Quantification of Signal-to-Noise Ratio in Cerebral Cortex Recordings Using Flexible MEAs With Co-localized Platinum Black, Carbon Nanotubes, and Gold Electrodes

Graphene-based microelectrodes with bidirectional functionality for next-generation retinal electronic interfaces

Neuroelectronic prostheses are being developed for restoring vision at the retinal level in patients who have lost their sight due to photoreceptor loss. The core component of these devices is the electrode array, which enables interfacing with retinal neurons. Generating the perception of meaningful images requires high-density microelectrode arrays (MEAs) capable of precisely activating targeted retinal neurons. Achieving this precision necessitates the downscaling of electrodes to micrometer dimensions. However, miniaturization increases electrode impedance, which poses challenges by limiting the amount of current that can be delivered, thereby impairing the electrode's capability for effective neural modulation. Additionally, it elevates noise levels, reducing the signal quality of the recorded neural activity. This report focuses on evaluating reduced graphene oxide (rGO) based devices for interfacing with the retina, showcasing their potential in vision restoration. Our findings reveal low impedance and high charge injection limit for microscale rGO electrodes, confirming their suitability for developing next-generation high-density retinal devices. We successfully demonstrated bidirectional interfacing with cell cultures and explanted retinal tissue, enabling the identification and modulation of multiple cells' activity. Additionally, calcium imaging allowed real-time monitoring of retinal cell dynamics, demonstrating a significant reduction in activated areas with small-sized electrodes. Overall, this study lays the groundwork for developing advanced rGO-based MEAs for high-acuity visual prostheses.

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Development of a novel, concentric micro-ECoG array enabling simultaneous detection of a single location by multiple electrode sizes

Objective.Detection of the epileptogenic zone is critical, especially for patients with drug-resistant epilepsy. Accurately mapping cortical regions exhibiting high activity during spontaneous seizure events while detecting neural activity up to 500 Hz can assist clinicians' surgical decisions and improve patient outcomes.Approach.We designed, fabricated, and tested a novel hybrid, multi-scale micro-electrocorticography (micro-ECoG) array with a unique embedded configuration. This array was compared to a commercially available microelectrode array (Neuronexus) for recording neural activity in rodent sensory cortex elicited by somatosensory evoked potentials and pilocarpine-induced seizures.Main results.Evoked potentials and spatial maps recorded by the multi-scale array ('micros', 'mesos', and 'macros' refering to the relative electrode sizes, 40 micron, 1 mm, and 4 mm respectively) were comparable to the Neuronexus array. The SSEPs recorded with the micros had higher peak amplitudes and greater signal power than those recorded by the larger mesos and macro. Seizure onset events and high-frequency oscillations (∼450 Hz) were detected on the multi-scale, similar to the commercially available array. The micros had greater SNR than the mesos and macro over the 5-1000 Hz frequency range during seizure monitoring. During cortical stimulation experimentation, the mesos successfully elicited motor effects.Significance.Previous studies have compared macro- and microelectrodes for localizing seizure activity in adjacent regions. The multi-scale design validated here is the first to simultaneously measure macro- and microelectrode signals from the same overlapping cortical area. This enables direct comparison of microelectrode recordings to the macroelectrode recordings used in standard neurosurgical practice. Previous studies have also shown that cortical regions generating high-frequency oscillations are at an increased risk for becoming epileptogenic zones. More accurate mapping of these micro seizures may improve surgical outcomes for epilepsy patients.

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. Increase of the overall synaptic strength led to synchronized global SO, while decrease of synaptic connectivity produced only local slow-waves that would not propagate beyond local area. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Power-integrated, wireless neural recording systems on the cranium using a direct printing method for deep-brain analysis

Conventional power-integrated wireless neural recording devices suffer from bulky, rigid batteries in head-mounted configurations, hindering the precise interpretation of the subject's natural behaviors. These power sources also pose risks of material leakage and overheating. We present the direct printing of a power-integrated wireless neural recording system that seamlessly conforms to the cranium. A quasi-solid-state Zn-ion microbattery was 3D-printed as a built-in power source geometrically synchronized to the shape of a mouse skull. Soft deep-brain neural probes, interconnections, and auxiliary electronics were also printed using liquid metals on the cranium with high resolutions. In vivo studies using mice demonstrated the reliability and biocompatibility of this wireless neural recording system, enabling the monitoring of neural activities across extensive brain regions without notable heat generation. This all-printed neural interface system revolutionizes brain research, providing bio-conformable, customizable configurations for improved data quality and naturalistic experimentation.

