Neural Recording Research Articles

A 1024-Channel Simultaneous Electrophysiological and Electrochemical Neural Recording System with In-Pixel Digitization and Crosstalk Compensation.

Simultaneous electrophysiological and chemical recording allows for multi-modal neural instrumentation and provides insights into chemical synapses and ion channels across the cell membrane. However, intermodal interference can hinder highly synchronized recording in large-scale systems with high temporal and spatial resolution. In this work, we propose a 1024-channel lab-on-CMOS system for dual-modal neural recording with in-pixel digitization and interference suppression. A foreground calibration scheme with tunable capacitance is implemented in-pixel to compensate for the crosstalk between electrical and chemical recording. Active pixels for both electrical and chemical modalities are designed based on a pulse width modulation (PWM) analog-to-digital conversion scheme. CMOS-compatible post-processing is implemented to realize in-pixel electrodes and chemical sensing membranes. The prototype, implemented in a 180nm CMOS technology, occupies a total area of 33mm2 with 1024 pixels, and each unit pixel includes one electrical recording site and two chemical recording sites, with dimensions of 150μm×130 μm. The total system power consumption is 19.68mW at a frame rate of 9k and 3k for electrical and chemical imaging respectively. The in-vitro experiment demonstrated the concurrent high density electrophysilogical and electrochemical recording with sub millisecond temporal resolution.

A nanowell-based MoS2 neuroelectrode for high-sensitivity neural recording

Implantable neural electrodes are crucial in neurological diagnosis and therapy because of their ultra-high spatial resolution, but they are constrained by high impedance and insufficient charge injection capacity, resulting in noise that often obscures valuable signals. Emerging nanotechnologies are powerful tools to improve sensitivity and biocompatibility. Herein, we developed quantized 2D MoS2 electrodes by incorporating bioactive MoS2 nanosheets onto bare electrodes, achieving sensitive, compatible recording. The 2D materials can create tiny nanowells, which behaved as quantized charge storage units and thus improved sensitivity. The key sensitivity indicators, impedance and cathode charge storage capacity, showed a multifold increase. The 17.7-fold improvement in catalytic activity of MoS2 electrodes facilitated effective current transmission and reduced inflammatory response. Invivo recording showed that the sensitivity of local field potentials increased throughout frequency range and peaked at a 4.7-fold in β rhythm. This work provides a general strategy for achieving effective diagnoses of neurological disorders.

Quantifying physical degradation alongside recording and stimulation performance of 980 intracortical microelectrodes chronically implanted in three humans for 956-2246 days.

The clinical success of brain-machine interfaces depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. Therefore, there is a need to quantify any damage that microelectrodes sustain when they are chronically implanted in the human cortex. Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from Neuroport arrays chronically implanted in the cortex of three people with tetraplegia for 956-2246 days. We analyzed eleven multi-electrode arrays in total: eight arrays with platinum (Pt) electrode tips and three with sputtered iridium oxide tips (SIROF); one Pt array was left in sterile packaging, serving as a control. The arrays were implanted/explanted across three different clinical sites surgeries (Caltech/UCLA, Caltech/USC and APL/Johns Hopkins) in the anterior intraparietal area, Brodmann's area 5, motor cortex, and somatosensory cortex.Human experts rated the electron micrographs of electrodes with respect to five damage metrics: the loss of metal at the electrode tip, the amount of separation between the silicon shank and tip metal, tissue adherence or bio-material to the electrode, damage to the shank insulation and silicone shaft. These metrics were compared to functional outcomes (recording quality, noise, impedance and stimulation ability). Despite higher levels of physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt electrodes (measured by SNR), at the time of explant. Additionally, 1 kHz impedance (measured in vivo prior to explant) significantly correlated with all physical damage metrics, recording, and stimulation performance for SIROF electrodes (but not Pt), suggesting a reliable measurement of in vivo degradation.We observed a new degradation type, primarily occurring on stimulated electrodes ("pockmarked" vs "cracked") electrodes; however, tip metalization damage was not significantly higher due to stimulation or amount of charge. Physical damage was centralized to specific regions of an array often with differences between outer and inner electrodes. This is consistent with degradation due to contact with the biologic milieu, influenced by variations in initial manufactured state. From our data, we hypothesize that erosion of the silicon shank often precedes damage to the tip metal, accelerating damage to the electrode / tissue interface. These findings link quantitative measurements, such as impedance, to the physical condition of the microelectrodes and their capacity to record and stimulate. These data could lead to improved manufacturing or novel electrode designs to improve long-term performance of BMIs making them are vitally important as multi-year clinical trials of BMIs are becoming more common.

