Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex

Phasic oxygen dynamics confounds fast choline-sensitive biosensor signals in the brain of behaving rodents

Cholinergic fast time-scale modulation of cortical physiology is critical for cognition, but direct local measurement of neuromodulators in vivo is challenging. Choline oxidase (ChOx)-based electrochemical biosensors have been used to capture fast cholinergic signals in behaving animals. However, these transients might be biased by local field potential and O₂-evoked enzymatic responses. Using a novel Tetrode-based Amperometric ChOx (TACO) sensor, we performed highly sensitive and selective simultaneous measurement of ChOx activity (COA) and O₂. In vitro and in vivo experiments, supported by mathematical modeling, revealed that non-steady-state enzyme responses to O₂ give rise to phasic COA dynamics. This mechanism accounts for most of COA transients in the hippocampus, including those following locomotion bouts and sharp-wave/ripples. Our results suggest that it is unfeasible to probe phasic cholinergic signals under most behavioral paradigms with current ChOx biosensors. This confound is generalizable to any oxidase-based biosensor, entailing rigorous controls and new biosensor designs.

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.

Multielectrode Recordings From Identified Neurons Involved in Visually Elicited Escape Behavior

A major challenge in current neuroscience is to understand the concerted functioning of distinct neurons involved in a particular behavior. This goal first requires achieving an adequate characterization of the behavior as well as an identification of the key neuronal elements associated with that action. Such conditions have been considerably attained for the escape response to visual stimuli in the crab Neohelice. During the last two decades a combination of in vivo intracellular recordings and staining with behavioral experiments and modeling, led us to postulate that a microcircuit formed by four classes of identified lobula giant (LG) neurons operates as a decision-making node for several important visually-guided components of the crab's escape behavior. However, these studies were done by recording LG neurons individually. To investigate the combined operations performed by the group of LG neurons, we began to use multielectrode recordings. Here we describe the methodology and show results of simultaneously recorded activity from different lobula elements. The different LG classes can be distinguished by their differential responses to particular visual stimuli. By comparing the response profiles of extracellular recorded units with intracellular recorded responses to the same stimuli, two of the four LG classes could be faithfully recognized. Additionally, we recorded units with stimulus preferences different from those exhibited by the LG neurons. Among these, we found units sensitive to optic flow with marked directional preference. Units classified within a single group according to their response profiles exhibited similar spike waveforms and similar auto-correlograms, but which, on the other hand, differed from those of groups with different response profiles. Additionally, cross-correlograms revealed excitatory as well as inhibitory relationships between recognizable units. Thus, the extracellular multielectrode methodology allowed us to stably record from previously identified neurons as well as from undescribed elements of the brain of the crab. Moreover, simultaneous multiunit recording allowed beginning to disclose the connections between central elements of the visual circuits. This work provides an entry point into studying the neural networks underlying the control of visually guided behaviors in the crab brain.

Neurons under genetic control: What are the next steps towards the treatment of movement disorders?

Since the implementation of deep-brain stimulation as a therapy for movement disorders, there has been little progress in the clinical application of novel alternative treatments. Movement disorders are a group of neurological conditions, which are characterised with impairment of voluntary movement and share similar anatomical loci across the basal ganglia. The focus of the current review is on Parkinson's disease and Huntington's disease as they are the most investigated hypokinetic and hyperkinetic movement disorders, respectively. The last decade has seen enormous advances in the development of laboratory techniques that control neuronal activity. The two major ways to genetically control the neuronal function are: 1) expression of light-sensitive proteins that allow for the optogenetic control of the neuronal spiking and 2) expression or suppression of genes that control the transcription and translation of proteins. However, the translation of these methodologies from the laboratories into the clinics still faces significant challenges. The article summarizes the latest developments in optogenetics and gene therapy. Here, I compare the physiological mechanisms of established electrical deep brain stimulation to the experimental optogenetical deep brain stimulation. I compare also the advantages of DNA- and RNA-based techniques for gene therapy of familial movement disorders. I highlight the benefits and the major issues of each technique and I discuss the translational potential and clinical feasibility of optogenetic stimulation and gene expression control. The review emphasises recent technical breakthroughs that could initiate a notable leap in the treatment of movement disorders.

Early olfactory, but not gustatory processing, is affected by the selection of heritable cognitive phenotypes in honey bee

Associative learning enables animals to predict rewards or punishments by their associations with predictive stimuli, while non-associative learning occurs without reinforcement. The latter includes latent inhibition (LI), whereby animals learn to ignore an inconsequential 'familiar' stimulus. Individual honey bees display heritable differences in expression of LI. We examined the behavioral and neuronal responses between honey bee genetic lines exhibiting high and low LI. We observed, as in previous studies, that high LI lines learned a familiar odor more slowly than low LI bees. By measuring gustatory responses to sucrose, we determined that perception of sucrose reward was similar between both lines, thereby not contributing to the LI phenotype. We then used extracellular electrophysiology to determine differences in neural responses of the antennal lobe (AL) to familiar and novel odors between the lines. Low LI bees responded significantly more strongly to both familiar and novel odors than the high LI bees, but the lines showed equivalent differences in response to the novel and familiar odors. This work suggests that some effects of genotype are present in early olfactory processing, and those effects could complement how LI is manifested at later stages of processing in brains of bees in the different lines.

