Large-scale, high-resolution electrophysiological imaging of field potentials in brain slices with microelectronic multielectrode arrays

Low glycemic index diet restrains epileptogenesis in a gender-specific fashion

Dietary restriction, such as low glycemic index diet (LGID), have been successfully used to treat drug-resistant epilepsy. However, if such diet could also counteract antiepileptogenesis is still unclear. Here, we investigated whether the administration of LGID during the latent pre-epileptic period, prevents or delays the appearance of the overt epileptic phenotype. To this aim, we used the Synapsin II knockout (SynIIKO) mouse, a model of temporal lobe epilepsy in which seizures manifest 2-3 months after birth, offering a temporal window in which LGID may affect epileptogenesis. Pregnant SynIIKO mice were fed with either LGID or standard diet during gestation and lactation. Both diets were maintained in weaned mice up to 5 months of age. LGID delayed the seizure onset and induced a reduction of seizures severity only in female SynIIKO mice. In parallel with the epileptic phenotype, high-density multielectrode array recordings revealed a reduction of frequency, amplitude, duration, velocity of propagation and spread of interictal events by LGID in the hippocampus of SynIIKO females, but not mutant males, confirming the gender-specific effect. ELISA-based analysis revealed that LGID increased cortico-hippocampal allopregnanolone (ALLO) levels only in females, while it was unable to affect ALLO plasma concentrations in either sex. The results indicate that the gender-specific interference of LGID with the epileptogenic process can be ascribed to a gender-specific increase in cortical ALLO, a neurosteroid known to strengthen GABAergic transmission. The study highlights the possibility of developing a personalized gender-based therapy for temporal lobe epilepsy.

Trametinib activates endogenous neurogenesis and recovers neuropathology in a model of Alzheimer's disease

Enhancing adult neurogenesis in the brain has been suggested as a potential therapeutic strategy for AD. We developed a screening platform, ATRIVIEW®, for molecules that activate neuronal differentiation of adult mouse NSCs. The most potent hit from an FDA-approved drug library was SNR1611 (trametinib), a selective MEK1/2 inhibitor. We found that trametinib increases the levels of P15INK4b and Neurog2, suggesting a mechanism by which MEK1/2 inhibition induces neuronal differentiation. Oral administration of trametinib increased adult neurogenesis in the dentate gyrus and subventricular zone of the 5XFAD AD mouse model. Surprisingly, we also found that trametinib enhanced adult neurogenesis in the cortex. Consequently, trametinib rescued AD pathologies such as neuronal loss and cognitive impairment in 5XFAD mice. Finally, trametinib induced neurogenic differentiation of NSCs derived from AD patient iPSCs, which suggests its potential therapeutic application. Altogether, we suggest that restoration of endogenous adult neurogenesis by trametinib may be a promising therapeutic approach to AD.

A deep generative adversarial network capturing complex spiral waves in disinhibited circuits of the cerebral cortex

BACKGROUND

In the cerebral cortex, disinhibited activity is characterized by propagating waves that spread across neural tissue. In this pathological state, a widely reported form of activity are spiral waves that travel in a circular pattern around a fixed spatial locus termed the center of mass. Spiral waves exhibit stereotypical activity and involve broad patterns of co-fluctuations, suggesting that they may be of lower complexity than healthy activity.

RESULTS

To evaluate this hypothesis, we performed dense multi-electrode recordings of cortical networks where disinhibition was induced by perfusing a pro-epileptiform solution containing 4-Aminopyridine as well as increased potassium and decreased magnesium. Spiral waves were identified based on a spatially delimited center of mass and a broad distribution of instantaneous phases across electrodes. Individual waves were decomposed into "snapshots" that captured instantaneous neural activation across the entire network. The complexity of these snapshots was examined using a measure termed the participation ratio. Contrary to our expectations, an eigenspectrum analysis of these snapshots revealed a broad distribution of eigenvalues and an increase in complexity compared to baseline networks. A deep generative adversarial network was trained to generate novel exemplars of snapshots that closely captured cortical spiral waves. These synthetic waves replicated key features of experimental data including a tight center of mass, a broad eigenvalue distribution, spatially-dependent correlations, and a high complexity. By adjusting the input to the model, new samples were generated that deviated in systematic ways from the experimental data, thus allowing the exploration of a broad range of states from healthy to pathologically disinhibited neural networks.

CONCLUSIONS

Together, results show that the complexity of population activity serves as a marker along a continuum from healthy to disinhibited brain states. The proposed generative adversarial network opens avenues for replicating the dynamics of cortical seizures and accelerating the design of optimal neurostimulation aimed at suppressing pathological brain activity.

A Neurospheroid-Based Microrobot for Targeted Neural Connections in a Hippocampal Slice

Functional restoration by the re-establishment of cellular or neural connections remains a major challenge in targeted cell therapy and regenerative medicine. Recent advances in magnetically powered microrobots have shown potential for use in controlled and targeted cell therapy. In this study, a magnetic neurospheroid (Mag-Neurobot) that can form both structural and functional connections with an organotypic hippocampal slice (OHS) is assessed using an ex vivo model as a bridge toward in vivo application. The Mag-Neurobot consists of hippocampal neurons and superparamagnetic nanoparticles (SPIONs); it is precisely and skillfully manipulated by an external magnetic field. Furthermore, the results of patch-clamp recordings of hippocampal neurons indicate that neither the neuronal excitabilities nor the synaptic functions of SPION-loaded cells are significantly affected. Analysis of neural activity propagation using high-density multi-electrode arrays shows that the delivered Mag-Neurobot is functionally connected with the OHS. The applications of this study include functional verification for targeted cell delivery through the characterization of novel synaptic connections and the functionalities of transported and transplanted cells. The success of the Mag-Neurobot opens up new avenues of research and application; it offers a test platform for functional neural connections and neural regenerative processes through cell transplantation.

Functional neuronal circuitry and oscillatory dynamics in human brain organoids

Human brain organoids replicate much of the cellular diversity and developmental anatomy of the human brain. However, the physiology of neuronal circuits within organoids remains under-explored. With high-density CMOS microelectrode arrays and shank electrodes, we captured spontaneous extracellular activity from brain organoids derived from human induced pluripotent stem cells. We inferred functional connectivity from spike timing, revealing a large number of weak connections within a skeleton of significantly fewer strong connections. A benzodiazepine increased the uniformity of firing patterns and decreased the relative fraction of weakly connected edges. Our analysis of the local field potential demonstrate that brain organoids contain neuronal assemblies of sufficient size and functional connectivity to co-activate and generate field potentials from their collective transmembrane currents that phase-lock to spiking activity. These results point to the potential of brain organoids for the study of neuropsychiatric diseases, drug action, and the effects of external stimuli upon neuronal networks.