A heterogenous integrated neural recording system with elastocapillary self-assembled Au-PDMS-PEG neural probe and customized ASIC

Abstract This study presents the design and implementation of a heterogenous integrated neural recording system consisting of a flexible Au-PDMS-PEG probe and customized complementary metal oxide semiconductor (CMOS) application-specific integrated circuit (ASIC) in a standard 0.18 μm process. The flexible Au-PDMS-PEG probe was prepared by an elastocapillary self-assembled process, achieving an electrode impedance of 250 kΩ (@1 kHz). The customized CMOS ASIC contains 36 modular digital pixels (MDP). It achieves 5.69 μVrms input referred noise, 10.29 effective number of bits, 49.5 μW power consumption, and 0.092 mm2 area for a single MDP unit. Spontaneous spikes were also recorded in the mouse cortex, with a peak-to-peak amplitude of 389.2 μVPP and a signal-to-noise ratio of 19.36. Benchtop and in-vivo experiments were conducted to validate the functionality and performance of the proposed neural recording system.

Circular Clustering With Polar Coordinate Reconstruction.

There is a growing interest in characterizing circular data found in biological systems. Such data are wide-ranging and varied, from the signal phase in neural recordings to nucleotide sequences in round genomes. Traditional clustering algorithms are often inadequate due to their limited ability to distinguish differences in the periodic component θ. Current clustering schemes for polar coordinate systems have limitations, such as being only angle-focused or lacking generality. To overcome these limitations, we propose a new analysis framework that utilizes projections onto a cylindrical coordinate system to represent objects in a polar coordinate system optimally. Using the mathematical properties of circular data, we show that our approach always finds the correct clustering result within the reconstructed dataset, given sufficient periodic repetitions of the data. This framework is generally applicable and adaptable to most state-of-the-art clustering algorithms. We demonstrate on synthetic and real data that our method generates more appropriate and consistent clustering results than standard methods. In summary, our proposed analysis framework overcomes the limitations of existing polar coordinate-based clustering methods and provides an accurate and efficient way to cluster circular data.

A Soft-Fiber Bioelectronic Device with Axon-Like Architecture Enables Reliable Neural Recording In Vivo under Vigorous Activities.

Implantable neural devices that record neurons in various states, including static states, light activities such as walking, and vigorous activities such as running, offer opportunities for understanding brain functions and dysfunctions. However, recording neurons under vigorous activities remains a long-standing challenge because it leads to intense brain deformation. Thus, three key requirements are needed simultaneously for neural devices, that is, low modulus, low specific interfacial impedance, and high electrical conductivity, to realize stable device/brain interfaces and high-quality transmission of neural signals. However, they always contradict each other in current material strategies. Here, a soft fiber neural device capable of stably tracking individual neurons in the deep brain of medium-sized animals under vigorous activity is reported. Inspired by the axon architecture, this fiber neural device is constructed with a conductive gel fiber possessing a network-in-liquid structure using conjugated polymers and liquid matrices and then insulated with soft fluorine rubber. This strategy reconciles the contradictions and simultaneously confers the fiber neural device with low modulus (300kPa), low specific impedance (579kΩµm2), and high electrical conductivity (32700Sm-1) - ≈1-3 times higher than hydrogels. Stable single-unit spike tracking in running cats, which promises new opportunities for neuroscience is demonstrated.