Integrated Neurophotonics: Toward Dense Volumetric Interrogation of Brain Circuit Activity-at Depth and in Real Time

We propose a new paradigm for dense functional imaging of brain activity to surmount the limitations of present methodologies. We term this approach "integrated neurophotonics"; it combines recent advances in microchip-based integrated photonic and electronic circuitry with those from optogenetics. This approach has the potential to enable lens-less functional imaging from within the brain itself to achieve dense, large-scale stimulation and recording of brain activity with cellular resolution at arbitrary depths. We perform a computational study of several prototype 3D architectures for implantable probe-array modules that are designed to provide fast and dense single-cell resolution (e.g., within a 1-mm3 volume of mouse cortex comprising ∼100,000 neurons). We describe progress toward realizing integrated neurophotonic imaging modules, which can be produced en masse with current semiconductor foundry protocols for chip manufacturing. Implantation of multiple modules can cover extended brain regions.

Free-viewing as experimental system to test the Temporal Correlation Hypothesis: A case of theory-generative experimental practice

Theory-free characterizations of experimental systems miss normative and conceptual components that sometimes are crucial to understanding their historical development. In the following paper, we show that these components may be part of the intrinsic capacities of experimental systems themselves. We study a case of non-exploratory and theory-oriented research in experimental neuroscience that concerns the construction of free-viewing as an experimental system to test one particular pre-existing hypothesis, the Temporal Correlation Hypothesis (TCH), at a laboratory in Santiago de Chile, during 2002-2008. We show that the system does not take well-formulated pre-existing predictions or hypotheses to test them directly, but re-creates them and re-signifies them in terms that are not implied by the theoretical background from which they originally derived. Therefore, we conclude that there is a sui generis way in which experimental systems produce proper theoretical knowledge.

Gels, jets, mosquitoes, and magnets: a review of implantation strategies for soft neural probes

Implantable neuroelectronic interfaces have enabled breakthrough advances in the clinical diagnosis and treatment of neurological disorders, as well as in fundamental studies of brain function, behavior, and disease. Intracranial electroencephalography (EEG) mapping with stereo-EEG (sEEG) depth electrodes is routinely adopted for precise epilepsy diagnostics and surgical treatment, while deep brain stimulation has become the standard of care for managing movement disorders. Intracortical microelectrode arrays for high-fidelity recordings of neural spiking activity have led to impressive demonstrations of the power of brain-machine interfaces for motor and sensory functional recovery. Yet, despite the rapid pace of technology development, the issue of establishing a safe, long-term, stable, and functional interface between neuroelectronic devices and the host brain tissue still remains largely unresolved. A body of work spanning at least the last 15 years suggests that safe, chronic integration between invasive electrodes and the brain requires a close match between the mechanical properties of man-made components and the neural tissue. In other words, the next generation of invasive electrodes should be soft and compliant, without sacrificing biological and chemical stability. Soft neuroelectronic interfaces, however, pose a new and significant surgical challenge: bending and buckling during implantation that can preclude accurate and safe device placement. In this topical review, we describe the next generation of soft electrodes and the surgical implantation methods for safe and precise insertion into brain structures. We provide an overview of the most recent innovations in the field of insertion strategies for flexible neural electrodes such as dissolvable or biodegradable carriers, microactuators, biologically-inspired support structures, and electromagnetic drives. In our analysis, we also highlight approaches developed in different fields, such as robotic surgery, which could be potentially adapted and translated to the insertion of flexible neural probes.

Immunohistological and Ultrastructural Study of the Inflammatory Response to Perforated Polyimide Cortical Implants: Mechanisms Underlying Deterioration of Electrophysiological Recording Quality

The deterioration of field potential (FP) recording quality and yield by in vivo multielectrode arrays (MEA) within days to weeks of implantation severely limits progress in basic and applied brain research. The prevailing hypothesis is that implantation of MEA platforms initiate and perpetuate inflammatory processes which culminate in the formation of scar tissue (the foreign body response, FBR) around the implant. The FBR leads to progressive degradation of the recording qualities by displacing neurons away from the electrode surfaces, increasing the resistance between neurons (current source) and the sensing pads and by reducing the neurons’ excitable membrane properties and functional synaptic connectivity through the release of pro-inflammatory cytokines. Meticulous attempts to causally relate the cellular composition, cell density, and electrical properties of the FBR have failed to unequivocally correlate the deterioration of recording quality with the histological severity of the FBR. Based on confocal and electron microscope analysis of thin sections of polyimide based MEA implants along with the surrounding brain tissue at different points in time after implantation, we propose that abrupt FP amplitude attenuation occurs at the implant/brain-parenchyma junction as a result of high seal resistance insulation formed by adhering microglia to the implant surfaces. In contrast to the prevailing hypothesis, that FP decrease occurs across the encapsulating scar of the implanted MEA, this mechanism potentially explains why no correlations have been found between the dimensions and density of the FBR and the recording quality. Recognizing that the seal resistance formed by adhering-microglia to the implant constitutes a downstream element undermining extracellular FP recordings, suggests that approaches to mitigate the formation of the insulating glial could lead to improved recording quality and yield.

Ultraflexible Neural Electrodes for Long-Lasting Intracortical Recording

Implanted electrodes provide one of the most important neurotechniques for fundamental and translational neurosciences by permitting time-resolved electrical detection of individual neurons in vivo. However, conventional rigid electrodes typically cannot provide stable, long-lasting recordings. Numerous interwoven biotic and abiotic factors at the tissue-electrode interface lead to short- and long-term instability of the recording performance. Making neural electrodes flexible provides a promising approach to mitigate these challenges on the implants and at the tissue-electrode interface. Here we review the recent progress of ultraflexible neural electrodes and discuss the engineering principles, the material properties, and the implantation strategies to achieve stable tissue-electrode interface and reliable unit recordings in living brains.

