High-density Microelectrode Arrays Research Articles

Functional specialization and distributed processing across marmoset lateral prefrontal subregions.

A prominent aspect of primate lateral prefrontal cortex organization is its division into several cytoarchitecturally distinct subregions. Neurophysiological investigations in macaques have provided evidence for the functional specialization of these subregions, but an understanding of the relative representational topography of sensory, social, and cognitive processes within them remains elusive. One explanatory factor is that evidence for functional specialization has been compiled largely from a patchwork of findings across studies, in many animals, and with considerable variation in stimulus sets and tasks. Here, we addressed this by leveraging the common marmoset (Callithrix jacchus) to carry out large-scale neurophysiological mapping of the lateral prefrontal cortex using high-density microelectrode arrays, and a diverse suite of test stimuli including faces, marmoset calls, and spatial working memory task. Task-modulated units and units responsive to visual and auditory stimuli were distributed throughout the lateral prefrontal cortex, while those with saccade-related activity or face-selective responses were restricted to 8aV, 8aD, 10, 46V, and 47. Neurons with contralateral visual receptive fields were limited to areas 8aV and 8aD. These data reveal a mixed pattern of functional specialization in the lateral prefrontal cortex, in which responses to some stimuli and tasks are distributed broadly across lateral prefrontal cortex subregions, while others are more limited in their representation.

Hyperexcitability and translational phenotypes in a preclinical mouse model of SYNGAP1-related intellectual disability

Disruption of SYNGAP1 directly causes a genetically identifiable neurodevelopmental disorder (NDD) called SYNGAP1-related intellectual disability (SRID). Without functional SynGAP1 protein, individuals are developmentally delayed and have prominent features of intellectual disability (ID), motor impairments, and epilepsy. Over the past two decades, there have been numerous discoveries indicating the critical role of Syngap1. Several rodent models with a loss of Syngap1 have been engineered, identifying precise roles in neuronal structure and function, as well as key biochemical pathways key for synapse integrity. Homozygous loss of SYNGAP1/Syngap1 is lethal. Heterozygous mutations of Syngap1 result in a broad range of behavioral phenotypes. Our in vivo functional data, using the original mouse model from the Huganir laboratory, corroborated behaviors including robust hyperactivity and deficits in learning and memory in young adults. Furthermore, we described impairments in the domain of sleep, characterized using neurophysiological data that was collected with wireless, telemetric electroencephalography (EEG). Syngap1+/− mice exhibited elevated spiking events and spike trains, in addition to elevated power, most notably in the delta power frequency. For the first time, we illustrated that primary neurons from Syngap1+/− mice displayed: 1) increased network firing activity, 2) greater bursts, 3) and shorter inter-burst intervals between peaks, by utilizing high density microelectrode arrays (HD-MEA). Our work bridges in vitro electrophysiological neuronal activity and function with in vivo neurophysiological brain activity and function. These data elucidate quantitative, translational biomarkers in vivo and in vitro that can be utilized for the development and efficacy assessment of targeted treatments for SRID.

Localization of neurons from extracellular footprints

Background:High density microelectrode arrays (HD-MEAs) are now widely used for both in-vitro and in-vivo recordings, as they allow spikes from hundreds of neurons to be recorded simultaneously. Since extracellular recordings do not allow visualization of the recorded neurons, algorithms are needed to estimate their physical positions, especially to track their movements when the are drifting away from recording devices. New Method:The objective of this study was to evaluate the performance of multiple algorithms for neuron localization solely from extracellular traces (MEA recordings), either artificial or obtained from mouse retina. The algorithms compared included center-of-mass, monopolar, and grid-based algorithms. The first method is a barycenter calculation. The second algorithm infers the position of the cell using triangulation with the assumption that the neuron behaves as a monopole. Finally, grid-based methods rely on comparing the recorded spike with a projection of spikes of hypothetical neurons with different positions. Results:The Grid-Based algorithm yielded the most satisfactory outcomes. The center-of-mass exhibited a minimal computational cost, yet its average localization was suboptimal. Monopolar algorithms gave cell localizations with an average error of less than 10μm, but they had considerable variability and a high computational cost. For the grid-based method, the variability was smaller, with satisfactory performance and low computational cost. Comparison with Existing Method(s):The accuracy of the different localization methods benchmarked in this article had not been properly tested with ground-truth recordings before. Conclusion:The objective of this article is to provide guidance to researchers on the selection of optimal methods for localizing neurons based on MEA recordings.

