with convulsive activity in human TLE hippocampi of patients with and without associated neurodegenerative damage.

Methods

Hippocampal mRNA- and protein-expression of NLRP3 and associated signalling molecules were analysed longitudinally in pilocarpine- and kainic acid-induced SE TLE mouse models. Complementarily, we studied NLRP3 inflammasome-associated transcript patterns in epileptogenic hippocampi with different damage patterns of pharmacoresistant TLE patients that had undergone epilepsy surgery for seizure relief.

Results

Pilocarpine- and kainic acid-induced SE elicit distinct hippocampal Nlrp3-associated molecular signalling. Transcriptional activation of NLRP3 pathway elements is associated with seizure activity but independent of the particular neuronal damage phenotype in KA-induced and in human TLE hippocampi.

Significance

These data suggest highly dynamic inflammasome signalling in SE-induced TLE and highlight a vicious cycle associated with seizure activity. Our results provide promising perspectives for the inflammasome signalling pathway as a target for anti-epileptogenic and -convulsive therapeutic strategies. The latter may even applicable to a particularly broad spectrum of TLE patients with currently pharmacoresistant disease.

Patterns of synchronization in 2D networks of inhibitory neurons

Neural firing in many inhibitory networks displays synchronous assembly or clustered firing, in which subsets of neurons fire synchronously, and these subsets may vary with different inputs to, or states of, the network. Most prior analytical and computational modeling of such networks has focused on 1D networks or 2D networks with symmetry (often circular symmetry). Here, we consider a 2D discrete network model on a general torus, where neurons are coupled to two or more nearest neighbors in three directions (horizontal, vertical, and diagonal), and allow different coupling strengths in all directions. Using phase model analysis, we establish conditions for the stability of different patterns of clustered firing behavior in the network. We then apply our results to study how variation of network connectivity and the presence of heterogeneous coupling strengths influence which patterns are stable. We confirm and supplement our results with numerical simulations of biophysical inhibitory neural network models. Our work shows that 2D networks may exhibit clustered firing behavior that cannot be predicted as a simple generalization of a 1D network, and that heterogeneity of coupling can be an important factor in determining which patterns are stable.

From Isles of Königsberg to Islets of Langerhans: Examining the Function of the Endocrine Pancreas Through Network Science

Islets of Langerhans are multicellular microorgans located in the pancreas that play a central role in whole-body energy homeostasis. Through secretion of insulin and other hormones they regulate postprandial storage and interprandial usage of energy-rich nutrients. In these clusters of hormone-secreting endocrine cells, intricate cell-cell communication is essential for proper function. Electrical coupling between the insulin-secreting beta cells through gap junctions composed of connexin36 is particularly important, as it provides the required, most important, basis for coordinated responses of the beta cell population. The increasing evidence that gap-junctional communication and its modulation are vital to well-regulated secretion of insulin has stimulated immense interest in how subpopulations of heterogeneous beta cells are functionally arranged throughout the islets and how they mediate intercellular signals. In the last decade, several novel techniques have been proposed to assess cooperation between cells in islets, including the prosperous combination of multicellular imaging and network science. In the present contribution, we review recent advances related to the application of complex network approaches to uncover the functional connectivity patterns among cells within the islets. We first provide an accessible introduction to the basic principles of network theory, enumerating the measures characterizing the intercellular interactions and quantifying the functional integration and segregation of a multicellular system. Then we describe methodological approaches to construct functional beta cell networks, point out possible pitfalls, and specify the functional implications of beta cell network examinations. We continue by highlighting the recent findings obtained through advanced multicellular imaging techniques supported by network-based analyses, giving special emphasis to the current developments in both mouse and human islets, as well as outlining challenges offered by the multilayer network formalism in exploring the collective activity of islet cell populations. Finally, we emphasize that the combination of these imaging techniques and network-based analyses does not only represent an innovative concept that can be used to describe and interpret the physiology of islets, but also provides fertile ground for delineating normal from pathological function and for quantifying the changes in islet communication networks associated with the development of diabetes mellitus.

Shed CNTNAP2 ectodomain is detectable in CSF and regulates Ca2+ homeostasis and network synchrony via PMCA2/ATP2B2

Although many neuronal membrane proteins undergo proteolytic cleavage, little is known about the biological significance of neuronal ectodomain shedding (ES). Here, we show that the neuronal sheddome is detectable in human cerebrospinal fluid (hCSF) and is enriched in neurodevelopmental disorder (NDD) risk factors. Among shed synaptic proteins is the ectodomain of CNTNAP2 (CNTNAP2-ecto), a prominent NDD risk factor. CNTNAP2 undergoes activity-dependent ES via MMP9 (matrix metalloprotease 9), and CNTNAP2-ecto levels are reduced in the hCSF of individuals with autism spectrum disorder. Using mass spectrometry, we identified the plasma membrane Ca2+ ATPase (PMCA) extrusion pumps as novel CNTNAP2-ecto binding partners. CNTNAP2-ecto enhances the activity of PMCA2 and regulates neuronal network dynamics in a PMCA2-dependent manner. Our data underscore the promise of sheddome analysis in discovering neurobiological mechanisms, provide insight into the biology of ES and its relationship with the CSF, and reveal a mechanism of regulation of Ca2+ homeostasis and neuronal network synchrony by a shed ectodomain.

pH-Dependence of Glucose-Dependent Activity of Beta Cell Networks in Acute Mouse Pancreatic Tissue Slice

