An Integrated Brain-Machine Interface Platform With Thousands of Channels

Soft bioelectronics for diagnostic and therapeutic applications in neurological diseases

Physical and chemical signals in the central nervous system yield crucial information that is clinically relevant under both physiological and pathological conditions. The emerging field of bioelectronics focuses on the monitoring and manipulation of neurophysiological signals with high spatiotemporal resolution and minimal invasiveness. Significant advances have been realized through innovations in materials and structural design, which have markedly enhanced mechanical and electrical properties, biocompatibility, and overall device performance. The diagnostic and therapeutic potential of soft bioelectronics has been corroborated across a diverse array of pre-clinical settings. This review summarizes recent studies that underscore the developments and applications of soft bioelectronics in neurological disorders, including neuromonitoring, neuromodulation, tumor treatment, and biosensing. Limitations and outlooks of soft devices are also discussed in terms of power supply, wireless control, biocompatibility, and the integration of artificial intelligence. This review highlights the potential of soft bioelectronics as a future platform to promote deciphering brain functions and clinical outcomes of neurological diseases.

The speech neuroprosthesis

Loss of speech after paralysis is devastating, but circumventing motor-pathway injury by directly decoding speech from intact cortical activity has the potential to restore natural communication and self-expression. Recent discoveries have defined how key features of speech production are facilitated by the coordinated activity of vocal-tract articulatory and motor-planning cortical representations. In this Review, we highlight such progress and how it has led to successful speech decoding, first in individuals implanted with intracranial electrodes for clinical epilepsy monitoring and subsequently in individuals with paralysis as part of early feasibility clinical trials to restore speech. We discuss high-spatiotemporal-resolution neural interfaces and the adaptation of state-of-the-art speech computational algorithms that have driven rapid and substantial progress in decoding neural activity into text, audible speech, and facial movements. Although restoring natural speech is a long-term goal, speech neuroprostheses already have performance levels that surpass communication rates offered by current assistive-communication technology. Given this accelerated rate of progress in the field, we propose key evaluation metrics for speed and accuracy, among others, to help standardize across studies. We finish by highlighting several directions to more fully explore the multidimensional feature space of speech and language, which will continue to accelerate progress towards a clinically viable speech neuroprosthesis.

Advances in AI-assisted biochip technology for biomedicine

The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.

Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods

Objective.Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended speech directly. Many studies have demonstrated promising results using invasive micro-electrode arrays and electrocorticography. However, the use of stereo-electroencephalography (sEEG) for speech decoding has not been fully recognized.Approach.In this research, recently released sEEG data were used to decode Dutch words spoken by epileptic participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, an recurrent neural network (RNN)-based sequence-to-sequence model (RNN), and a transformer model.Main results.Our RNN and transformer models outperformed the linear regression significantly, while no significant difference was found between the two deep-learning methods. Further investigation on individual electrodes showed that the same decoding result can be obtained using only a few of the electrodes.Significance.This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.

A high-density 1,024-channel probe for brain-wide recordings in non-human primates

Large-scale neural population recordings with single-cell resolution across the primate brain remain challenging. Here we introduce the Neuroscroll probe that isolates single neuronal activities simultaneously from 1,024 densely spaced channels that are flexibly distributed across the shank of the probe. The Neuroscroll probe length is easily tunable for individual probes from 10 mm to 90 mm, covering the brain size of non-human primates and humans, and the probes remain intact and functional after repeated bending deformations. The Neuroscroll probes provided reliable recordings from large neural populations with high chronic stability up to 105 weeks in rats. Recording with each Neuroscroll probe yielded hundreds of well-isolated single units simultaneously from multiple brain regions distributed across the entire depth of the rhesus macaque brain. With the thousand simultaneously recorded channels, unprecedented probe length, excellent mechanical stability and flexible recording site distribution, the Neuroscroll probes enable a wide range of new experimental paradigms in system neuroscience studies with great versatility.

A scalable platform for acquisition of high-fidelity human intracranial EEG with minimal clinical burden

Human neuroscience research has been significantly advanced by neuroelectrophysiological studies from people with refractory epilepsy-the only routine clinical intervention that acquires multi-day, multi-electrode human intracranial electroencephalography (iEEG). While a sampling rate below 2 kHz is sufficient for manual iEEG review by epileptologists, computational methods and research studies may benefit from higher resolution, which requires significant technical development. At adult and pediatric Stanford hospitals, research ports of commercial clinical acquisition systems were configured to collect 10 kHz iEEG of up to 256 electrodes simultaneously with the clinical data. The research digital stream was designed to be acquired post-digitization, resulting in no loss in clinical signal quality. This novel framework implements a near-invisible research platform to facilitate the secure, routine collection of high-resolution iEEG that minimizes research hardware footprint and clinical workflow interference. The addition of a pocket-sized router in the patient room enabled an encrypted tunnel to securely transmit research-quality iEEG across hospital networks to a research computer within the hospital server room, where data was coded, de-identified, and uploaded to cloud storage. Every eligible patient undergoing iEEG clinical evaluation at both hospitals since September 2017 has been recruited; participant recruitment is ongoing. Over 350+ terabytes (representing 1000+ days) of neuroelectrophysiology were recorded across 200+ participants of diverse demographics. To our knowledge, this is the first report of such a research integration within a hospital setting. It is a promising approach to promoting equitable participant enrollment and building comprehensive data repositories with consistent, high-fidelity specifications towards new discoveries in human neuroscience.

Biocompatible Electrical and Optical Interfaces for Implantable Sensors and Devices

Implantable bioelectronics hold tremendous potential in the field of healthcare, yet the performance of these systems heavily relies on the interfaces between artificial machines and living tissues. In this paper, we discuss the recent developments of tethered interfaces, as well as those of non-tethered interfaces. Among them, systems that study neural activity receive significant attention due to their innovative developments and high relevance in contemporary research, but other functional types of interface systems are also explored to provide a comprehensive overview of the field. We also analyze the key considerations, including perforation site selection, fixing strategies, long-term retention, and wireless communication, highlighting the challenges and opportunities with stable, effective, and biocompatible interfaces. Furthermore, we propose a primitive model of biocompatible electrical and optical interfaces for implantable systems, which simultaneously possesses biocompatibility, stability, and convenience. Finally, we point out the future directions of interfacing strategies.

Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces

This review systematically explores the application of transformer-based models in EEG signal processing and brain-computer interface (BCI) development, with a distinct focus on ensuring methodological rigour and adhering to empirical validations within the existing literature. By examining various transformer architectures, such as the Temporal Spatial Transformer Network (TSTN) and EEG Conformer, this review delineates their capabilities in mitigating challenges intrinsic to EEG data, such as noise and artifacts, and their subsequent implications on decoding and classification accuracies across disparate mental tasks. The analytical scope extends to a meticulous examination of attention mechanisms within transformer models, delineating their role in illuminating critical temporal and spatial EEG features and facilitating interpretability in model decision-making processes. The discourse additionally encapsulates emerging works that substantiate the efficacy of transformer models in noise reduction of EEG signals and diversifying applications beyond the conventional motor imagery paradigm. Furthermore, this review elucidates evident gaps and propounds exploratory avenues in the applications of pre-trained transformers in EEG analysis and the potential expansion into real-time and multi-task BCI applications. Collectively, this review distils extant knowledge, navigates through the empirical findings, and puts forward a structured synthesis, thereby serving as a conduit for informed future research endeavours in transformer-enhanced, EEG-based BCI systems.

Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes

We introduce Ultra-Flexible Tentacle Electrodes (UFTEs), packing many independent fibers with the smallest possible footprint without limitation in recording depth using a combination of mechanical and chemical tethering for insertion. We demonstrate a scheme to implant UFTEs simultaneously into many brain areas at arbitrary locations without angle-of-insertion limitations, and a 512-channel wireless logger. Immunostaining reveals no detectable chronic tissue damage even after several months. Mean spike signal-to-noise ratios are 1.5-3x compared to the state-of-the-art, while the highest signal-to-noise ratios reach 89, and average cortical unit yields are ~1.75/channel. UFTEs can track the same neurons across sessions for at least 10 months (longest duration tested). We tracked inter- and intra-areal neuronal ensembles (neurons repeatedly co-activated within 25 ms) simultaneously from hippocampus, retrosplenial cortex, and medial prefrontal cortex in freely moving rodents. Average ensemble lifetimes were shorter than the durations over which we can track individual neurons. We identify two distinct classes of ensembles. Those tuned to sharp-wave ripples display the shortest lifetimes, and the ensemble members are mostly hippocampal. Yet, inter-areal ensembles with members from both hippocampus and cortex have weak tuning to sharp wave ripples, and some have unusual months-long lifetimes. Such inter-areal ensembles occasionally remain inactive for weeks before re-emerging.

Brain implantation of tissue-level-soft bioelectronics via embryonic development

The design of bioelectronics capable of stably tracking brain-wide, single-cell, and millisecond-resolved neural activities in the developing brain is critical to the study of neuroscience and neurodevelopmental disorders. During development, the three-dimensional (3D) structure of the vertebrate brain arises from a 2D neural plate 1,2 . These large morphological changes previously posed a challenge for implantable bioelectronics to track neural activity throughout brain development 3-9 . Here, we present a tissue-level-soft, sub-micrometer-thick, stretchable mesh microelectrode array capable of integrating into the embryonic neural plate of vertebrates by leveraging the 2D-to-3D reconfiguration process of the tissue itself. Driven by the expansion and folding processes of organogenesis, the stretchable mesh electrode array deforms, stretches, and distributes throughout the entire brain, fully integrating into the 3D tissue structure. Immunostaining, gene expression analysis, and behavioral testing show no discernible impact on brain development or function. The embedded electrode array enables long-term, stable, brain-wide, single-unit-single-spike-resolved electrical mapping throughout brain development, illustrating how neural electrical activities and population dynamics emerge and evolve during brain development.

Invasive Brain Computer Interface for Motor Restoration in Spinal Cord Injury: A Systematic Review

STUDY DESIGN

Systematic review of the literature.

OBJECTIVES

In recent years, brain-computer interface (BCI) has emerged as a potential treatment for patients with spinal cord injury (SCI). This is the first systematic review of the literature on invasive closed-loop BCI technologies for the treatment of SCI in humans.

MATERIALS AND METHODS

A comprehensive search of PubMed MEDLINE, Web of Science, and Ovid EMBASE was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

RESULTS

Of 8316 articles collected, 19 studies met all the inclusion criteria. Data from 21 patients were extracted from these studies. All patients sustained a cervical SCI and were treated using either a BCI with intracortical microelectrode arrays (n = 18, 85.7%) or electrocorticography (n = 3, 14.3%). To decode these neural signals, machine learning and statistical models were used: support vector machine in eight patients (38.1%), linear estimator in seven patients (33.3%), Hidden Markov Model in three patients (14.3%), and other in three patients (14.3%). As the outputs, ten patients (47.6%) underwent noninvasive functional electrical stimulation (FES) with a cuff; one (4.8%) had an invasive FES with percutaneous stimulation, and ten (47.6%) used an external device (neuroprosthesis or virtual avatar). Motor function was restored in all patients for each assigned task. Clinical outcome measures were heterogeneous across all studies.

CONCLUSIONS

Invasive techniques of BCI show promise for the treatment of SCI, but there is currently no technology that can restore complete functional autonomy in patients with SCI. The current techniques and outcomes of BCI vary greatly. Because invasive BCIs are still in the early stages of development, further clinical studies should be conducted to optimize the prognosis for patients with SCI.