High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System

High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System

Enhancing Brain-Computer Interfaces through Kriging-Based Fusion of Sparse Regression Partial Differential Equations to Counter Injection Molding View of Node Displacement Effects.

Injection molding is an efficient and precise manufacturing technology that is widely used in the production of plastic products. In recent years, injection molding technology has made significant progress, especially with the combination of in-mold electronics (IME) technology, which makes it possible to embed electronic components directly into the surface of a product. IME technology improves the integration and performance of a product by embedding conductive materials and functional components in the mold. Brain-computer interfaces (BCIs) are a rapidly growing field of research that aims to capture, analyze, and feedback brain signals by directly connecting the brain to external devices. The Utah array, a high-density microelectrode array, has been widely used for the recording and transmission of brain signals. However, the traditional fabrication method of the Utah array suffers from high cost and low integration, which limits its promotion in practical applications. The lines that receive EEG signals are one of the key parts of a brain-computer interface system. The optimization of injection molding parameters is particularly important in order to effectively embed these lines into thin films and to ensure the precise displacement of the line nodes and the stability of signal transmission during the injection molding process. In this study, a method based on the Kriging prediction model and sparse regression partial differential equations (PDEs) is proposed to optimize the key parameters in the injection molding process. This method can effectively predict and control the displacement of nodes in the film, ensure the stability and reliability of the line during the injection process, and improve the accuracy of EEG signal transmission and system performance. The optimal injection parameters were finally obtained: a holding pressure of 525 MPa, a holding time of 50 s, and a melting temperature of 285 °C. Under this condition, the average node displacement of UA was reduced from the initial 0.19 mm to 0.89 µm, with an optimization rate of 95.32%.

A modular and flexible open source cell incubator system for mobile and stationary use

Culturing living cells in vitro requires the maintenance of physiological conditions for extended periods of time. Here, we introduce a versatile and affordable incubation system, addressing the limitations of traditional incubation systems. Conventionally, stationary cell incubators maintain constant temperature and gas levels for in vitro cell culturing. Combining such incubators with additional lab equipment proves challenging. The presented platform offers modularity and adaptability, enabling customization to diverse experimental needs. The system includes a main unit with a user-friendly interface as well as an interchangeable incubation chamber. We present two incubation chambers targeting two completely different use cases. The first chamber, named “inkugo” facilitates the transportation of cells for up to two hours without external power and for more than a day without an external CO2 source. The second chamber termed “inkubox” was designed to enable continuous electrophysiological recordings. Recordings from up to four neural cultures growing on high-density microelectrode arrays can be performed in parallel. The system’s unique feature lies in its separability of control and incubation components, allowing one control unit to manage various custom chambers. The design’s simplicity and the use of widely accessible components make the here proposed incubation system replicable for any laboratory. This platform fosters collaboration and experimentation in both decentralized and traditional laboratory settings, making it an invaluable addition to any cell culturing pipeline.

Developing Microelectrode Arrays for the Point-of-Care Multiplex Detection of Metabolites.

DNA-aptamer-functionalized electrode arrays can provide an intriguing method for detecting pathogen-derived exometabolites. This work addresses the limitations of previous aptamer-based pathogen detection methods by introducing a novel surface design that bridges the gap between initial efforts in this area and the demands of a point-of-care device. Specifically, the use of a diblock copolymer coating on a high-density microelectrode array and Cu-mediated cross coupling reactions that allow for the exclusive functionalization of that coating by any electrode or set of electrodes in the array provides a device that is stable for 1 year and compatible with the multiplex detection of small-molecule targets. The new chemistry developed allows one to take advantage of a large number of electrodes in the array with one experiment described herein capitalizing on the use of 960 individually addressable electrodes.