Xenon LFP Analysis Platform Is a Novel Graphical User Interface for Analysis of Local Field Potential From Large-Scale MEA Recordings

High-density multi-electrode array (HD-MEA) has enabled neuronal measurements at high spatial resolution to record local field potentials (LFP), extracellular action potentials, and network-wide extracellular recording on an extended spatial scale. While we have advanced recording systems with over 4,000 electrodes capable of recording data at over 20 kHz, it still presents computational challenges to handle, process, extract, and view information from these large recordings. We have created a computational method, and an open-source toolkit built in Python, rendered on a web browser using Plotly’s Dash for extracting and viewing the data and creating interactive visualization. In addition to extracting and viewing entire or small chunks of data sampled at lower or higher frequencies, respectively, it provides a framework to collect user inputs, analyze channel groups, generate raster plots, view quick summary measures for LFP activity, detect and isolate noise channels, and generate plots and visualization in both time and frequency domain. Incorporated into our Graphical User Interface (GUI), we also created a novel seizure detection method, which can be used to detect the onset of seizures in all or a selected group of channels and provide the following measures of seizures: distance, duration, and propagation across the region of interest. We demonstrate the utility of this toolkit, using datasets collected from an HD-MEA device comprising of 4,096 recording electrodes. For the current analysis, we demonstrate the toolkit and methods with a low sampling frequency dataset (300 Hz) and a group of approximately 400 channels. Using this toolkit, we present novel data demonstrating increased seizure propagation speed from brain slices of Scn1aHet mice compared to littermate controls. While there have been advances in HD-MEA recording systems with high spatial and temporal resolution, limited tools are available for researchers to view and process these big datasets. We now provide a user-friendly toolkit to analyze LFP activity obtained from large-scale MEA recordings with translatable applications to EEG recordings and demonstrate the utility of this new graphic user interface with novel biological findings.

Classification of antiseizure drugs in cultured neuronal networks using multielectrode arrays and unsupervised learning

OBJECTIVE

Antiseizure drugs (ASDs) modulate synaptic and ion channel function to prevent abnormal hypersynchronous or excitatory activity arising in neuronal networks, but the relationship between ASDs with respect to their impact on network activity is poorly defined. In this study, we first investigated whether different ASD classes exert differential impact upon network activity, and we then sought to classify ASDs according to their impact on network activity.

METHODS

We used multielectrode arrays (MEAs) to record the network activity of cultured cortical neurons after applying ASDs from two classes: sodium channel blockers (SCBs) and γ-aminobutyric acid type A receptor-positive allosteric modulators (GABA PAMs). A two-dimensional representation of changes in network features was then derived, and the ability of this low-dimensional representation to classify ASDs with different molecular targets was assessed.

RESULTS

A two-dimensional representation of network features revealed a separation between the SCB and GABA PAM drug classes, and could classify several test compounds known to act through these molecular targets. Interestingly, several ASDs with novel targets, such as cannabidiol and retigabine, had closer similarity to the SCB class with respect to their impact upon network activity.

SIGNIFICANCE

These results demonstrate that the molecular target of two common classes of ASDs is reflected through characteristic changes in network activity of cultured neurons. Furthermore, a low-dimensional representation of network features can be used to infer an ASDs molecular target. This approach may allow for drug screening to be performed based on features extracted from MEA recordings.

Discovering Microcircuit Secrets With Multi-Spot Imaging and Electrophysiological Recordings: The Example of Cerebellar Network Dynamics

The cerebellar cortex microcircuit is characterized by a highly ordered neuronal architecture having a relatively simple and stereotyped connectivity pattern. For a long time, this structural simplicity has incorrectly led to the idea that anatomical considerations would be sufficient to understand the dynamics of the underlying circuitry. However, recent experimental evidence indicates that cerebellar operations are much more complex than solely predicted by anatomy, due to the crucial role played by neuronal and synaptic properties. To be able to explore neuronal and microcircuit dynamics, advanced imaging, electrophysiological techniques and computational models have been combined, allowing us to investigate neuronal ensembles activity and to connect microscale to mesoscale phenomena. Here, we review what is known about cerebellar network organization, neural dynamics and synaptic plasticity and point out what is still missing and would require experimental assessments. We consider the available experimental techniques that allow a comprehensive assessment of circuit dynamics, including voltage and calcium imaging and extracellular electrophysiological recordings with multi-electrode arrays (MEAs). These techniques are proving essential to investigate the spatiotemporal pattern of activity and plasticity in the cerebellar network, providing new clues on how circuit dynamics contribute to motor control and higher cognitive functions.

Arhgap22 Disruption Leads to RAC1 Hyperactivity Affecting Hippocampal Glutamatergic Synapses and Cognition in Mice

Rho GTPases are a class of G-proteins involved in several aspects of cellular biology, including the regulation of actin cytoskeleton. The most studied members of this family are RHOA and RAC1 that act in concert to regulate actin dynamics. Recently, Rho GTPases gained much attention as synaptic regulators in the mammalian central nervous system (CNS). In this context, ARHGAP22 protein has been previously shown to specifically inhibit RAC1 activity thus standing as critical cytoskeleton regulator in cancer cell models; however, whether this function is maintained in neurons in the CNS is unknown. Here, we generated a knockout animal model for arhgap22 and provided evidence of its role in the hippocampus. Specifically, we found that ARHGAP22 absence leads to RAC1 hyperactivity and to an increase in dendritic spine density with defects in synaptic structure, molecular composition, and plasticity. Furthermore, arhgap22 silencing causes impairment in cognition and a reduction in anxiety-like behavior in mice. We also found that inhibiting RAC1 restored synaptic plasticity in ARHGAP22 KO mice. All together, these results shed light on the specific role of ARHGAP22 in hippocampal excitatory synapse formation and function as well as in learning and memory behaviors.

