Polytrodes: High-Density Silicon Electrode Arrays for Large-Scale Multiunit Recording

Modeling and Analysis of Environmental Electromagnetic Interference in Multiple-Channel Neural Recording Systems for High Common-Mode Interference Rejection Performance

Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10-3 V2/Hz to 4.04 × 10-6 V2/Hz without affecting the recorded AP signal quality in an unshielded experiment environment.

Active Micro-Nano-Collaborative Bioelectronic Device for Advanced Electrophysiological Recording

The development of precise and sensitive electrophysiological recording platforms holds the utmost importance for research in the fields of cardiology and neuroscience. In recent years, active micro/nano-bioelectronic devices have undergone significant advancements, thereby facilitating the study of electrophysiology. The distinctive configuration and exceptional functionality of these active micro-nano-collaborative bioelectronic devices offer the potential for the recording of high-fidelity action potential signals on a large scale. In this paper, we review three-dimensional active nano-transistors and planar active micro-transistors in terms of their applications in electro-excitable cells, focusing on the evaluation of the effects of active micro/nano-bioelectronic devices on electrophysiological signals. Looking forward to the possibilities, challenges, and wide prospects of active micro-nano-devices, we expect to advance their progress to satisfy the demands of theoretical investigations and medical implementations within the domains of cardiology and neuroscience research.

Chronic, Reusable, Multiday Neuropixels Recordings during Free-Moving Operant Behavior

Electrophysiological recording is a powerful technique to examine neuronal substrates underlying cognition and behavior. Neuropixels probes provide a unique capacity to capture neuronal activity across many brain areas with high spatiotemporal resolution. Neuropixels are also expensive and optimized for acute, head-fixed use, both of which limit the types of behaviors and manipulations that can be studied. Recent advances have addressed the cost issue by showing chronic implant, explant, and reuse of Neuropixels probes, but the methods were not optimized for use in free-moving behavior. There were specific needs for improvement in cabling/connection stability. Here, we extend that work to demonstrate chronic Neuropixels recording, explant, and reuse in a rat model during fully free-moving operant behavior. Similar to prior approaches, we house the probe and headstage within a 3D-printed housing that avoids direct fixation of the probe to the skull, enabling eventual explant. We demonstrate innovations to allow chronic headstage connection with protection against environmental factors and a more stable cabling setup to reduce the tension that can interrupt recording. We demonstrate this approach with rats performing two different behavioral tasks, in each case showing: (1) chronic single- or dual-probe recordings in free-moving rats in operant chambers and (2) reusability of Neuropixels 1.0 probes with continued good single-unit yield after retrieval and reimplant. We thus demonstrate the potential for Neuropixels recordings in a wider range of species and preparations.

In vivo recordings in freely behaving mice using independent silicon probes targeting multiple brain regions

In vivo recordings in freely behaving animals are crucial to understand the neuronal circuit basis of behavior. Although current multi-channel silicon probes provide unparalleled sampling density, the study of interacting neuronal populations requires the implantation of multiple probes across different regions of the brain. Ideally, these probes should be independently adjustable, to maximize the yield, and recoverable, to mitigate costs. In this work, we describe the implementation of a miniaturized 3D-printed headgear system for chronic in vivo recordings in mice using independently movable silicon probes targeting multiple brain regions. We successfully demonstrated the performance of the headgear by simultaneously recording the neuronal activity in the prelimbic cortex and dorsal hippocampus. The system proved to be sturdy, ensuring high-quality stable recordings and permitted reuse of the silicon probes, with no observable interference in mouse innate behaviors.

Characterization of extracellular spike waveforms recorded in wallaby primary visual cortex

Extracellular recordings were made from 642 units in the primary visual cortex (V1) of a highly visual marsupial, the Tammar wallaby. The receptive field (RF) characteristics of the cells were objectively estimated using the non-linear input model (NIM), and these were correlated with spike shapes. We found that wallaby cortical units had 68% regular spiking (RS), 12% fast spiking (FS), 4% triphasic spiking (TS), 5% compound spiking (CS) and 11% positive spiking (PS). RS waveforms are most often associated with recordings from pyramidal or spiny stellate cell bodies, suggesting that recordings from these cell types dominate in the wallaby cortex. In wallaby, 70-80% of FS and RS cells had orientation selective RFs and had evenly distributed linear and nonlinear RFs. We found that 47% of wallaby PS units were non-orientation selective and they were dominated by linear RFs. Previous studies suggest that the PS units represent recordings from the axon terminals of non-orientation selective cells originating in the lateral geniculate nucleus (LGN). If this is also true in wallaby, as strongly suggested by their low response latencies and bursty spiking properties, the results suggest that significantly more neurons in wallaby LGN are already orientation selective. In wallaby, less than 10% of recorded spikes had triphasic (TS) or sluggish compound spiking (CS) waveforms. These units had a mixture of orientation selective and non-oriented properties, and their cellular origins remain difficult to classify.

Design and fabrication of a microelectrode array for studying epileptiform discharges from rodents

Local field potentials, the extracellular electrical activities from brain regions, provide clinically relevant information about the status of neurophysiological conditions, including epilepsy. In this study, a 13-channel silicon-based single-shank microelectrode array (MEA) was designed and fabricated to record local field potentials (LFPs) from the different depths of a rat's brain. A titanium/gold layer was patterned as electrodes on an oxidized silicon substrate, and silicon dioxide was deposited as a passivation layer. The fabricated array was implanted in the somatosensory cortex of the right hemisphere of an anesthetized rat. The developed MEA was interfaced with an OpenBCI Cyton Daisy Biosensing Board to acquire the local field potentials. The LFPs were acquired at three different neurophysiological conditions, including baseline signals, chemically-induced epileptiform discharges, and recovered baseline signals after anti-epileptic drug (AED) administration. Further, time-frequency analyses were performed on the acquired biopotentials to study the difference in spatiotemporal features. The processed signals and time-frequency analyses clearly distinguish between pre-convulsant and post-AED baselines and evoked epileptiform discharges.

Cross-frequency coupling between slow harmonics via the real brainstem oscillators: An in vivo animal study

Brain waves of discrete rhythms (gamma to delta frequency ranges) are ubiquitously recorded and interpreted with respect to probable corresponding specific functions. The most challenging idea of interpreting varied frequencies of brain waves has been postulated as a communication mechanism in which different neuronal assemblies use specific ranges of frequencies cooperatively. One promising candidate is cross-frequency coupling (CFC), in which some neuronal assemblies efficiently utilize the fastest gamma range brain waves as an information carrier (phase-amplitude CFC); however, phase-phase CFC via the slowest delta and theta waves has rarely been described to date. Moreover, CFC has rarely been reported in the animal brainstem including humans, which most likely utilizes the slowest waves (delta and theta ranges). Harmonic waves are characterized by the presence of a fundamental frequency with several overtones, multiples of the fundamental frequency. Rat brainstem waves seemed to consist of slow harmonics with different frequencies that could cooperatively produce a phase-phase CFC. Harmonic rhythms of different frequency ranges can cross-couple with each other to sustain robust and resilient consonance via real oscillators, notwithstanding any perturbations.

A Five-Channel Weighted Real-Time Algorithm for High-Density Electrodes Spike Sorting

With the application of high-density neural probes, a neuron can be detected by multiple adjacent probes, and the traditional single-channel spike sorting is no longer suitable. In this paper, we propose a five-channel weighted real-time spike sorting algorithm based on template-matching to process neural signals recorded by high-density probes. This work uses the signals of the center channel and the adjacent four channels to form a five-channel template by weighting, and employs a modified OSort algorithm with unsupervised learning to update the template. We implemented automatic online spike sorting, and tested it with both ground truth recordings and simulated datasets. The experiments show that our algorithm utilizing the information of adjacent channels has a higher sorting accuracy than traditional single-channel spike sorting. The average sorting accuracy reaches 89%, compared to 78% for single-channel.