Extracellular pH has the potential to affect various aspects of the pancreatic beta cell function. To explain this effect, a number of mechanisms was proposed involving both extracellular and intracellular targets and pathways. Here, we focus on reassessing the influence of extracellular pH on glucose-dependent beta cell activation and collective activity in physiological conditions. To this end we employed mouse pancreatic tissue slices to perform high-temporally resolved functional imaging of cytosolic Ca²⁺ oscillations. We investigated the effect of either physiological H⁺ excess or depletion on the activation properties as well as on the collective activity of beta cell in an islet. Our results indicate that lowered pH invokes activation of a subset of beta cells in substimulatory glucose concentrations, enhances the average activity of beta cells, and alters the beta cell network properties in an islet. The enhanced average activity of beta cells was determined indirectly utilizing cytosolic Ca²⁺ imaging, while direct measuring of insulin secretion confirmed that this enhanced activity is accompanied by a higher insulin release. Furthermore, reduced functional connectivity and higher functional segregation at lower pH, both signs of a reduced intercellular communication, do not necessary result in an impaired insulin release.

Interictal spikes with and without high-frequency oscillation have different single-neuron correlates

Interictal epileptiform discharges (IEDs) are a widely used biomarker in patients with epilepsy but lack specificity. It has been proposed that there are truly epileptogenic and less pathological or even protective IEDs. Recent studies suggest that highly pathological IEDs are characterized by high-frequency oscillations (HFOs). Here, we aimed to dissect these 'HFO-IEDs' at the single-neuron level, hypothesizing that the underlying mechanisms are distinct from 'non-HFO-IEDs'. Analysing hybrid depth electrode recordings from patients with temporal lobe epilepsy, we found that single-unit firing rates were higher in HFO- than in non-HFO-IEDs. HFO-IEDs were characterized by a pronounced pre-peak increase in firing, which coincided with the preferential occurrence of HFOs, whereas in non-HFO-IEDs, there was only a mild pre-peak increase followed by a post-peak suppression. Comparing each unit's firing during HFO-IEDs to its baseline activity, we found many neurons with a significant increase during the HFO component or ascending part, but almost none with a decrease. No such imbalance was observed during non-HFO-IEDs. Finally, comparing each unit's firing directly between HFO- and non-HFO-IEDs, we found that most cells had higher rates during HFO-IEDs and, moreover, identified a distinct subset of neurons with a significant preference for this IED subtype. In summary, our study reveals that HFO- and non-HFO-IEDs have different single-unit correlates. In HFO-IEDs, many neurons are moderately activated, and some participate selectively, suggesting that both types of increased firing contribute to highly pathological IEDs.

Visualizing Oscillations in Brain Slices With Genetically Encoded Voltage Indicators

Genetically encoded voltage indicators (GEVIs) expressed pan-neuronally were able to optically resolve bicuculline induced spontaneous oscillations in brain slices of the mouse motor cortex. Three GEVIs were used that differ in their timing of response to voltage transients as well as in their voltage ranges. The duration, number of cycles, and frequency of the recorded oscillations reflected the characteristics of each GEVI used. Multiple oscillations imaged in the same slice never originated at the same location, indicating the lack of a “hot spot” for induction of the voltage changes. Comparison of pan-neuronal, Ca²⁺/calmodulin-dependent protein kinase II α restricted, and parvalbumin restricted GEVI expression revealed distinct profiles for the excitatory and inhibitory cells in the spontaneous oscillations of the motor cortex. Resolving voltage fluctuations across space, time, and cell types with GEVIs represent a powerful approach to dissecting neuronal circuit activity.

Identification of neural oscillations and epileptiform changes in human brain organoids

Brain organoids represent a powerful tool for studying human neurological diseases, particularly those that affect brain growth and structure. However, many diseases manifest with clear evidence of physiological and network abnormality in the absence of anatomical changes, raising the question of whether organoids possess sufficient neural network complexity to model these conditions. Here, we explore the network-level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex network dynamics reminiscent of intact brain preparations. We demonstrate highly abnormal and epileptiform-like activity in organoids derived from induced pluripotent stem cells from individuals with Rett syndrome, accompanied by transcriptomic differences revealed by single-cell analyses. We also rescue key physiological activities with an unconventional neuroregulatory drug, pifithrin-α. Together, these findings provide an essential foundation for the utilization of brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.

Maximally selective single-cell target for circuit control in epilepsy models

Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. Here, we studied epileptic circuits using a newly developed computational pipeline that leveraged single-cell calcium imaging of larval zebrafish and chronically epileptic mice, biologically constrained effective connectivity modeling, and higher-order motif-focused network analysis. We uncovered a novel functional cell type that preferentially emerged in the preseizure state, the superhub, that was unusually richly connected to the rest of the network through feedforward motifs, critically enhancing downstream excitation. Perturbation simulations indicated that disconnecting superhubs was significantly more effective in stabilizing epileptic circuits than disconnecting hub cells that were defined traditionally by connection count. In the dentate gyrus of chronically epileptic mice, superhubs were predominately modeled adult-born granule cells. Collectively, these results predict a new maximally selective and minimally invasive cellular target for seizure control.