The Flow of Axonal Information Among Hippocampal Subregions: 1. Feed-Forward and Feedback Network Spatial Dynamics Underpinning Emergent Information Processing

The tri-synaptic pathway in the mammalian hippocampus enables cognitive learning and memory. Despite decades of reports on anatomy and physiology, the functional architecture of the hippocampal network remains poorly understood in terms of the dynamics of axonal information transfer between subregions. Information inputs largely flow from the entorhinal cortex (EC) to the dentate gyrus (DG), and then are processed further in the CA3 and CA1 before returning to the EC. Here, we reconstructed elements of the rat hippocampus in a novel device over an electrode array that allowed for monitoring the directionality of individual axons between the subregions. The direction of spike propagation was determined by the transmission delay of the axons recorded between two electrodes in microfluidic tunnels. The majority of axons from the EC to the DG operated in the feed-forward direction, with other regions developing unexpectedly large proportions of feedback axons to balance excitation. Spike timing in axons between each region followed single exponential log-log distributions over two orders of magnitude from 0.01 to 1 s, indicating that conventional descriptors of mean firing rates are misleading assumptions. Most of the spiking occurred in bursts that required two exponentials to fit the distribution of inter-burst intervals. This suggested the presence of up-states and down-states in every region, with the least up-states in the DG to CA3 feed-forward axons and the CA3 subregion. The peaks of the log-normal distributions of intra-burst spike rates were similar in axons between regions with modes around 95 Hz distributed over an order of magnitude. Burst durations were also log-normally distributed around a peak of 88 ms over two orders of magnitude. Despite the diversity of these spike distributions, spike rates from individual axons were often linearly correlated to subregions. These linear relationships enabled the generation of structural connectivity graphs, not possible previously without the directional flow of axonal information. The rich axonal spike dynamics between subregions of the hippocampus reveal both constraints and broad emergent dynamics of hippocampal architecture. Knowledge of this network architecture may enable more efficient computational artificial intelligence (AI) networks, neuromorphic hardware, and stimulation and decoding from cognitive implants.

Wireless and battery-free platforms for collection of biosignals

Recent progress in biosensors have quantitively expanded current capabilities in exploratory research tools, diagnostics and therapeutics. This rapid pace in sensor development has been accentuated by vast improvements in data analysis methods in the form of machine learning and artificial intelligence that, together, promise fantastic opportunities in chronic sensing of biosignals to enable preventative screening, automated diagnosis, and tools for personalized treatment strategies. At the same time, the importance of widely accessible personal monitoring has become evident by recent events such as the COVID-19 pandemic. Progress in fully integrated and chronic sensing solutions is therefore increasingly important. Chronic operation, however, is not truly possible with tethered approaches or bulky, battery-powered systems that require frequent user interaction. A solution for this integration challenge is offered by wireless and battery-free platforms that enable continuous collection of biosignals. This review summarizes current approaches to realize such device architectures and discusses their building blocks. Specifically, power supplies, wireless communication methods and compatible sensing modalities in the context of most prevalent implementations in target organ systems. Additionally, we highlight examples of current embodiments that quantitively expand sensing capabilities because of their use of wireless and battery-free architectures.

Linear Response of General Observables in Spiking Neuronal Network Models

We establish a general linear response relation for spiking neuronal networks, based on chains with unbounded memory. This relation allow us to predict the influence of a weak amplitude time dependent external stimuli on spatio-temporal spike correlations, from the spontaneous statistics (without stimulus) in a general context where the memory in spike dynamics can extend arbitrarily far in the past. Using this approach, we show how the linear response is explicitly related to the collective effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike train statistics. We illustrate our results with numerical simulations performed over a discrete time integrate and fire model.

Electrical Imaging of Light-Induced Signals Across and Within Retinal Layers

The mammalian retina processes sensory signals through two major pathways: a vertical excitatory pathway, which involves photoreceptors, bipolar cells, and ganglion cells, and a horizontal inhibitory pathway, which involves horizontal cells, and amacrine cells. This concept explains the generation of an excitatory center—inhibitory surround sensory receptive fields—but fails to explain the modulation of the retinal output by stimuli outside the receptive field. Electrical imaging of light-induced signal propagation at high spatial and temporal resolution across and within different retinal layers might reveal mechanisms and circuits involved in the remote modulation of the retinal output. Here we took advantage of a high-density complementary metal oxide semiconductor-based microelectrode array and investigated the light-induced propagation of local field potentials (LFPs) in vertical mouse retina slices. Surprisingly, the LFP propagation within the different retinal layers depends on stimulus duration and stimulus background. Application of the same spatially restricted light stimuli to flat-mounted retina induced ganglion cell activity at remote distances from the stimulus center. This effect disappeared if a global background was provided or if gap junctions were blocked. We hereby present a neurotechnological approach and demonstrated its application, in which electrical imaging evaluates stimulus-dependent signal processing across different neural layers.

Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Current labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescent indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended time scales. We report on a dual-mode high-density microelectrode array, which can simultaneously record in (i) full-frame mode with 19,584 recording sites and (ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we develop reliable analysis tools, which drastically increase the throughput to infer axonal morphology and conduction speed.

Current methods of neuronal network imaging cannot be used for continuous, long-term functional recordings. Here, the authors present a dual-mode high-density microelectrode array, which can simultaneously record in full-frame and high-signal-to-noise modes for label-free electrophysiological measurements.

CHIME: CMOS-Hosted in vivo Microelectrodes for Massively Scalable Neuronal Recordings

Mammalian brains consist of 10s of millions to 100s of billions of neurons operating at millisecond time scales, of which current recording techniques only capture a tiny fraction. Recording techniques capable of sampling neural activity at high spatiotemporal resolution have been difficult to scale. The most intensively studied mammalian neuronal networks, such as the neocortex, show a layered architecture, where the optimal recording technology samples densely over large areas. However, the need for application-specific designs as well as the mismatch between the three-dimensional architecture of the brain and largely two-dimensional microfabrication techniques profoundly limits both neurophysiological research and neural prosthetics. Here, we discuss a novel strategy for scalable neuronal recording by combining bundles of glass-ensheathed microwires with large-scale amplifier arrays derived from high-density CMOS in vitro MEA systems or high-speed infrared cameras. High signal-to-noise ratio (<25 μV RMS noise floor, SNR up to 25) is achieved due to the high conductivity of core metals in glass-ensheathed microwires allowing for ultrathin metal cores (down to <1 μm) and negligible stray capacitance. Multi-step electrochemical modification of the tip enables ultra-low access impedance with minimal geometric area, which is largely independent of the core diameter. We show that the microwire size can be reduced to virtually eliminate damage to the blood-brain-barrier upon insertion and we demonstrate that microwire arrays can stably record single-unit activity. Combining microwire bundles and CMOS arrays allows for a highly scalable neuronal recording approach, linking the progress in electrical neuronal recordings to the rapid progress in silicon microfabrication. The modular design of the system allows for custom arrangement of recording sites. Our approach of employing bundles of minimally invasive, highly insulated and functionalized microwires to extend a two-dimensional CMOS architecture into the 3rd dimension can be translated to other CMOS arrays, such as electrical stimulation devices.