Synthetic Chemistry and Microelectrode Arrays for Point of Care Diagnostics

Molecular diagnostics typically require a molecular interaction that identifies a specific target, a device that detects, quantifies, and records that interaction, and an interface between those two elements. Each feature is critical for the success of the device. Yet while the first two are matters of intense interest an research, the surface itself is often ignored. This is problematic because it provides the platform for the whole "experiment". For a "point-of-care" diagnostic, the surface on the device must be stable both in terms of chemistry and time while still allowing the signal from the molecular interaction to be sensed and recorded.With this in mind, we have been focusing on the use of diblock copolymers as molecular surfaces for the construction of "point-of-care" diagnostics. We have developed a toolbox of chemical reactions that can be used site-selectively on such surfaces, and we have recently demonstrated that the approach taken is compatible with the use of DNA-aptamers to do multiplex sensing on a high density microelectrode array using a surface that is stable for a year. We have also illustrated how the quality of the signaling study conducted on an array is dependent on optimization of the chemical reaction.But exactly how good are the chemical reactions employed on the arrays to build a surface in a site-selective manner? One can tell with the use of fluorescence based studies that reactions happen where you want then to happen, but are there background reactions that can be problematic for a multiplex sensing experiment, do the reactions happen in high yield, and how much of an expensive, difficult to obtain biological reagent might be lost due to the confinement strategy? Are some reactions better than others in this regard? In short, if we are to optimize the surfaces constructed on a diagnostic device, then we need not only have tools in place for conducting synthetic chemistry on the device, but also the tools in place necessary to analyze those reactions. Understanding the chemistry that can and/or does occur at any electrode in a high density array requires more than looking at pretty pictures of fluorescent spots. In the talk to be given, we will utilize a combination of a "Kenner-safety-catch" linker strategy and molecular biology techniques to answer key questions about the synthetic chemistry used to construct the DNA-aptamer based multiplex sensor mentioned above.

Imaging Algorithm and Optimal Measurement Protocol for 3-D Impedance Tomography on 2-D High-Density Microelectrode Array

Imaging Algorithm and Optimal Measurement Protocol for 3-D Impedance Tomography on 2-D High-Density Microelectrode Array

A Multimodal Fitting Approach to Construct Single-Neuron Models with Patch Clamp and High-Density Microelectrode Arrays.

In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated.

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains.

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.

Flexible high-density microelectrode arrays for closed-loop brain-machine interfaces: a review.

Flexible high-density microelectrode arrays (HDMEAs) are emerging as a key component in closed-loop brain-machine interfaces (BMIs), providing high-resolution functionality for recording, stimulation, or both. The flexibility of these arrays provides advantages over rigid ones, such as reduced mismatch between interface and tissue, resilience to micromotion, and sustained long-term performance. This review summarizes the recent developments and applications of flexible HDMEAs in closed-loop BMI systems. It delves into the various challenges encountered in the development of ideal flexible HDMEAs for closed-loop BMI systems and highlights the latest methodologies and breakthroughs to address these challenges. These insights could be instrumental in guiding the creation of future generations of flexible HDMEAs, specifically tailored for use in closed-loop BMIs. The review thoroughly explores both the current state and prospects of these advanced arrays, emphasizing their potential in enhancing BMI technology.

198 Human Focal Seizures Induce Wavelike States in Bursts of Neuronal Activity

INTRODUCTION: Recent advances in high-density microelectrode arrays (MEAs) have allowed clinicians to record from individual neurons (single-unit activity) or from ensembles of neurons (multi-unit activity, MUA) in the human brain. These recordings have yielded fundamental advances both in epilepsy and in normal brain function. However, the properties of bursts of neuronal activity during seizures have not been characterized. METHODS: Five patients at our institution were implanted with microelectrode arrays and met inclusion criteria. Bursts were captured using custom code (MATLAB). We used statistical techniques to compute the rate similarity or sequence similarity between any two sequences. RESULTS: We found strong internal consistency within groups of pathological discharges -- that is, IED and seizure related bursts -- for both rate and sequence similarity. We found that baseline, IED, and seizure-related bursts all reside in particular subspaces, after dimensionality reduction using UMAP. During seizures, the LFP exhibits strong wavelike properties. Interestingly, the MUA direction echoes that of the LFP in this patient. However, MUA discharges are only partially wavelike. In IEDs, while the LFP is quite wavelike, the MUA does not exhibit wavelike properties. We found that LFP wave directions, for both seizures and IEDs, tend to arise from the seizure source, as determined using the ECoG. The relationship between the ECoG source and the MUA wave direction is less consistent. Seizure sequences start near the baseline subspace. Over time, seizure bursts are recruited to a distinct subspace, at which time they develop wavelike properties. CONCLUSIONS: Seizure events induce wavelike states in neuronal bursts. These findings may help explain why seizures alter cognition.