Precision of neuronal localization in 2D cell cultures by using high-performance electropolymerized microelectrode arrays correlated with optical imaging

Recently, the development of electronic devices to extracellularly record the simultaneous electrical activities of numerous neurons has been blooming, opening new possibilities to interface and decode neuronal activity. In this work, we tested how the use of EDOT electropolymerization to tune post-fabrication materials could optimize the cell/electrode interface of such devices. Our results showed an improved signal-to-noise ratio, better biocompatibility, and a higher number of neurons detected in comparison with gold electrodes. Then, using such enhanced recordings with 2D neuronal cultures combined with fluorescent optical imaging, we checked the extent to which the positions of the recorded neurons could be estimated solely via their extracellular signatures. Our results showed that assuming neurons behave as monopoles, positions could be estimated with a precision of approximately tens of micrometers.

Multiscale imaging informs translational mouse modeling of neurological disease

Multiscale neurophysiology reveals that simple motor actions are associated with changes in neuronal firing in virtually every brain region studied. Accordingly, the assessment of focal pathology such as stroke or progressive neurodegenerative diseases must also extend widely across brain areas. To derive mechanistic information through imaging, multiple resolution scales and multimodal factors must be included, such as the structure and function of specific neurons and glial cells and the dynamics of specific neurotransmitters. Emerging multiscale methods in preclinical animal studies that span micro- to macroscale examinations fill this gap, allowing a circuit-based understanding of pathophysiological mechanisms. Combined with high-performance computation and open-source data repositories, these emerging multiscale and large field-of-view techniques include live functional ultrasound, multi- and single-photon wide-scale light microscopy, video-based miniscopes, and tissue-penetrating fiber photometry, as well as variants of post-mortem expansion microscopy. We present these technologies and outline use cases and data pipelines to uncover new knowledge within animal models of stroke, Alzheimer's disease, and movement disorders.

Robotic multi-probe single-actuator inchworm neural microdrive

A wide range of techniques in neuroscience involve placing individual probes at precise locations in the brain. However, large-scale measurement and manipulation of the brain using such methods have been severely limited by the inability to miniaturize systems for probe positioning. Here, we present a fundamentally new, remote-controlled micropositioning approach composed of novel phase-change material-filled resistive heater micro-grippers arranged in an inchworm motor configuration. The microscopic dimensions, stability, gentle gripping action, individual electronic control, and high packing density of the grippers allow micrometer-precision independent positioning of many arbitrarily shaped probes using a single piezo actuator. This multi-probe single-actuator design significantly reduces the size and weight and allows for potential automation of microdrives. We demonstrate accurate placement of multiple electrodes into the rat hippocampus in vivo in acute and chronic preparations. Our robotic microdrive technology should therefore enable the scaling up of many types of multi-probe applications in neuroscience and other fields.

In Vivo Neural Interfaces—From Small- to Large-Scale Recording

Brain functions arise from the coordinated activation of neuronal assemblies distributed across multiple brain regions. The electrical potential from the neuron captured by the electrode can be processed to extract brain information. A large number of densely and simultaneously recorded neuronal potential signals from neurons spanning multiple brain regions contribute to the insight of specific behaviors encoded by the neural ensembles. In this review, we focused on the neural interfaces developed for small- to large-scale recordings and discussed the developmental challenges and strategies in microsystem, electrode device, and interface material levels for the future larger-scale neural ensemble recordings.

A Taste Bud Organoid-Based Microelectrode Array Biosensor for Taste Sensing

The biological taste system has the unique ability to detect taste substances. Biomaterials originating from a biological taste system have been recognized as ideal candidates to serve as sensitive elements in the development of taste-based biosensors. In this study, we developed a taste bud organoid-based biosensor for the research of taste sensation. Taste bud organoids prepared from newborn mice were cultured and loaded onto the surface of a 64-channel microelectrode array (MEA) chip to explore the electrophysiological changes upon taste; an MEA chip was used to simultaneously record multiple-neuron firing activities from taste bud organoids under different taste stimuli, which helped to reveal the role of taste buds in taste sensing. The obtained results show that taste cells separated from the taste epithelium grew well into spherical structures under 3D culture conditions. These structures were composed of multiple cells with obvious budding structures. Moreover, the multicellular spheres were seeded on a 64-channel microelectrode array and processed with different taste stimuli. It was indicated that the MEA chip could efficiently monitor the electrophysiological signals from taste bud organoids in response to various taste stimuli. This biosensor provides a new method for the study of taste sensations and taste bud functions.

A universal, multimodal cell-based biosensing platform for optimal intracellular action potential recording

Intracellular recording of action potentials is an essential mean for studying disease mechanisms, and for electrophysiological studies, particularly in excitable cells as cardiomyocytes or neurons. Current strategies to obtain intracellular recordings include three-dimensional (3D) nanoelectrodes that can effectively penetrate the cell membrane and achieve high-quality intracellular recordings in a minimally invasive manner, or transient electroporation of the membrane that can yield temporary intracellular access. However, the former strategy requires a complicated and costly fabrication process, and the latter strategy suffers from high dependency on the method of application of electroporation, yielding inconsistent, suboptimal recordings. These factors hinder the high throughput use of these strategies in electrophysiological studies. In this work, we propose an advanced cell-based biosensing platform that relies on electroporation to produce consistent, high-quality intracellular recordings. The suggested universal system can be integrated with any electrode array, and it enables tunable electroporation with controllable pulse parameters, while the recorded potentials can be analyzed in real time to provide instantaneous feedback on the electroporation effectiveness. This integrated system enables the user to perform electroporation, record and assess the obtained signals in a facile manner, to ultimately achieve stable, reliable, intracellular recording. Moreover, the proposed platform relies on microelectrode arrays which are suited for large-scale production, and additional modules that are low-cost. Using this platform, we demonstrate the tuning of electroporation pulse width, pulse number, and amplitude, to achieve effective electroporation and high-quality intracellular recordings. This integrated platform has the potential to enable larger scale, repeatable, convenient, and low-cost electrophysiological studies.

jULIEs: nanostructured polytrodes for low traumatic extracellular recordings and stimulation in the mammalian brain

Objective Extracellular microelectrode techniques are the most widely used approach to interrogate neuronal populations. Regardless of the manufacturing method, damage to the vasculature and circuit function during probe insertion remains a concern. Reducing the footprint of the penetrating probes is a potential solution to this issue. However, coupling to the extracellular signals requires careful surface engineering. Approach Here, we show that continuously drawn SiO2-insulated ultra-microelectrode fibres offer an attractive substrate to address these challenges. Individual fibres can be fabricated to >10m continuous stretches and a selection of diameters below 30 µm with a low resistance (<100 Ω/m), continuous metal core of <10 µm and atomically flat smooth shank surfaces. To optimize the properties of the miniaturised electrode-tissue interface, we electrodeposit rough Au structures followed by ~20nm IrOx film by electrodeposition resulting in reduction of the interfacial impedance to <500kΩ at 1 kHz. Main results We demonstrate that these ultra-low impedance electrodes (jULIEs) can record and stimulate single and multi-unit activity with minimal tissue disturbance and exceptional signal-to-noise ratio in both superficial (~40µm) and deep (~6mm) structures of the mouse brain. We further show that sensor modifications are stable and probe manufacturing is reproducible. Significance Minimally perturbing bidirectional neural interfacing can reveal circuit function in the mammalian brain in vivo.