Microengineered neuronal networks: enhancing brain-machine interfaces

The brain-machine interface (BMI), a crucial conduit between the human brain and computers, holds transformative potential for various applications in neuroscience. This manuscript explores the role of micro-engineered neuronal networks (MNNs) in advancing BMI technologies and their therapeutic applications. As the interdisciplinary collaboration intensifies, the need for innovative and user-friendly BMI technologies becomes paramount. A comprehensive literature review sourced from reputable databases (PubMed Central, Medline, EBSCOhost, and Google Scholar) aided in the foundation of the manuscript, emphasizing the pivotal role of MNNs. This study aims to synthesize and analyze the diverse facets of MNNs in the context of BMI technologies, contributing insights into neural processes, technological advancements, therapeutic potentials, and ethical considerations surrounding BMIs. MNNs, exemplified by dual-mode neural microelectrodes, offer a controlled platform for understanding complex neural processes. Through case studies, we showcase the pivotal role of MNNs in BMI innovation, addressing challenges, and paving the way for therapeutic applications. The integration of MNNs with BMI technologies marks a revolutionary stride in neuroscience, refining brain-computer interactions and offering therapeutic avenues for neurological disorders. Challenges, ethical considerations, and future trends in BMI research necessitate a balanced approach, leveraging interdisciplinary collaboration to ensure responsible and ethical advancements. Embracing the potential of MNNs is paramount for the betterment of individuals with neurological conditions and the broader community.

Efficient in Vivo Neural Signal Compression Using an Autoencoder-Based Neural Network

Conventional in vivo neural signal processing involves extracting spiking activity within the recorded signals from an ensemble of neurons and transmitting only spike counts over an adequate interval. However, for brain-computer interface (BCI) applications utilizing continuous local field potentials (LFPs) for cognitive decoding, the volume of neural data to be transmitted to a computer imposes relatively high data rate requirements. This is particularly true for BCIs employing high-density intracortical recordings with hundreds or thousands of electrodes. This article introduces the first autoencoder-based compression digital circuit for the efficient transmission of LFP neural signals. Various algorithmic and architectural-level optimizations are implemented to significantly reduce the computational complexity and memory requirements of the designed in vivo compression circuit. This circuit employs an autoencoder-based neural network, providing a robust signal reconstruction. The application-specific integrated circuit (ASIC) of the in vivo compression logic occupies the smallest silicon area and consumes the lowest power among the reported state-of-the-art compression ASICs. Additionally, it offers a higher compression rate and a superior signal-to-noise and distortion ratio.

Guest Editorial: Implantable bioelectronics

The realm of implantable bioelectronics represents a frontier in medical science, merging technology, biology, and medicine to innovate treatments that enhance, restore, or monitor physiological functions. This field has yielded devices like cochlear implants, cardiac pacemakers, deep brain stimulators, and vagus nerve stimulators, each designed to address a specific health condition, ranging from sensorineural hearing loss to chronic pain, neurological disorders, and heart rhythm irregularities. Such devices underscore the potential of bioelectronics to significantly improve patient outcomes and quality of life. Recent technological breakthroughs in materials science, nanotechnology, and microfabrication have enabled the development of more sophisticated, smaller, and biocompatible bioelectronic devices. However, the field also encounters challenges, particularly in extending the capabilities of devices such as retinal prostheses, which aim to restore vision but currently offer limited visual acuity. Research in implantable bioelectronics is highly timely, driven by an aging global population with a growing prevalence of chronic diseases that could benefit from these technologies. The convergence of societal health needs, advancing technological capabilities, and a supportive ecosystem for innovation marks this era as pivotal for bioelectronic research.

Ultrastable N-Type Semiconducting Fiber Organic Electrochemical Transistors for Highly Sensitive Biosensors

Organic electrochemical transistors (OECTs) have attracted increasing attention due to their merits of high transconductance, low operating voltage, and good biocompatibility, ideal for biosensors. However, further advances in their practical applications face challenges of low n-type performance and poor stability. Here, it is demonstrated that wet-spinning the commercially available n-type conjugated polymer poly(benzimidazobenzophenanthroline) (BBL) into highly aligned and crystalline fibers enhances both OECT performance and stability. Although BBL is only soluble in high-boiling-point strong acids, it can be wet-spun into high-quality fibers with adjustable diameters. The BBL fiber OECTs exhibit a record-high area-normalized transconductance (gm,A) of 2.40 µS µm-2 and over 10 times higher figure-of-merit (µC*) than its thin-film counterparts. More importantly, these fiber OECTs exhibit remarkable stability with no noticeable performance attenuation after 1500 cycles over 4 h operation, outperforming all previously reported n-type OECTs. The superior performance and stability can be attributed to shorter π-π stacking distance and ordered molecular arrangement in the fibers, endowing the BBL fiber OECT-based biosensors with outstanding sensitivity while keeping a miniaturized form factor. This work demonstrates that, beyond new material development, developing new fabrication technology is also crucial for addressing the performance and stability issues in n-type OECTs.

Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device

Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-μm-thick, mechanically flexible micro-electrocorticography (μECoG) BCI, integrating 256×256 electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording and 16,384 stimulation channels, from which we can simultaneously record up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.

A brain machine interface framework for exploring proactive control of smart environments

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.

Implantable Neural Microelectrodes: How to Reduce Immune Response

Implantable neural microelectrodes exhibit the great ability to accurately capture the electrophysiological signals from individual neurons with exceptional submillisecond precision, holding tremendous potential for advancing brain science research, as well as offering promising avenues for neurological disease therapy. Although significant advancements have been made in the channel and density of implantable neural microelectrodes, challenges persist in extending the stable recording duration of these microelectrodes. The enduring stability of implanted electrode signals is primarily influenced by the chronic immune response triggered by the slight movement of the electrode within the neural tissue. The intensity of this immune response increases with a higher bending stiffness of the electrode. This Review thoroughly analyzes the sequential reactions evoked by implanted electrodes in the brain and highlights strategies aimed at mitigating chronic immune responses. Minimizing immune response mainly includes designing the microelectrode structure, selecting flexible materials, surface modification, and controlling drug release. The purpose of this paper is to provide valuable references and ideas for reducing the immune response of implantable neural microelectrodes and stimulate their further exploration in the field of brain science.

A roadmap for implanting microelectrode arrays to evoke tactile sensations through intracortical microstimulation

Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other brain-computer interface studies to ensure successful placement of stimulation electrodes.