Fabrication of Out-of-Plane High Channel Density Microelectrode Neural Array with 3D Recording and Stimulation Capabilities

The Utah Electrode Array (UEA) and its different variants have become a gold standard in penetrating high channel count neural electrode for bi-directional neuroprostheses (simultaneous recording and stimulation). However, despite its usage in numerous applications, it has one major drawback of having only one active site per shaft, which is at the tip of the shaft. In this work, we are demonstrating a next-generation device, the Utah Multisite Electrode Array (UMEA), which is capable of having multiple sites around the shaft and also retaining the site at the tip. The UMEA can have up to 9 sites per shaft (hence can accommodate 900 active sites) while retaining the form factor of the conventional UEA with 100 sites. However, in this work and to show the proof of concept, the UMEA was fabricated with one active site at the tip and two around the shaft at different heights; thus, three active sites per shaft. The UMEA device is fabricated using a 3D shadow mask patterning technology, which is suitable for a batch fabrication process for these out-of-plane structures. The UMEA was characterized by in-vitro tests to showcase the electrochemical properties of the shaft sites for bi-directional neuroprostheses in contrast to the traditional tip sites of the standard UEA. The UMEA not only improves the channel density of conventional UEAs and hence can access a larger population of neurons, but also enhances the recording and stimulation capabilities from different layers of the human cortex without further increasing the risk of neuronal damage.

A versatile and modular tetrode-based device for single-unit recordings in rodent ex vivo and in vivo acute preparations

BACKGROUND

The demand for affordable tools for recording extracellular activity and successfully isolating single units from different brain preparations has pushed researchers and companies to invest in developing and fabricating new recording devices. However, depending on the brain region of interest, experimental question or type of preparation, different devices are required thus adding substantial financial burden to laboratories.

NEW METHOD

We have developed a simple and affordable tetrode-based device that allows interchangeable extracellular recordings of neuronal activity between in vivo and ex vivo preparations and can be easily implemented in all wet-bench laboratories.

RESULTS

Spontaneous activity from several putative single neurons could be easily recorded and isolated by lowering the device into ex vivo cerebellum brain slices. The same device was also used in vivo, lowered into primary auditory cortex of adult anesthetized transgenic mice expressing channelrhodopsin in cortical neurons. Acoustic stimulation of the contralateral ear or direct laser optogenetic stimulation successfully evoked cortical activity at the recording site. Several isolated putative single neurons presented time-locked activity response to the different stimuli.

COMPARISON WITH EXISTING METHODS

Besides low fabrication cost, our device uses an omnetics connector compatible with the majority of headstages already available at most electrophysiology laboratories. The device allows custom tetrode configuration arrays and extensions for optogenetics and pharmacology, providing experimental flexibility not available in commercial off-the-shelf microelectrode arrays and silicon probes.

CONCLUSIONS

We developed an affordable, versatile and modular device to facilitate tetrode extracellular recordings interchangeably between in vivo anaesthetized animals and ex vivo brain slice recordings.

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.

Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm

Dynamic correlation is the correlation between two time series across time. Two approaches that currently exist in neuroscience literature for dynamic correlation estimation are the sliding window method and dynamic conditional correlation. In this paper, we first show the limitations of these two methods especially in the presence of extreme values. We present an alternate approach for dynamic correlation estimation based on a weighted graph and show using simulations and real data analyses the advantages of the new approach over the existing ones. We also provide some theoretical justifications and present a framework for quantifying uncertainty and testing hypotheses.

Twister3: a simple and fast microwire twister

Objective.

Twisted wire probes (TWPs, e.g. stereotrodes and tetrodes) provide a cheap and reliable method for obtaining high quality, multiple single-unit neural recordings in freely moving animals. Despite their ubiquity, TWPs are constructed using a tedious procedure consisting of manually folding, turning, and fusing microwire. This imposes a significant labor burden on research personnel who use TWPs in their experiments.

Approach.

To address this issue, we created Twister3, an open-source microwire twisting machine. This machine features a quick-draw wire feeder that eliminates manual wire folding, an auto-aligning motor attachment mechanism which results in consistently straight probes, and a high speed motor for rapid probe turning.

Main results.

Twister3 greatly increases the speed and repeatability of constructing twisted microwire probes compared to existing options. Users with less than one hour of experience using the device were able to make ~70 tetrodes per hour, on average. It is cheap, well documented, and all associated designs and source code are open-source.

Significance.

Twister3 significantly reduces the labor burden of creating high-quality TWPs so electrophysiologists can spend more of their time performing recordings rather than making probes. Therefore, this device is of interest to any lab performing TWP neural recordings, for example, using microdrives.

SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters

Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike sorting packages are available, there is little consensus about which are most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of ground-truth electrophysiological recordings, displaying results interactively on a continuously-updating website. With contributions from eleven laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include paired intracellular/extracellular recordings and state-of-the-art simulated recordings. Ten of the most popular spike sorting codes are wrapped in a Python package and evaluated on a compute cluster using an automated pipeline. SpikeForest documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.

Recording local field potential and neuronal activity with tetrodes in epileptic patients

BACKGROUND

Recordings with tetrodes have proven to be more effective in isolating single neuron spiking activity than with single microwires. However, tetrodes have never been used in humans. We report on the characteristics, safety, compatibility with clinical intracranial recordings in epileptic patients, and performance, of a new type of hybrid electrode equipped with tetrodes.