Long-termin-vivorecording performance of flexible penetrating microelectrode arrays

Objective. Neural interfaces are an essential tool to enable the human body to directly communicate with machines such as computers or prosthetic robotic arms. Since invasive electrodes can be located closer to target neurons, they have advantages such as precision in stimulation and high signal-to-noise ratio (SNR) in recording, while they often exhibit unstable performance in long-termin-vivoimplantation because of the tissue damage caused by the electrodes insertion. In the present study, we investigated the electrical functionality of flexible penetrating microelectrode arrays (FPMAs) up to 3 months inin-vivoconditions.Approach. Thein-vivoexperiment was performed by implanting FPMAs in five rats. Thein-vivoimpedance as well as the action potential (AP) amplitude and SNR were analyzed over weeks. Additionally, APs were tracked over time to investigate the possibility of single neuron recording.Main results. It was observed that the FPMAs exhibited dramatic increases in impedance for the first 4 weeks after implantation, accompanied by decreases in AP amplitude. However, the increase/decrease in AP amplitude was always accompanied by the increase/decrease in background noise, resulting in quite consistently maintained SNRs. After 4 weeks of implantation, we observed two distinctive issues regarding long-term implantation, each caused by chronic tissue responses or by the delamination of insulation layer. The results demonstrate that the FPMAs successfully recorded neuronal signals up to 12 weeks, with very stably maintained SNRs, reduced by only 16.1% on average compared to the first recordings, although biological tissue reactions or physical degradation of the FPMA were present.Significance. The fabricated FPMAs successfully recorded intracortical signals for 3 months. The SNR was maintained up to 3 months and the chronic function of FPMA was comparable with other silicon based implantable electrodes.

Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks

In 1943, McCulloch and Pitts introduced a discrete recurrent neural network as a model for computation in brains. The work inspired breakthroughs such as the first computer design and the theory of finite automata. We focus on learning in Hopfield networks, a special case with symmetric weights and fixed-point attractor dynamics. Specifically, we explore minimum energy flow (MEF) as a scalable convex objective for determining network parameters. We catalog various properties of MEF, such as biological plausibility, and then compare to classical approaches in the theory of learning. Trained Hopfield networks can perform unsupervised clustering and define novel error-correcting coding schemes. They also efficiently find hidden structures (cliques) in graph theory. We extend this known connection from graphs to hypergraphs and discover n-node networks with robust storage of 2Ω(n1-ϵ) memories for any ϵ>0. In the case of graphs, we also determine a critical ratio of training samples at which networks generalize completely.

A Miniaturized 256-Channel Neural Recording Interface with Area-Efficient Hybrid Integration of Flexible Probes and CMOS Integrated Circuits

We report a miniaturized, minimally invasive high-density neural recording interface that occupies only a 1.53 mm2 footprint for hybrid integration of a flexible probe and a 256-channel integrated circuit chip. To achieve such a compact form factor, we developed a custom flip-chip bonding technique using anisotropic conductive film and analog circuit-under-pad in a tiny pitch of 75 m. To enhance signal-to-noise ratios, we applied a reference-replica topology that can provide the matched input impedance for signal and reference paths in low-noise aimpliers (LNAs). The analog front-end (AFE) consists of LNAs, buffers, programmable gain amplifiers, 10b ADCs, a reference generator, a digital controller, and serial-peripheral interfaces (SPIs). The AFE consumes 51.92 W from 1.2 V and 1.8 V supplies in an area of 0.0161 mm2 per channel, implemented in a 180 nm CMOS process. The AFE shows > 60 dB mid-band CMRR, 6.32 Vrms input-referred noise from 0.5 Hz to 10 kHz, and 48 M input impedance at 1 kHz. The fabricated AFE chip was directly flip-chip bonded with a 256-channel flexible polyimide neural probe and assembled in a tiny head-stage PCB. Full functionalities of the fabricated 256-channel interface were validated in both in vitro and in vivo experiments, demonstrating the presented hybrid neural recording interface is suitable for various neuroscience studies in the quest of large scale, miniaturized recording systems.

Metal microdrive and head cap system for silicon probe recovery in freely moving rodent

High-yield electrophysiological extracellular recording in freely moving rodents provides a unique window into the temporal dynamics of neural circuits. Recording from unrestrained animals is critical to investigate brain activity during natural behaviors. The use and implantation of high-channel-count silicon probes represent the largest cost and experimental complexity associated with such recordings making a recoverable and reusable system desirable. To address this, we have designed and tested a novel 3D printed head-gear system for freely moving mice and rats. The system consists of a recoverable microdrive printed in stainless steel and a plastic head cap system, allowing researchers to reuse the silicon probes with ease, decreasing the effective cost, and the experimental effort and complexity. The cap designs are modular and provide structural protection and electrical shielding to the implanted hardware and electronics. We provide detailed procedural instructions allowing researchers to adapt and flexibly modify the head-gear system.

In-Cell Nanoelectronics: Opening the Door to Intracellular Electrophysiology

Establishing a reliable electrophysiological recording platform is crucial for cardiology and neuroscience research. Noninvasive and label-free planar multitransistors and multielectrode arrays are conducive to perform the large-scale cellular electrical activity recordings, but the signal attenuation limits these extracellular devices to record subthreshold activities. In recent decade, in-cell nanoelectronics have been rapidly developed to open the door to intracellular electrophysiology. With the unique three-dimensional nanotopography and advanced penetration strategies, high-throughput and high-fidelity action potential like signal recordings is expected to be realized. This review summarizes in-cell nanoelectronics from versatile nano-biointerfaces, penetration strategies, active/passive nanodevices, systematically analyses the applications in electrogenic cells and especially evaluates the influence of nanodevices on the high-quality intracellular electrophysiological signals. Further, the opportunities, challenges and broad prospects of in-cell nanoelectronics are prospected, expecting to promote the development of in-cell electrophysiological platforms to meet the demand of theoretical investigation and clinical application.

Voltage distributions in extracellular brain recordings

Extracellular recordings of brain voltage signals have many uses, including the identification of spikes and the characterization of brain states via analysis of local field potential (LFP) or EEG recordings. Though the factors underlying the generation of these signals are time varying and complex, their analysis may be facilitated by an understanding of their statistical properties. To this end, we analyzed the voltage distributions of high-pass extracellular recordings from a variety of structures, including cortex, thalamus, and hippocampus, in monkeys, cats, and rodents. We additionally investigated LFP signals in these recordings as well as human EEG signals obtained during different sleep stages. In all cases, the distributions were accurately described by a Gaussian within ±1.5 standard deviations from zero. Outside these limits, voltages tended to be distributed exponentially, that is, they fell off linearly on log-linear frequency plots, with variable heights and slopes. A possible explanation for this is that sporadically and independently occurring events with individual Gaussian size distributions can sum to produce approximately exponential distributions. For the high-pass recordings, a second explanation results from a model of the noisy behavior of ion channels that produce action potentials via Hodgkin-Huxley kinetics. The distributions produced by this model, relative to the averaged potential, were also Gaussian with approximately exponential flanks. The model also predicted time-varying noise distributions during action potentials, which were observed in the extracellular spike signals. These findings suggest a principled method for detecting spikes in high-pass recordings and transient events in LFP and EEG signals.NEW & NOTEWORTHY We show that the voltage distributions in brain recordings, including high-pass extracellular recordings, the LFP, and human EEG, are accurately described by a Gaussian within ±1.5 standard deviations from zero, with heavy, exponential tails outside these limits. This offers a principled way of setting event detection thresholds in high-pass recordings. It also offers a means for identifying event-like, transient signals in LFP and EEG recordings which may correlate with other neural phenomena.

