An Integrated Brain-Machine Interface Platform With Thousands of Channels

Interface, interaction, and intelligence in generalized brain-computer interfaces

A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.

Understanding the Impact of Neural Variations and Random Connections on Inference

Recent research suggests that in vitro neural networks created from dissociated neurons may be used for computing and performing machine learning tasks. To develop a better artificial intelligent system, a hybrid bio-silicon computer is worth exploring, but its performance is still inferior to that of a silicon-based computer. One reason may be that a living neural network has many intrinsic properties, such as random network connectivity, high network sparsity, and large neural and synaptic variability. These properties may lead to new design considerations, and existing algorithms need to be adjusted for living neural network implementation. This work investigates the impact of neural variations and random connections on inference with learning algorithms. A two-layer hybrid bio-silicon platform is constructed and a five-step design method is proposed for the fast development of living neural network algorithms. Neural variations and dynamics are verified by fitting model parameters with biological experimental results. Random connections are generated under different connection probabilities to vary network sparsity. A multi-layer perceptron algorithm is tested with biological constraints on the MNIST dataset. The results show that a reasonable inference accuracy can be achieved despite the presence of neural variations and random network connections. A new adaptive pre-processing technique is proposed to ensure good learning accuracy with different living neural network sparsity.

Neurochips: Considerations from a neurosurgeon's standpoint

A neurochip comprises a small device based on the brain-machine interfaces that emulate the functioning synapses. Its implant in the human body allows the interaction of the brain with a computer. Although the data-processing speed is still slower than that of the human brain, they are being developed. There is no ethical conflict as long as it is used for neural rehabilitation or to supply impaired or missing neurological functions. However, other applications emerge as controversial.

To the best of our knowledge, there have no been publications about the neurosurgical role in the application of this neurotechnological advance. Deliberation on neurochips is primarily limited to a small circle of scholars such as neurotechnological engineers, artists, philosophers, and bioethicists. Why do we address neurosurgeons? They will be directly involved as they could be required to perform invasive procedures.

Future neurosurgeons will have to be a different type of neurosurgeon. They will be part of interdisciplinary teams interacting with computer engineers, neurobiologist, and ethicists. Although a neurosurgeon is not expected to be an expert in all areas, they have to be familiar with them; they have to be prepared to determine indications, contraindications and risks of the procedures, participating in the decision-making processes, and even collaborating in the design of devices to preserve anatomic structures. Social, economic, and legal aspects are also inherent to the neurosurgical activity; therefore, these aspects should also be considered.

Low-Cutoff Frequency Reduction in Neural Amplifiers: Analysis and Implementation in CMOS 65 nm

Scaling down technology demotes the parameters of AC-coupled neural amplifiers, such as increasing the low-cutoff frequency due to the short-channel effects. To improve the low-cutoff frequency, one solution is to increase the feedback capacitors' value. This solution is not desirable, as the input capacitors have to be increased to maintain the same gain, which increases the area and decreases the input impedance of the neural amplifier. We analytically analyze the small-signal behavior of the neural amplifier and prove that the main reason for the increase of the low-cutoff frequency in advanced CMOS technologies is the reduction of the input resistance of the operational transconductance amplifier (OTA). We also show that the reduction of the input resistance of the OTA is due to the increase in the gate oxide leakage in the input transistors. In this paper, we explore this fact and propose two solutions to reduce the low-cutoff frequency without increasing the value of the feedback capacitor. The first solution is performed by only simulation and is called cross-coupled positive feedback that uses pseudoresistors to provide a negative resistance to increase the input resistance of the OTA. As an advantage, only standard CMOS transistors are used in this method. Simulation results show that a low-cutoff frequency of 1.5 Hz is achieved while the midband gain is 30.4 dB at 1 V. In addition, the power consumption is 0.6 μW. In the second method, we utilize thick-oxide MOS transistors in the input differential pair of the OTA. We designed and fabricated the second method in the 65 nm TSMC CMOS process. Measured results are obtained by in vitro recordings on slices of mouse brainstem. The measurement results show that the bandwidth is between 2 Hz and 5.6 kHz. The neural amplifier has 34.3 dB voltage gain in midband and consumes 3.63 μW at 1 V power supply. The measurement results show an input-referred noise of 6.1 μV_rms and occupy 0.04 mm² silicon area.