Application of spike sorting algorithm to neuronal signals originated from boron doped diamond micro-electrode arrays

In this work we report on the implementation of methods for data processing signals from microelectrode arrays (MEA) and the application of these methods for signals originated from two types of MEAs to detect putative neurons and sort them into subpopulations. We recorded electrical signals from firing neurons using titanium nitride (TiN) and boron doped diamond (BDD) MEAs. In previous research, we have shown that these methods have the capacity to detect neurons using commercially-available TiN-MEAs. We have managed to cultivate and record hippocampal neurons for the first time using a newly developed custom-made multichannel BDD-MEA with 20 recording sites. We have analysed the signals with the algorithms developed and employed them to inspect firing bursts and enable spike sorting. We did not observe any significant difference between BDD- and TiN-MEAs over the parameters, which estimated spike shape variability per each detected neuron. This result supports the hypothesis that we have detected real neurons, rather than noise, in the BDD-MEA signal. BDD materials with suitable mechanical, electrical and biocompatibility properties have a large potential in novel therapies for treatments of neural pathologies, such as deep brain stimulation in Parkinson's disease.

Human Cerebrospinal Fluid Induces Neuronal Excitability Changes in Resected Human Neocortical and Hippocampal Brain Slices

Human cerebrospinal fluid (hCSF) has proven advantageous over conventional medium for culturing both rodent and human brain tissue. In addition, increased activity and synchrony, closer to the dynamic states exclusively recorded in vivo, were reported in rodent slices and cell cultures switching from artificial cerebrospinal fluid (aCSF) to hCSF. This indicates that hCSF possesses properties that are not matched by the aCSF, which is generally used for most electrophysiological recordings. To evaluate the possible significance of using hCSF as an electrophysiological recording medium, also for human brain tissue, we compared the network and single-cell firing properties of human brain slice cultures during perfusion with hCSF and aCSF. For measuring the overall activity from a majority of neurons within neocortical and hippocampal human slices, we used a microelectrode array (MEA) recording technique with 252 electrodes covering an area of 3.2 × 3.2 mm2. A second CMOS-based MEA with 4225 sensors on a 2 × 2 mm2 area was used for detailed mapping of action potential waveforms and cell identification. We found that hCSF increased the number of active electrodes and neurons and the firing rate of the neurons in the slices and induced an increase in the numbers of single channel and population bursts. Interestingly, not only an increase in the overall activity in the slices was observed, but a reconfiguration of the network could also be detected with specific activation and inactivation of subpopulations of neuronal ensembles. In conclusion, hCSF is an important component to consider for future human brain slice studies, especially for experiments designed to mimic parts of physiology and disease observed in vivo.

Ultrasensitive System for Electrophysiology of Cancer Cell Populations: A Review

Bioelectricity is the electrical activity produced by living organisms. Understanding the role of bioelectricity in a disease context is important as it contributes to both disease diagnosis and therapeutic intervention. Electrophysiology tools work well for neuronal cultures; however, they are limited in their ability to detect the electrical activity of non-neuronal cells, wherein the majority of cancers arise. Electronic structures capable of detecting and modulating signaling, in real-time, in electrically quiescent cells are urgently required. One of the limitations to understanding the role of bioelectricity in cancer is the inability to detect low-level signals. In this study, we review our latest advances in devising bidirectional transducers with large electrode areas and concomitant low impedances. The resulting high sensitivity is demonstrated by the extracellular detection of electrical activity in Rat-C6 glioma and prostate cancer (PC-3) cell populations. By using specific inhibitors, we further demonstrated that the large electrical activity in Rat-C6 glioma populations is acidosis driven. For PC-3 cells, the use of a calcium inhibitor together with the slowly varying nature of the signal suggests that Ca²⁺ channels are involved in the cohort electrogenicity.

Large-Scale, High-Resolution Microelectrode Arrays for Interrogation of Neurons and Networks

High-density microelectrode arrays (HD-MEAs) are increasingly being used for the observation and manipulation of neurons and networks in vitro. Large-scale electrode arrays allow for long-term extracellular recording of the electrical activity from thousands of neurons simultaneously. Beyond population activity, it has also become possible to extract information of single neurons at subcellular level (e.g., the propagation of action potentials along axons). In effect, HD-MEAs have become an electrical imaging platform for label-free extraction of the structure and activation of cells in cultures and tissues. The quality of HD-MEA data depends on the resolution of the electrode array and the signal-to-noise ratio. In this chapter, we begin with an introduction to HD-MEA signals. We provide an overview of the developments on complementary metal-oxide-semiconductor or CMOS-based HD-MEA technology. We also discuss the factors affecting the performance of HD-MEAs and the trending application requirements that drive the efforts for future devices. We conclude with an outlook on the potential of HD-MEAs for advancing basic neuroscience and drug discovery.

Epilepsy-on-a-Chip System for Antiepileptic Drug Discovery

OBJECTIVE

Hippocampal slice cultures spontaneously develop chronic epilepsy several days after slicing and are used as an in vitro model of post-traumatic epilepsy. Here, we describe a hybrid microfluidic-microelectrode array (μflow-MEA) technology that incorporates a microfluidic perfusion network and electrodes into a miniaturized device for hippocampal slice culture based antiepileptic drug discovery.

METHODS

Field potential simulation was conducted to help optimize the electrode design to detect a seizure-like population activity. Epilepsy-on-a-chip model was validated by chronic electrical recording, neuronal survival quantification, and anticonvulsant test. To demonstrate the application of μflow-MEA in drug discovery, we utilized a two-stage screening platform to identify potential targets for antiepileptic drugs. In Stage I, lactate and lactate dehydrogenase biomarker assays were performed to identify potential drug candidates. In Stage II, candidate compounds were retested with μflow-MEA-based chronic electrical assay to provide electrophysiological confirmation of biomarker results.