Analysis of extracellular spike waveforms and associated receptive fields of neurons in cat primary visual cortex

KEY POINTS

Extracellular spikes recorded in the visual cortex (Area 17/18, V1) are commonly classified into either regular-spiking (RS) or fast-spiking (FS). Using multi-electrode arrays positioned in cat V1 and a broadband stimulus, we show that there is also a distinct class with positive-spiking (PS) waveforms. PS units were associated mainly with non-oriented receptive fields while RS and FS units had orientation-selective receptive fields. We suggest that PS units are recordings of axons originating from the thalamus. This conclusion was reinforced by our finding that we could record PS units after cortical silencing, but not record RS and FS units. The importance of our findings is that we were able to correlate spike shapes with receptive field characteristics with high precision using multi-electrode extracellular recording techniques. This allows considerable increases in the amount of information that can be extracted from future cortical experiments.

ABSTRACT

Extracellular spike waveforms from recordings in the visual cortex have been classified into either regular-spiking (RS) or fast-spiking (FS) units. While both these types of spike waveforms are negative-dominant, we show that there are also distinct classes of spike waveforms in visual Area 17/18 (V1) of anaesthetised cats with positive-dominant waveforms, which are not regularly reported. The spatial receptive fields (RFs) of these different spike waveform types were estimated, which objectively revealed the existence of oriented and non-oriented RFs. We found that units with positive-dominant spikes, which have been associated with recordings from axons in the literature, had mostly non-oriented RFs (84%), which are similar to the centre-surround RFs observed in the dorsal lateral geniculate nucleus (dLGN). Thus, we hypothesise that these positive-dominant waveforms may be recordings from dLGN afferents. We recorded from V1 before and after the application of muscimol (a cortical silencer) and found that the positive-dominant spikes (PS) remained while the RS and FS cells did not. We also noted that the PS units had spiking characteristics normally associated with dLGN units (i.e. higher response spike rates, lower response latencies and higher proportion of burst spikes). Our findings show quantitatively that it is possible to correlate the RF properties of cortical neurons with particular spike waveforms. This has implications for how extracellular recordings should be interpreted and complex experiments can now be contemplated that would have been very challenging previously, such as assessing the feedforward connectivity between brain areas in the same location of cortical tissue.

Trends and recent development of the microelectrode arrays (MEAs)

In this paper, we reviewed the history of microelectrode arrays (MEAs), compared different microfabrication techniques applied to modern MEAs in terms of their material characters, device properties and application scenarios. Then we discussed the biocompatibility of different MEAs as well as corresponding strategy of improvement. At last, we analyzed the growing trend of MEAs' technical route, expected application of MEAs in the field of Electrical impedance tomography (EIT).

Spiking Correlation Analysis of Synchronous Spikes Evoked by Acupuncture Mechanical Stimulus

Acupuncturing the ST36 acupoint can evoke the response of the sensory nervous system, which is translated into output electrical signals in the spinal dorsal root. Neural response activities, especially synchronous spike events, evoked by different acupuncture manipulations have remarkable differences. In order to identify these network collaborative activities, we analyze the underlying spike correlation in the synchronous spike event. In this paper, we adopt a log-linear model to describe network response activities evoked by different acupuncture manipulations. Then the state-space model and Bayesian theory are used to estimate network spike correlations. Two sets of simulation data are used to test the effectiveness of the estimation algorithm and the model goodness-of-fit. In addition, simulation data are also used to analyze the relationship between spike correlations and synchronous spike events. Finally, we use this method to identify network spike correlations evoked by four different acupuncture manipulations. Results show that reinforcing manipulations (twirling reinforcing and lifting-thrusting reinforcing) can evoke the third-order spike correlation but reducing manipulations (twirling reducing and lifting-thrusting reducing) does not. This is the main reason why synchronous spikes evoked by reinforcing manipulations are more abundant than reducing manipulations.

Cellular-scale silicon probes for high-density, precisely localized neurophysiology

Neural implants with large numbers of electrodes have become an important tool for examining brain functions. However, these devices typically displace a large intracranial volume compared with the neurons they record. This large size limits the density of implants, provokes tissue reactions that degrade chronic performance, and impedes the ability to accurately visualize recording sites within intact circuits. Here we report next-generation silicon-based neural probes at a cellular scale (5 × 10 µm cross section), with ultra-high-density packing (as little as 66 µm between shanks) and 64 or 256 closely spaced recording sites per probe. We show that these probes can be inserted into superficial or deep brain structures and record large spikes in freely behaving rats for many weeks. Finally, we demonstrate a slice-in-place approach for the precise registration of recording sites relative to nearby neurons and anatomical features, including striatal µ-opioid receptor patches. This scalable technology provides a valuable tool for examining information processing within neural circuits and potentially for human brain-machine interfaces.NEW & NOTEWORTHY Devices with many electrodes penetrating into the brain are an important tool for investigating neural information processing, but they are typically large compared with neurons. This results in substantial damage and makes it harder to reconstruct recording locations within brain circuits. This paper presents high-channel-count silicon probes with much smaller features and a method for slicing through probe, brain, and skull all together. This allows probe tips to be directly observed relative to immunohistochemical markers.

Impact of Brain Surface Boundary Conditions on Electrophysiology and Implications for Electrocorticography

Volume conduction of electrical potentials in the brain is highly influenced by the material properties and geometry of the tissue and recording devices implanted into the tissue. These effects are very large in EEG due to the volume conduction through the skull and scalp but are often neglected in intracranial electrophysiology. When considering penetrating electrodes deep in the brain, the assumption of an infinite and homogenous medium can be used when the sources are far enough from the brain surface and the electrodes to minimize the boundary effect. When the electrodes are recording from the brain's surface the effect of the boundary cannot be neglected, and the large surface area and commonly used insulating materials in surface electrode arrays may further increase the effect by altering the nature of the boundary in the immediate vicinity of the electrodes. This gives the experimenter some control over the spatial profiles of the potentials by appropriate design of the electrode arrays. We construct a simple three-layer model to describe the effect of material properties and geometry above the brain surface on the electric potentials and conduct empirical experiments to validate this model. A laminar electrode array is used to measure the effect of insulating and relatively conducting layers above the cortical surface by recording evoked potentials alternating between a dried surface and saline covering layer, respectively. Empirically, we find that an insulating boundary amplifies the potentials relative to conductive saline by about a factor of 4, and that the effect is not constrained to potentials that originate near the surface. The model is applied to predict the influence of array design and implantation procedure on the recording amplitude and spatial selectivity of the surface electrode arrays.

Interfacing Neurons with Nanostructured Electrodes Modulates Synaptic Circuit Features

Understanding neural physiopathology requires advances in nanotechnology-based interfaces, engineered to monitor the functional state of mammalian nervous cells. Such interfaces typically contain nanometer-size features for stimulation and recording as in cell-non-invasive extracellular microelectrode arrays. In such devices, it turns crucial to understand specific interactions of neural cells with physicochemical features of electrodes, which could be designed to optimize performance. Herein, versatile flexible nanostructured electrodes covered by arrays of metallic nanowires are fabricated and used to investigate the role of chemical composition and nanotopography on rat brain cells in vitro. By using Au and Ni as exemplary materials, nanostructure and chemical composition are demonstrated to play major roles in the interaction of neural cells with electrodes. Nanostructured devices are interfaced to rat embryonic cortical cells and postnatal hippocampal neurons forming synaptic circuits. It is shown that Au-based electrodes behave similarly to controls. Contrarily, Ni-based nanostructured electrodes increase cell survival, boost neuronal differentiation, and reduce glial cells with respect to flat counterparts. Nonetheless, Au-based electrodes perform superiorly compared to Ni-based ones. Under electrical stimulation, Au-based nanostructured substrates evoke intracellular calcium dynamics compatible with neural networks activation. These studies highlight the opportunity for these electrodes to excite a silent neural network by direct neuronal membranes depolarization.

Spatial Information Based OSort for Real-Time Spike Sorting Using FPGA

OBJECTIVE

Spiking activity of individual neurons can be separated from the acquired multi-unit activity with spike sorting methods. Processing the recorded high-dimensional neural data can take a large amount of time when performed on general-purpose computers.