Small footprint optoelectrodes using ring resonators for passive light localization

The combination of electrophysiology and optogenetics enables the exploration of how the brain operates down to a single neuron and its network activity. Neural probes are in vivo invasive devices that integrate sensors and stimulation sites to record and manipulate neuronal activity with high spatiotemporal resolution. State-of-the-art probes are limited by tradeoffs involving their lateral dimension, number of sensors, and ability to access independent stimulation sites. Here, we realize a highly scalable probe that features three-dimensional integration of small-footprint arrays of sensors and nanophotonic circuits to scale the density of sensors per cross-section by one order of magnitude with respect to state-of-the-art devices. For the first time, we overcome the spatial limit of the nanophotonic circuit by coupling only one waveguide to numerous optical ring resonators as passive nanophotonic switches. With this strategy, we achieve accurate on-demand light localization while avoiding spatially demanding bundles of waveguides and demonstrate the feasibility with a proof-of-concept device and its scalability towards high-resolution and low-damage neural optoelectrodes.

Gateway reflexes, neuronal circuits that regulate the gateways for autoreactive T cells in organs that have blood barriers

Gateway reflexes are neural circuits that maintain homeostasis of the immune system. They form gateways for autoreactive T cells to infiltrate the central nervous system in a noradrenaline-dependent manner despite the blood-brain barrier. This mechanism is critical not only for maintaining organ homeostasis but also for inflammatory disease development. Gateway reflexes can be regulated by environmental or artificial stimuli including electrical stimulation, suggesting that the infiltration of immune cells can be controlled by bioelectronic medicine. In this review, we describe the discovery of gateway reflexes and their future directions with special focus on bioelectronic medicine.

High-performance brain-to-text communication via handwriting

Brain-computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping^1-5 or point-and-click typing with a computer cursor^6,7. However, rapid sequences of highly dexterous behaviours, such as handwriting or touch typing, might enable faster rates of communication. Here we developed an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time, using a recurrent neural network decoding approach. With this BCI, our study participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect. To our knowledge, these typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant (115 characters per minute)⁸. Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.

Virtual Reality Rehabilitation and Exergames—Physical and Psychological Impact on Fall Prevention among the Elderly—A Literature Review

The present review is aimed at the effectiveness of virtual reality (VR) and exergames in the prevention of falls among the elderly. Falls become a significant problem in the aging population and lead to psychological, social, and physical impairment. Prevention of falls is crucial to the well-being of the elderly population and is one of the challenges of contemporary rehabilitation. Recently, in view of the threat of the SARS-CoV-2 pandemic, contactless methods of rehabilitation, including telerehabilitation, appear as valuable rehabilitation tools. This review is based on the PRISMA guidelines and was carried out in five databases: PubMed, Medline, Web of Science, Scopus, and PEDro. Twenty-one randomized controlled trials, focused on the application of VR and exergames in the prevention of falls, were included. This review suggests that VR training in rehabilitation appears to be a promising complement to traditional techniques of physiotherapy to improve specific physical outcomes. VR and exergames could be considered as a complement of standard physiotherapy and its possible continuation at home for elderly. However, further high-quality studies, with carefully designed protocols and proper blinding, are needed.

Analysis and Reduction of Nonlinear Distortion in AC-Coupled CMOS Neural Amplifiers with Tunable Cutoff Frequencies

Integrated CMOS neural amplifiers are key elements of modern large-scale neuroelectronic interfaces. The neural amplifiers are routinely AC-coupled to electrodes to remove the DC voltage. The large resistances required for the AC coupling circuit are usually realized using MOSFETs that are nonlinear. Specifically, designs with tunable cutoff frequency of the input high‑pass filter may suffer from excessive nonlinearity, since the gate-source voltages of the transistors forming the pseudoresistors vary following the signal being amplified. Consequently, the nonlinear distortion in such circuits may be high for signal frequencies close to the cutoff frequency of the input filter. Here we propose a simple modification of the architecture of a tunable AC-coupled amplifier, in which the bias voltages V_gs of the transistors forming the pseudoresistor are kept constant independently of the signal levels, what results in significantly improved linearity. Based on numerical simulations of the proposed circuit designed in 180 nm technology we analyze the Total Harmonic Distortion levels as a function of signal frequency and amplitude. We also investigate the impact of basic amplifier parameters—gain, cutoff frequency of the AC coupling circuit, and silicon area—on the distortion and noise performance. The post-layout simulations of the complete test ASIC show that the distortion is very significantly reduced at frequencies near the cutoff frequency, when compared to the commonly used circuits. The THD values are below 1.17% for signal frequencies 1 Hz–10 kHz and signal amplitudes up to 10 mV peak-to-peak. The preamplifier area is only 0.0046 mm² and the noise is 8.3 µV_rms in the 1 Hz–10 kHz range. To our knowledge this is the first report on a CMOS neural amplifier with systematic characterization of THD across complete range of frequencies and amplitudes of neuronal signals recorded by extracellular electrodes.

