High-density Microelectrode Arrays Research Articles

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.

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.

P22-21: A 3D human iPSC-derived brain model coupled to multi-well high-density microelectrode arrays (HD-MEA) to improve in vitro neurotoxicity screening of environmental compounds

P22-21: A 3D human iPSC-derived brain model coupled to multi-well high-density microelectrode arrays (HD-MEA) to improve in vitro neurotoxicity screening of environmental compounds

Developing evaluation method for peripheral sensitization in cultured sensory neurons

AbstractPeripheral sensitization, decrease in pain threshold in sensory neurons, can cause chronic pain. Little is known about how peripheral sensitization led to chronic pain. Here, we aimed to develop a method for evaluating peripheral sensitization in cultured sensory neurons with electrical recording. Sensory neurons from rat dorsal root ganglion were cultured on high‐density microelectrode arrays (HD‐MEA), and their activity was evoked by capsaicin stimulation with or without substance P. Fluorescent imaging‐based electrode selection effectively selected five times as many capsaicin‐sensitive sensory neurons as the existing method. Peripheral sensitization by substance P was detected from 31.9% of selected sensory neurons, and majority of these neurons co‐expressed capsaicin and substance P receptors. These results indicate that our method is suitable for evaluating peripheral sensitization by substance P in cultured sensory neurons.

Optimizing mRNA transfection on a high-definition electroporation microelectrode array results in 98% efficiency and multiplexed gene delivery

Spatially resolved transfection, intracellular delivery of proteins and nucleic acids, has the potential to drastically speed up the discovery of biologically active cargos, for instance for the development of cell therapies or new genome engineering tools. We recently demonstrated the use of a high-density microelectrode array for the targeted electrotransfection of cells grown on its surface, a process called High-Definition Electroporation (HD-EP). We also developed a framework based on Design of Experiments to quickly establish optimized electroporation conditions across five different electrical pulse parameters. Here, we used this framework to optimize the transfection efficiency of primary fibroblasts with a mCherry-encoding mRNA, resulting in 98% of the cells expressing the desired fluorescent protein without any sign of cell death. That transfection yield is the highest reported so far for electroporation. Moreover, varying the pulse number was shown to modulate the fluorescence intensity of cells, indicating the dosage-controlled delivery of mRNA and protein expression. Finally, exploiting the single-electrode addressability of the microelectrode array, we demonstrated spatially resolved, high efficiency, sequential transfection of cells with three distinct mRNAs. Since the chip can be easily redesigned to feature a much large number of electrodes, we anticipate that this methodology will enable the development of dedicated screening platforms for analysis of mRNA variants at scale.

Functional imaging of conduction dynamics in cortical and spinal axons.

Mammalian axons are specialized for transmitting action potentials to targets within the central and peripheral nervous system. A growing body of evidence suggests that, besides signal conduction, axons play essential roles in neural information processing, and their malfunctions are common hallmarks of neurodegenerative diseases. The technologies available to study axonal function and structure integrally limit the comprehension of axon neurobiology. High-density microelectrode arrays (HD-MEAs) allow for accessing axonal action potentials at high spatiotemporal resolution, but provide no insights on axonal morphology. Here, we demonstrate a method for electrical visualization of axonal morphologies based on extracellular action potentials recorded from cortical and motor neurons using HD-MEAs. The method enabled us to reconstruct up to 5-cm-long axonal arbors and directly monitor axonal conduction across thousands of recording sites. We reconstructed 1.86 m of cortical and spinal axons in total and found specific features in their structure and function.

Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays

Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks' early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation.

Development of a Hypersensitivity Evaluation Method for Cultured Sensory Neurons Using Electrical Activity Recording.

Investigation of hypersensitivity caused by peripheral sensitization progression is important for developing novel pain treatments. Existing methods cannot record plastic changes in neuronal activity because they occur over a few days. We aimed to establish an efficient method to evaluate neuronal activity alterations caused by peripheral sensitization on high-density microelectrode arrays (HD-MEAs) which can record neuronal activity for a long time. Rat dorsal root ganglion (DRG) neurons were dissected from rat embryos and cultured on HD-MEAs. DRG neurons were labeled with NeuO, live staining dye. Neurons were detected with the fluorescence signal and electrodes were selected with the fluorescence images. The number of DRG neurons, whose activity were recorded, detected based on fluorescence observation was five times greater than that based on neuronal activity. Analysis of changes in neuronal activity observed in pharmacological stimulation experiments suggested that substance P induced peripheral sensitization and enhanced capsaicin sensitivity. In addition, results of immunofluorescence staining suggested that peripheral sensitization occurred mostly in neurons that co-expressed transient receptor potential vanilloid 1 (TRPV1) and neurokinin 1 receptor (NK1R). In conclusion, we established an efficient method for assessing the effects of peripheral sensitization on DRG neurons cultured on HD-MEAs.