RESULTS AND CONCLUSION

We screened 12 receptor tyrosine kinases inhibitors, and EGFR/ErbB-2 and cFMS inhibitors were identified as novel antiepileptic compounds.

SIGNIFICANCE

This epilepsy-on-a-chip system provides the means for rapid dissection of complex signaling pathways in epileptogenesis, paving the way for high-throughput antiepileptic drug discovery.

Technologies to Study Action Potential Propagation With a Focus on HD-MEAs

Axons convey information in neuronal circuits via reliable conduction of action potentials (APs) from the axon initial segment (AIS) to the presynaptic terminals. Recent experimental findings increasingly evidence that the axonal function is not limited to the simple transmission of APs. Advances in subcellular-resolution recording techniques have shown that axons display activity-dependent modulation in spike shape and conduction velocity, which influence synaptic strength and latency. We briefly review here, how recent methodological developments facilitate the understanding of the axon physiology. We included the three most common methods, i.e., genetically encoded voltage imaging (GEVI), subcellular patch-clamp and high-density microelectrode arrays (HD-MEAs). We then describe the potential of using HD-MEAs in studying axonal physiology in more detail. Due to their robustness, amenability to high-throughput and high spatiotemporal resolution, HD-MEAs can provide a direct functional electrical readout of single cells and cellular ensembles at subcellular resolution. HD-MEAs can, therefore, be employed in investigating axonal pathologies, the effects of large-scale genomic interventions (e.g., with RNAi or CRISPR) or in compound screenings. A combination of extracellular microelectrode arrays (MEAs), intracellular microelectrodes and optical imaging may potentially reveal yet unexplored repertoires of axonal functions.

Fabrication of Multielectrode Arrays for Neurobiology Applications

Substrate-integrated multielectrode arrays (MEAs) enable multisite, long-term, and label-free sensing and actuation of neuronal electrical signals in reduced cell culture models for network electrophysiology. Conventional, thin-film fabricated passive MEAs typically provide a few tens of electrode sites. New generations of active CMOS-based high-resolution arrays provide the capabilities of simultaneous recordings from thousands of neurons over fields of view of several square millimeters, yet allowing extracellular electrical imaging to be achieved down to the subcellular scale. In turn, such advancement in chip-based electrical readouts can significantly complement recently developed biotechnological and bimolecular techniques for neurobiology applications. Here, we describe (1) a simple method to fabricate passive MEAs and (2) protocols for preparing and growing primary rat hippocampal neuronal cultures and human iPS-derived neurons on MEAs. The aim is to provide reliable protocols for initiating the reader to this technology and for stimulating their further development and experimental use in neurobiology.

Accurate signal-source localization in brain slices by means of high-density microelectrode arrays

Extracellular recordings by means of high-density microelectrode arrays (HD-MEAs) have become a powerful tool to resolve subcellular details of single neurons in active networks grown from dissociated cells. To extend the application of this technology to slice preparations, we developed models describing how extracellular signals, produced by neuronal cells in slices, are detected by microelectrode arrays. The models help to analyze and understand the electrical-potential landscape in an in vitro HD-MEA-recording scenario based on point-current sources. We employed two modeling schemes, (i) a simple analytical approach, based on the method of images (MoI), and (ii) an approach, based on finite-element methods (FEM). We compared and validated the models with large-scale, high-spatiotemporal-resolution recordings of slice preparations by means of HD-MEAs. We then developed a model-based localization algorithm and compared the performance of MoI and FEM models. Both models provided accurate localization results and a comparable and negligible systematic error, when the point source was in saline, a condition similar to cell-culture experiments. Moreover, the relative random error in the x-y-z-localization amounted only up to 4.3% for z-distances up to 200 μm from the HD-MEA surface. In tissue, the systematic errors of both, MoI and FEM models were significantly higher, and a pre-calibration was required. Nevertheless, the FEM values proved to be closer to the tissue experimental results, yielding 5.2 μm systematic mean error, compared to 22.0 μm obtained with MoI. These results suggest that the medium volume or "saline height", the brain slice thickness and anisotropy, and the location of the reference electrode, which were included in the FEM model, considerably affect the extracellular signal and localization performance, when the signal source is at larger distance to the array. After pre-calibration, the relative random error of the z-localization in tissue was only 3% for z-distances up to 200 μm. We then applied the model and related detailed understanding of extracellular recordings to achieve an electrically-guided navigation of a stimulating micropipette, solely based on the measured HD-MEA signals, and managed to target spontaneously active neurons in an acute brain slice for electroporation.

Brain Somatic Mutations in Epileptic Disorders

During the cortical development, cells in the brain acquire somatic mutations that can be implicated in various neurodevelopmental disorders. There is increasing evidence that brain somatic mutations lead to sporadic form of epileptic disorders with previously unknown etiology. In particular, malformation of cortical developments (MCD), ganglioglioma (GG) associated with intractable epilepsy and non-lesional focal epilepsy (NLFE) are known to be attributable to brain somatic mutations in mTOR pathway genes and others. In order to identify such somatic mutations presenting as low-level in epileptic brain tissues, the mutated cells should be enriched and sequenced with high-depth coverage. Nevertheless, there are a lot of technical limitations to accurately detect low-level of somatic mutations. Also, it is important to validate whether identified somatic mutations are truly causative for epileptic seizures or not. Furthermore, it will be necessary to understand the molecular mechanism of how brain somatic mutations disturb neuronal circuitry since epilepsy is a typical example of neural network disorder. In this review, we overview current genetic techniques and experimental tools in neuroscience that can address the existence and significance of brain somatic mutations in epileptic disorders as well as their effect on neuronal circuitry.

SiNAPS: An implantable active pixel sensor CMOS-probe for simultaneous large-scale neural recordings

Large-scale neural recordings with high spatial and temporal accuracy are instrumental to understand how the brain works. To this end, it is of key importance to develop probes that can be conveniently scaled up to a high number of recording channels. Despite recent achievements in complementary metal-oxide semiconductor (CMOS) multi-electrode arrays probes, in current circuit architectures an increase in the number of simultaneously recording channels would significantly increase the total chip area. A promising approach for overcoming this scaling issue consists in the use of the modular Active Pixel Sensor (APS) concept, in which a small front-end circuit is located beneath each electrode. However, this approach imposes challenging constraints on the area of the in-pixel circuit, power consumption and noise. Here, we present an APS CMOS-probe technology for Simultaneous Neural recording that successfully addresses all these issues for whole-array read-outs at 25 kHz/channel from up to 1024 electrode-pixels. To assess the circuit performances, we realized in a 0.18 μm CMOS technology an implantable single-shaft probe with a regular array of 512 electrode-pixels with a pitch of 28 μm. Extensive bench tests showed an in-pixel gain of 45.4 ± 0.4 dB (low pass, F_-3 dB = 4 kHz), an input referred noise of 7.5 ± 0.67 μV_RMS (300 Hz to 7.5 kHz) and a power consumption <6 μW/pixel. In vivo acute recordings demonstrate that our SiNAPS CMOS-probe can sample full-band bioelectrical signals from each electrode, with the ability to resolve and discriminate activity from several packed neurons both at the spatial and temporal scale. These results pave the way to new generations of compact and scalable active single/multi-shaft brain recording systems.