A simple method for implanting free-floating microdevices into the nervous tissue

Objective. Free-floating implantable neural interfaces are an emerging powerful paradigm for mapping and modulation of brain activity. Minuscule wirelessly-powered devices have the potential to provide minimally-invasive interactions with neurons in chronic research and medical applications. However, these devices face a seemingly simple problem-how can they be placed into nervous tissue rapidly, efficiently and in an essentially arbitrary location?Approach. We introduce a novel injection tool and describe a controlled injection approach that minimizes damage to the tissue.Main results.To validate the needle injectable tool and the presented delivery approach, we evaluate the spatial precision and rotational alignment of the microdevices injected into agarose, brain, and sciatic nerve with the aid of tissue clearing and MRI imaging. In this research, we limited the number of injections into the brain to four per rat as we are using microdevices that are designed for an adult head size on a rat model. We then present immunohistology data to assess the damage caused by the needle.Significance. By virtue of its simplicity, the proposed injection method can be used to inject microdevices of all sizes and shapes and will do so in a fast, minimally-invasive, and cost-effective manner. As a result, the introduced technique can be broadly used to accelerate the validation of these next-generation types of electrodes in animal models.

A comparison of insertion methods for surgical placement of penetrating neural interfaces

Many implantable electrode arrays exist for the purpose of stimulating or recording electrical activity in brain, spinal, or peripheral nerve tissue, however most of these devices are constructed from materials that are mechanically rigid. A growing body of evidence suggests that the chronic presence of these rigid probes in the neural tissue causes a significant immune response and glial encapsulation of the probes, which in turn leads to gradual increase in distance between the electrodes and surrounding neurons. In recording electrodes, the consequence is the loss of signal quality and, therefore, the inability to collect electrophysiological recordings long term. In stimulation electrodes, higher current injection is required to achieve a comparable response which can lead to tissue and electrode damage. To minimize the impact of the immune response, flexible neural probes constructed with softer materials have been developed. These flexible probes, however, are often not strong enough to be inserted on their own into the tissue, and instead fail via mechanical buckling of the shank under the force of insertion. Several strategies have been developed to allow the insertion of flexible probes while minimizing tissue damage. It is critical to keep these strategies in mind during probe design in order to ensure successful surgical placement. In this review, existing insertion strategies will be presented and evaluated with respect to surgical difficulty, immune response, ability to reach the target tissue, and overall limitations of the technique. Overall, the majority of these insertion techniques have only been evaluated for the insertion of a single probe and do not quantify the accuracy of probe placement. More work needs to be performed to evaluate and optimize insertion methods for accurate placement of devices and for devices with multiple probes.

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

Review of 3D-printing technologies for wearable and implantable bio-integrated sensors

Thin-film microfabrication-based bio-integrated sensors are widely used for a broad range of applications that require continuous measurements of biophysical and biochemical signals from the human body. Typically, they are fabricated using standard photolithography and etching techniques. This traditional method is capable of producing a precise, thin, and flexible bio-integrated sensor system. However, it has several drawbacks, such as the fact that it can only be used to fabricate sensors on a planar surface, it is highly complex requiring specialized high-end facilities and equipment, and it mostly allows only 2D features to be fabricated. Therefore, developing bio-integrated sensors via 3D-printing technology has attracted particular interest. 3D-printing technology offers the possibility to develop sensors on nonplanar substrates, which is beneficial for noninvasive bio-signal sensing, and to directly print on complex 3D nonplanar organ structures. Moreover, this technology introduces a highly flexible and precisely controlled printing process to realize patient-specific sensor systems for ultimate personalized medicine, with the potential of rapid prototyping and mass customization. This review summarizes the latest advancements in 3D-printed bio-integrated systems, including 3D-printing methods and employed printing materials. Furthermore, two widely used 3D-printing techniques are discussed, namely, ex-situ and in-situ fabrication techniques, which can be utilized in different types of applications, including wearable and smart-implantable biosensor systems.