Nanoporous graphene-based thin-film microelectrodes for in vivo high-resolution neural recording and stimulation

One of the critical factors determining the performance of neural interfaces is the electrode material used to establish electrical communication with the neural tissue, which needs to meet strict electrical, electrochemical, mechanical, biological and microfabrication compatibility requirements. This work presents a nanoporous graphene-based thin-film technology and its engineering to form flexible neural interfaces. The developed technology allows the fabrication of small microelectrodes (25 µm diameter) while achieving low impedance (∼25 kΩ) and high charge injection (3-5 mC cm-2). In vivo brain recording performance assessed in rodents reveals high-fidelity recordings (signal-to-noise ratio >10 dB for local field potentials), while stimulation performance assessed with an intrafascicular implant demonstrates low current thresholds (<100 µA) and high selectivity (>0.8) for activating subsets of axons within the rat sciatic nerve innervating tibialis anterior and plantar interosseous muscles. Furthermore, the tissue biocompatibility of the devices was validated by chronic epicortical (12 week) and intraneural (8 week) implantation. This work describes a graphene-based thin-film microelectrode technology and demonstrates its potential for high-precision and high-resolution neural interfacing.

Chronic full-band recordings with graphene microtransistors as neural interfaces for discrimination of brain states

Brain states such as sleep, anesthesia, wakefulness, or coma are characterized by specific patterns of cortical activity dynamics, from local circuits to full-brain emergent properties. We previously demonstrated that full-spectrum signals, including the infraslow component (DC, direct current-coupled), can be recorded acutely in multiple sites using flexible arrays of graphene solution-gated field-effect transistors (gSGFETs). Here, we performed chronic implantation of 16-channel gSGFET arrays over the rat cerebral cortex and recorded full-band neuronal activity with two objectives: (1) to test the long-term stability of implanted devices; and (2) to investigate full-band activity during the transition across different levels of anesthesia. First, we demonstrate it is possible to record full-band signals with stability, fidelity, and spatiotemporal resolution for up to 5.5 months using chronic epicortical gSGFET implants. Second, brain states generated by progressive variation of levels of anesthesia could be identified as traditionally using the high-pass filtered (AC, alternating current-coupled) spectrogram: from synchronous slow oscillations in deep anesthesia through to asynchronous activity in the awake state. However, the DC signal introduced a highly significant improvement for brain-state discrimination: the DC band provided an almost linear information prediction of the depth of anesthesia, with about 85% precision, using a trained algorithm. This prediction rose to about 95% precision when the full-band (AC + DC) spectrogram was taken into account. We conclude that recording infraslow activity using gSGFET interfaces is superior for the identification of brain states, and further supports the preclinical and clinical use of graphene neural interfaces for long-term recordings of cortical activity.

A Nanozyme-Based Electrode for High-Performance Neural Recording

Implanted neural electrodes have been widely used to treat brain diseases that require high sensitivity and biocompatibility at the tissue-electrode interface. However, currently used clinical electrodes cannot meet both these requirements simultaneously, which hinders the effective recording of electronic signals. Herein, nanozyme-based neural electrodes incorporating bioinspired atomically precise clusters are developed as a general strategy with a heterogeneous design for multiscale and ultrasensitive neural recording via quantum transport and biocatalytic processes. Owing to the dual high-speed electronic and ionic currents at the electrode-tissue interface, the impedance of nanozyme electrodes is 26 times lower than that of state-of-the-art metal electrodes, and the acquisition sensitivity for the local field potential is ≈10 times higher than that of clinical PtIr electrodes, enabling a signal-to-noise ratio (SNR) of up to 14.7 dB for single-neuron recordings in rats. The electrodes provide more than 100-fold higher antioxidant and multi-enzyme-like activities, which effectively decrease 67% of the neuronal injury area by inhibiting glial proliferation and allowing sensitive and stable neural recording. Moreover, nanozyme electrodes can considerably improve the SNR of seizures in acute epileptic rats and are expected to achieve precise localization of seizure foci in clinical settings.