Early Appearance and Spread of Fast Ripples in the Hippocampus in a Model of Cortical Traumatic Brain Injury

Fast ripples (FRs; activity of >250 Hz) have been considered as a biomarker of epileptic activity in the hippocampus and entorhinal cortex; it is thought that they signal the focus of seizure generation. Similar high-frequency network activity has been produced in vitro by changing extracellular medium composition, by using pro-epileptic substances, or by electrical stimulation. Here we study the propagation of these events between different subregions of the male rat hippocampus in a recently introduced experimental model of FRs in entorhinal cortex-hippocampal slices in vitro By using a matrix of 4096 microelectrodes, the sites of initiation, propagation pathways, and spatiotemporal characteristics of activity patterns could be studied with unprecedented high resolution. To this end, we developed an analytic tool based on bidimensional current source density estimation, which delimits sinks and sources with a high precision and evaluates their trajectories using the concept of center of mass. With this methodology, we found that FRs can arise almost simultaneously at noncontiguous sites in the CA3-to-CA1 direction, underlying the spatial heterogeneity of epileptogenic foci, while continuous somatodendritic waves of activity develop. An unexpected, yet important propagation route is the propagation of activity from CA3 into the hilus and dentate gyrus. This pathway may cause reverberating activation of both regions, supporting sustained pathological network events and altered information processing in hippocampal networks.SIGNIFICANCE STATEMENT Fast ripples (FRs) have been considered as a biomarker of epileptic activity and may signal the focus of seizure generation. In a model of traumatic brain injury in the rat, FRs appear in the hippocampus within a couple of hours after an extrahippocampal, cortical lesion. We analyzed the origin and dynamics of the FRs in the hippocampus using massive electrophysiological recordings, allowing an unprecedented high spatiotemporal resolution. We show that FRs originate in distinct and noncontiguous locations within the CA3 region and uncover, with high precision, the extent and dynamics of their current density. This activity propagates toward CA1 but also backpropagates to the hilus and the dentate gyrus, suggesting activation of defined microcircuits that can sustain recurrent excitation.

Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains

We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. To find the maximum entropy Markov chain, we use the thermodynamic formalism, which provides insightful connections with statistical physics and thermodynamics from which large deviations properties arise naturally. We provide an accessible introduction to the maximum entropy Markov chain inference problem and large deviations theory to the community of computational neuroscience, avoiding some technicalities while preserving the core ideas and intuitions. We review large deviations techniques useful in spike train statistics to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability, and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.

High-performance, inexpensive setup for simultaneous multisite recording of electrophysiological signals and mesoscale voltage imaging in the mouse cortex

Simultaneous recording of optical and electrophysiological signals from multiple cortical areas may provide crucial information to expand our understanding of cortical function. However, the insertion of multiple electrodes into the brain may compromise optical imaging by both restricting the field of view and interfering with the approaches used to stabilize the specimen. Existing methods that combine electrophysiological recording and optical imaging in vivo implement either multiple surface electrodes, silicon probes, or a single electrode for deeper recordings. To address such limitation, we built a microelectrode array (hyperdrive, patent US5928143 A) compatible with wide-field imaging that allows insertion of up to 12 probes into a large brain area (8 mm diameter). The hyperdrive is comprised of a circle of individual microdrives where probes are positioned at an angle leaving a large brain area unobstructed for wide-field imaging. Multiple tetrodes and voltage-sensitive dye imaging were used for acute simultaneous registration of spontaneous and evoked cortical activity in anesthetized mice. The electrophysiological signals were used to extract local field potential (LFP) traces, multiunit, and single-unit spiking activity. To demonstrate our approach, we compared LFP and VSD signals over multiple regions of the cortex and analyzed the relationship between single-unit and global cortical population activities. The study of the interactions between cortical activity at local and global scales, such as the one presented in this work, can help to expand our knowledge of brain function.

Image-guided recording system for spatial and temporal mapping of neuronal activities in brain slice

In this study, we introduce the novel image-guided recording system (IGRS) for efficient interpretation of neuronal activities in the brain slice. IGRS is designed to combine microelectrode array (MEA) and optical coherence tomography at the customized upright microscope. It allows to record multi-site neuronal signals and image of the volumetric brain anatomy in a single body configuration. For convenient interconnection between a brain image and neuronal signals, we developed the automatic mapping protocol that enables us to project acquired neuronal signals on a brain image. To evaluate the performance of IGRS, hippocampal signals of the brain slice were monitored, and corresponding with two-dimensional neuronal maps were successfully reconstructed. Our results indicated that IGRS and mapping protocol can provide the intuitive information regarding long-term and multi-sites neuronal signals. In particular, the temporal and spatial mapping capability of neuronal signals would be a very promising tool to observe and analyze the massive neuronal activity and connectivity in MEA-based electrophysiological studies.

Revealing the distribution of transmembrane currents along the dendritic tree of a neuron from extracellular recordings

Revealing the current source distribution along the neuronal membrane is a key step on the way to understanding neural computations; however, the experimental and theoretical tools to achieve sufficient spatiotemporal resolution for the estimation remain to be established. Here, we address this problem using extracellularly recorded potentials with arbitrarily distributed electrodes for a neuron of known morphology. We use simulations of models with varying complexity to validate the proposed method and to give recommendations for experimental applications. The method is applied to in vitro data from rat hippocampus.