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

Adaptive spike detection and hardware optimization towards autonomous, high-channel-count BMIs

BACKGROUND

The progress in microtechnology has enabled an exponential trend in the number of neurons that can be simultaneously recorded. The data bandwidth requirement is however increasing with channel count. The vast majority of experimental work involving electrophysiology stores the raw data and then processes this offline; to detect the underlying spike events. Emerging applications however require new methods for local, real-time processing.

NEW METHODS

We have developed an adaptive, low complexity spike detection algorithm that combines three novel components for: (1) removing the local field potentials; (2) enhancing the signal-to-noise ratio; and (3) computing an adaptive threshold. The proposed algorithm has been optimised for hardware implementation (i.e. minimising computations, translating to a fixed-point implementation), and demonstrated on low-power embedded targets.

MAIN RESULTS

The algorithm has been validated on both synthetic datasets and real recordings yielding a detection sensitivity of up to 90%. The initial hardware implementation using an off-the-shelf embedded platform demonstrated a memory requirement of less than 0.1 kb ROM and 3 kb program flash, consuming an average power of 130 μW.

COMPARISON WITH EXISTING METHODS

The method presented has the advantages over other approaches, that it allows spike events to be robustly detected in real-time from neural activity in a completely autonomous way, without the need for any calibration, and can be implemented with low hardware resources.

CONCLUSION

The proposed method can detect spikes effectively and adaptively. It alleviates the need for re-calibration, which is critical towards achieving a viable BMI, and more so with future 'high bandwidth' systems' targeting 1000s of channels.

Wireless and battery-free platforms for collection of biosignals

Recent progress in biosensors have quantitively expanded current capabilities in exploratory research tools, diagnostics and therapeutics. This rapid pace in sensor development has been accentuated by vast improvements in data analysis methods in the form of machine learning and artificial intelligence that, together, promise fantastic opportunities in chronic sensing of biosignals to enable preventative screening, automated diagnosis, and tools for personalized treatment strategies. At the same time, the importance of widely accessible personal monitoring has become evident by recent events such as the COVID-19 pandemic. Progress in fully integrated and chronic sensing solutions is therefore increasingly important. Chronic operation, however, is not truly possible with tethered approaches or bulky, battery-powered systems that require frequent user interaction. A solution for this integration challenge is offered by wireless and battery-free platforms that enable continuous collection of biosignals. This review summarizes current approaches to realize such device architectures and discusses their building blocks. Specifically, power supplies, wireless communication methods and compatible sensing modalities in the context of most prevalent implementations in target organ systems. Additionally, we highlight examples of current embodiments that quantitively expand sensing capabilities because of their use of wireless and battery-free architectures.

Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

An Examination of Prospective Uses and Future Directions of Neuralink: The Brain-Machine Interface

The human brain is one of the most mystifying biological structures in nature. Overwhelming research, technology, and innovations in neuroscience have augmented clinical assessments, diagnosis, and treatment capabilities. Nonetheless, there is still much to be discovered about nervous system disorders and defects. Neuralink, a neurotechnology company, is advancing the field of neuroscience and neuroengineering. The company’s initial aim is to develop an implantable brain-machine interface device that will enhance the lives of people with severe brain and spinal cord injuries. Here, we provide insight into Neuralink’s design, early testing, and future applications in neurosurgery. While early testing with small and large animals show promising results, no clinical trials have been conducted to date. Additionally, a term search for “Neuralink” was performed in PubMed. The literature search yielded only 28 references, of which most indirectly mentioned the device but not in direct testing. In order to conclude the safety and viability of the Neuralink device, further research studies are needed to move forward beyond speculation.