METHODS

In this paper, an FPGA-based real-time spike sorting system is presented which takes into account the spatial correlation between the electrical signals recorded with closely-packed recording sites to cluster multi-channel neural data. The system uses a spatial window-based version of the Online Sorting algorithm, which uses unsupervised template-matching for clustering.

RESULTS

The test results show that the proposed system can reach an average accuracy of 86% using simulated data (16-32 neurons, 4-10 dB Signal-to-Noise Ratio), while the single-channel clustering version achieves only 74% average accuracy in the same cases on a 128-channel electrode array. The developed system was also tested on in vivo cortical recordings obtained from an anesthetized rat.

CONCLUSION

The proposed FPGA-based spike sorting system can process more than 11000 spikes/second, so it can be used during in vivo experiments providing real-time feedback on the location and electrophysiological properties of well-separable single units.

SIGNIFICANCE

The proposed spike sorting system could be used to reduce the positioning error of the closely-packed recording site during a neural measurement.

Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting

Deciphering useful information from electrophysiological data recorded from the brain, in-vivo or in-vitro, is dependent on the capability to analyse spike patterns efficiently and accurately. The spike analysis mechanisms are heavily reliant on the clustering algorithms that enable separation of spike trends based on their spatio-temporal behaviors. Literature review report several clustering algorithms over decades focused on different applications. Although spike analysis algorithms employ only a small subset of clustering algorithms, however, not much work has been reported on the compliance and suitability of such clustering algorithms for spike analysis. In our study, we have attempted to comment on the suitability of available clustering algorithms and performance capacity when exposed to spike analysis. In this regard, the study reports a compatibility evaluation on algorithms previously employed in spike sorting as well as the algorithms yet to be investigated for application in sorting neural spikes. The performance of the algorithms is compared in terms of their accuracy, confusion matrix and accepted validation indices. Three data sets comprising of easy, difficult, and real spike similarity with known ground-truth are chosen for assessment, ensuring a uniform testbed. The procedure also employs two feature-sets, principal component analysis and wavelets. The report also presents a statistical score scheme to evaluate the performance individually and overall. The open nature of the data sets, the clustering algorithms and the evaluation criteria make the proposed evaluation framework widely accessible to the research community. We believe that the study presents a reference guide for emerging neuroscientists to select the most suitable algorithms for their spike analysis requirements.

Principles of functional neural mapping using an intracortical ultra-density microelectrode array (ultra-density MEA)

OBJECTIVE

Intracortical electrical neural recording using solid-state electrodes is a prevalent approach in addressing neurophysiological queries and implementing brain-computer interfacing systems. As a variety of ultra-density microelectrode arrays (ultra-density MEAs) are being created more recently, this paper answers to the rising demand for a more rigorous theory concerning this new type of neural electrode technology, both to guide the proper design and to inform the proper usage.

APPROACH

This design and use problem of ultra-density MEAs for functional intracortical neuronal circuit mapping is approached from a signal analysis perspective. Starting with quantitative derivations of key basic concepts, the concept of ultra-density MEA is defined in the context for fully resolving the voltage sources within its view volume. Then, the principle of using such an ultra-density MEA for functional neural mapping is elaborated, and a recursive approach to completely resolve all voltage sources from the ultra-density MEA's recordings is proposed. This approach is further validated using a simulated experiment. Last, the limitations and implications of this work are discussed.

MAIN RESULTS

MEAs can only be used to map the extracellular somatic action potential (esAP) sources in a neural microcircuit, and AP propagation along individual axons cannot be detected. The key for the ultra-density MEA design is to make sure that each spatial unit of analysis (SUA) contains no more than one active esAP source. The unique neural resolving capability of ultra-density MEAs comparing to conventional MEAs is to be able to spatiotemporally resolve each esAP source within its view volume.

SIGNIFICANCE

The ultimate capability and limitation of neural electrode array technology at such an unprecedented fabrication resolution is unraveled. This work strives to further the discussions on this topic into a more quantitative and rational direction, while providing a theoretical guideline for the rational development and neuroscientific application of an ultra-density MEA for intracortical functional mapping.

A template-matching algorithm for laminar identification of cortical recording sites from evoked response potentials

In recent years, the advent of the so-called silicon probes has made it possible to homogeneously sample spikes and local field potentials (LFPs) from a regular grid of cortical recording sites. In principle, this allows inferring the laminar location of the sites based on the spatiotemporal pattern of LFPs recorded along the probe, as in the well-known current source-density (CSD) analysis. This approach, however, has several limitations, since it relies on visual identification of landmark features (i.e., current sinks and sources) by human operators - features that can be absent from the CSD pattern if the probe does not span the whole cortical thickness, thus making manual labelling harder. Furthermore, as any manual annotation procedure, the typical CSD-based workflow for laminar identification of recording sites is affected by subjective judgment undermining the consistency and reproducibility of results. To overcome these limitations, we developed an alternative approach, based on finding the optimal match between the LFPs recorded along a probe in a given experiment and a template LFP profile that was computed using 18 recording sessions, in which the depth of the recording sites had been recovered through histology. We show that this method can achieve an accuracy of 79 µm in recovering the cortical depth of recording sites and a 76% accuracy in inferring their laminar location. As such, our approach provides an alternative to CSD that, being fully automated, is less prone to the idiosyncrasies of subjective judgment and works reliably also for recordings spanning a limited cortical stretch.

Volumetric mapping of the functional neuroanatomy of the respiratory network in the perfused brainstem preparation of rats

KEY POINTS

The functional neuroanatomy of the mammalian respiratory network is far from being understood since experimental tools that measure neural activity across this brainstem-wide circuit are lacking. Here, we use silicon multi-electrode arrays to record respiratory local field potentials (rLFPs) from 196-364 electrode sites within 8-10 mm³ of brainstem tissue in single arterially perfused brainstem preparations with respect to the ongoing respiratory motor pattern of inspiration (I), post-inspiration (PI) and late-expiration (E2). rLFPs peaked specifically at the three respiratory phase transitions, E2-I, I-PI and PI-E2. We show, for the first time, that only the I-PI transition engages a brainstem-wide network, and that rLFPs during the PI-E2 transition identify a hitherto unknown role for the dorsal respiratory group. Volumetric mapping of pontomedullary rLFPs in single preparations could become a reliable tool for assessing the functional neuroanatomy of the respiratory network in health and disease.

ABSTRACT

While it is widely accepted that inspiratory rhythm generation depends on the pre-Bötzinger complex, the functional neuroanatomy of the neural circuits that generate expiration is debated. We hypothesized that the compartmental organization of the brainstem respiratory network is sufficient to generate macroscopic local field potentials (LFPs), and if so, respiratory (r) LFPs could be used to map the functional neuroanatomy of the respiratory network. We developed an approach using silicon multi-electrode arrays to record spontaneous LFPs from hundreds of electrode sites in a volume of brainstem tissue while monitoring the respiratory motor pattern on phrenic and vagal nerves in the perfused brainstem preparation. Our results revealed the expression of rLFPs across the pontomedullary brainstem. rLFPs occurred specifically at the three transitions between respiratory phases: (1) from late expiration (E2) to inspiration (I), (2) from I to post-inspiration (PI), and (3) from PI to E2. Thus, respiratory network activity was maximal at respiratory phase transitions. Spatially, the E2-I, and PI-E2 transitions were anatomically localized to the ventral and dorsal respiratory groups, respectively. In contrast, our data show, for the first time, that the generation of controlled expiration during the post-inspiratory phase engages a distributed neuronal population within ventral, dorsal and pontine network compartments. A group-wise independent component analysis demonstrated that all preparations exhibited rLFPs with a similar temporal structure and thus share a similar functional neuroanatomy. Thus, volumetric mapping of rLFPs could allow for the physiological assessment of global respiratory network organization in health and disease.