A high‐performance asynchronous readout circuit with cascade function for a neural recording micro‐electrode array

AbstractThe data‐driven machine learning technology used for neural decoding emphasizes the requirements of in vivo and in vitro neural signal acquisition with high spatial and temporal resolution. However, micro‐electrode arrays (MEAs) that simultaneously achieve high spatial and temporal resolution neural signal acquisition are not yet available. Meanwhile, the high data bandwidth of large‐scale MEA brings challenges in power consumption, data transmission, storage, and neural signal processing. This research aims to improve the spatial and temporal resolution of MEA, reduce the cost and time of large‐scale MEA customization through cascading, and reduce the bandwidth of large‐scale MEA through spike compression. Firstly, based on in‐pixel spike detection, a row‐based neural spike readout mechanism and related array circuit to improve the neural spike readout performance and temporal resolution of neural signal acquisition is proposed. To further enhance the spatial resolution and reduce the risk of large‐scale MEA fabrication, the cascading capability of the readout circuit is explored. Lastly, spatial correlation‐based neural spike encoding is proposed to reduce the data bandwidth, achieving a 5.2× compression rate. This is a study on implementing large‐scale MEA through cascaded readout circuits and novel study to utilize the spatial correlation between detected neural spikes for further compression.

Brain–computer interface for simultaneous dual‐region spatial coding in hippocampal and somatosensory cortex of freely behaving rats

AbstractDecoding of spatial information from place cells is currently limited to individual brain regions, severely constraining the understanding of the brain's spatial navigation mechanisms. In this study, synchronized detection of dual‐brain region spatial encoding was conducted using brain–computer interface (BCI). Therefore, an implantable microelectrode array (MEA) was developed tailored for the simultaneous detection of neuronal activities in the CA1 of the hippocampus and the Barrel Cortex (BC) of the somatosensory cortex in rats as BCI. The MEA was improved by modifying the nanocomposite PtNP/PEDOT:PSS to improve their electrical properties (reducing impedance from 1.82 ± 0.17 MΩ to 3.4 ± 0.3 kΩ), facilitating neural recordings in freely behaving rats. The neural activities were obtained from the CA1 and the BC simultaneously and validated the existence of place cells in both brain regions. This study highlighted the potential of PtNP/PEDOT:PSS‐modified MEA as BCI in concurrently detecting neural activity associated with spatial encoding in two brain regions, offering novel perspectives on the processing of tactile stimuli and the formation of cognitive maps for navigation.

Neural population dynamics underlying evidence accumulation in multiple rat brain regions.

Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions - the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS) - while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal's choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally-inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.

Sapphire-Based Optrode for Low Noise Neural Recording and Optogenetic Manipulation.

Electrophysiological recordings of neurons in deep brain regions using optogenetic stimulation are essential to understanding and regulating the role of complex neural activity in biological behavior and cognitive function. Optogenetic techniques have significantly advanced neuroscience research by enabling the optical manipulation of neural activities. Because of the significance of the technique, constant advancements in implantable optrodes that integrate optical stimulation with low-noise, large-scale electrophysiological recording are in demand to improve the spatiotemporal resolution for various experimental designs and future clinical applications. However, robust and easy-to-use neural optrodes that integrate neural recording arrays with high-intensity light emitting diodes (LEDs) are still lacking. Here, we propose a neural optrode based on Gallium Nitride (GaN) on sapphire technology, which integrates a high-intensity blue LED with a 5x2 recording array monolithically for simultaneous neural recording and optogenetic manipulation. To reduce the noise interference between the recording electrodes and the LED, which is in close physical proximity, three metal grounding interlayers were incorporated within the optrode, and their ability to reduce LED-induced artifacts during neural recording was confirmed through both electromagnetic simulations and experimental demonstrations. The capability of the sapphire optrode to record action potentials has been demonstrated by recording the firing of mitral/tuft cells in the olfactory bulbs of mice in vivo. Additionally, the elevation of action potential firing due to optogenetic stimulation observed using the sapphire probe in medial superior olive (MSO) neurons of the gerbil auditory brainstem confirms the capability of this sapphire optrode to precisely access neural activities in deep brain regions under complex experimental designs.

Model selection for spectral parameterization.

Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which challenges the replicability and robustness of findings. Here, we introduce a principled approach to model selection, relying on Bayesian information criterion, for static and time-resolved spectral parameterization of neurophysiological data. We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral and spectrogram decompositions, even in non-stationary contexts. Overall, the proposed spectral decomposition with data-driven model selection minimizes the reliance on user expertise and subjective choices, enabling more robust, reproducible, and interpretable research findings.