Application of deep reinforcement learning for spike sorting under multi-class imbalance

Spike sorting is the basis for analyzing spike firing patterns encoded in high-dimensional information spaces. With the fact that high-density microelectrode arrays record multiple neurons simultaneously, the data collected often suffers from two problems: a few overlapping spikes and different neuronal firing rates, which both belong to the multi-class imbalance problem. Since deep reinforcement learning (DRL) assign targeted attention to categories through reward functions, we propose ImbSorter to implement spike sorting under multi-class imbalance. We describe spike sorting as a Markov sequence decision and construct a dynamic reward function (DRF) to improve the sensitivity of the agent to minor classes based on the inter-class imbalance ratios. The agent is eventually guided by the optimal strategy to classify spikes. We consider the Wave_Clus dataset, which contains overlapping spikes and diverse noise levels, and the macaque dataset, which has a multi-scale imbalance. ImbSorter is compared with classical DRL architectures, traditional machine learning algorithms, and advanced overlapping spike sorting techniques on these two above datasets. ImbSorter obtained improved results on the Macro_F1. The results show ImbSorter has a promising ability to resist overlapping and noise interference. It has high stability and promising performance in processing spikes with different degrees of skewed distribution.

A 512-Ch Dual-Mode Microchip for Simultaneous Measurements of Electrophysiological and Neurochemical Activities

In the study of the brain, large and high-density microelectrode arrays have been widely used to study the behavior of neurotransmission. CMOS technology has facilitated these devices by enabling the integration of high-performance amplifiers directly on-chip. Usually, these large arrays measure only the voltage spikes resulting from action potentials traveling along firing neuronal cells. However, at synapses, communication between neurons occurs by the release of neurotransmitters, which cannot be measured on typical CMOS electrophysiology devices. Development of electrochemical amplifiers has resulted in the measurement of neurotransmitter exocytosis down to the level of a single vesicle. To effectively monitor the complete picture of neurotransmission, measurement of both action potentials and neurotransmitter activity is needed. Current efforts have not resulted in a device that is capable of the simultaneous measurement of action potential and neurotransmitter release at the same spatiotemporal resolution needed for a comprehensive study of neurotransmission. In this paper, we present a true dual-mode CMOS device that fully integrates 256-ch electrophysiology amplifiers and 256-ch electrochemical amplifiers, along with an on-chip 512 electrode microelectrode array capable of simultaneous measurement from all 512 channels.

Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks.

Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABA A receptor antagonist. Our results suggest that the joint representation of node features and functional connectivity, extracted from a baseline recording, was informative for predicting firing rate changes of individual neurons after the perturbation. Specifically, our implementation of a GNN model with inductive learning capability (GraphSAGE) outperformed other prediction models that relied only on single-neuron features. We tested the generalizability of the results on two additional datasets of HD-MEA recordings-a second dataset with cultures perturbed with Bicuculline and a dataset perturbed with the GABA A receptor antagonist Gabazine. GraphSAGE models showed improved prediction accuracy over other prediction models. Our results demonstrate the added value of taking into account the functional connectivity between neurons and the potential of GNNs to study complex interactions between neurons.

Microfluidic cell engineering on high-density microelectrode arrays for assessing structure-function relationships in living neuronal networks.

Neuronal networks in dissociated culture combined with cell engineering technology offer a pivotal platform to constructively explore the relationship between structure and function in living neuronal networks. Here, we fabricated defined neuronal networks possessing a modular architecture on high-density microelectrode arrays (HD-MEAs), a state-of-the-art electrophysiological tool for recording neural activity with high spatial and temporal resolutions. We first established a surface coating protocol using a cell-permissive hydrogel to stably attach a polydimethylsiloxane microfluidic film on the HD-MEA. We then recorded the spontaneous neural activity of the engineered neuronal network, which revealed an important portrait of the engineered neuronal network-modular architecture enhances functional complexity by reducing the excessive neural correlation between spatially segregated modules. The results of this study highlight the impact of HD-MEA recordings combined with cell engineering technologies as a novel tool in neuroscience to constructively assess the structure-function relationships in neuronal networks.

CMU Array: A 3D nanoprinted, fully customizable high-density microelectrode array platform.

Microelectrode arrays provide the means to record electrophysiological activity critical to brain research. Despite its fundamental role, there are no means to customize electrode layouts to address specific experimental or clinical needs. Moreover, current electrodes demonstrate substantial limitations in coverage, fragility, and expense. Using a 3D nanoparticle printing approach that overcomes these limitations, we demonstrate the first in vivo recordings from electrodes that make use of the flexibility of the 3D printing process. The customizable and physically robust 3D multi-electrode devices feature high electrode densities (2600 channels/cm2 of footprint) with minimal gross tissue damage and excellent signal-to-noise ratio. This fabrication methodology also allows flexible reconfiguration consisting of different individual shank lengths and layouts, with low overall channel impedances. This is achieved, in part, via custom 3D printed multilayer circuit boards, a fabrication advancement itself that can support several biomedical device possibilities. This effective device design enables both targeted and large-scale recording of electrical signals throughout the brain.