NEW METHOD

240 standard clinical macroelectrodes and 102 hybrid electrodes were implanted in 28 patients. Hybrids (diameter 800 μm) are made of 6 or 9 macro-contacts and 2 or 3 tetrodes (diameter 70-80 μm).

RESULTS

No clinical complication or adverse event was associated with the hybrids. Impedance and noise of recordings were stable over time. The design enabled multiscale spatial analyses that revealed physiopathological events which were sometimes specific to one tetrode, but could not be recorded on the macro-contacts. After spike sorting, the single-unit yield was similar to other hybrid electrodes and was sometimes as high as >10 neurons per tetrode.

COMPARISON WITH EXISTING METHOD(S)

This new hybrid electrode has a smaller diameter than other available hybrid electrodes. It provides novel spatial information due to the configuration of the tetrodes. The single-unit yield appears promising.

CONCLUSIONS

This new hybrid electrode is safe, easy to use, and works satisfactorily for conducting multi-scale seizure and physiological analyses.

Rapid Learning of Odor-Value Association in the Olfactory Striatum

Rodents can successfully learn multiple novel stimulus-response associations after only a few repetitions when the contingencies predict reward. The circuits modified during such reinforcement learning to support decision-making are not known, but the olfactory tubercle (OT) and posterior piriform cortex (pPC) are candidates for decoding reward category from olfactory sensory input and relaying this information to cognitive and motor areas. Through single-cell recordings in behaving male and female C57BL/6 mice, we show here that an explicit representation for reward category emerges in the OT within minutes of learning a novel odor-reward association, whereas the pPC lacks an explicit representation even after weeks of overtraining. The explicit reward category representation in OT is visible in the first sniff (50-100 ms) of an odor on each trial, and precedes the motor action. Together, these results suggest that the coding of stimulus information required for reward prediction does not occur within olfactory cortex, but rather in circuits involving the olfactory striatum.SIGNIFICANCE STATEMENT Rodents are olfactory specialists and can use odors to learn contingencies quickly and well. We have found that mice can readily learn to place multiple odors into rewarded and unrewarded categories. Once they have learned the rule, they can do such categorization in a matter of minutes (<10 trials). We found that neural activity in olfactory cortex largely reflects sensory coding, with very little explicit information about categories. By contrast, neural activity in a brain region in the ventral striatum is rapidly modified in a matter of minutes to reflect reward category. Our experiments set up a paradigm for studying rapid sensorimotor reinforcement in a circuit that is right at the interface of sensory input and reward areas.

RatHat: A Self-Targeting Printable Brain Implant System

There has not been a major change in how neuroscientists approach stereotaxic methods in decades. Here, we present a new stereotaxic method that provides an alternative approach to a traditional u-frame stereotaxic device and reduces costs, surgical time, and aids repeatability. The RatHat brain implantation system is a 3D-printable stereotaxic device for rats that is fabricated prior to surgery and fits to the shape of the skull. RatHat builds are directly implanted into the brain without the need for head-leveling or coordinate-mapping during surgery. The RatHat can be used in conjunction with the traditional u-frame stereotaxic device, but does not require the use of a micromanipulator for successful implantations. Each RatHat contains several primary components including the implant for mounting intracranial components, the surgical stencil for targeting drill sites, and the protective cap for preventing damage from impacts and debris. Each component serves a unique function and can be used together or separately. We demonstrate the feasibility of the RatHat in four different proof-of-principle experiments: (1) a three-pole cannula apparatus, (2) an optrode-electrode assembly, (3) a fixed-electrode array, and (4) a tetrode hyperdrive. Implants were successful, durable, and long-lasting (up to nine months). RatHat print files are easily created, can be modified in computer aided design (CAD) software for a variety of applications, and are easily shared, contributing to open science goals and replications. The RatHat has been adapted to multiple experimental paradigms in our lab and should be a useful new way to conduct stereotaxic implant surgeries in rodents.

64-Channel Carbon Fiber Electrode Arrays for Chronic Electrophysiology

A chief goal in neuroscience is to understand how neuronal activity relates to behavior, perception, and cognition. However, monitoring neuronal activity over long periods of time is technically challenging, and limited, in part, by the invasive nature of recording tools. While electrodes allow for recording in freely-behaving animals, they tend to be bulky and stiff, causing damage to the tissue they are implanted in. One solution to this invasiveness problem may be probes that are small enough to fly under the immune system's radar. Carbon fiber (CF) electrodes are thinner and more flexible than typical metal or silicon electrodes, but the arrays described in previous reports had low channel counts and required time-consuming manual assembly. Here we report the design of an expanded-channel-count carbon fiber electrode array (CFEA) as well as a method for fast preparation of the recording sites using acid etching and electroplating with PEDOT-TFB, and demonstrate the ability of the 64-channel CFEA to record from rat visual cortex. We include designs for interfacing the system with micro-drives or flex-PCB cables for recording from multiple brain regions, as well as a facilitated method for coating CFs with the insulator Parylene-C. High-channel-count CFEAs may thus be an alternative to traditional microwire-based electrodes and a practical tool for exploring the neural code.