Quantitative Confocal Microscopy for Grouping of Dose-Response Data: Deciphering Calcium Sequestration and Subsequent Cell Death in the Presence of Excess Norepinephrine

Fluorescent calcium (Ca²⁺) imaging is one of the preferred methods to record cellular activity during in vitro preclinical studies, high-content drug screening, and toxicity analysis. Visualization and analysis for dose-response data obtained using high-resolution imaging remain challenging, due to the inherent heterogeneity present in the Ca²⁺ spiking. To address this challenge, we propose measurement of cytosolic Ca²⁺ ions using spinning-disk confocal microscopy and machine learning-based analytics that is scalable. First, we implemented uniform manifold approximation and projection (UMAP) for visualizing the multivariate time-series dataset in the two-dimensional (2D) plane using Python. The dataset was obtained through live imaging experiments with norepinephrine-induced Ca²⁺ oscillation in HeLa cells for a large range of doses. Second, we demonstrate that the proposed framework can be used to depict the grouping of the spiking pattern for lower and higher drug doses. To the best of our knowledge, this is the first attempt at UMAP visualization of the time-series dose response and identification of the Ca²⁺ signature during lytic death. Such quantitative microscopy can be used as a component of a high-throughput data analysis workflow for toxicity analysis.

In vivo calcium imaging reveals disordered interictal network dynamics in epileptic stxbp1b zebrafish

STXBP1 mutations are associated with encephalopathy, developmental delay, intellectual disability, and epilepsy. While neural networks are known to operate at a critical state in the healthy brain, network behavior during pathological epileptic states remains unclear. Examining activity during periods between well-characterized ictal-like events (i.e., interictal period) could provide a valuable step toward understanding epileptic networks. To study these networks in the context of STXBP1 mutations, we combine a larval zebrafish model with in vivo fast confocal calcium imaging and extracellular local field potential recordings. Stxbp1b mutants display transient periods of elevated activity among local clusters of interacting neurons. These network "cascade" events were significantly larger in size and duration in mutants. At mesoscale resolution, cascades exhibit neurodevelopmental abnormalities. At single-cell scale, we describe spontaneous hyper-synchronized neuronal ensembles. That calcium imaging reveals uniquely disordered brain states during periods between pathological ictal-like seizure events is striking and represents a potential interictal biomarker.

Spatially localized cluster solutions in inhibitory neural networks

Neurons in the inhibitory network of the striatum display cell assembly firing patterns which recent results suggest may consist of spatially compact neural clusters. Previous computational modeling of striatal neural networks has indicated that non-monotonic, distance-dependent coupling may promote spatially localized cluster firing. Here, we identify conditions for the existence and stability of cluster firing solutions in which clusters consist of spatially adjacent neurons in inhibitory neural networks. We consider simple non-monotonic, distance-dependent connectivity schemes in weakly coupled 1-D networks where cells make stronger connections with their kth nearest neighbors on each side and weaker connections with closer neighbors. Using the phase model reduction of the network system, we prove the existence of cluster solutions where neurons that are spatially close together are also synchronized in the same cluster, and find stability conditions for these solutions. Our analysis predicts the long-term behavior for networks of neurons, and we confirm our results by numerical simulations of biophysical neuron network models. Our results demonstrate that an inhibitory network with non-monotonic, distance-dependent connectivity can exhibit cluster solutions where adjacent cells fire together.

Dynamic core-periphery structure of information sharing networks in entorhinal cortex and hippocampus

Neural computation is associated with the emergence, reconfiguration, and dissolution of cell assemblies in the context of varying oscillatory states. Here, we describe the complex spatiotemporal dynamics of cell assemblies through temporal network formalism. We use a sliding window approach to extract sequences of networks of information sharing among single units in hippocampus and entorhinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve through time and as a function of changing global brain state (theta vs. slow-wave oscillations). First, we find that information sharing networks display, at any time, a core-periphery structure in which an integrated core of more tightly functionally interconnected units links to more loosely connected network leaves. However the units participating to the core or to the periphery substantially change across time windows, with units entering and leaving the core in a smooth way. Second, we find that discrete network states can be defined on top of this continuously ongoing liquid core-periphery reorganization. Switching between network states results in a more abrupt modification of the units belonging to the core and is only loosely linked to transitions between global oscillatory states. Third, we characterize different styles of temporal connectivity that cells can exhibit within each state of the sharing network. While inhibitory cells tend to be central, we show that, otherwise, anatomical localization only poorly influences the patterns of temporal connectivity of the different cells. Furthermore, cells can change temporal connectivity style when the network changes state. Altogether, these findings reveal that the sharing of information mediated by the intrinsic dynamics of hippocampal and entorhinal cortex cell assemblies have a rich spatiotemporal structure, which could not have been identified by more conventional time- or state-averaged analyses of functional connectivity.

It is generally thought that computations performed by local brain circuits rely on complex neural processes, associated with the flexible waxing and waning of cell assemblies, that is, an ensemble of cells firing in tight synchrony. Although cell assembly formation is inherently and unavoidably dynamical, it is still common to find studies in which essentially “static” approaches are used to characterize this process. In the present study, we adopt instead a temporal network approach. Avoiding usual time-averaging procedures, we reveal that hub neurons are not hardwired but that cells vary smoothly their degree of integration within the assembly core. Furthermore, our temporal network framework enables the definition of alternative possible styles of “hubness.” Some cells may share information with a multitude of other units but only in an intermittent manner, as “activists” in a flash mob. In contrast, some other cells may share information in a steadier manner, as resolute “lobbyists.” Finally, by avoiding averages over preimposed states, we show that within each global oscillatory state rich switching dynamics can take place between a repertoire of many available network states. We thus show that the temporal network framework provides a natural and effective language to rigorously describe the rich spatiotemporal patterns of information sharing instantiated by cell assembly evolution.