Defining Surgical Terminology and Risk for Brain Computer Interface Technologies

With the emergence of numerous brain computer interfaces (BCI), their form factors, and clinical applications the terminology to describe their clinical deployment and the associated risk has been vague. The terms “minimally invasive” or “non-invasive” have been commonly used, but the risk can vary widely based on the form factor and anatomic location. Thus, taken together, there needs to be a terminology that best accommodates the surgical footprint of a BCI and their attendant risks. This work presents a semantic framework that describes the BCI from a procedural standpoint and its attendant clinical risk profile. We propose extending the common invasive/non-invasive distinction for BCI systems to accommodate three categories in which the BCI anatomically interfaces with the patient and whether or not a surgical procedure is required for deployment: (1) Non-invasive—BCI components do not penetrate the body, (2) Embedded—components are penetrative, but not deeper than the inner table of the skull, and (3) Intracranial –components are located within the inner table of the skull and possibly within the brain volume. Each class has a separate risk profile that should be considered when being applied to a given clinical population. Optimally, balancing this risk profile with clinical need provides the most ethical deployment of these emerging classes of devices. As BCIs gain larger adoption, and terminology becomes standardized, having an improved, more precise language will better serve clinicians, patients, and consumers in discussing these technologies, particularly within the context of surgical procedures.

Deep brain stimulation of midbrain locomotor circuits in the freely moving pig

Background:

Deep brain stimulation (DBS) of the mesencephalic locomotor region (MLR) has been studied as a therapeutic target in rodent models of stroke, parkinsonism, and spinal cord injury. Clinical DBS trials have targeted the closely related pedunculopontine nucleus in patients with Parkinson’s disease as a therapy for gait dysfunction, with mixed reported outcomes. Recent studies suggest that optimizing the MLR target could improve its effectiveness.

Objective:

We sought to determine if stereotaxic targeting and DBS in the midbrain of the pig, in a region anatomically similar to that previously identified as the MLR in other species, could initiate and modulate ongoing locomotion, as a step towards generating a large animal neuromodulation model of gait.

Methods:

We implanted Medtronic 3389 electrodes into putative MLR structures in Yucatan micropigs to characterize the locomotor effects of acute DBS in this region, using EMG recordings, joint kinematics, and speed measurements on a manual treadmill.

Results:

MLR DBS initiated and augmented locomotion in freely moving micropigs. Effective locomotor sites centered around the cuneiform nucleus and stimulation frequency controlled locomotor speed and stepping frequency. Off-target stimulation evoked defensive and aversive behaviors that precluded locomotion in the animals.

Conclusion:

Pigs appear to have an MLR and can be used to model neuromodulation of this gait-promoting center. These results indicate that the pig is a useful model to guide future clinical studies for optimizing MLR DBS in cases of gait deficiencies associated with such conditions as Parkinson’s disease, spinal cord injury, or stroke.

The Argo: a high channel count recording system for neural recording in vivo

OBJECTIVE

Decoding neural activity has been limited by the lack of tools available to record from large numbers of neurons across multiple cortical regions simultaneously with high temporal fidelity. To this end, we developed the Argo system to record cortical neural activity at high data rates.

APPROACH

Here we demonstrate a massively parallel neural recording system based on platinum-iridium microwire electrode arrays bonded to a CMOS voltage amplifier array. The Argo system is the highest channel count in vivo neural recording system, supporting simultaneous recording from 65 536 channels, sampled at 32 kHz and 12-bit resolution. This system was designed for cortical recordings, compatible with both penetrating and surface microelectrodes.

MAIN RESULTS

We validated this system through initial bench testing to determine specific gain and noise characteristics of bonded microwires, followed by in-vivo experiments in both rat and sheep cortex. We recorded spiking activity from 791 neurons in rats and surface local field potential activity from over 30 000 channels in sheep.

SIGNIFICANCE

These are the largest channel count microwire-based recordings in both rat and sheep. While currently adapted for head-fixed recording, the microwire-CMOS architecture is well suited for clinical translation. Thus, this demonstration helps pave the way for a future high data rate intracortical implant.

Implanted Flexible Electronics: Set Device Lifetime with Smart Nanomaterials

Flexible electronics is one of the most attractive and anticipated markets in the internet-of-things era, covering a broad range of practical and industrial applications from displays and energy harvesting to health care devices. The mechanical flexibility, combined with high performance electronics, and integrated on a soft substrate offer unprecedented functionality for biomedical applications. This paper presents a brief snapshot on the materials of choice for niche flexible bio-implanted devices that address the requirements for both biodegradable and long-term operational streams. The paper also discusses potential future research directions in this rapidly growing field.