PRANAS: A New Platform for Retinal Analysis and Simulation

The retina encodes visual scenes by trains of action potentials that are sent to the brain via the optic nerve. In this paper, we describe a new free access user-end software allowing to better understand this coding. It is called PRANAS (https://pranas.inria.fr), standing for Platform for Retinal ANalysis And Simulation. PRANAS targets neuroscientists and modelers by providing a unique set of retina-related tools. PRANAS integrates a retina simulator allowing large scale simulations while keeping a strong biological plausibility and a toolbox for the analysis of spike train population statistics. The statistical method (entropy maximization under constraints) takes into account both spatial and temporal correlations as constraints, allowing to analyze the effects of memory on statistics. PRANAS also integrates a tool computing and representing in 3D (time-space) receptive fields. All these tools are accessible through a friendly graphical user interface. The most CPU-costly of them have been implemented to run in parallel.

Inflammatory cytokine-induced changes in neural network activity measured by waveform analysis of high-content calcium imaging in murine cortical neurons

During acute neuroinflammation, increased levels of cytokines within the brain may contribute to synaptic reorganization that results in long-term changes in network hyperexcitability. Indeed, inflammatory cytokines are implicated in synaptic dysfunction in epilepsy and in an array of degenerative and autoimmune diseases of the central nervous system. Current tools for studying the impact of inflammatory factors on neural networks are either insufficiently fast and sensitive or require complicated and costly experimental rigs. Calcium imaging offers a reasonable surrogate for direct measurement of neuronal network activity, but traditional imaging paradigms are confounded by cellular heterogeneity and cannot readily distinguish between glial and neuronal calcium transients. While the establishment of pure neuron cultures is possible, the removal of glial cells ignores physiologically relevant cell-cell interactions that may be critical for circuit level disruptions induced by inflammatory factors. To overcome these issues, we provide techniques and algorithms for image processing and waveform feature extraction using automated analysis of spontaneous and evoked calcium transients in primary murine cortical neuron cultures transduced with an adeno-associated viral vector driving the GCaMP6f reporter behind a synapsin promoter. Using this system, we provide evidence of network perturbations induced by the inflammatory cytokines TNFα, IL1β, and IFNγ.

Intracellular and Extracellular Recording of Spontaneous Action Potentials in Mammalian Neurons and Cardiac Cells with 3D Plasmonic Nanoelectrodes

Three-dimensional vertical micro- and nanostructures can enhance the signal quality of multielectrode arrays and promise to become the prime methodology for the investigation of large networks of electrogenic cells. So far, access to the intracellular environment has been obtained via spontaneous poration, electroporation, or by surface functionalization of the micro/nanostructures; however, these methods still suffer from some limitations due to their intrinsic characteristics that limit their widespread use. Here, we demonstrate the ability to continuously record both extracellular and intracellular-like action potentials at each electrode site in spontaneously active mammalian neurons and HL-1 cardiac-derived cells via the combination of vertical nanoelectrodes with plasmonic optoporation. We demonstrate long-term and stable recordings with a very good signal-to-noise ratio. Additionally, plasmonic optoporation does not perturb the spontaneous electrical activity; it permits continuous recording even during the poration process and can regulate extracellular and intracellular contributions by means of partial cellular poration.

Direct Interfacing of Neurons to Highly Integrated Microsystems

The use of large high-density transducer arrays enables fundamentally new neuroscientific insights through enabling high-throughput monitoring of action potentials of larger neuronal networks (> 1000 neurons) over extended time to see effects of disturbances or developmental effects, and through facilitating detailed investigations of neuronal signaling characteristics at subcellular level, for example, the study of axonal signal propagation that has been largely inaccessible to established methods. Applications include research in neural diseases and pharmacology.

Trends and Challenges in Neuroengineering: Toward "Intelligent" Neuroprostheses through Brain-"Brain Inspired Systems" Communication

Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace "intelligent" neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes.

Seizure Prediction: Science Fiction or Soon to Become Reality?

This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.

A Device for Long-Term Perfusion, Imaging, and Electrical Interfacing of Brain Tissue In vitro

Distributed microelectrode array (MEA) recordings from consistent, viable, ≥500 μm thick tissue preparations over time periods from days to weeks may aid in studying a wide range of problems in neurobiology that require in vivo-like organotypic morphology. Existing tools for electrically interfacing with organotypic slices do not address necrosis that inevitably occurs within thick slices with limited diffusion of nutrients and gas, and limited removal of waste. We developed an integrated device that enables long-term maintenance of thick, functionally active, brain tissue models using interstitial perfusion and distributed recordings from thick sections of explanted tissue on a perforated multi-electrode array. This novel device allows for automated culturing, in situ imaging, and extracellular multi-electrode interfacing with brain slices, 3-D cell cultures, and potentially other tissue culture models. The device is economical, easy to assemble, and integrable with standard electrophysiology tools. We found that convective perfusion through the culture thickness provided a functional benefit to the preparations as firing rates were generally higher in perfused cultures compared to their respective unperfused controls. This work is a step toward the development of integrated tools for days-long experiments with more consistent, healthier, thicker, and functionally more active tissue cultures with built-in distributed electrophysiological recording and stimulation functionality. The results may be useful for the study of normal processes, pathological conditions, and drug screening strategies currently hindered by the limitations of acute (a few hours long) brain slice preparations.

Aberrant Activity in Degenerated Retinas Revealed by Electrical Imaging

In this review, I present and discuss the current understanding of aberrant electrical activity found in the ganglion cell layer (GCL) of rod-degenerated (rd) mouse retinas. The reported electrophysiological properties revealed by electrical imaging using high-density microelectrode arrays can be subdivided between spiking activity originating from retinal ganglion cells (RGCs) and local field potentials (LFPs) reflecting strong trans-membrane currents within the GCL. RGCs in rd retinas show increased and rhythmic spiking compared to age-matched wild-type retinas. Fundamental spiking frequencies range from 5 to 15 Hz in various mouse models. The rhythmic RGC spiking is driven by a presynaptic network comprising AII amacrine and bipolar cells. In the healthy retina this rhythm-generating circuit is inhibited by photoreceptor input. A unique physiological feature of rd retinas is rhythmic LFP manifested as spatially-restricted low-frequency (5-15 Hz) voltage changes. Their spatiotemporal characterization revealed propagation and correlation with RGC spiking. LFPs rely on gap-junctional coupling and are shaped by glycinergic and by GABAergic transmission. The aberrant RGC spiking and LFPs provide a simple readout of the functionality of the remaining retinal circuitry which can be used in the development of improved vision restoration strategies.