Magnetically Compatible Brain Electrode Arrays Based on Single-Walled Carbon Nanotubes for Long-Term Implantation

Advancements in brain-machine interfaces and neurological treatments urgently require the development of improved brain electrodes applied for long-term implantation, where traditional and polymer options face challenges like size, tissue damage, and signal quality. Carbon nanotubes are emerging as a promising alternative, combining excellent electronic properties and biocompatibility, which ensure better neuron coupling and stable signal acquisition. In this study, a new flexible brain electrode array based on 99.99% purity of single-walled carbon nanotubes (SWCNTs) was developed, which has 30 um × 40 um size, about 5.1 kΩ impedance, and 14.01 dB signal-to-noise ratio (SNR). The long-term implantation experiment in vivo in mice shows the proposed brain electrode can maintain stable LFP signal acquisition over 12 weeks while still achieving an SNR of 3.52 dB. The histological analysis results show that SWCNT-based brain electrodes induced minimal tissue damage and showed significantly reduced glial cell responses compared to platinum wire electrodes. Long-term stability comes from SWCNT's biocompatibility and chemical inertness, the electrode's flexible and fine structure. Furthermore, the new brain electrode array can function effectively during 7-Tesla magnetic resonance imaging, enabling the collection of local field potential and even epileptic discharges during the magnetic scan. This study provides a comprehensive study of carbon nanotubes as invasive brain electrodes, providing a new path to address the challenge of long-term brain electrode implantation.

Recording physiological and pathological cortical activity and exogenous electric fields using graphene microtransistor arrays in vitro

Graphene-based solution-gated field-effect transistors (gSGFETs) allow the quantification of the brain's full-band signal. Extracellular alternating current (AC) signals include local field potentials (LFP, population activity within a reach of hundreds of micrometers), multiunit activity (MUA), and ultimately single units. Direct current (DC) potentials are slow brain signals with a frequency under 0.1 Hz, and commonly filtered out by conventional AC amplifiers. This component conveys information about what has been referred to as "infraslow" activity. We used gSGFET arrays to record full-band patterns from both physiological and pathological activity generated by the cerebral cortex. To this end, we used an in vitro preparation of cerebral cortex that generates spontaneous rhythmic activity, such as that occurring in slow wave sleep. This examination extended to experimentally induced pathological activities, including epileptiform discharges and cortical spreading depression. Validation of recordings obtained via gSGFETs, including both AC and DC components, was accomplished by cross-referencing with well-established technologies, thereby quantifying these components across different activity patterns. We then explored an additional gSGFET potential application, which is the measure of externally induced electric fields such as those used in therapeutic neuromodulation in humans. Finally, we tested the gSGFETs in human cortical slices obtained intrasurgically. In conclusion, this study offers a comprehensive characterization of gSGFETs for brain recordings, with a focus on potential clinical applications of this emerging technology.

Human acute microelectrode array recordings with broad cortical access, single-unit resolution, and parallel behavioral monitoring

There are vast gaps in our understanding of the organization and operation of the human nervous system at the level of individual neurons and their networks. Here, we report reliable and robust acute multichannel recordings using planar microelectrode arrays (MEAs) implanted intracortically in awake brain surgery with open craniotomies that grant access to large parts of the cortical hemisphere. We obtained high-quality extracellular neuronal activity at the microcircuit, local field potential level and at the cellular, single-unit level. Recording from the parietal association cortex, a region rarely explored in human single-unit studies, we demonstrate applications on these complementary spatial scales and describe traveling waves of oscillatory activity as well as single-neuron and neuronal population responses during numerical cognition, including operations with uniquely human number symbols. Intraoperative MEA recordings are practicable and can be scaled up to explore cellular and microcircuit mechanisms of a wide range of human brain functions.

Real-time noise cancellation with deep learning

Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm's performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode. With this setup, an average of 4dB and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was achieved by removing wide band muscle noise. This concept has the potential to not only adaptively improve the signal-to-noise ratio of EEG but can be applied to a wide range of biological, industrial and consumer applications such as industrial sensing or noise cancelling headphones.

Electrochemical Testing of a New Polyimide Thin Film Electrode for Stimulation, Recording, and Monitoring of Brain Activity

Subdural electrode arrays are used for monitoring cortical activity and functional brain mapping in patients with seizures. Until recently, the only commercially available arrays were silicone-based, whose thickness and lack of conformability could impact their performance. We designed, characterized, manufactured, and obtained FDA clearance for 29-day clinical use (510(k) K192764) of a new thin-film polyimide-based electrode array. This study describes the electrochemical characterization undertaken to evaluate the quality and reliability of electrical signal recordings and stimulation of these new arrays. Two testing paradigms were performed: a short-term active soak with electrical stimulation and a 29-day passive soak. Before and after each testing paradigm, the arrays were evaluated for their electrical performance using Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV) and Voltage Transients (VT). In all tests, the impedance remained within an acceptable range across all frequencies. The different CV curves showed no significant changes in shape or area, which is indicative of stable electrode material. The electrode polarization remained within appropriate limits to avoid hydrolysis.