Multimodal in vivo recording using transparent graphene microelectrodes illuminates spatiotemporal seizure dynamics at the microscale

Neurological disorders such as epilepsy arise from disrupted brain networks. Our capacity to treat these disorders is limited by our inability to map these networks at sufficient temporal and spatial scales to target interventions. Current best techniques either sample broad areas at low temporal resolution (e.g. calcium imaging) or record from discrete regions at high temporal resolution (e.g. electrophysiology). This limitation hampers our ability to understand and intervene in aberrations of network dynamics. Here we present a technique to map the onset and spatiotemporal spread of acute epileptic seizures in vivo by simultaneously recording high bandwidth microelectrocorticography and calcium fluorescence using transparent graphene microelectrode arrays. We integrate dynamic data features from both modalities using non-negative matrix factorization to identify sequential spatiotemporal patterns of seizure onset and evolution, revealing how the temporal progression of ictal electrophysiology is linked to the spatial evolution of the recruited seizure core. This integrated analysis of multimodal data reveals otherwise hidden state transitions in the spatial and temporal progression of acute seizures. The techniques demonstrated here may enable future targeted therapeutic interventions and novel spatially embedded models of local circuit dynamics during seizure onset and evolution.

Hippocampal adult-born granule cells drive network activity in a mouse model of chronic temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is characterized by recurrent seizures driven by synchronous neuronal activity. The reorganization of the dentate gyrus (DG) in TLE may create pathological conduction pathways for synchronous discharges in the temporal lobe, though critical microcircuit-level detail is missing from this pathophysiological intuition. In particular, the relative contribution of adult-born (abGC) and mature (mGC) granule cells to epileptiform network events remains unknown. We assess dynamics of abGCs and mGCs during interictal epileptiform discharges (IEDs) in mice with TLE as well as sharp-wave ripples (SPW-Rs) in healthy mice, and find that abGCs and mGCs are desynchronized and differentially recruited by IEDs compared to SPW-Rs. We introduce a neural topic model to explain these observations, and find that epileptic DG networks organize into disjoint, cell-type specific pathological ensembles in which abGCs play an outsized role. Our results characterize identified GC subpopulation dynamics in TLE, and reveal a specific contribution of abGCs to IEDs.

Spontaneous Activity of Neuronal Ensembles in Mouse Motor Cortex: Changes after GABAergic Blockade

The mouse motor cortex exhibits spontaneous activity in the form of temporal sequences of neuronal ensembles in vitro without the need of tissue stimulation. These neuronal ensembles are defined as groups of neurons with a strong correlation between its firing patterns, generating what appears to be a predetermined neural conduction mode that needs study. Each ensemble is commonly accompanied by one or more parvalbumin expressing neurons (PV+) or fast spiking interneurons. Many of these interneurons have functional connections between them, helping to form a circuit configuration similar to a small-world network. However, rich club metrics show that most connected neurons are neurons not expressing parvalbumin, mainly pyramidal neurons (PV-) suggesting feed-forward propagation through pyramidal cells. Ensembles with PV+ neurons are connected to these hubs. When ligand-gated fast GABAergic transmission is blocked, temporal sequences of ensembles collapse into a unique synchronous and recurrent ensemble, showing the need of inhibition for coding cortical spontaneous activity. This new ensemble has a duration and electrophysiological characteristics of brief recurrent interictal epileptiform discharges (IEDs) composed by the coactivity of both PV- and PV+ neurons, demonstrating that GABA transmission impedes its occurrence. Synchronous ensembles are clearly divided into two clusters one of them lasting longer and mainly composed by PV+ neurons. Because an ictal-like event was not recorded after several minutes of IEDs recording, it is inferred that an external stimulus and/or fast GABA transmission are necessary for its appearance, making this preparation ideal to study both the neuronal machinery to encode cortical spontaneous activity and its transformation into brief non-ictal epileptiform discharges.

Functional connectivity of the irritative zone identified by electrical source imaging, and EEG-correlated fMRI analyses

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The irritative zone differs on Electrical Source Imaging (ESI) and EEG-fMRI.

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Findings differ in functional connectivity and show low temporal correlation.

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ESI and EEG-fMRI reveal distinct aspects of the irritative zone.

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The differences may reflect differences in temporal resolution of the techniques.

Objective

The irritative zone - the area generating epileptic spikes - can be studied non-invasively during the interictal period using Electrical Source Imaging (ESI) and simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI). Although the techniques yield results which may overlap spatially, differences in spatial localization of the irritative zone within the same patient are consistently observed. To investigate this discrepancy, we used Blood Oxygenation Level Dependent (BOLD) functional connectivity measures to examine the underlying relationship between ESI and EEG-fMRI findings.

Methods

Fifteen patients (age 20–54), who underwent presurgical epilepsy investigation, were scanned using a single-session resting-state EEG-fMRI protocol. Structural MRI was used to obtain the electrode localisation of a high-density 64-channel EEG cap. Electrical generators of interictal epileptiform discharges were obtained using a distributed local autoregressive average (LAURA) algorithm as implemented in Cartool EEG software. BOLD activations were obtained using both spike-related and voltage-map EEG-fMRI analysis. The global maxima of each method were used to investigate the temporal relationship of BOLD time courses and to assess the spatial similarity using the Dice similarity index between functional connectivity maps.