Highly scalable parallel processing of extracellular recordings of Multielectrode Arrays

Technological advances of Multielectrode Arrays (MEAs) used for multisite, parallel electrophysiological recordings, lead to an ever increasing amount of raw data being generated. Arrays with hundreds up to a few thousands of electrodes are slowly seeing widespread use and the expectation is that more sophisticated arrays will become available in the near future. In order to process the large data volumes resulting from MEA recordings there is a pressing need for new software tools able to process many data channels in parallel. Here we present a new tool for processing MEA data recordings that makes use of new programming paradigms and recent technology developments to unleash the power of modern highly parallel hardware, such as multi-core CPUs with vector instruction sets or GPGPUs. Our tool builds on and complements existing MEA data analysis packages. It shows high scalability and can be used to speed up some performance critical pre-processing steps such as data filtering and spike detection, helping to make the analysis of larger data sets tractable.

Nanomaterial mediated optogenetics: opportunities and challenges

This review elaborates on the possible applications of nanomaterials in optogenetics and analyses the benefits of nanomaterial-mediated optogenetics.

Spatiotemporal norepinephrine mapping using a high-density CMOS microelectrode array

A high-density amperometric electrode array containing 8192 individually addressable platinum working electrodes with an integrated potentiostat fabricated using Complementary Metal Oxide Semiconductor (CMOS) processes is reported. The array was designed to enable electrochemical imaging of chemical gradients with high spatiotemporal resolution. Electrodes are arranged over a 2 mm × 2 mm surface area into 64 subarrays consisting of 128 individual Pt working electrodes as well as Pt pseudo-reference and auxiliary electrodes. Amperometric measurements of norepinephrine in tissue culture media were used to demonstrate the ability of the array to measure concentration gradients in complex media. Poly(dimethylsiloxane) microfluidics were incorporated to control the chemical concentrations in time and space, and the electrochemical response at each electrode was monitored to generate electrochemical heat maps, demonstrating the array's imaging capabilities. A temporal resolution of 10 ms can be achieved by simultaneously monitoring a single subarray of 128 electrodes. The entire 2 mm × 2 mm area can be electrochemically imaged in 64 seconds by cycling through all subarrays at a rate of 1 Hz per subarray. Monitoring diffusional transport of norepinephrine is used to demonstrate the spatiotemporal resolution capabilities of the system.

A direct-to-drive neural data acquisition system

Driven by the increasing channel count of neural probes, there is much effort being directed to creating increasingly scalable electrophysiology data acquisition (DAQ) systems. However, all such systems still rely on personal computers for data storage, and thus are limited by the bandwidth and cost of the computers, especially as the scale of recording increases. Here we present a novel architecture in which a digital processor receives data from an analog-to-digital converter, and writes that data directly to hard drives, without the need for a personal computer to serve as an intermediary in the DAQ process. This minimalist architecture may support exceptionally high data throughput, without incurring costs to support unnecessary hardware and overhead associated with personal computers, thus facilitating scaling of electrophysiological recording in the future.

Electrographic waveform structure predicts laminar focus location in a model of temporal lobe seizures in vitro

Temporal lobe epilepsy is the most common form of partial-onset epilepsy and accounts for the majority of adult epilepsy cases in most countries. A critical role for the hippocampus (and to some extent amygdala) in the pathology of these epilepsies is clear, with selective removal of these regions almost as effective as temporal lobectomy in reducing subsequent seizure risk. However, there is debate about whether hippocampus is 'victim' or 'perpetrator': The structure is ideally placed to 'broadcast' epileptiform activity to a great many other brain regions, but removal often leaves epileptiform events still occurring in cortex, particularly in adjacent areas, and recruitment of the hippocampus into seizure-like activity has been shown to be difficult in clinically-relevant models. Using a very simple model of acute epileptiform activity with known, single primary pathology (GABAA Receptor partial blockade), we track the onset and propagation of epileptiform events in hippocampus, parahippocampal areas and neocortex. In this model the hippocampus acts as a potential seizure focus for the majority of observed events. Events with hippocampal focus were far more readily propagated throughout parahippocampal areas and into neocortex than vice versa. The electrographic signature of events of hippocampal origin was significantly different to those of primary neocortical origin - a consequence of differential laminar activation. These data confirm the critical role of the hippocampus in epileptiform activity generation in the temporal lobe and suggest the morphology of non-invasive electrical recording of neocortical interictal events may be useful in confirming this role.

Revealing neuronal function through microelectrode array recordings

Microelectrode arrays and microprobes have been widely utilized to measure neuronal activity, both in vitro and in vivo. The key advantage is the capability to record and stimulate neurons at multiple sites simultaneously. However, unlike the single-cell or single-channel resolution of intracellular recording, microelectrodes detect signals from all possible sources around every sensor. Here, we review the current understanding of microelectrode signals and the techniques for analyzing them. We introduce the ongoing advancements in microelectrode technology, with focus on achieving higher resolution and quality of recordings by means of monolithic integration with on-chip circuitry. We show how recent advanced microelectrode array measurement methods facilitate the understanding of single neurons as well as network function.

Asynchronous GABA Release Is a Key Determinant of Tonic Inhibition and Controls Neuronal Excitability: A Study in the Synapsin II-/- Mouse

Idiopathic epilepsies have frequently been linked to mutations in voltage-gated channels (channelopathies); recently, mutations in several genes encoding presynaptic proteins have been shown to cause epilepsy in humans and mice, indicating that epilepsy can also be considered a synaptopathy. However, the functional mechanisms by which presynaptic dysfunctions lead to hyperexcitability and seizures are not well understood. We show that deletion of synapsin II (Syn II), a presynaptic protein contributing to epilepsy predisposition in humans, leads to a loss of tonic inhibition in mouse hippocampal slices due to a dramatic decrease in presynaptic asynchronous GABA release. We also show that the asynchronous GABA release reduces postsynaptic cell firing, and the parallel impairment of asynchronous GABA release and tonic inhibition results in an increased excitability at both single-neuron and network levels. Restoring tonic inhibition with THIP (4,5,6,7-tetrahydroisoxazolo[5,4-c]pyridin-3-ol; gaboxadol), a selective agonist of δ subunit-containing GABA_A receptors, fully rescues the SynII^−/− epileptic phenotype both ex vivo and in vivo. The results demonstrate a causal relationship between the dynamics of GABA release and the generation of tonic inhibition, and identify a novel mechanism of epileptogenesis generated by dysfunctions in the dynamics of release that can be effectively targeted by novel antiepileptic strategies.