All-Polymeric Electrode Based on PEDOT:PSS for In Vivo Neural Recording

One of the significant challenges today in the brain-machine interfaces that use invasive methods is the stability of the chronic record. In recent years, polymer-based electrodes have gained notoriety for achieving mechanical strength values close to that of brain tissue, promoting a lower immune response to the implant. In this work, we fabricated fully polymeric electrodes based on PEDOT:PSS for neural recording in Wistar rats. We characterized the electrical properties and both in vitro and in vivo functionality of the electrodes. Additionally, we employed histological processing and microscopical visualization to evaluate the tecidual immune response at 7, 14, and 21 days post-implant. Electrodes with 400-micrometer channels showed a 12 dB signal-to-noise ratio. Local field potentials were characterized under two conditions: anesthetized and free-moving. There was a proliferation of microglia at the tissue-electrode interface in the early days, though there was a decrease after 14 days. Astrocytes also migrated to the interface, but there was not continuous recruitment of these cells in the tissue; there was inflammatory stability by day 21. The signal was not affected by this inflammatory action, demonstrating that fully polymeric electrodes can be an alternative means to prolong the valuable time of neural recordings.

Resource Allocation in the Noise-Free Striatal Beat Frequency Model of Interval Timing

The Striatal Beat Frequency (SBF) model of interval timing uses many neural oscillators, presumably located in the frontal cortex (FC), to produce beats at a specific criterion time Tc. The coincidence detection produces the beats in the basal ganglia spiny neurons by comparing the current state of the FC neural oscillators against the long-term memory values stored at reinforcement time Tc. The neurobiologically realistic SBF model has been previously used for producing precise and scalar timing in the presence of noise. Here we simplified the SBF model to gain insight into the resource allocation problem in interval timing networks. Specifically, we used a noise-free SBF model to explore the lower limits of the number of neural oscillators required for producing accurate timing. Using abstract sine-wave neural oscillators in the SBF-sin model, we found that the lower limit of the number of oscillators needed is proportional to the criterion time Tc and the frequency span (fmax − fmin) of the FC neural oscillators. Using biophysically realistic Morris–Lecar model neurons in the SBF-ML model, the lower bound increased by one to two orders of magnitude compared to the SBF-sin model.

A High-Performance Electrode Based on van der Waals Heterostructure for Neural Recording

Neural electrodes have been widely used to monitor neurological disorders and have a major impact on neuroscience, whereas traditional electrodes are limited to their inherent high impedance, which makes them insensitive to weak signals during recording neural signals. Herein, we developed a neural electrode based on the graphene/Ag van der Waals heterostructure for improving the detection sensitivity and signal-to-noise ratio (SNR). The impedance of the graphene/Ag electrode is reduced to 161.4 ± 13.4 MΩ μm², while the cathode charge-storage capacity (CSCc) reaches 24.2 ± 1.9 mC cm^-2, which is 6.3 and 48.4 times higher than those of the commercial Ag electrodes, respectively. Density functional theory (DFT) results find that the Ag-graphene interface has more doped electronic states, providing faster electron transfer and enhanced interfacial transport. In vivo detection sensitivity and SNR of graphene/Ag electrodes are significantly improved. The current work provides a feasible solution for designing brain electrodes to monitor neural signals more sensitively and accurately.

Restoring Sensorimotor Function Through Neuromodulation After Spinal Cord Injury: Progress and Remaining Challenges

Spinal cord injury (SCI) is a major disability that results in motor and sensory impairment and extensive complications for the affected individuals which not only affect the quality of life of the patients but also result in a heavy burden for their families and the health care system. Although there are few clinically effective treatments for SCI, research over the past few decades has resulted in several novel treatment strategies which are related to neuromodulation. Neuromodulation-the use of neuromodulators, electrical stimulation or optogenetics to modulate neuronal activity-can substantially promote the recovery of sensorimotor function after SCI. Recent studies have shown that neuromodulation, in combination with other technologies, can allow paralyzed patients to carry out intentional, controlled movement, and promote sensory recovery. Although such treatments hold promise for completely overcoming SCI, the mechanisms by which neuromodulation has this effect have been difficult to determine. Here we review recent progress relative to electrical neuromodulation and optogenetics neuromodulation. We also examine potential mechanisms by which these methods may restore sensorimotor function. We then highlight the strengths of these approaches and remaining challenges with respect to its application.