Results

ESI, voltage-map and spike-related EEG-fMRI methods identified peaks in 15 (100%), 13 (67%) and 8 (53%) of the 15 patients, respectively. For all methods, maxima were localised within the same lobe, but differed in sub-lobar localisation, with a median distance of 22.8 mm between the highest peak for each method. The functional connectivity analysis showed that the temporal correlation between maxima only explained 38% of the variance between the time course of the BOLD response at the maxima. The mean Dice similarity index between seed-voxel functional connectivity maps showed poor spatial agreement.

Significance

Non-invasive methods for the localisation of the irritative zone have distinct spatial and temporal sensitivity to different aspects of the local cortical network involved in the generation of interictal epileptiform discharges.

Detection of Specific Templates in Calcium Spiking in HeLa Cells Using Hierarchical DBSCAN: Clustering and Visualization of CellDrug Interaction at Multiple Doses

One of the major challenges in analyzing large scale intracellular calcium spiking data obtained through fluorescent imaging is to identify various patterns present in time series data. Such an analysis identifying the distinct frequency and amplitude encoding during cell-drug interaction study is expected to provide new insights into the drug action patterns over a time course. Here, we present the HDBSCAN clustering algorithm to find a clustering pattern present in calcium spiking obtained by confocal imaging of single cells. Our methodology uncovers the specific templates present in dynamic responses obtained through treatment with multiple doses of the drug. First, we attempt to visualize the clustering pattern present in time-series data corresponding to various doses of the drug. Secondly, we show that the HDBSCAN can be used for the detection of specific signatures corresponding to low and high cell density regions selected from in vitro experiments. To the best of our knowledge, this is the first attempt to optimize the clustering of calcium dynamics using HDBSCAN. Finally, we emphasize that HDBSCAN offers a high-level grasp on systems biology data, including complex spiking pattern and can be used as a visual analytic tool for drug dose selection.

Introduction to Focus Issue: Symmetry and optimization in the synchronization and collective behavior of complex systems

Synchronization phenomena and collective behavior are commonplace in complex systems with applications ranging from biological processes such as coordinated neuron firings and cell cycles to the stability of alternating current power grids. A fundamental pursuit is the study of how various types of symmetry-e.g., as manifest in network structure or coupling dynamics-impact a system's collective behavior. Understanding the intricate relations between structural and dynamical symmetry/asymmetry also provides new paths to develop strategies that enhance or inhibit synchronization. Previous research has revealed symmetry as a key factor in identifying optimization mechanisms, but the particular ways that symmetry/asymmetry influence collective behavior can generally depend on the type of dynamics, networks, and form of synchronization (e.g., phase synchronization, group synchronization, and chimera states). Other factors, such as time delay, noise, time-varying structure, multilayer connections, basin stability, and transient dynamics, also play important roles, and many of these remain underexplored. This Focus Issue brings together a survey of theoretical and applied research articles that push forward this important line of questioning.

Organizing principles of whole-brain functional connectivity in zebrafish larvae

Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized, including geometric constraints, a balance between segregative and integrative features, and functionally flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we analyze whole-brain functional networks reconstructed from calcium imaging data recorded in larval zebrafish. Our analyses reveal that functional connections are distance-dependent and that networks exhibit hierarchical modular structure and hubs that span module boundaries. We go on to show that spontaneous network structure places constraints on stimulus-evoked reconfigurations of connections and that networks are highly consistent across individuals. Our analyses reveal basic organizing principles of whole-brain functional brain networks at the mesoscale. Our overarching methodological framework provides a blueprint for studying correlated activity at the cellular level using a low-dimensional network representation. Our work forms a conceptual bridge between macro- and mesoscale network neuroscience and opens myriad paths for future studies to investigate network structure of nervous systems at the cellular level.

Quantitative properties of a feedback circuit predict frequency-dependent pattern separation

Feedback inhibitory motifs are thought to be important for pattern separation across species. How feedback circuits may implement pattern separation of biologically plausible, temporally structured input in mammals is, however, poorly understood. We have quantitatively determined key properties of netfeedback inhibition in the mouse dentate gyrus, a region critically involved in pattern separation. Feedback inhibition is recruited steeply with a low dynamic range (0% to 4% of active GCs), and with a non-uniform spatial profile. Additionally, net feedback inhibition shows frequency-dependent facilitation, driven by strongly facilitating mossy fiber inputs. Computational analyses show a significant contribution of the feedback circuit to pattern separation of theta modulated inputs, even within individual theta cycles. Moreover, pattern separation was selectively boosted at gamma frequencies, in particular for highly similar inputs. This effect was highly robust, suggesting that frequency-dependent pattern separation is a key feature of the feedback inhibitory microcircuit.

You can probably recall where you left your car this morning without too much trouble. But assuming you use the same busy parking lot every day, can you remember which space you parked in yesterday? Or the day before that? Most people find this difficult not because they cannot remember what happened two or three days ago, but because it requires distinguishing between very similar memories. The car, the parking lot, and the time of day were the same on each occasion. So how do you remember where you parked this morning?

This ability to distinguish between memories of similar events depends on a brain region called the hippocampus. A subregion of the hippocampus called the dentate gyrus generates different patterns of activity in response to events that are similar but distinct. This process is called pattern separation, and it helps ensure that you do not look for your car in yesterday’s parking space.

Pattern separation in the dentate gyrus is thought to involve a form of negative feedback called feedback inhibition, a phenomenon where the output of a process acts to limit or stop the same process. To test this idea, Braganza et al. studied feedback inhibition in the dentate gyrus of mice, before building a computer model simulating the inhibition process and supplying the model with two types of realistic input. The first consisted of low-frequency theta brainwaves, which occur, for instance, in the dentate gyrus when animals explore their environment. The second consisted of higher frequency gamma brainwaves, which occur, for example, when animals experience something new.