Through-polymer, via technology-enabled, flexible, lightweight, and integrated devices for implantable neural probes

In implantable electrophysiological recording systems, the headstage typically comprises neural probes that interface with brain tissue and integrated circuit chips for signal processing. While advancements in MEMS and CMOS technology have significantly improved these components, their interconnection still relies on conventional printed circuit boards and sophisticated adapters. This conventional approach adds considerable weight and volume to the package, especially for high channel count systems. To address this issue, we developed a through-polymer via (TPV) method inspired by the through-silicon via (TSV) technique in advanced three-dimensional packaging. This innovation enables the vertical integration of flexible probes, amplifier chips, and PCBs, realizing a flexible, lightweight, and integrated device (FLID). The total weight of the FLIDis only 25% that of its conventional counterparts relying on adapters, which significantly increased the activity levels of animals wearing the FLIDs to nearly match the levels of control animals without implants. Furthermore, by incorporating a platinum-iridium alloy as the top layer material for electrical contact, the FLID realizes exceptional electrical performance, enabling in vivo measurements of both local field potentials and individual neuron action potentials. These findings showcase the potential of FLIDs in scaling up implantable neural recording systems and mark a significant advancement in the field of neurotechnology.

Flexible high-density microelectrode arrays for closed-loop brain-machine interfaces: a review

Flexible high-density microelectrode arrays (HDMEAs) are emerging as a key component in closed-loop brain-machine interfaces (BMIs), providing high-resolution functionality for recording, stimulation, or both. The flexibility of these arrays provides advantages over rigid ones, such as reduced mismatch between interface and tissue, resilience to micromotion, and sustained long-term performance. This review summarizes the recent developments and applications of flexible HDMEAs in closed-loop BMI systems. It delves into the various challenges encountered in the development of ideal flexible HDMEAs for closed-loop BMI systems and highlights the latest methodologies and breakthroughs to address these challenges. These insights could be instrumental in guiding the creation of future generations of flexible HDMEAs, specifically tailored for use in closed-loop BMIs. The review thoroughly explores both the current state and prospects of these advanced arrays, emphasizing their potential in enhancing BMI technology.

Ethical Considerations of Neuralink and Brain-Computer Interfaces

Neuralink is a neurotechnology company founded by Elon Musk in 2016, which has been quietly developing revolutionary technology allowing for ultra-high precision bidirectional communication between external devices and the brain. In this paper, we explore the multifaceted ethical considerations surrounding neural interfaces, analyzing potential societal impacts, risks, and call for a need for responsible innovation. Despite the technological, medical, medicolegal, and ethical challenges ahead, neural interface technology remains extremely promising and has the potential to create a new era of medicine.

Highly Customizable 3D Microelectrode Arrays for In Vitro and In Vivo Neuronal Tissue Recordings

Planar microelectrode arrays (MEAs) for - in vitro or in vivo - neuronal signal recordings lack the spatial resolution and sufficient signal-to-noise ratio (SNR) required for a detailed understanding of neural network function and synaptic plasticity. To overcome these limitations, a highly customizable three-dimensional (3D) printing process is used in combination with thin film technology and a self-aligned template-assisted electrochemical deposition process to fabricate 3D-printed-based MEAs on stiff or flexible substrates. Devices with design flexibility and physical robustness are shown for recording neural activity in different in vitro and in vivo applications, achieving high-aspect ratio 3D microelectrodes of up to 33:1. Here, MEAs successfully record neural activity in 3D neuronal cultures, retinal explants, and the cortex of living mice, thereby demonstrating the versatility of the 3D MEA while maintaining high-quality neural recordings. Customizable 3D MEAs provide unique opportunities to study neural activity under regular or various pathological conditions, both in vitro and in vivo, and contribute to the development of drug screening and neuromodulation systems that can accurately monitor the activity of large neural networks over time.

Brain-implanted conductors amplify radiofrequency fields in rodents: Advantages and risks

Over the past few decades, daily exposure to radiofrequency (RF) fields has been increasing due to the rapid development of wireless and medical imaging technologies. Under extreme circumstances, exposure to very strong RF energy can lead to heating of body tissue, even resulting in tissue injury. The presence of implanted devices, moreover, can amplify RF effects on surrounding tissue. Therefore, it is important to understand the interactions of RF fields with tissue in the presence of implants, in order to establish appropriate wireless safety protocols, and also to extend the benefits of medical imaging to increasing numbers of people with implanted medical devices. This study explored the neurological effects of RF exposure in rodents implanted with neuronal recording electrodes. We exposed freely moving and anesthetized rats and mice to 950 MHz RF energy while monitoring their brain activity, temperature, and behavior. We found that RF exposure could induce fast onset firing of single neurons without heat injury. In addition, brain implants enhanced the effect of RF stimulation resulting in reversible behavioral changes. Using an optical temperature measurement system, we found greater than tenfold increase in brain temperature in the vicinity of the implant. On the one hand, our results underline the importance of careful safety assessment for brain-implanted devices, but on the other hand, we also show that metal implants may be used for neurostimulation if brain temperature can be kept within safe limits.

A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling

Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 μ V rms input-referred noise over 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 μV rms over 0.001Hz-1Hz) and 435 M Ω input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.

Pre-hospital stroke monitoring past, present, and future: a perspective

Integrated brain-machine interface signifies a transformative advancement in neurological monitoring and intervention modalities for events such as stroke, the leading cause of disability. Historically, stroke management relied on clinical evaluation and imaging. While today's stroke landscape integrates artificial intelligence for proactive clinical decision-making, mainly in imaging and stroke detection, it depends on clinical observation for early detection. Cardiovascular monitoring and detection systems, which have become standard throughout healthcare and wellness settings, provide a model for future cerebrovascular monitoring and detection. This commentary reviews the progression of continuous stroke monitoring, spotlighting contemporary innovations and prospective avenues, and emphasizes the influential roles of cutting-edge technologies in shaping stroke care.