Stimulation and Recording of the Hippocampus Using the Same Pt-Ir Coated Microelectrodes

Same-electrode stimulation and recording with high spatial resolution, signal quality, and power efficiency is highly desirable in neuroscience and neural engineering. High spatial resolution and signal-to-noise ratio is necessary for obtaining unitary activities and delivering focal stimulations. Power efficiency is critical for battery-operated implantable neural interfaces. This study demonstrates the capability of recording single units as well as evoked potentials in response to a wide range of electrochemically safe stimulation pulses through high-resolution microelectrodes coated with co-deposition of Pt-Ir. It also compares signal-to-noise ratio, single unit activity, and power efficiencies between Pt-Ir coated and uncoated microelectrodes. To enable stimulation and recording with the same microelectrodes, microelectrode arrays were treated with electrodeposited platinum-iridium coating (EPIC) and tested in the CA1 cell body layer of rat hippocampi. The electrodes' ability to (1) inject a large range of electrochemically reversable stimulation pulses to the tissue, and (2) record evoked potentials and single unit activities were quantitively assessed over an acute time period. Compared to uncoated electrodes, EPIC electrodes recorded signals with higher signal-to-noise ratios (coated: 9.77 ± 1.95 dB; uncoated: 1.95 ± 0.40 dB) and generated lower voltages (coated: 100 mV; uncoated: 650 mV) for a given stimulus (5 μA). The improved performance corresponded to lower energy consumptions and electrochemically safe stimulation above 5 μA (>0.38 mC/cm2), which enabled elicitation of field excitatory post synaptic potentials and population spikes. Spontaneous single unit activities were also modulated by varying stimulation intensities and monitored through the same electrodes. This work represents an example of stimulation and recording single unit activities from the same microelectrode, which provides a powerful tool for monitoring and manipulating neural circuits at the single neuron level.

Totally transparent hydrogel-based subdural electrode with patterned salt bridge

A totally transparent subdural electrode was developed by embedding a conductive poly (vinyl alcohol) (PVA)-filled microchannel made of poly(dimethylsiloxane) (PDMS) into an another PVA hydrogel substrate. Tight bonding between the PVA substrate and the PDMS microchannel (salt bridge) was achieved by mechanical interlocking utilizing the microprotrusions formed on the microchannel. This simple method of bonding without the use of any additives such as silane molecules or nanofibers is very suitable for constructing biomedical devices. The salt bridge electrode (total thickness, ca. 1.5 mm) was sufficiently soft, and showed superior shape conformability that makes it an excellent choice as a subdural electrode used on the brain surface. In vivo measurement proved that the salt bridge electrode makes close contact to the exposed porcine brain and can record brain wave signals of frequencies 1 ~ 15 Hz. In addition, the high transparency of the electrode provided a clear view of the brain surface that would assist the effective surgical operation and optogenetic research.

Hydrogel-Based Organic Subdural Electrode with High Conformability to Brain Surface

A totally soft organic subdural electrode has been developed by embedding an array of poly(3,4-ethylenedioxythiophene)-modified carbon fabric (PEDOT-CF) into the polyvinyl alcohol (PVA) hydrogel substrate. The mesh structure of the stretchable PEDOT-CF allowed stable structural integration with the PVA substrate. The electrode performance for monitoring electrocorticography (ECoG) was evaluated in saline solution, on ex vivo brains, and in vivo animal experiments using rats and porcines. It was demonstrated that the large double-layer capacitance of the PEDOT-CF brings low impedance at the frequency of brain wave including epileptic seizures, and PVA hydrogel substrate minimized the contact impedance on the brain. The most important unique feature of the hydrogel-based ECoG electrode was its shape conformability to enable tight adhesion even to curved, grooved surface of brains by just being placed. In addition, since the hydrogel-based electrode is totally organic, the simultaneous ECoG-fMRI measurements could be conducted without image artifacts, avoiding problems induced by conventional metallic electrodes.