Testing the model showed that feedback inhibition contributes to pattern separation with both theta and gamma inputs. However, pattern separation is stronger with gamma input. This suggests that high frequency brainwaves in the hippocampus could help animals distinguish new events from old ones by promoting pattern separation.

Various brain disorders, including Alzheimer’s disease, schizophrenia and epilepsy, involve changes in the dentate gyrus and altered brain rhythms. The current findings could help reveal how these changes contribute to memory impairments and to a reduced ability to distinguish similar experiences.

Emerging roles of network analysis for epilepsy

In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals.

Stability of spontaneous, correlated activity in mouse auditory cortex

Neural systems can be modeled as complex networks in which neural elements are represented as nodes linked to one another through structural or functional connections. The resulting network can be analyzed using mathematical tools from network science and graph theory to quantify the system’s topological organization and to better understand its function. Here, we used two-photon calcium imaging to record spontaneous activity from the same set of cells in mouse auditory cortex over the course of several weeks. We reconstruct functional networks in which cells are linked to one another by edges weighted according to the correlation of their fluorescence traces. We show that the networks exhibit modular structure across multiple topological scales and that these multi-scale modules unfold as part of a hierarchy. We also show that, on average, network architecture becomes increasingly dissimilar over time, with similarity decaying monotonically with the distance (in time) between sessions. Finally, we show that a small fraction of cells maintain strongly-correlated activity over multiple days, forming a stable temporal core surrounded by a fluctuating and variable periphery. Our work indicates a framework for studying spontaneous activity measured by two-photon calcium imaging using computational methods and graphical models from network science. The methods are flexible and easily extended to additional datasets, opening the possibility of studying cellular level network organization of neural systems and how that organization is modulated by stimuli or altered in models of disease.

Neurons coordinate their activity with one another, forming networks that help support adaptive, flexible behavior. Still, little is known about the organization of these networks at the cellular scale and their stability over time. Here, we reconstruct networks from calcium imaging data recorded in mouse primary auditory cortex. We show that these networks exhibit spatially constrained, hierarchical modular structure, which may facilitate specialized information processing. However, we show that connection weights and modular structure are also variable over time, changing on a timescale of days and adopting novel network configurations. Despite this, a small subset of neurons maintain their connections to one another and preserve their modular organization across time, forming a stable temporal core surrounded by a flexible periphery. These findings represent a conceptual bridge linking network analyses of macroscale and cellular-level neuroimaging data. They also represent a complementary approach to existing circuits- and systems-based interrogation of nervous system function, opening the door for deeper and more targeted analysis in the future.

Acute Focal Seizures Start As Local Synchronizations of Neuronal Ensembles

Understanding seizure formation and spread remains a critical goal of epilepsy research. We used fast in vivo two-photon calcium imaging in male mouse neocortex to reconstruct, with single-cell resolution, the dynamics of acute (4-aminopyridine) focal cortical seizures as they originate within a spatially confined seizure initiation site (intrafocal region), and subsequently propagate into neighboring cortical areas (extrafocal region). We find that seizures originate as local neuronal ensembles within the initiation site. This abnormal hyperactivity engages increasingly larger areas in a saltatory fashion until it breaks into neighboring cortex, where it proceeds smoothly and is then detected electrophysiologically (LFP). Interestingly, PV inhibitory interneurons have spatially heterogeneous activity in intrafocal and extrafocal territories, ruling out a simple role of inhibition in seizure formation and spread. We propose a two-step model for the progression of focal seizures, where neuronal ensembles activate first, generating a microseizure, followed by widespread neural activation in a traveling wave through neighboring cortex during macroseizures.SIGNIFICANCE STATEMENT We have used calcium imaging in mouse sensory cortex in vivo to reconstruct the onset of focal seizures elicited by local injection of the chemoconvulsant 4-aminopyridine. We demonstrate at cellular resolution that acute focal seizures originate as increasingly synchronized local neuronal ensembles. Because of its spatial confinement, this process may at first be undetectable even by nearby LFP electrodes. Further, we establish spatial footprints of local neural subtype activity that correspond to consecutive steps of seizure microprogression. Such footprints could facilitate determining the recording location (e.g., inside/outside an epileptogenic focus) in high-resolution studies, even in the absence of a priori knowledge about where exactly a seizure started.

Presence of synchrony-generating hubs in the human epileptic neocortex

KEY POINTS

•Initiation of pathological synchronous events such as epileptic spikes and seizures is linked to the hyperexcitability of the neuronal network in both humans and animals. •In the present study, we show that epileptiform interictal-like spikes and seizures emerged in human neocortical slices by blocking GABA_A receptors, following the disappearance of the spontaneously occurring synchronous population activity. •Large variability of temporally and spatially simple and complex spikes was generated by tissue from epileptic patients, whereas only simple events appeared in samples from non-epileptic patients. •Physiological population activity was associated with a moderate level of principal cell and interneuron firing, with a slight dominance of excitatory neuronal activity, whereas epileptiform events were mainly initiated by the synchronous and intense discharge of inhibitory cells. •These results help us to understand the role of excitatory and inhibitory neurons in synchrony-generating mechanisms, in both epileptic and non-epileptic conditions.