Open source silicon microprobes for high throughput neural recording

OBJECTIVE

Microfabricated multielectrode arrays are widely used for high throughput recording of extracellular neural activity, which is transforming our understanding of brain function in health and disease. Currently there is a plethora of electrode-based tools being developed at higher education and research institutions. However, taking such tools from the initial research and development phase to widespread adoption by the neuroscience community is often hindered by several obstacles. The objective of this work is to describe the development, application, and open dissemination of silicon microprobes for recording neural activity in vivo.

APPROACH

We propose an open source dissemination platform as an alternative to commercialization. This framework promotes recording tools that are openly and inexpensively available to the community. The silicon microprobes are designed in house, but the fabrication and assembly processes are carried out by third party companies. This enables mass production, a key requirement for large-scale dissemination.

MAIN RESULTS

We demonstrate the operation of silicon microprobes containing up to 256 electrodes in conjunction with optical fibers for optogenetic manipulations or fiber photometry. These data provide new insights about the relationship between calcium activity and neural spiking activity. We also describe the current state of dissemination of these tools. A file repository of resources related to designing, using, and sharing these tools is maintained online.

SIGNIFICANCE

This paper is likely to be a valuable resource for both current and prospective users, as well as developers of silicon microprobes. Based on their extensive usage by a number of labs including ours, these tools present a promising alternative to other types of electrode-based technologies aimed at high throughput recording in head-fixed animals. This work also demonstrates the importance of validating fiber photometry measurements with simultaneous electrophysiological recordings.

Fast simulation of extracellular action potential signatures based on a morphological filtering approximation

Simulating extracellular recordings of neuronal populations is an important and challenging task both for understanding the nature and relationships between extracellular field potentials at different scales, and for the validation of methodological tools for signal analysis such as spike detection and sorting algorithms. Detailed neuronal multicompartmental models with active or passive compartments are commonly used in this objective. Although using such realistic NEURON models could lead to realistic extracellular potentials, it may require a high computational burden making the simulation of large populations difficult without a workstation. We propose in this paper a novel method to simulate extracellular potentials of firing neurons, taking into account the NEURON geometry and the relative positions of the electrodes. The simulator takes the form of a linear geometry based filter that models the shape of an action potential by taking into account its generation in the cell body / axon hillock and its propagation along the axon. The validity of the approach for different NEURON morphologies is assessed. We demonstrate that our method is able to reproduce realistic extracellular action potentials in a given range of axon/dendrites surface ratio, with a time-efficient computational burden.

Cooperative Phase Adaptation and Amplitude Amplification of Neuronal Activity in the Vagal Complex: An Interplay Between Microcircuits and Macrocircuits

Clusters of neurons can communicate with others through the cross-frequency coupling mechanism of oscillatory synchrony. We addressed the hypothesis that neuronal networks at various levels from micro- to macrocircuits implement this communication strategy. An abundance of local recurrent axons of vagal complex (VC) cells establish dense local microcircuits and seem to generate high-frequency noise-causing stochastic resonance (reverberation) and coherence resonance, even in in vitro slice preparations. These phenomena were observed in vitro as the generation of episodes of higher-frequency noise after an external stimulation and as stimulus-induced or spontaneous high-amplitude signals (postsynaptic activities). The in vitro microcircuit networks rarely sustained the stochastic resonance and coherence resonance cooperatively; however, in vivo networks involving additional intrabulbar mesocircuits and large-scale macrocircuits were able to sustain them cooperatively. This gave rise to large-scale oscillatory synchrony leading to robust power and coherence of signals with high amplitudes, reaching several millivolts in amplitude from a noise level of ~100 microV through cardiorespiratory frequency coupling. A regenerative mechanism of neuronal circuits might work for the generation of large-scale oscillatory synchrony. The amplitude and phase of neuronal activity in vivo may interact cooperatively to give rise to varying degrees of power and coherence of robust rhythmic activity for distinct physiological roles. The cooperative interaction between phase adaptation and amplitude amplification of neuronal activity may provide diverse nervous systems with both robustness and resilience.

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Neuroprosthetics and neuromodulation represent a promising field for several related applications in the central and peripheral nervous system, such as the treatment of neurological disorders, the control of external robotic devices, and the restoration of lost tactile functions. These actions are allowed by the neural interface, a miniaturized implantable device that most commonly exploits electrical energy to fulfill these operations. A neural interface must be biocompatible, stable over time, low invasive, and highly selective; the challenge is to develop a safe, compact, and reliable tool for clinical applications. In case of anatomical impairments, neuroprosthetics is bound to the need of exploring the surrounding environment by fast-responsive and highly sensitive artificial tactile sensors that mimic the natural sense of touch. Tactile sensors and neural interfaces are closely interconnected since the readouts from the first are required to convey information to the neural implantable apparatus. The role of these devices is pivotal hence technical improvements are essential to ensure a secure system to be eventually adopted in daily life. This review highlights the fundamental criteria for the design and microfabrication of neural interfaces and artificial tactile sensors, their use in clinical applications, and future enhancements for the release of a second generation of devices.

Neuropixels Data-Acquisition System: A Scalable Platform for Parallel Recording of 10 000+ Electrophysiological Signals

Although CMOS fabrication has enabled a quick evolution in the design of high-density neural probes and neural-recording chips, the scaling and miniaturization of the complete data-acquisition systems has happened at a slower pace. This is mainly due to the complexity and the many requirements that change depending on the specific experimental settings. In essence, the fundamental challenge of a neural-recording system is getting the signals describing the largest possible set of neurons out of the brain and down to data storage for analysis. This requires a complete system optimization that considers the physical, electrical, thermal and signal-processing requirements, while accounting for available technology, manufacturing constraints and budget. Here we present a scalable and open-standards-based open-source data-acquisition system capable of recording from over 10,000 channels of raw neural data simultaneously. The components and their interfaces have been optimized to ensure robustness and minimum invasiveness in small-rodent electrophysiology.

Enabling single cell electrical stimulation and response recording via a microfluidic platform

Electrical stimulation (ES) has been recognized to play important roles in regulating cell behaviors. A microfluidic device was developed for the electrical stimulation of single cells and simultaneous recording of extracellular field potential (EFP). Each single cell was trapped onto an electrode surface by a constriction channel for ES testing and was then driven to the outlet by the pressure afterward. This design allows the application of ES on and detection of EFP of single cells continuously in a microfluidic channel. Human cardiomyocytes and primary rat cortex neurons were tested with specific ES with the device. Each cell's EFP signal was detected and analyzed during the ES process. Results have shown that after applying specific ES on the excitable single cells, the cells evoked electrical responses. In addition, increased secretion of glutamic acid was detected from the stimulated neurons. Altogether, these results indicated that the developed device can be used to continuously apply ES on and accurately determine cell responses of single cells with shorter probing time. The throughput of the measurement can achieve 1 cell per minute, which is higher than the traditional ES methods that need culturing cells or manually positioning the cells onto the electrode surface. Before and after the application of ES, the cell viability had no significant change. Such a device can be used to study the biological process of various types of cells under electrical stimulation.