Growing evidence for separate neural mechanisms for attention and consciousness

Our conscious experience of the world seems to go in lockstep with our attentional focus: We tend to see, hear, taste, and feel what we attend to, and vice versa. This tight coupling between attention and consciousness has given rise to the idea that these two phenomena are indivisible. In the late 1950s, the honoree of this special issue, Charles Eriksen, was among a small group of early pioneers that sought to investigate whether a transient increase in overall level of attention (alertness) in response to a noxious stimulus can be decoupled from conscious perception using experimental techniques. Recent years saw a similar debate regarding whether attention and consciousness are two dissociable processes. Initial evidence that attention and consciousness are two separate processes primarily rested on behavioral data. However, the past couple of years witnessed an explosion of studies aimed at testing this conjecture using neuroscientific techniques. Here we provide an overview of these and related empirical studies on the distinction between the neuronal correlates of attention and consciousness, and detail how advancements in theory and technology can bring about a more detailed understanding of the two. We argue that the most promising approach will combine ever-evolving neurophysiological and interventionist tools with quantitative, empirically testable theories of consciousness that are grounded in a mathematically formalized understanding of phenomenology.

3D Interfacing between Soft Electronic Tools and Complex Biological Tissues

Recent developments in soft functional materials have created opportunities for building bioelectronic devices with tissue-like mechanical properties. Their integration with the human body could enable advanced sensing and stimulation for medical diagnosis and therapies. However, most of the available soft electronics are constructed as planar sheets, which are difficult to interface with the target organs and tissues that have complex 3D structures. Here, the recent approaches are highlighted to building 3D interfaces between soft electronic tools and complex biological organs and tissues. Examples involve mesh devices for conformal contact, imaging-guided fabrication of organ-specific electronics, miniaturized probes for neurointerfaces, instrumented scaffold for tissue engineering, and many other soft 3D systems. They represent diverse routes for reconciling the interfacial mismatches between electronic tools and biological tissues. The remaining challenges include device scaling to approach the complexity of target organs, biological data acquisition and processing, 3D manufacturing techniques, etc., providing a range of opportunities for scientific research and technological innovation.

Technological Challenges in the Development of Optogenetic Closed-Loop Therapy Approaches in Epilepsy and Related Network Disorders of the Brain

Epilepsy is a chronic, neurological disorder affecting millions of people every year. The current available pharmacological and surgical treatments are lacking in overall efficacy and cause side-effects like cognitive impairment, depression, tremor, abnormal liver and kidney function. In recent years, the application of optogenetic implants have shown promise to target aberrant neuronal circuits in epilepsy with the advantage of both high spatial and temporal resolution and high cell-specificity, a feature that could tackle both the efficacy and side-effect problems in epilepsy treatment. Optrodes consist of electrodes to record local field potentials and an optical component to modulate neurons via activation of opsin expressed by these neurons. The goal of optogenetics in epilepsy is to interrupt seizure activity in its earliest state, providing a so-called closed-loop therapeutic intervention. The chronic implantation in vivo poses specific demands for the engineering of therapeutic optrodes. Enzymatic degradation and glial encapsulation of implants may compromise long-term recording and sufficient illumination of the opsin-expressing neural tissue. Engineering efforts for optimal optrode design have to be directed towards limitation of the foreign body reaction by reducing the implant's elastic modulus and overall size, while still providing stable long-term recording and large-area illumination, and guaranteeing successful intracerebral implantation. This paper presents an overview of the challenges and recent advances in the field of electrode design, neural-tissue illumination, and neural-probe implantation, with the goal of identifying a suitable candidate to be incorporated in a therapeutic approach for long-term treatment of epilepsy patients.

Compressed sensing of large-scale local field potentials using adaptive sparsity analysis and Non-convex Optimization

OBJECTIVE

Energy consumption is a critical issue in resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) has emerged as a powerful framework in addressing this issue owing to its highly efficient data compression procedure. In this paper, a CS-based approach termed Simultaneous Analysis Non-Convex Optimization (SANCO) is proposed for large-scale, multi-channel local field potentials (LFPs) recording.