A 1024-Channel 268 nW/pixel 36×36 μm2/channel Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces.

This paper presents a data-compressive neural recording IC for single-cell resolution high-bandwidth brain-computer interfaces. The IC features wired-OR lossy compression during digitization, thus preventing data deluge and massive data movement. By discarding unwanted baseline samples of the neural signals, the output data rate is reduced by 146× on average while allowing the reconstruction of spike samples. The recording array consists of pulse position modulation-based active digital pixels with a global single-slope analog-to-digital conversion scheme, which enables a low-power and compact pixel design with significantly simple routing and low array readout energy. Fabricated in a 28-nm CMOS process, the neural recording IC features 1024 channels (i.e., 32 × 32 array) with a pixel pitch of 36 μm that can be directly matched to a high-density microelectrode array. The pixel achieves 7.4 μVrms input-referred noise with a -3 dB bandwidth of 300-Hz to 5-kHz while consuming only 268 nW from a single 1-V supply. The IC achieves the smallest area per channel (36 × 36 μm2) and the highest energy efficiency among the state-of-the-art neural recording ICs published to date.

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Mechanical stimulation and electrophysiological monitoring at subcellular resolution reveals differential mechanosensation of neurons within networks

A growing consensus that the brain is a mechanosensitive organ is driving the need for tools that mechanically stimulate and simultaneously record the electrophysiological response of neurons within neuronal networks. Here we introduce a synchronized combination of atomic force microscopy, high-density microelectrode array and fluorescence microscopy to monitor neuronal networks and to mechanically characterize and stimulate individual neurons at piconewton force sensitivity and nanometre precision while monitoring their electrophysiological activity at subcellular spatial and millisecond temporal resolution. No correlation is found between mechanical stiffness and electrophysiological activity of neuronal compartments. Furthermore, spontaneously active neurons show exceptional functional resilience to static mechanical compression of their soma. However, application of fast transient (∼500 ms) mechanical stimuli to the neuronal soma can evoke action potentials, which depend on the anchoring of neuronal membrane and actin cytoskeleton. Neurons show higher responsivity, including bursts of action potentials, to slower transient mechanical stimuli (∼60 s). Moreover, transient and repetitive application of the same compression modulates the neuronal firing rate. Seemingly, neuronal networks can differentiate and respond to specific characteristics of mechanical stimulation. Ultimately, the developed multiparametric tool opens the door to explore manifold nanomechanobiological responses of neuronal systems and new ways of mechanical control.

Novel high-dense microelectrode array based multimodal bioelectronic monitoring system for cardiac arrhythmia re-entry analysis

In recent decades, significant progress has been made in the treatment of heart diseases, particularly in the field of personalized medicine. Despite the development of genetic tests, phenotyping and risk stratification are performed based on clinical findings and invasive in vivo techniques, such as stimulation conduction mapping techniques and programmed ventricular pacing. Consequently, label-free non-invasive in vitro functional analysis systems are urgently needed for more accurate and effective in vitro risk stratification, model-based therapy planning, and clinical safety profile evaluation of drugs. To overcome these limitations, a novel multilayer high-density microelectrode array (HD-MEA), with an optimized configuration of 512 sensing and 4 pacing electrodes on a sensor area of 100 mm2, was developed for the bioelectronic detection of re-entry arrhythmia patterns. Together with a co-developed front-end, we monitored label-free and in parallel cardiac electrophysiology based on field potential monitoring and mechanical contraction using impedance spectroscopy at the same microelectrode. In proof of principle experiments, human induced pluripotent stem cell (hiPS)-derived cardiomyocytes were cultured on HD-MEAs and used to demonstrate the sensitive quantification of contraction strength modulation by cardioactive drugs such as blebbistatin (IC50 = 4.2 μM), omecamtiv and levosimendan. Strikingly, arrhythmia-typical rotor patterns (re-entry) can be induced by optimized electrical stimulation sequences and detected with high spatial resolution. Therefore, we provide a novel cardiac re-entry analysis system as a promising reference point for diagnostic approaches based on in vitro assays using patient-specific hiPS-derived cardiomyocytes.