Neural signal recording and processing in somatic neuroprosthetic applications. A review

Neurointerfaces have acquired major relevance as both rehabilitative and therapeutic tools for patients with spinal cord injury, limb amputations and other neural disorders. Bidirectional neural interfaces are a key component for the functional control of neuroprosthetic devices. The two main neuroprosthetic applications of interfaces with the peripheral nervous system (PNS) are: the refined control of artificial prostheses with sensory neural feedback, and functional electrical stimulation (FES) systems attempting to generate motor or visceral responses in paralyzed organs. The results obtained in experimental and clinical studies with both, extraneural and intraneural electrodes are very promising in terms of the achieved functionality for the neural stimulation mode. However, the results of neural recordings with peripheral nerve interfaces are more limited. In this paper we review the different existing approaches for PNS signals recording, denoising, processing and classification, enabling their use for bidirectional interfaces. PNS recordings can provide three types of signals: i) population activity signals recorded by using extraneural electrodes placed on the outer surface of the nerve, which carry information about cumulative nerve activity; ii) spike activity signals recorded with intraneural electrodes placed inside the nerve, which carry information about the electrical activity of a set of individual nerve fibers; and iii) hybrid signals, which contain both spiking and cumulative signals. Finally, we also point out some of the main limitations, which are hampering clinical translation of neural decoding, and indicate possible solutions for improvement.

Constant Sub-second Cycling between Representations of Possible Futures in the Hippocampus

Cognitive faculties such as imagination, planning, and decision-making entail the ability to represent hypothetical experience. Crucially, animal behavior in natural settings implies that the brain can represent hypothetical future experience not only quickly but also constantly over time, as external events continually unfold. To determine how this is possible, we recorded neural activity in the hippocampus of rats navigating a maze with multiple spatial paths. We found neural activity encoding two possible future scenarios (two upcoming maze paths) in constant alternation at 8 Hz: one scenario per ∼125-ms cycle. Further, we found that the underlying dynamics of cycling (both inter- and intra-cycle dynamics) generalized across qualitatively different representational correlates (location and direction). Notably, cycling occurred across moving behaviors, including during running. These findings identify a general dynamic process capable of quickly and continually representing hypothetical experience, including that of multiple possible futures.

Multimaterial and multifunctional neural interfaces: from surface-type and implantable electrodes to fiber-based devices

Development of neural interfaces from surface electrodes to fibers with various type, functionality, and materials.

Multiplexing of Information about Self and Others in Hippocampal Ensembles

In addition to coding a subject's location in space, the hippocampus has been suggested to code social information, including the spatial position of conspecifics. "Social place cells" have been reported for tasks in which an observer mimics the behavior of a demonstrator. We examine whether rat hippocampal neurons may encode the behavior of a minirobot, but without requiring the animal to mimic it. Rather than finding social place cells, we observe that robot behavioral patterns modulate place fields coding animal position. This modulation may be confounded by correlations between robot movement and changes in the animal's position. Although rat position indeed significantly predicts robot behavior, we find that hippocampal ensembles code additional information about robot movement patterns. Fast-spiking interneurons are particularly informative about robot position and global behavior. In conclusion, when the animal's own behavior is conditional on external agents, the hippocampus multiplexes information about self and others.

Mecp2 Deletion from Cholinergic Neurons Selectively Impairs Recognition Memory and Disrupts Cholinergic Modulation of the Perirhinal Cortex

Rett Syndrome is a neurological disorder caused by mutations in the gene encoding methyl CpG binding protein 2 (MeCP2) and characterized by severe intellectual disability. The cholinergic system is a critical modulator of cognitive ability and is affected in patients with Rett Syndrome. To better understand the importance of MeCP2 function in cholinergic neurons, we studied the effect of selective Mecp2 deletion from cholinergic neurons in mice. Mice with Mecp2 deletion from cholinergic neurons were selectively impaired in assays of recognition memory, a cognitive task largely mediated by the perirhinal cortex (PRH). Deletion of Mecp2 from cholinergic neurons resulted in profound alterations in baseline firing of L5/6 neurons and eliminated the responses of these neurons to optogenetic stimulation of cholinergic input to PRH. Both the behavioral and the electrophysiological deficits of cholinergic Mecp2 deletion were rescued by inhibiting ACh breakdown with donepezil treatment.

An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting

Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience. This network is designed to process an extracellular neural signal in an online and unsupervised fashion. The signal stream is continuously fed to the network and processed through several layers to output spike trains matching the truth after a short learning period requiring only few data. The network features an attention mechanism to handle the scarcity of action potential occurrences in the signal, and a threshold adaptation mechanism to handle patterns with different sizes. This method outperforms two existing spike-sorting algorithms at low signal-to-noise ratio (SNR) and can be adapted to process several channels simultaneously in the case of tetrode recordings. Such attention-based STDP network applied to spike-sorting opens perspectives to embed neuromorphic processing of neural data in future brain implants.

Presence of synchrony-generating hubs in the human epileptic neocortex

KEY POINTS

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

ABSTRACT

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

Miniaturized neural system for chronic, local intracerebral drug delivery

Recent advances in medications for neurodegenerative disorders are expanding opportunities for improving the debilitating symptoms suffered by patients. Existing pharmacologic treatments, however, often rely on systemic drug administration, which result in broad drug distribution and consequent increased risk for toxicity. Given that many key neural circuitries have sub-cubic millimeter volumes and cell-specific characteristics, small-volume drug administration into affected brain areas with minimal diffusion and leakage is essential. We report the development of an implantable, remotely controllable, miniaturized neural drug delivery system permitting dynamic adjustment of therapy with pinpoint spatial accuracy. We demonstrate that this device can chemically modulate local neuronal activity in small (rodent) and large (nonhuman primate) animal models, while simultaneously allowing the recording of neural activity to enable feedback control.

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.