The neural basis of swap errors in working memory

When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once-such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally and theoretical explanations have been proposed, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms by analyzing neural population recordings from monkeys performing two multistimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location or will be shown at the corresponding location. Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results provide evidence that swap errors emerge during selection of correctly remembered information from working memory, and highlight this selection as a crucial-yet surprisingly brittle-neural process.

Motor somatotopy impacts imagery strategy success in human intracortical brain-computer interfaces.

The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain-computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control. Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder. We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g., reaching vs. wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex. These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.

Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation

Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.

A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature.

Objective: The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy-but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these algorithms.Approach: Here we report our Graph nEtwork Multichannel sorting (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features.Main results: Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.Significance: GEMsort allows rapidly sort neural spikes and is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability.

A 64-Channel Inverter-Based Neural Signal Recording Amplifier With a Novel Differential-Like OTA Achieving an NEF of 0.84

A 64-Channel Inverter-Based Neural Signal Recording Amplifier With a Novel Differential-Like OTA Achieving an NEF of 0.84

A Wirelessly Powered Scattered Neural Recording Wearable System.

This paper introduces a wirelessly powered scattered neural recording wearable system that can facilitate continuous, untethered, and long-term electroencephalogram (EEG) recording. The proposed system, including 32 standalone EEG recording devices and a central controller, is incorporated in a wearable form factor. The standalone devices are sparsely distributed on the scalp, allowing for flexible placement and varying quantities to provide extensive spatial coverage and scalability. Each standalone device featuring a low-power EEG recording application-specific integrated circuit (ASIC) wirelessly receives power through a 60 MHz inductive link. The low-power ASIC design (84.6 μW) ensures sufficient wireless power reception through a small receiver (Rx) coil. The 60 MHz inductive link also serves as the data carrier for wireless communication between standalone devices and the central controller, eliminating the need for additional data antennas. All these efforts contribute to the miniaturization of standalone devices with dimensions of 12×12×5 mm3, enhancing device wearability. The central controller applies the pulse width modulation (PWM) scheme on the 60 MHz carrier, transmitting user commands at 4 Mbps to EEG recording ASICs. The ASIC employs a novel synchronized PWM demodulator to extract user commands, operating signal digitization and data transmission. The analog frontend (AFE) amplifies the EEG signal with a gain of 45 dB and applies band-pass filtering from 0.03 Hz to 400 Hz, with an input-referred noise (IRN) of 3.62 μVRMS. The amplified EEG signal is then digitized by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with a peak signal-to-noise and distortion ratio (SNDR) of 55.4 dB. The resulting EEG data is transmitted to an external software-defined radio (SDR) Rx through load-shift-keying (LSK) backscatter at 3.75 Mbps. The system's functionality is fully evaluated in human experiments.

A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System With Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4 × and 4.3 × in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39 × with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6 × and 2.8 × savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1 × in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μVrms and 2.4 μVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.

Neural representational geometries reflect behavioral differences in monkeys and recurrent neural networks

Animals likely use a variety of strategies to solve laboratory tasks. Traditionally, combined analysis of behavioral and neural recording data across subjects employing different strategies may obscure important signals and give confusing results. Hence, it is essential to develop techniques that can infer strategy at the single-subject level. We analyzed an experiment in which two male monkeys performed a visually cued rule-based task. The analysis of their performance shows no indication that they used a different strategy. However, when we examined the geometry of stimulus representations in the state space of the neural activities recorded in dorsolateral prefrontal cortex, we found striking differences between the two monkeys. Our purely neural results induced us to reanalyze the behavior. The new analysis showed that the differences in representational geometry are associated with differences in the reaction times, revealing behavioral differences we were unaware of. All these analyses suggest that the monkeys are using different strategies. Finally, using recurrent neural network models trained to perform the same task, we show that these strategies correlate with the amount of training, suggesting a possible explanation for the observed neural and behavioral differences.

Ferromagnetic Fiber Systems for Multiplexing Neural Recording and Modulation with Spatial Selectivity

Ferromagnetic Fiber Systems for Multiplexing Neural Recording and Modulation with Spatial Selectivity