Activity-dependent recruitment of inhibition and excitation in the awake mammalian cortex during electrical stimulation

Electrical stimulation is an effective tool for mapping and altering brain connectivity, with applications ranging from treating pharmacology-resistant neurological disorders to providing sensory feedback for neural prostheses. Paramount to the success of these applications is the ability to manipulate electrical currents to precisely control evoked neural activity patterns. However, little is known about stimulation-evoked responses in inhibitory neurons nor how stimulation-evoked activity patterns depend on ongoing neural activity. In this study, we used 2-photon imaging and cell-type specific labeling to measure single-cell responses of excitatory and inhibitory neurons to electrical stimuli in the visual cortex of awake mice. Our data revealed strong interactions between electrical stimulation and pre-stimulus activity of single neurons in awake animals and distinct recruitment and response patterns for excitatory and inhibitory neurons. This work demonstrates the importance of cell-type-specific labeling of neurons in future studies.

3D spatiotemporally scalable in vivo neural probes based on fluorinated elastomers

Electronic devices for recording neural activity in the nervous system need to be scalable across large spatial and temporal scales while also providing millisecond and single-cell spatiotemporal resolution. However, existing high-resolution neural recording devices cannot achieve simultaneous scalability on both spatial and temporal levels due to a trade-off between sensor density and mechanical flexibility. Here we introduce a three-dimensional (3D) stacking implantable electronic platform, based on perfluorinated dielectric elastomers and tissue-level soft multilayer electrodes, that enables spatiotemporally scalable single-cell neural electrophysiology in the nervous system. Our elastomers exhibit stable dielectric performance for over a year in physiological solutions and are 10,000 times softer than conventional plastic dielectrics. By leveraging these unique characteristics we develop the packaging of lithographed nanometre-thick electrode arrays in a 3D configuration with a cross-sectional density of 7.6 electrodes per 100 µm2. The resulting 3D integrated multilayer soft electrode array retains tissue-level flexibility, reducing chronic immune responses in mouse neural tissues, and demonstrates the ability to reliably track electrical activity in the mouse brain or spinal cord over months without disrupting animal behaviour.

A perspective on translating genomic discoveries into targets for brain-machine interface and deep brain stimulation devices

Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.

Concept for intrathecal delivery of brain recording and stimulation device

Neurological disorders are common, yet many neurological diseases don't have efficacious treatments. The protected nature of the brain both anatomically and physiologically through the blood brain barrier (BBB) make it exceptionally hard to access. Recent advancements in interventional approaches, like the Stentrode™, have opened the possibility of using the cerebral vasculature as a highway for minimally invasive therapeutic delivery to the brain. Despite the immense success that the Stentrode™ has faced recently, it is limited to major cerebral vasculature and exists outside the BBB, making drug eluting configurations largely ineffective. The present study seeks to identify a separate anatomical pathway for therapeutic delivery to the deep brain using the ventricular system. The intrathecal route, in which drug pumps and spinal cord stimulators are delivered through a lumbar puncture, is a well-established route for delivering therapies to the spinal cord as high as C1. The present study identifies an extension of this anatomical pathway through the foramen of Magendie and into the brains ventricular system. To test this pathway, a narrow self-expanding electrical recording device was manufactured and its potential to navigate the ventricular system was assessed on human anatomical brain samples. While the results of this paper are largely preliminary and a substantial amount of safety and efficacy data is needed, this paper identifies an important anatomical pathway for delivery of therapeutic and diagnostics tools to the brain that is minimally invasive, can access limbic structures, and is within the BBB.

Recent Advances in Implantable Neural Interfaces for Multimodal Electrical Neuromodulation

Electrical neuromodulation plays a pivotal role in enhancing patient outcomes among individuals suffering from neurological disorders. Implantable neural interfaces are vital components of the electrical neuromodulation system to ensure desirable performance; However, conventional devices are limited to a single function and are constructed with bulky and rigid materials, which often leads to mechanical incompatibility with soft tissue and an inability to adapt to the dynamic and complex 3D structures of biological systems. In addition, current implantable neural interfaces utilized in clinical settings primarily rely on wire-based techniques, which are associated with complications such as increased risk of infection, limited positioning options, and movement restrictions. Here, the state-of-art applications of electrical neuromodulation are presented. Material schemes and device structures that can be employed to develop robust and multifunctional neural interfaces, including flexibility, stretchability, biodegradability, self-healing, self-rolling, or morphing are discussed. Furthermore, multimodal wireless neuromodulation techniques, including optoelectronics, mechano-electrics, magnetoelectrics, inductive coupling, and electrochemically based self-powered devices are reviewed. In the end, future perspectives are given.

Implantable bioelectrodes: challenges, strategies, and future directions

Implantable bioelectrodes for regulating and monitoring biological behaviors have become indispensable medical devices in modern healthcare, alleviating pathological symptoms such as epilepsy and arrhythmia, and assisting in reversing conditions such as deafness and blindness. In recent years, developments in the fields of materials science and biomedical engineering have contributed to advances in research on implantable bioelectrodes. However, the foreign body reaction (FBR) is still a major constraint for the long-term application of electrodes. In this paper, four types of commonly used implantable bioelectrodes are reviewed, concentrating on their background, development, and a series of complications caused by FBR after long-term implantation. Strategies for resisting FBRs are then devised in terms of physics, chemistry, and nanotechnology. We analyze the major trends in the future development of implantable bioelectrodes and outline some promising research to optimize the long-term operational stability of electrodes. Although current implantable bioelectrodes have been able to achieve good biocompatibility, low impedance, and low mechanical mismatch and trauma, these devices still face the challenge of FBR. Resistance to FBR is still the key for the long-term effectiveness of bioelectrodes, and a better understanding of the mechanisms of FBR, as well as miniaturization, long-term passivation, and coupling with gene therapy may be the way forward for the next generation of implantable bioelectrodes.