Neural Circuit and Clinical Insights from Intraoperative Recordings During Deep Brain Stimulation Surgery

Observations using invasive neural recordings from patient populations undergoing neurosurgical interventions have led to critical breakthroughs in our understanding of human neural circuit function and malfunction. The opportunity to interact with patients during neurophysiological mapping allowed for early insights in functional localization to improve surgical outcomes, but has since expanded into exploring fundamental aspects of human cognition including reward processing, language, the storage and retrieval of memory, decision-making, as well as sensory and motor processing. The increasing use of chronic neuromodulation, via deep brain stimulation, for a spectrum of neurological and psychiatric conditions has in tandem led to increased opportunity for linking theories of cognitive processing and neural circuit function. Our purpose here is to motivate the neuroscience and neurosurgical community to capitalize on the opportunities that this next decade will bring. To this end, we will highlight recent studies that have successfully leveraged invasive recordings during deep brain stimulation surgery to advance our understanding of human cognition with an emphasis on reward processing, improving clinical outcomes, and informing advances in neuromodulatory interventions.

Highly Flexible Precisely Braided Multielectrode Probes and Combinatorics for Future Neuroprostheses

The braided multielectrode probe (BMEP) is an ultrafine microwire bundle interwoven into a precise tubular braided structure, which is designed to be used as an invasive neural probe consisting of multiple microelectrodes for electrophysiological neural recording and stimulation. Significant advantages of BMEPs include highly flexible mechanical properties leading to decreased immune responses after chronic implantation in neural tissue and dense recording/stimulation sites (24 channels) within the 100-200 μm diameter. In addition, because BMEPs can be manufactured using various materials in any size and shape without length limitations, they could be expanded to applications in deep central nervous system (CNS) regions as well as peripheral nervous system (PNS) in larger animals and humans. Finally, the 3D topology of wires supports combinatoric rearrangements of wires within braids, and potential neural yield increases. With the newly developed next generation micro braiding machine, we can manufacture more precise and complex microbraid structures. In this article, we describe the new machine and methods, and tests of simulated combinatoric separation methods. We propose various promising BMEP designs and the potential modifications to these designs to create probes suitable for various applications for future neuroprostheses.

An Unsupervised Compressed Sensing Algorithm for Multi-Channel Neural Recording and Spike Sorting

We propose an unsupervised compressed sensing (CS)-based framework to compress, recover, and cluster neural action potentials. This framework can be easily integrated into high-density multi-electrode neural recording VLSI systems. Embedding spectral clustering and group structures in dictionary learning, we extend the proposed framework to unsupervised spike sorting without prior label information. Additionally, we incorporate group sparsity concepts in the dictionary learning to enable the framework for multi-channel neural recordings, as in tetrodes. To further improve spike sorting success rates in the CS framework, we embed template matching in sparse coding to jointly predict clusters of spikes. Our experimental results demonstrate that the proposed CS-based framework can achieve a high compression ratio (8:1 to 20:1), with a high quality reconstruction performance (>8 dB) and a high spike sorting accuracy (>90%).

Novel electrode technologies for neural recordings

Neural recording electrode technologies have contributed considerably to neuroscience by enabling the extracellular detection of low-frequency local field potential oscillations and high-frequency action potentials of single units. Nevertheless, several long-standing limitations exist, including low multiplexity, deleterious chronic immune responses and long-term recording instability. Driven by initiatives encouraging the generation of novel neurotechnologies and the maturation of technologies to fabricate high-density electronics, novel electrode technologies are emerging. Here, we provide an overview of recently developed neural recording electrode technologies with high spatial integration, long-term stability and multiple functionalities. We describe how these emergent neurotechnologies can approach the ultimate goal of illuminating chronic brain activity with minimal disruption of the neural environment, thereby providing unprecedented opportunities for neuroscience research in the future.

Layer-specific representation of long-lasting sustained activity in the rat auditory cortex

In the auditory system, distinct and reproducible transient activities responding to the onset of sound have long been the focus when characterizing the auditory cortex, i.e., tonotopic maps, subregions, and layer-specific representation. There is limited information on sustained activities because the rapid adaptation impairs reproducibility and the signal-to-noise ratio. We recently overcame this problem by focusing on neural synchrony and machine learning demonstrated that band-specific power and the phase locking value (PLV) represent sound information in a tonotopic and region-specific manner. Here, we attempted to reveal the layer-specific representation of sustained activities. A microelectrode array recorded sustained activities from layers 2/3, 4, and 5/6 of the rat auditory cortex. We characterized band-specific power and PLV patterns and applied sparse logistic regression (SLR) to discriminate (1) between the sound-induced and spontaneous activities and (2) five test frequencies from the sound-induced activities in each layer. SLR achieved the highest discrimination performance in high-gamma activities in layers 4 and 5/6, higher than in layer 2/3, indicating poor sound representation in layer 2/3. Moreover, the recording sites that contributed to the discrimination in layers 4 and 5/6 had a characteristic frequency similar to the test frequency and were often located in the belt area, indicating tonotopic and region-specific representation. These results indicate that information processing of sustained activities may depend on high-gamma oscillators, i.e., cortical inhibitory interneurons, and reflects layer-specific thalamocortical and corticocortical neural circuits in the auditory system, which may contribute to associative information processing beyond sound frequency in auditory perception.

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.