Electrical and chemical modulation of homogeneous and heterogeneous human-iPSCs-derived neuronal networks on high density arrays.

The delicate "Excitatory/Inhibitory balance" between neurons holds significance in neurodegenerative and neurodevelopmental diseases. With the ultimate goal of creating a faithful in vitro model of the human brain, in this study, we investigated the critical factor of heterogeneity, focusing on the interplay between excitatory glutamatergic (E) and inhibitory GABAergic (I) neurons in neural networks. We used high-density Micro-Electrode Arrays (MEA) with 2304 recording electrodes to investigate two neuronal culture configurations: 100% glutamatergic (100E) and 75% glutamatergic / 25% GABAergic (75E25I) neurons. This allowed us to comprehensively characterize the spontaneous electrophysiological activity exhibited by mature cultures at 56 Days in vitro, a time point in which the GABA shift has already occurred. We explored the impact of heterogeneity also through electrical stimulation, revealing that the 100E configuration responded reliably, while the 75E25I required more parameter tuning for improved responses. Chemical stimulation with BIC showed an increase in terms of firing and bursting activity only in the 75E25I condition, while APV and CNQX induced significant alterations on both dynamics and functional connectivity. Our findings advance understanding of diverse neuron interactions and their role in network activity, offering insights for potential therapeutic interventions in neurological conditions. Overall, this work contributes to the development of a valuable human-based in vitro system for studying physiological and pathological conditions, emphasizing the pivotal role of neuron diversity in neural network dynamics.

DeePhys: A machine learning–assisted platform for electrophysiological phenotyping of human neuronal networks

Reproducible functional assays to study invitro neuronal networks represent an important cornerstone in the quest to develop physiologically relevant cellular models of human diseases. Here, we introduce DeePhys, a MATLAB-based analysis tool for data-driven functional phenotyping of invitro neuronal cultures recorded by high-density microelectrode arrays. DeePhys is a modular workflow that offers a range of techniques to extract features from spike-sorted data, allowing for the examination of functional phenotypes both at the individual cell and network levels, as well as across development. In addition, DeePhys incorporates the capability to integrate novel features and to use machine-learning-assisted approaches, which facilitates a comprehensive evaluation of pharmacological interventions. To illustrate its practical application, we apply DeePhys to human induced pluripotent stem cell-derived dopaminergic neurons obtained from both patients and healthy individuals and showcase how DeePhys enables phenotypic screenings.

High-density transparent graphene arrays for predicting cellular calcium activity at depth from surface potential recordings.

Optically transparent neural microelectrodes have facilitated simultaneous electrophysiological recordings from the brain surface with the optical imaging and stimulation of neural activity. A remaining challenge is to scale down the electrode dimensions to the single-cell size and increase the density to record neural activity with high spatial resolution across large areas to capture nonlinear neural dynamics. Here we developed transparent graphene microelectrodes with ultrasmall openings and a large, transparent recording area without any gold extensions in the field of view with high-density microelectrode arrays up to 256 channels. We used platinum nanoparticles to overcome the quantum capacitance limit of graphene and to scale down the microelectrode diameter to 20 µm. An interlayer-doped double-layer graphene was introduced to prevent open-circuit failures. We conducted multimodal experiments, combining the recordings of cortical potentials of microelectrode arrays with two-photon calcium imaging of the mouse visual cortex. Our results revealed that visually evoked responses are spatially localized for high-frequency bands, particularly for the multiunit activity band. The multiunit activity power was found to be correlated with cellular calcium activity. Leveraging this, we employed dimensionality reduction techniques and neural networks to demonstrate that single-cell and average calcium activities can be decoded from surface potentials recorded by high-density transparent graphene arrays.