Comparative analysis of energy transfer mechanisms for neural implants

As neural implant technologies advance rapidly, a nuanced understanding of their powering mechanisms becomes indispensable, especially given the long-term biocompatibility risks like oxidative stress and inflammation, which can be aggravated by recurrent surgeries, including battery replacements. This review delves into a comprehensive analysis, starting with biocompatibility considerations for both energy storage units and transfer methods. The review focuses on four main mechanisms for powering neural implants: Electromagnetic, Acoustic, Optical, and Direct Connection to the Body. Among these, Electromagnetic Methods include techniques such as Near-Field Communication (RF). Acoustic methods using high-frequency ultrasound offer advantages in power transmission efficiency and multi-node interrogation capabilities. Optical methods, although still in early development, show promising energy transmission efficiencies using Near-Infrared (NIR) light while avoiding electromagnetic interference. Direct connections, while efficient, pose substantial safety risks, including infection and micromotion disturbances within neural tissue. The review employs key metrics such as specific absorption rate (SAR) and energy transfer efficiency for a nuanced evaluation of these methods. It also discusses recent innovations like the Sectored-Multi Ring Ultrasonic Transducer (S-MRUT), Stentrode, and Neural Dust. Ultimately, this review aims to help researchers, clinicians, and engineers better understand the challenges of and potentially create new solutions for powering neural implants.

Conversion of a medical implant into a versatile computer-brain interface

BACKGROUND

Information transmission into the human nervous system is the basis for a variety of prosthetic applications. Spinal cord stimulation (SCS) systems are widely available, have a well documented safety record, can be implanted minimally invasively, and are known to stimulate afferent pathways. Nonetheless, SCS devices are not yet used for computer-brain-interfacing applications.

OBJECTIVE

Here we aimed to establish computer-to-brain communication via medical SCS implants in a group of 20 individuals who had been operated for the treatment of chronic neuropathic pain.

METHODS

In the initial phase, we conducted interface calibration with the aim of determining personalized stimulation settings that yielded distinct and reproducible sensations. These settings were subsequently utilized to generate inputs for a range of behavioral tasks. We evaluated the required calibration time, task training duration, and the subsequent performance in each task.

RESULTS

We could establish a stable spinal computer-brain interface in 18 of the 20 participants. Each of the 18 then performed one or more of the following tasks: A rhythm-discrimination task (n = 13), a Morse-decoding task (n = 3), and/or two different balance/body-posture tasks (n = 18; n = 5). The median calibration time was 79 min. The median training time for learning to use the interface in a subsequent task was 1:40 min. In each task, every participant demonstrated successful performance, surpassing chance levels.

CONCLUSION

The results constitute the first proof-of-concept of a general purpose computer-brain interface paradigm that could be deployed on present-day medical SCS platforms.

Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces

Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.

Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

Cholesterol-Conjugated Supramolecular Multimeric siRNAs: Effect of siRNA Length on Accumulation and Silencing In Vitro and In Vivo

Conjugation of small interfering RNA (siRNA) with lipophilic molecules is one of the most promising approaches for delivering siRNA in vivo. The rate of molecular weight-dependent siRNA renal clearance is critical for the efficiency of this process. In this study, we prepared cholesterol-containing supramolecular complexes containing from three to eight antisense strands and examined their accumulation and silencing activity in vitro and in vivo. We have shown for the first time that such complexes with 2'F, 2'OMe, and LNA modifications exhibit interfering activity both in carrier-mediated and carrier-free modes. Silencing data from a xenograft tumor model show that 4 days after intravenous injection of cholesterol-containing monomers and supramolecular trimers, the levels of MDR1 mRNA in the tumor decreased by 85% and 68%, respectively. The in vivo accumulation data demonstrated that the formation of supramolecular structures with three or four antisense strands enhanced their accumulation in the liver. After addition of two PS modifications at the ends of antisense strands, 47% and 67% reductions of Ttr mRNA levels in the liver tissue were detected 7 days after administration of monomers and supramolecular trimers, respectively. Thus, we have obtained a new type of RNAi inducer that is convenient for synthesis and provides opportunities for modifications.

Multifunctional IrOx Neural Probe for In Situ Dynamic Brain Hypoxia Evaluation

Perioperative cerebral hypoxia and neonatal hypoxia-ischemic encephalopathy are the main triggers that lead to temporary or permanent brain dysfunction. The pathogenesis is intimately correlated to neural activities and the pH of the microenvironment, which calls for a high demand for in situ multitype physiological signal acquisition in the brain. However, conventional pH sensing neural interfaces cannot obtain the characteristics of multimodes, multichannels, and high spatial resolution of physiological signals simultaneously. Here, we report a multifunctional implantable iridium oxide (IrOx) neural probe (MIIONP) combined with electrophysiology recording, in situ pH sensing, and neural stimulation for real-time dynamic brain hypoxia evaluation. The neural probe modified with IrOx films exhibits outstanding electrophysiology recording and neural stimulation performance and long-term stable high spatial pH sensing resolution of about 100 μm, and the cytotoxicity of IrOx microelectrodes was investigated as well. In addition, 4 weeks' tracking of the same neuron firing and instantaneous population spike captured during electrical stimulation was achieved by MIIONP. Finally, in a mouse brain hypoxia model, the MIIONP has demonstrated the capability of synchronous in situ recording of the pH and neural firing changes in the brain, which has a valuable application in dynamic brain disease evaluation through real-time acquisition of multiple physiological signals.

Compact Electrochromic Optical Recording of Bioelectric Potentials

Electrochromic optical recording (ECORE) is a label-free method that utilizes electrochromism to optically detect electrical signals in biological cells with a high signal-to-noise ratio and is suitable for long-term recording. However, ECORE usually requires a large and intricate optical setup, making it relatively difficult to transport and to study specimens on a large scale. Here, we present a Compact ECORE (CECORE) apparatus that drastically reduces the spatial footprint and complexity of the ECORE setup whilst maintaining high sensitivity. An autobalancing differential photodetector automates common-mode noise rejection, removing the need for manually adjustable optics, and a compact laser module conserves space compared to a typical laser mount. The result is a simple, easy-to-use, and relatively low cost system that achieves a sensitivity of 16.7 μV (within a factor of 5 of the shot noise limit), and reliably detects action potentials from Human-induced pluripotent stem cell (HiPSC) derived cardiomyocytes. This setup can be further improved to within 1.5 dB of the shot noise limit by filtering out power-line interference.