LFP clustering in cortex reveals a taxonomy of Up states and near-millisecond, ordered phase-locking in cortical neurons

During slow-wave sleep and anesthesia, mammalian cortex exhibits a synchronized state during which neurons shift from a largely nonfiring to a firing state, known as an Up-state transition. Up-state transitions may constitute the default activity pattern of the entire cortex (Neske GT. Front Neural Circuits 9: 88, 2016) and could be critical to understanding cortical function, yet the genesis of such transitions and their interaction with single neurons is not well understood. It was recently shown that neurons firing at rates >2 Hz fire spikes in a stereotyped order during Up-state transitions (Luczak A, McNaughton BL, Harris KD. Nat Rev Neurosci 16: 745-755, 2015), yet it is still unknown if Up states are homogeneous and whether spiking order is present in neurons with rates <2 Hz (the majority). Using extracellular recordings from anesthetized cats and mice and from naturally sleeping rats, we show for the first time that Up-state transitions can be classified into several types based on the shape of the local field potential (LFP) during each transition. Individual LFP events could be localized in time to within 1-4 ms, more than an order of magnitude less than in previous studies. The majority of recorded neurons synchronized their firing to within ±5-15 ms relative to each Up-state transition. Simultaneous electrophysiology and wide-field imaging in mouse confirmed that LFP event clusters are cortex-wide phenomena. Our findings show that Up states are of different types and point to the potential importance of temporal order and millisecond-scale signaling by cortical neurons.NEW & NOTEWORTHY During cortical Up-state transitions in sleep and anesthesia, neurons undergo brief periods of increased firing in an order similar to that occurring in awake states. We show that these transitions can be classified into distinct types based on the shape of the local field potential. Transition times can be defined to <5 ms. Most neurons synchronize their firing to within ±5-15 ms of the transitions and fire in a consistent order.

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.

Visual cortex neurons phase-lock selectively to subsets of LFP oscillations

It is generally thought that apart from receptive field differences, such as preferred orientation and spatial frequency selectivity, primary visual cortex neurons are functionally similar to each other. However, the genetic diversity of cortical neurons plus the existence of inputs additional to those required to explain known receptive field properties might suggest otherwise. Here we report the existence of desynchronized states in anesthetized cat area 17 lasting up to 45 min, characterized by variable narrow-band local field potential (LFP) oscillations in the range 2-100 Hz and the absence of a synchronized 1/f frequency spectrum. During these periods, spontaneously active neurons phase-locked to variable subsets of LFP oscillations. Individual neurons often ignored frequencies that others phase-locked to. We suggest that these desynchronized periods may correspond to REM sleep-like episodes occurring under anesthesia. Frequency-selective codes may be used for signaling during these periods. Hence frequency-selective combination and frequency-labeled pathways may represent a previously unsuspected dimension of cortical organization. NEW & NOTEWORTHY We investigated spontaneous neuronal firing during periods of desynchronized local field potential (LFP) activity, resembling REM sleep, in anesthetized cats. During these periods, neurons synchronized their spikes to specific phases of multiple LFP frequency components, with some neurons ignoring frequencies that others were synchronized to. Some neurons fired at phase alignments of frequency pairs, thereby acting as phase coincidence detectors. These results suggest that internal brain signaling may use frequency combination codes to generate temporally structured spike trains.

In vivo Recording Quality of Mechanically Decoupled Floating Versus Skull-Fixed Silicon-Based Neural Probes

Throughout the past decade, silicon-based neural probes have become a driving force in neural engineering. Such probes comprise sophisticated, integrated CMOS electronics which provide a large number of recording sites along slender probe shanks. Using such neural probes in a chronic setting often requires them to be mechanically anchored with respect to the skull. However, any relative motion between brain and implant causes recording instabilities and tissue responses such as glial scarring, thereby shielding recordable neurons from the recording sites integrated on the probe and thus decreasing the signal quality. In the current work, we present a comparison of results obtained using mechanically fixed and floating silicon neural probes chronically implanted into the cortex of a non-human primate. We demonstrate that the neural signal quality estimated by the quality of the spiking and local field potential (LFP) recordings over time is initially superior for the floating probe compared to the fixed device. Nonetheless, the skull-fixed probe also allowed long-term recording of multi-unit activity (MUA) and low frequency signals over several months, especially once pulsations of the brain were properly controlled.

Optimal Electrode Size for Multi-Scale Extracellular-Potential Recording From Neuronal Assemblies

Advances in microfabrication technology have enabled the production of devices containing arrays of thousands of closely spaced recording electrodes, which afford subcellular resolution of electrical signals in neurons and neuronal networks. Rationalizing the electrode size and configuration in such arrays demands consideration of application-specific requirements and inherent features of the electrodes. Tradeoffs among size, spatial density, sensitivity, noise, attenuation, and other factors are inevitable. Although recording extracellular signals from neurons with planar metal electrodes is fairly well established, the effects of the electrode characteristics on the quality and utility of recorded signals, especially for small, densely packed electrodes, have yet to be fully characterized. Here, we present a combined experimental and computational approach to elucidating how electrode size, and size-dependent parameters, such as impedance, baseline noise, and transmission characteristics, influence recorded neuronal signals. Using arrays containing platinum electrodes of different sizes, we experimentally evaluated the electrode performance in the recording of local field potentials (LFPs) and extracellular action potentials (EAPs) from the following cell preparations: acute brain slices, dissociated cell cultures, and organotypic slice cultures. Moreover, we simulated the potential spatial decay of point-current sources to investigate signal averaging using known signal sources. We demonstrated that the noise and signal attenuation depend more on the electrode impedance than on electrode size, per se, especially for electrodes <10 μm in width or diameter to achieve high-spatial-resolution readout. By minimizing electrode impedance of small electrodes (<10 μm) via surface modification, we could maximize the signal-to-noise ratio to electrically visualize the propagation of axonal EAPs and to isolate single-unit spikes. Due to the large amplitude of LFP signals, recording quality was high and nearly independent of electrode size. These findings should be of value in configuring in vitro and in vivo microelectrode arrays for extracellular recordings with high spatial resolution in various applications.

Multichannel Silicon Probes for Awake Hippocampal Recordings in Large Animals

Decoding laminar information across deep brain structures and cortical regions is necessary in order to understand the neuronal ensembles that represent cognition and memory. Large animal models are essential for translational research due to their gyrencephalic neuroanatomy and significant white matter composition. A lack of long-length probes with appropriate stiffness allowing penetration to deeper structures with minimal damage to the neural interface is one of the major technical limitations to applying the approaches currently utilized in lower order animals to large animals. We therefore tested the performance of multichannel silicon probes of various solutions and designs that were developed specifically for large animal electrophysiology. Neurophysiological signals from dorsal hippocampus were recorded in chronically implanted awake behaving Yucatan pigs. Single units and local field potentials were analyzed to evaluate performance of given silicon probes over time. EDGE-style probes had the highest yields during intra-hippocampal recordings in pigs, making them the most suitable for chronic implantations and awake behavioral experimentation. In addition, the cross-sectional area of silicon probes was found to be a crucial determinant of silicon probe performance over time, potentially due to reduction of damage to the neural interface. Novel 64-channel EDGE-style probes tested acutely produced an optimal single unit separation and a denser sampling of the laminar structure, identifying these research silicon probes as potential candidates for chronic implantations. This study provides an analysis of multichannel silicon probes designed for large animal electrophysiology of deep laminar brain structures, and suggests that current designs are reaching the physical thresholds necessary for long-term (∼1 month) recordings with single-unit resolution.

High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification

Different neuron types serve distinct roles in neural processing. Extracellular electrical recordings are extensively used to study brain function but are typically blind to cell identity. Morphoelectrical properties of neurons measured on spatially dense electrode arrays have the potential to distinguish neuron types. We used high-density silicon probes to record from cortical and subcortical regions of the mouse brain. Extracellular waveforms of each neuron were detected across many channels and showed distinct spatiotemporal profiles among brain regions. Classification of neurons by brain region was improved with multichannel compared with single-channel waveforms. In visual cortex, unsupervised clustering identified the canonical regular-spiking (RS) and fast-spiking (FS) classes but also indicated a subclass of RS units with unidirectional backpropagating action potentials (BAPs). Moreover, BAPs were observed in many hippocampal RS cells. Overall, waveform analysis of spikes from high-density probes aids neuron identification and can reveal dendritic backpropagation. NEW & NOTEWORTHY It is challenging to identify neuron types with extracellular electrophysiology in vivo. We show that spatiotemporal action potentials measured on high-density electrode arrays can capture cell type-specific morphoelectrical properties, allowing classification of neurons across brain structures and within the cortex. Moreover, backpropagating action potentials are reliably detected in vivo from subpopulations of cortical and hippocampal neurons. Together, these results enhance the utility of dense extracellular electrophysiology for cell-type interrogation of brain network function.