Flexible Micropillar Electrode Arrays for In Vivo Neural Activity Recordings

Flexible electronics that can form tight interfaces with neural tissues hold great promise for improving the diagnosis and treatment of neurological disorders and advancing brain/machine interfaces. Here, the facile fabrication of a novel flexible micropillar electrode array (µPEA) is described based on a biotemplate method. The flexible and compliant µPEA can readily integrate with the soft surface of a rat cerebral cortex. Moreover, the recording sites of the µPEA consist of protruding micropillars with nanoscale surface roughness that ensure tight interfacing and efficient electrical coupling with the nervous system. As a result, the flexible µPEA allows for in vivo multichannel recordings of epileptiform activity with a high signal-to-noise ratio of 252 ± 35. The ease of preparation, high flexibility, and biocompatibility make the µPEA an attractive tool for in vivo spatiotemporal mapping of neural activity.

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.

Cortical Electrocorticogram (ECoG) Is a Local Signal

Electrocorticogram (ECoG), obtained by low-pass filtering the brain signal recorded from a macroelectrode placed on the cortex, is extensively used to find the seizure focus in drug-resistant epilepsy and is of growing importance in cognitive and brain-machine-interfacing studies. To accurately estimate the epileptogenic cortex or to make inferences about cognitive processes, it is important to determine the "spatial spread" of ECoG (i.e., the extent of cortical tissue that contributes to its activity). However, the ECoG spread is currently unknown; even the spread of local field potential (LFP) obtained from microelectrodes is debated, with estimates ranging from a few hundred micrometers to several millimeters. Spatial spread can be estimated by measuring the receptive field (RF) and multiplying by the cortical magnification factor, but this method overestimates the spread because RF size gets inflated due to several factors. This issue can be partially addressed using a model that compares the RFs of two measures, such as LFP and multi-unit activity (MUA). To use this approach for ECoG, we designed a customized array containing both microelectrodes and ECoG electrodes to simultaneously map MUA, LFP, and ECoG RFs from the primary visual cortex of awake monkeys (three female Macaca radiata). The spatial spread of ECoG was surprisingly local (diameter ∼3 mm), only 3 times that of the LFP. Similar results were obtained using a model to simulate ECoG as a sum of LFPs of varying electrode sizes. Our results further validate the use of ECoG in clinical and basic cognitive research.SIGNIFICANCE STATEMENT Brains signals capture different attributes of the neural network depending on the size and location of the recording electrode. Electrocorticogram (ECoG), obtained by placing macroelectrodes (typically 2-3 mm diameter) on the exposed surface of the cortex, is widely used by neurosurgeons to identify the source of seizures in drug-resistant epileptic patients. The brain area responsible for seizures is subsequently surgically removed. Accurate estimation of the epileptogenic cortex and its removal requires the estimation of spatial spread of ECoG. Here, we estimated the spatial spread of ECoG in five behaving monkeys using two different approaches. Our results suggest that ECoG is a local signal (diameter of ∼3 mm), which can provide a useful tool for clinical, cognitive neuroscience, and brain-machine-interfacing applications.

Flexible fiber-based optoelectronics for neural interfaces

Neurological and psychiatric conditions pose an increasing socioeconomic burden on our aging society. Our ability to understand and treat these conditions relies on the development of reliable tools to study the dynamics of the underlying neural circuits. Despite significant progress in approaches and devices to sense and modulate neural activity, further refinement is required on the spatiotemporal resolution, cell-type selectivity, and long-term stability of neural interfaces. Guided by the principles of neural transduction and by the materials properties of the neural tissue, recent advances in neural interrogation approaches rely on flexible and multifunctional devices. Among these approaches, multimaterial fibers have emerged as integrated tools for sensing and delivering of multiple signals to and from the neural tissue. Fiber-based neural probes are produced by thermal drawing process, which is the manufacturing approach used in optical fiber fabrication. This technology allows straightforward incorporation of multiple functional components into microstructured fibers at the level of their macroscale models, preforms, with a wide range of geometries. Here we will introduce the multimaterial fiber technology, its applications in engineering fields, and its adoption for the design of multifunctional and flexible neural interfaces. We will discuss examples of fiber-based neural probes tailored to the electrophysiological recording, optical neuromodulation, and delivery of drugs and genes into the rodent brain and spinal cord, as well as their emerging use for studies of nerve growth and repair.

Assessing the Impacts of Correlated Variability with Dissociated Timescales

Despite the profound influence on coding capacity of sensory neurons, the measurements of noise correlations have been inconsistent. This is, possibly, because nonstationarity, i.e., drifting baselines, engendered the spurious long-term correlations even if no actual short-term correlation existed. Although attempts to separate them have been made previously, they were ad hoc for specific cases or computationally too demanding. Here we proposed an information-geometric method to unbiasedly estimate pure short-term noise correlations irrespective of the background brain activities without demanding computational resources. First, the benchmark simulations demonstrated that the proposed estimator is more accurate and computationally efficient than the conventional correlograms and the residual correlations with Kalman filters or moving averages of length three or more, while the best moving average of length two coincided with the propose method regarding correlation estimates. Next, we analyzed the cat V1 neural responses to demonstrate that the statistical test accompanying the proposed method combined with the existing nonstationarity test enabled us to dissociate short-term and long-term noise correlations. When we excluded the spurious noise correlations of purely long-term nature, only a small fraction of neuron pairs showed significant short-term correlations, possibly reconciling the previous inconsistent observations on existence of significant noise correlations. The decoding accuracy was slightly improved by the short-term correlations. Although the long-term correlations deteriorated the generalizability, the generalizability was recovered by the decoder with trend removal, suggesting that brains could overcome nonstationarity. Thus, the proposed method enables us to elucidate the impacts of short-term and long-term noise correlations in a dissociated manner.