Epistemology of synthetic biology: a new theoretical framework based on its potential objects and objectives

Synthetic biology is a new research field which attempts to understand, modify, and create new biological entities by adopting a modular and systemic conception of the living organisms. The development of synthetic biology has generated a pluralism of different approaches, bringing together a set of heterogeneous practices and conceptualizations from various disciplines, which can lead to confusion within the synthetic biology community as well as with other biological disciplines. I present in this manuscript an epistemological analysis of synthetic biology in order to better define this new discipline in terms of objects of study and specific objectives. First, I present and analyze the principal research projects developed at the foundation of synthetic biology, in order to establish an overview of the practices in this new emerging discipline. Then, I analyze an important scientometric study on synthetic biology to complete this overview. Afterwards, considering this analysis, I suggest a three-level classification of the object of study for synthetic biology (which are different kinds of living entities that can be built in the laboratory), based on three successive criteria: structural hierarchy, structural origin, functional origin. Finally, I propose three successively linked objectives in which synthetic biology can contribute (where the achievement of one objective led to the development of the other): interdisciplinarity collaboration (between natural, artificial, and theoretical sciences), knowledge of natural living entities (past, present, future, and alternative), pragmatic definition of the concept of "living" (that can be used by biologists in different contexts). Considering this new theoretical framework, based on its potential objects and objectives, I take the position that synthetic biology has not only the potential to develop its own new approach (which includes methods, objects, and objectives), distinct from other subdisciplines in biology, but also the ability to develop new knowledge on living entities.

Amplitude- and frequency-dependent activation of layer II/III neurons by intracortical microstimulation

Intracortical microstimulation (ICMS) has been used for the development of brain machine interfaces. However, further understanding about the spatiotemporal responses of neurons to different electrical stimulation parameters is necessary to inform the design of optimal therapies. In this study, we employed in vivo electrophysiological recording, two-photon calcium imaging, and electric field simulation to evaluate the acute effect of ICMS on layer II/III neurons. Our results show that stimulation frequency non-linearly modulates neuronal responses, whereas the magnitude of responses is linearly correlated to the electric field strength and stimulation amplitude before reaching a steady state. Temporal dynamics of neurons' responses depends more on stimulation frequency and their distance to the stimulation electrode. In addition, amplitude-dependent post-stimulation suppression was observed within ∼500 μm of the stimulation electrode, as evidenced by both calcium imaging and local field potentials. These findings provide insights for selecting stimulation parameters to achieve desirable spatiotemporal specificity of ICMS.

Recent strategies for neural dynamics observation at a larger scale and wider scope

The tremendous technical progress in neuroscience offers opportunities to observe a more minor or/and broader dynamic picture of the brain. Moreover, the large-scale neural activity of individual neurons enables the dissection of detailed mechanistic links between neural populations and behaviors. To measure neural activity in-vivo, multi-neuron recording, and neuroimaging techniques are employed and developed to acquire more neurons. The tools introduced concurrently recorded dozens to hundreds of neurons in the coordinated brain regions and elucidated the neuronal ensembles from a massive population perspective of diverse neurons at cellular resolution. In particular, the increasing spatiotemporal resolution of neuronal monitoring across the whole brain dramatically facilitates our understanding of additional nervous system functions in health and disease. Here, we will introduce state-of-the-art neuroscience tools involving large-scale neural population recording and the long-range connections spanning multiple brain regions. Their synergic effects provide to clarify the controversial circuitry underlying neuroscience. These challenging neural tools present a promising outlook for the fundamental dynamic interplay across levels of synaptic cellular, circuit organization, and brain-wide. Hence, more observations of neural dynamics will provide more clues to elucidate brain functions and push forward innovative technology at the intersection of neural engineering disciplines. We hope this review will provide insight into the use or development of recent neural techniques considering spatiotemporal scales of brain observation.

Decoding the Spike-Band Subthreshold Motor Cortical Activity

Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.

Polymer Implantable Electrode Foundry: A shared resource for manufacturing polymer-based microelectrodes for neural interfaces

Large scale monitoring of neural activity at the single unit level can be achieved via electrophysiological recording using implanted microelectrodes. While neuroscience researchers have widely employed chronically implanted electrode-based interfaces for this purpose, a commonly encountered limitation is loss of highly resolved signals arising from immunological response over time. Next generation electrode-based interfaces improve longitudinal signal quality using the strategy of stabilizing the device-tissue interface with microelectrode arrays constructed from soft and flexible polymer materials. The limited availability of such polymer microelectrode arrays has restricted access to a small number of researchers able to build their own custom devices or who have developed specific collaborations with engineering researchers who can produce them. Here, a new technology resource model is introduced that seeks to widely increase access to polymer microelectrode arrays by the neuroscience research community. The Polymer Implantable Electrode (PIE) Foundry provides custom and standardized polymer microelectrode arrays as well as training and guidance on best-practices for implantation and chronic experiments.

An Ultraflexible Electrode Array for Large-Scale Chronic Recording in the Nonhuman Primate Brain

Single-unit (SU) recording in nonhuman primates (NHPs) is indispensible in the quest of how the brain works, yet electrodes currently used for the NHP brain are limited in signal longevity, stability, and spatial coverage. Using new structural materials, microfabrication, and penetration techniques, we develop a mechanically robust ultraflexible, 1 µm thin electrode array (MERF) that enables pial penetration and high-density, large-scale, and chronic recording of neurons along both vertical and horizontal cortical axes in the nonhuman primate brain. Recording from three monkeys yields 2,913 SUs from 1,065 functional recording channels (up to 240 days), with some SUs tracked for up to 2 months. Recording from the primary visual cortex (V1) reveals that neurons with similar orientation preferences for visual stimuli exhibited higher spike correlation. Furthermore, simultaneously recorded neurons in different cortical layers of the primary motor cortex (M1) show preferential firing for hand movements of different directions. Finally, it is shown that a linear decoder trained with neuronal spiking activity across M1 layers during monkey's hand movements can be used to achieve on-line control of cursor movement. Thus, the MERF electrode array offers a new tool for basic neuroscience studies and brain-machine interface (BMI) applications in the primate brain.