ABSTRACT

Understanding the role of different neuron types in synchrony generation is crucial for developing new therapies aiming to prevent hypersynchronous events such as epileptic seizures. Paroxysmal activity was linked to hyperexcitability and to bursting behaviour of pyramidal cells in animals. Human data suggested a leading role of either principal cells or interneurons, depending on the seizure morphology. In the present study, we aimed to uncover the role of excitatory and inhibitory processes in synchrony generation by analysing the activity of clustered single neurons during physiological and epileptiform synchronies in human neocortical slices. Spontaneous population activity was detected with a 24-channel laminar microelectrode in tissue derived from patients with or without preoperative clinical manifestations of epilepsy. This population activity disappeared by blocking GABA_A receptors, and several variations of spatially and temporally simple or complex interictal-like spikes emerged in epileptic tissue, whereas peritumoural slices generated only simple spikes. Around one-half of the clustered neurons participated with an elevated firing rate in physiological synchronies with a slight dominance of excitatory cells. By contrast, more than 90% of the neurons contributed to interictal-like spikes and seizures, and an intense and synchronous discharge of inhibitory neurons was associated with the start of these events. Intrinsically bursting principal cells fired later than other neurons. Our data suggest that a balanced excitation and inhibition characterized physiological synchronies, whereas disinhibition-induced epileptiform events were initiated mainly by non-synaptically synchronized inhibitory neurons. Our results further highlight the differences between humans and animal models, and between in vivo and (pharmacologically manipulated) in vitro conditions.

White Matter Network Architecture Guides Direct Electrical Stimulation through Optimal State Transitions

Optimizing direct electrical stimulation for the treatment of neurological disease remains difficult due to an incomplete understanding of its physical propagation through brain tissue. Here, we use network control theory to predict how stimulation spreads through white matter to influence spatially distributed dynamics. We test the theory's predictions using a unique dataset comprising diffusion weighted imaging and electrocorticography in epilepsy patients undergoing grid stimulation. We find statistically significant shared variance between the predicted activity state transitions and the observed activity state transitions. We then use an optimal control framework to posit testable hypotheses regarding which brain states and structural properties will efficiently improve memory encoding when stimulated. Our work quantifies the role that white matter architecture plays in guiding the dynamics of direct electrical stimulation and offers empirical support for the utility of network control theory in explaining the brain's response to stimulation.

Reduced Repertoire of Cortical Microstates and Neuronal Ensembles in Medically Induced Loss of Consciousness

Medically induced loss of consciousness (mLOC) during anesthesia is associated with a macroscale breakdown of brain connectivity, yet the neural microcircuit correlates of mLOC remain unknown. To explore this, we applied different analytical approaches (t-SNE/watershed segmentation, affinity propagation clustering, PCA, and LZW complexity) to two-photon calcium imaging of neocortical and hippocampal microcircuit activity and local field potential (LFP) measurements across different anesthetic depths in mice, and to micro-electrode array recordings in human subjects. We find that in both cases, mLOC disrupts population activity patterns by generating (1) fewer discriminable network microstates and (2) fewer neuronal ensembles. Our results indicate that local neuronal ensemble dynamics could causally contribute to the emergence of conscious states.

Resolving the Micro-Macro Disconnect to Address Core Features of Seizure Networks

Current drug treatments for epilepsy attempt to broadly restrict excitability to mask a symptom, seizures, with little regard for the heterogeneous mechanisms that underlie disease manifestation across individuals. Here, we discuss the need for a more complete view of epilepsy, outlining how key features at the cellular and microcircuit level can significantly impact disease mechanisms that are not captured by the most common methodology to study epilepsy, electroencephalography (EEG). We highlight how major advances in neuroscience tool development now enable multi-scale investigation of fundamental questions to resolve the currently controversial understanding of seizure networks. These findings will provide essential insight into what has emerged as a disconnect between the different levels of investigation and identify new targets and treatment options.

Network Properties Revealed during Multi-Scale Calcium Imaging of Seizure Activity in Zebrafish

Seizures are characterized by hypersynchronization of neuronal networks. Understanding these networks could provide a critical window for therapeutic control of recurrent seizure activity, i.e., epilepsy. However, imaging seizure networks has largely been limited to microcircuits in vitro or small “windows” in vivo. Here, we combine fast confocal imaging of genetically encoded calcium indicator (GCaMP)-expressing larval zebrafish with local field potential (LFP) recordings to study epileptiform events at whole-brain and single-neuron levels in vivo. Using an acute seizure model (pentylenetetrazole, PTZ), we reliably observed recurrent electrographic ictal-like events associated with generalized activation of all major brain regions and uncovered a well-preserved anterior-to-posterior seizure propagation pattern. We also examined brain-wide network synchronization and spatiotemporal patterns of neuronal activity in the optic tectum microcircuit. Brain-wide and single-neuronal level analysis of PTZ-exposed and 4-aminopyridine (4-AP)-exposed zebrafish revealed distinct network dynamics associated with seizure and non-seizure hyperexcitable states, respectively. Neuronal ensembles, comprised of coactive neurons, were also uncovered during interictal-like periods. Taken together, these results demonstrate that macro- and micro-network calcium motifs in zebrafish may provide a greater understanding of epilepsy.