Elastocapillary self-assembled neurotassels for stable neural activity recordings

Implantable neural probes that are mechanically compliant with brain tissue offer important opportunities for stable neural interfaces in both basic neuroscience and clinical applications. Here, we developed a Neurotassel consisting of an array of flexible and high-aspect ratio microelectrode filaments. A Neurotassel can spontaneously assemble into a thin and implantable fiber through elastocapillary interactions when withdrawn from a molten, tissue-dissolvable polymer. Chronically implanted Neurotassels elicited minimal neuronal cell loss in the brain and enabled stable activity recordings of the same population of neurons in mice learning to perform a task. Moreover, Neurotassels can be readily scaled up to 1024 microelectrode filaments, each with a neurite-scale cross-sectional footprint of 3 × 1.5 μm², to form implantable fibers with a total diameter of ~100 μm. With their ultrasmall sizes, high flexibility, and scalability, Neurotassels offer a new approach for stable neural activity recording and neuroprosthetics.

Optimizing the Yield of Multi-Unit Activity by Including the Entire Spiking Activity

Neurophysiological data acquisition using multi-electrode arrays and/or (semi-) chronic recordings frequently has to deal with low signal-to-noise ratio (SNR) of neuronal responses and potential failure of detecting evoked responses within random background fluctuations. Conventional methods to extract action potentials (spikes) from background noise often apply thresholds to the recorded signal, usually allowing reliable detection of spikes when data exhibit a good SNR, but often failing when SNR is poor. We here investigate a threshold-independent, fast, and automated procedure for analysis of low SNR data, based on fullwave-rectification and low-pass filtering the signal as a measure of the entire spiking activity (ESA). We investigate the sensitivity and reliability of the ESA-signal for detecting evoked responses by applying an automated receptive field (RF) mapping procedure to semi-chronically recorded data from primary visual cortex (V1) of five macaque monkeys. For recording sites with low SNR, the usage of ESA improved the detection rate of RFs by a factor of 2.5 in comparison to MUA-based detection. For recording sites with medium and high SNR, ESA delivered 30% more RFs than MUA. This significantly higher yield of ESA-based RF-detection still hold true when using an iterative procedure for determining the optimal spike threshold for each MUA individually. Moreover, selectivity measures for ESA-based RFs were quite compatible with MUA-based RFs. Regarding RF size, ESA delivered larger RFs than thresholded MUA, but size difference was consistent over all SNR fractions. Regarding orientation selectivity, ESA delivered more sites with significant orientation-dependent responses but with somewhat lower orientation indexes than MUA. However, preferred orientations were similar for both signal types. The results suggest that ESA is a powerful signal for applications requiring automated, fast, and reliable response detection, as e.g., brain-computer interfaces and neuroprosthetics, due to its high sensitivity and its independence from user-dependent intervention. Because the full information of the spiking activity is preserved, ESA also constitutes a valuable alternative for offline analysis of data with limited SNR.

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.

High-Density, Long-Lasting, and Multi-region Electrophysiological Recordings Using Polymer Electrode Arrays

The brain is a massive neuronal network, organized into anatomically distributed sub-circuits, with functionally relevant activity occurring at timescales ranging from milliseconds to years. Current methods to monitor neural activity, however, lack the necessary conjunction of anatomical spatial coverage, temporal resolution, and long-term stability to measure this distributed activity. Here we introduce a large-scale, multi-site, extracellular recording platform that integrates polymer electrodes with a modular stacking headstage design supporting up to 1,024 recording channels in freely behaving rats. This system can support months-long recordings from hundreds of well-isolated units across multiple brain regions. Moreover, these recordings are stable enough to track large numbers of single units for over a week. This platform enables large-scale electrophysiological interrogation of the fast dynamics and long-timescale evolution of anatomically distributed circuits, and thereby provides a new tool for understanding brain activity.

Developing Next-generation Brain Sensing Technologies - A Review

Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients.

Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects

A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes.

Differential recordings of local field potential: A genuine tool to quantify functional connectivity

Local field potential (LFP) recording is a very useful electrophysiological method to study brain processes. However, this method is criticized for recording low frequency activity in a large area of extracellular space potentially contaminated by distal activity. Here, we theoretically and experimentally compare ground-referenced (RR) with differential recordings (DR). We analyze electrical activity in the rat cortex with these two methods. Compared with RR, DR reveals the importance of local phasic oscillatory activities and their coherence between cortical areas. Finally, we show that DR provides a more faithful assessment of functional connectivity caused by an increase in the signal to noise ratio, and of the delay in the propagation of information between two cortical structures.

Recent Progress on Microelectrodes in Neural Interfaces

Brain‒machine interface (BMI) is a promising technology that looks set to contribute to the development of artificial limbs and new input devices by integrating various recent technological advances, including neural electrodes, wireless communication, signal analysis, and robot control. Neural electrodes are a key technological component of BMI, as they can record the rapid and numerous signals emitted by neurons. To receive stable, consistent, and accurate signals, electrodes are designed in accordance with various templates using diverse materials. With the development of microelectromechanical systems (MEMS) technology, electrodes have become more integrated, and their performance has gradually evolved through surface modification and advances in biotechnology. In this paper, we review the development of the extracellular/intracellular type of in vitro microelectrode array (MEA) to investigate neural interface technology and the penetrating/surface (non-penetrating) type of in vivo electrodes. We briefly examine the history and study the recently developed shapes and various uses of the electrode. Also, electrode materials and surface modification techniques are reviewed to measure high-quality neural signals that can be used in BMI.