Cryogel-based neurostimulation electrodes to activate endogenous neural precursor cells

The delivery of electrical pulses to the brain via penetrating electrodes, known as brain stimulation, has been recognized as an effective clinical approach for treating neurological disorders. Resident brain neural precursor cells (NPCs) are electrosensitive cells that respond to electrical stimulation by expanding in number, migrating and differentiating which are important characteristics that support neural repair. Here, we report the design of a conductive cryogel brain stimulation electrode specifically developed for NPC activation. The cryogel electrode has a modulus switching mechanism permitting facile penetration and reducing the mechanical mismatch between brain tissue and the penetrating electrode. The cryogel demonstrated good in vivo biocompatibility and reduced the interfacial impedance to deliver the stimulating electric field with lower voltage under charge-balanced current controlled stimulation. An ex vivo assay reveals that electrical stimulation using the cryogel electrodes results in significant expansion in the size of NPC pool. Hence, the cryogel electrodes have the potential to be used for NPC activation to support endogenous neural repair. STATEMENT OF SIGNIFICANCE: The objective of this study is to develop a cryogel-based stimulation electrode as an alternative to traditional electrode materials to be used in regenerative medicine applications for enhancing neural regeneration in brain. The electrode offers benefits such as adaptive modulus for implantation, high charge storage and injection capacities, and modulus matching with brain tissue, allowing for stable delivery of electric field for long-term neuromodulation. The electrochemical properties of cryogel electrodes were characterized in living tissue with an ex vivo set-up, providing a deeper understanding of stimulation capacity in brain environments. The cryogel electrode is biocompatible and enables low voltage, current-controlled stimulation for effective activation of endogenous neural precursor cells, revealing their potential utility in neural stem cell-mediated brain repair.

Flexible Electrodes for Brain-Computer Interface System

Brain-computer interface (BCI) has been the subject of extensive research recently. Governments and companies have substantially invested in relevant research and applications. The restoration of communication and motor function, the treatment of psychological disorders, gaming, and other daily and therapeutic applications all benefit from BCI. The electrodes hold the key to the essential, fundamental BCI precondition of electrical brain activity detection and delivery. However, the traditional rigid electrodes are limited due to their mismatch in Young's modulus, potential damages to the human body, and a decline in signal quality with time. These factors make the development of flexible electrodes vital and urgent. Flexible electrodes made of soft materials have grown in popularity in recent years as an alternative to conventional rigid electrodes because they offer greater conformance, the potential for higher signal-to-noise ratio (SNR) signals, and a wider range of applications. Therefore, the latest classifications and future developmental directions of fabricating these flexible electrodes are explored in this paper to further encourage the speedy advent of flexible electrodes for BCI. In summary, the perspectives and future outlook for this developing discipline are provided.

Advancing the interfacing performances of chronically implantable neural probes in the era of CMOS neuroelectronics

Tissue penetrating microelectrode neural probes can record electrophysiological brain signals at resolutions down to single neurons, making them invaluable tools for neuroscience research and Brain-Computer-Interfaces (BCIs). The known gradual decrease of their electrical interfacing performances in chronic settings, however, remains a major challenge. A key factor leading to such decay is Foreign Body Reaction (FBR), which is the cascade of biological responses that occurs in the brain in the presence of a tissue damaging artificial device. Interestingly, the recent adoption of Complementary Metal Oxide Semiconductor (CMOS) technology to realize implantable neural probes capable of monitoring hundreds to thousands of neurons simultaneously, may open new opportunities to face the FBR challenge. Indeed, this shift from passive Micro Electro-Mechanical Systems (MEMS) to active CMOS neural probe technologies creates important, yet unexplored, opportunities to tune probe features such as the mechanical properties of the probe, its layout, size, and surface physicochemical properties, to minimize tissue damage and consequently FBR. Here, we will first review relevant literature on FBR to provide a better understanding of the processes and sources underlying this tissue response. Methods to assess FBR will be described, including conventional approaches based on the imaging of biomarkers, and more recent transcriptomics technologies. Then, we will consider emerging opportunities offered by the features of CMOS probes. Finally, we will describe a prototypical neural probe that may meet the needs for advancing clinical BCIs, and we propose axial insertion force as a potential metric to assess the influence of probe features on acute tissue damage and to control the implantation procedure to minimize iatrogenic injury and subsequent FBR.

DualSort: online spike sorting with a running neural network

Objective.Spike sorting, i.e. the detection and separation of measured action potentials from different extracellularly recorded neurons, remains one of the bottlenecks in deciphering the brain. In recent years, the application of neural networks (NNs) for spike sorting has garnered significant attention. Most methods focus on specific sub-problems within the conventional spike sorting pipeline, such as spike detection or feature extraction, and attempt to solve them with complex network architectures. This paper presents DualSort, a simple NN that gets combined with downstream post-processing for real-time spike sorting. It shows high efficiency, low complexity, and requires a comparatively small amount of human interaction.Approach.Synthetic and experimentally obtained extracellular single-channel recordings were utilized to train and evaluate the proposed NN. For training, spike waveforms were labeled with respect to their associated neuron and position in the signal, allowing the detection and categorization of spikes in unison. DualSort classifies a single spike multiple times in succession, as it runs over the signal in a step-by-step manner and uses a post-processing algorithm that transmits the network output into spike trains. Main results.With the used datasets, DualSort was able to detect and distinguish different spike waveforms and separate them from background activity. The post-processing algorithm significantly strengthened the overall performance of the model, making the system more robust as a whole. Although DualSort is an end-to-end solution that efficiently transforms filtered signals into spike trains, it competes with contemporary state-of-the-art technologies that exclusively target single sub-problems in the conventional spike sorting pipeline.Significance.This work demonstrates that even under high noise levels, complex NNs are not necessary by any means to achieve high performance in spike detection and sorting. The utilization of data augmentation on a limited quantity of spikes could substantially decrease hand-labeling compared to other studies. Furthermore, the proposed framework can be utilized without human interaction when combined with an unsupervised technique that provides pseudo labels for DualSort. Due to the low complexity of our network, it works efficiently and enables real-time processing on basic hardware. The proposed approach is not limited to spike sorting, as it may also be used to process different signals, such as electroencephalogram (EEG), which needs to be investigated in future research.