On the Influence of Structural Connectivity on the Correlation Patterns and Network Synchronization

Since brain structural connectivity is the foundation of its functionality, in order to understand brain abilities, studying the relation between structural and functional connectivity is essential. Several approaches have been applied to measure the role of the structural connectivity in the emergent correlation/synchronization patterns. In this study, we investigates the cross-correlation and synchronization sensitivity to coupling strength between neural regions for different topological networks. We model the neural populations by a neural mass model that express an oscillatory dynamic. The results highlight that coupling between neural ensembles leads to various cross-correlation patterns and local synchrony even on an ordered network. Moreover, as the network departs from an ordered organization to a small-world architecture, correlation patterns, and synchronization dynamics change. Interestingly, at a certain range of the synaptic strength, by fixing the structural conditions, different organized patterns are seen at the different input signals. This variety switches to a bifurcation region by increasing the synaptic strength. We show that topological variations is a major factor of synchronization behavior and lead to alterations in correlated local clusters. We found the coupling strength (between cortical areas) to be especially important at conversions of correlation and synchronization states. Since correlation patterns generate functional connections and transitions of functional connectivity have been related to cognitive operations, these diverse correlation patterns may be considered as different dynamical states corresponding to various cognitive tasks.

Sensory coding is impaired in rat absence epilepsy

KEY POINTS

Absence epilepsy is characterized by the occurrence of spike-and-wave discharges concomitant with an alteration of consciousness and is associated with cognitive comorbidities. In a genetic model of absence epilepsy in the rat, the genetic absence epilepsy rat from Strasbourg (GAERS), spike-and-wave discharges are shown to be initiated in the barrel field primary somatosensory cortex that codes whisker-related information, therefore playing an essential role in the interactions of rodents with their environment. Sensory-information processing is impaired in the epileptic barrel field primary somatosensory cortex of GAERS, with a delayed sensory-evoked potential and a duplicated neuronal response to whisker stimulation in in vivo extracellular recordings. Yet, GAERS present no defaults of performance in a texture discrimination task, suggesting the existence of a compensatory mechanism within the epileptic neuronal network. The results of the present study indicate that physiological primary functions are processed differently in an epileptic cortical network.

ABSTRACT

Several neurodevelopmental pathologies are associated with disorganized cortical circuits that may alter primary functions such as sensory processes. In the present study, we investigated whether the function of a cortical area is altered in the seizure onset zone of absence epilepsy, a prototypical form of childhood genetic epilepsy associated with cognitive impairments. We first combined in vivo multichannel electrophysiological recordings and histology to precisely localize the seizure onset zone in the genetic absence epilepsy rat from Strasbourg (GAERS). We then investigated the functionality of this epileptic zone using extracellular silicon probe recordings of sensory-evoked local field potentials and multi-unit activity, as well as a behavioural test of texture discrimination. We show that seizures in this model are initiated in the barrel field part of the primary somatosensory cortex and are associated with high-frequency oscillations. In this cortex, we found an increased density of parvalbumin-expressing interneurons in layer 5 in GAERS compared to non-epileptic Wistar rats. Its functional investigation revealed that sensory abilities of GAERS are not affected in a texture-discrimination task, whereas the intracortical processing of sensory-evoked information is delayed and duplicated. Altogether, these results suggest that absence seizures are associated with an increase of parvalbumin-inhibitory neurons, which may promote the functional relationship between epileptic oscillations and high-frequency activities. Our findings suggest that cortical circuits operate differently in the epileptic onset zone and may adapt to maintain their ability to process highly specialized information.

Confocal Imaging and k-Means Clustering of GABAB and mGluR Mediated Modulation of Ca2+ Spiking in Hippocampal Neurons

Imaging cytosolic calcium in neurons is emerging as a new tool in neurological disease diagnosis, drug screening, and toxicity testing. Ca²⁺ oscillation signatures show a significant variation depending on GPCR targeting agonists. Quantification of Ca²⁺ spike trains in ligand induced Ca²⁺ oscillations remains challenging due to their inherent heterogeneity in primary culture. Moreover, there is no framework available for identification of optimal number of clusters and distance metric to cluster Ca²⁺ spike trains. Using quantitative confocal imaging and clustering analysis, we show the characterization of Ca²⁺ spiking in GPCR targeting drug-treated primary culture of hippocampal neurons. A systematic framework for selection of the clustering method instead of an intuition-based method was used to optimize the cluster number and distance metric. The results discern neurons with diverse Ca²⁺ response patterns, including higher amplitude fast spiking and lower spiking responses, and their relative percentage in a neuron population in absence and presence of GPCR-targeted drugs. The proposed framework was employed to show that the  clustering pattern of Ca²⁺ spiking can be controlled using GABA_B and mGluR targeting drugs. This approach can be used for unbiased measurement of neural activity and identification of spiking population with varying amplitude and frequencies, providing a platform for high-content drug screening.

Smart Data Analytics approach to model Complex Biochemical Oscillations in Hippocampal Neurons

Calcium spiking can be used for drug screening studies in pharmaceutical industries. However, performing experiments for multiple drugs and doses are highly expensive. The oscillatory behavior of calcium spiking data demonstrates extreme nonlinearity and phase singularity. This makes it more challenging to construct physics-based models for the experimental observations. In this scenario, data based modelling, such as Artificial Neural Networks (ANN), and thereafter the model based prediction of calcium profiles may offer a cost-effective and time saving solution. Therefore, a novel ANN building algorithm is presented in the current work, where data based simultaneous estimation of ANN architecture and nonlinear activation function stands out as the main highlight. The resultant ANN was then used to learn the oscillatory behavior in calcium ion concentration data, obtained from hippocampal neurons of rats by fluorescent labelling and confocal imaging. The paper shows that the novel technique can be used in general for emulating biochemical oscillations (with or without drug injection) and can be implemented to predict the cell-drug responses for intermediated doses. The proposed algorithm can also be used for obtaining high resolution data from low resolution experimental measurements.