A microfluidic device for noninvasive cell electrical stimulation and extracellular field potential analysis

We developed a device that can quickly apply versatile electrical stimulation (ES) signals to cells suspended in microfluidic channels and measure extracellular field potential simultaneously. The device could trap cells onto the surface of measurement electrodes for ES and push them to the downstream channel after ES by increasing pressure for continuous measurement. Cardiomyocytes, major functional cells in heart, together with human fibroblast cells and human umbilical vein endothelial cells, were tested with the device. Extracellular field potential signals generated from the cells were recorded. We found that under electrical stimulation, cardiomyocytes were triggered to alter their field potential, while non-excitable cells were not triggered. Hence this device can noninvasively distinguish electrically excitable cells from electrically non-excitable cells. Results have also shown that increased cardiomyocyte cell number led to increased magnitude and occurrence of the cell responses. This relationship could be used to detect the viable cells in a cardiac tissue. Application of variable ES signals on different cardiomyocyte clusters has shown that the application of ES clearly boosted cardiomyocytes electrical activities according to the stimulation frequency. In addition, we confirmed that the device can apply ES onto and detect the electrical responses from a mixed cell cluster; the responses from the mixed cluster is dependent on the ratio of cardiomyocytes. These results demonstrated that our device could be used as a tool to optimize ES conditions to facilitate the functional engineered cardiac tissue development.

How does the presence of neural probes affect extracellular potentials?

OBJECTIVE

Mechanistic modeling of neurons is an essential component of computational neuroscience that enables scientists to simulate, explain, and explore neural activity. The conventional approach to simulation of extracellular neural recordings first computes transmembrane currents using the cable equation and then sums their contribution to model the extracellular potential. This two-step approach relies on the assumption that the extracellular space is an infinite and homogeneous conductive medium, while measurements are performed using neural probes. The main purpose of this paper is to assess to what extent the presence of the neural probes of varying shape and size impacts the extracellular field and how to correct for them.

APPROACH

We apply a detailed modeling framework allowing explicit representation of the neuron and the probe to study the effect of the probes and thereby estimate the effect of ignoring it. We use meshes with simplified neurons and different types of probe and compare the extracellular action potentials with and without the probe in the extracellular space. We then compare various solutions to account for the probes' presence and introduce an efficient probe correction method to include the probe effect in modeling of extracellular potentials.

MAIN RESULTS

Our computations show that microwires hardly influence the extracellular electric field and their effect can therefore be ignored. In contrast, multi-electrode arrays (MEAs) significantly affect the extracellular field by magnifying the recorded potential. While MEAs behave similarly to infinite insulated planes, we find that their effect strongly depends on the neuron-probe alignment and probe orientation.

SIGNIFICANCE

Ignoring the probe effect might be deleterious in some applications, such as neural localization and parameterization of neural models from extracellular recordings. Moreover, the presence of the probe can improve the interpretation of extracellular recordings, by providing a more accurate estimation of the extracellular potential generated by neuronal models.

The dark matter of the brain

The bulk of brain energy expenditure is allocated for maintenance of perpetual intrinsic activity of neurons and neural circuits. Long-term electrophysiological and neuroimaging studies in anesthetized and behaving animals show, however, that the great majority of nerve cells in the intact brain do not fire action potentials, i.e., are permanently silent. Herein, I review emerging data suggesting massive redundancy of nerve cells in mammalian nervous system, maintained in inhibited state at high energetic costs. Acquired in the course of evolution, these collections of dormant neurons and circuits evade routine functional undertakings, and hence, keep out of the reach of natural selection. Under penetrating stress and disease, however, they occasionally switch in active state and drive a variety of neuro-psychiatric symptoms and behavioral abnormalities. The increasing evidence for widespread occurrence of silent neurons warrants careful revision of functional models of the brain and entails unforeseen reserves for rehabilitation and plasticity.

Characterization of Tetrodes Coated with Au Nanoparticles (AuNPs) and PEDOT and Their Application to Thalamic Neural Signal Detection in vivo

Tetrodes, consisting of four twisted micro-wires can simultaneously record the number of neurons in the brain. To improve the quality of neuronal activity detection, the tetrode tips should be modified to increase the surface area and lower the impedance properties. In this study, tetrode tips were modified by the electrodeposition of Au nanoparticles (AuNPs) and dextran (Dex) doped poly (3,4-ethylenedioxythiophene) (PEDOT). The electrochemical properties were measured using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). A decrease in the impedance value from 4.3 MΩ to 13 kΩ at 1 kHz was achieved by the modified tetrodes. The cathodic charge storage capacity (CSC_C) of AuNPs-PEDOT deposited tetrodes was 4.5 mC/cm², as determined by CV measurements. The tetrodes that were electroplated with AuNPs and PEDOT exhibited an increased surface area, which reduced the tetrode impedance. In vivo recording in the ventral posterior medial (VPM) nucleus of the thalamus was performed to investigate the single-unit activity in normal rats. To evaluate the recording performance of modified tetrodes, spontaneous spike signals were recorded. The values of the L-ratio, isolation distance and signal-to-noise (SNR) confirmed that electroplating the tetrode surface with AuNPs and PEDOT improved the recording performance, and these parameters could be used to effectively quantify the spikes of each cluster.

Spatiotemporal Structure and Dynamics of Spontaneous Oscillatory Synchrony in the Vagal Complex

Fundamental structure and dynamics of spontaneous neuronal activities without apparent peripheral inputs were analyzed in the vagal complex (VC), whose activities had been generally thought to be produced almost passively to peripheral cues. The analysis included the caudal nucleus of the tractus solitarius—a main gateway for viscerosensory peripheral afferents and involved dynamically and critically in cardiorespiratory brainstem networks. In the present study, a possibility of self-organized brain activity was addressed in the VC. While VC neurons exhibited sparse firing in anesthetized rats and in in vitro preparations, we identified peculiar features of the emergent electrical population activity: (1) Spontaneous neuronal activity, in most cases, comprised both respiration and cardiac cycle components. (2) Population potentials of polyphasic high amplitudes reaching several millivolts emerged in synchrony with the inspiratory phase of respiratory cycles and exhibited several other characteristic temporal dynamics. (3) The spatiotemporal dynamics of local field potentials (LFPs), recorded simultaneously over multiple sites, were characterized by a stochastic emergence of high-amplitude synchrony. By adjusting amplitude and frequency (phase) over both space and time, the traveling synchrony exhibited varied degrees of coherence and power with a fluctuating balance between mutual oscillators of respiratory and cardiac frequency ranges. Full-fledged large-scale oscillatory synchrony over a wide region of the VC emerged after achieving a maximal stable balance between the two oscillators. Distinct somatic (respiratory; ~1 Hz) and visceral (autonomic; ~5 Hz) oscillators seemed to exist and communicate co-operatively in the brainstem network. Fluctuating oscillatory coupling may reflect varied degrees of synchrony influenced by the varied amplitude and frequency of neuronal activity in the VC. Intranuclear micro-, intrabulbar meso-, and wide-ranging macro-circuits involving the VC are likely to form nested networks and strategically interact to maintain a malleable whole-body homeostasis. These two brainstem oscillators could orchestrate neuronal activities of the VC, and other neuronal groups, through a phase-phase coupling mechanism to perform specific physiological functions.