APPROACH

The SANCO method consists of three parts: (1) the analysis model is adopted to reinforce sparsity of the multi-channel LFPs, therefore overcoming the drawbacks of conventional synthesis models. (2) An optimal continuous order difference matrix is constructed as the analysis operator, enhancing the recovery performance while saving both computational resources and data storage space. (3) A non-convex optimizer that can by efficiently solved with alternating direction method of multipliers (ADMM) is developed for multi-channel LFPs reconstruction.

MAIN RESULTS

Experimental results on real datasets reveal that the proposed approach outperforms state-of-the-art CS methods in terms of both recovery quality and computational efficiency.

SIGNIFICANCE

Energy efficiency of the SANCO make it an ideal candidate for resource-constrained, large scale wireless neural recording. Particularly, the proposed method ensures that the key features of LFPs had little degradation even when data are compressed by 16x, making it very suitable for long term wireless neural recording applications.

Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology

Rapid developments are currently taking place in the fields of artificial intelligence (AI) and biotechnology, and applications arising from the convergence of these 2 fields are likely to offer immense opportunities that could greatly benefit human health and biosecurity. The combination of AI and biotechnology could potentially lead to breakthroughs in precision medicine, improved biosurveillance, and discovery of novel medical countermeasures as well as facilitate a more effective public health emergency response. However, as is the case with many preceding transformative technologies, new opportunities often present new risks in parallel. Understanding the current and emerging risks at the intersection of AI and biotechnology is crucial for health security specialists and unlikely to be achieved by examining either field in isolation. Uncertainties multiply as technologies merge, showcasing the need to identify robust assessment frameworks that could adequately analyze the risk landscape emerging at the convergence of these 2 domains.This paper explores the criteria needed to assess risks associated with Artificial intelligence and biotechnology and evaluates 3 previously published risk assessment frameworks. After highlighting their strengths and limitations and applying to relevant Artificial intelligence and biotechnology examples, the authors suggest a hybrid framework with recommendations for future approaches to risk assessment for convergent technologies.

How is flexible electronics advancing neuroscience research?

Innovative neurotechnology must be leveraged to experimentally answer the multitude of pressing questions in modern neuroscience. Driven by the desire to address the existing neuroscience problems with newly engineered tools, we discuss in this review the benefits of flexible electronics for neuroscience studies. We first introduce the concept and define the properties of flexible and stretchable electronics. We then categorize the four dimensions where flexible electronics meets the demands of modern neuroscience: chronic stability, interfacing multiple structures, multi-modal compatibility, and neuron-type-specific recording. Specifically, with the bending stiffness now approaching that of neural tissue, implanted flexible electronic devices produce little shear motion, minimizing chronic immune responses and enabling recording and stimulation for months, and even years. The unique mechanical properties of flexible electronics also allow for intimate conformation to the brain, the spinal cord, peripheral nerves, and the retina. Moreover, flexible electronics enables optogenetic stimulation, microfluidic drug delivery, and neural activity imaging during electrical stimulation and recording. Finally, flexible electronics can enable neuron-type identification through analysis of high-fidelity recorded action potentials facilitated by its seamless integration with the neural circuitry. We argue that flexible electronics will play an increasingly important role in neuroscience studies and neurological therapies via the fabrication of neuromorphic devices on flexible substrates and the development of enhanced methods of neuronal interpenetration.

Laser Sharpening of Carbon Fiber Microelectrode Arrays for Brain Recording

Microwire microelectrode arrays (MEAs) are implanted in the brain for recording neuron activities to study the brain function. Among various microwire materials, carbon fiber stands out due to its small diameter (5-10 μm), relatively high Young's modulus, and low electrical resistance. Microwire tips in MEAs are often sharpened to reduce the insertion force and prevent the thin microwires from buckling. Currently, carbon fiber MEAs are sharpened by either torch burning, which limits the positions of wire tips to a water bath surface plane, or electrical discharge machining, which is difficult to implement to the nonelectrically conductive carbon fiber with parylene-C insulation. A laser-based carbon fiber sharpening method proposed in this study enables the fabrication of carbon fiber MEAs with sharp tips and custom lengths. Experiments were conducted to study effects of laser input voltage and transverse speed on carbon fiber tip geometry. Results of the tip sharpness and stripped length of the insulation as well as the electrochemical impedance spectroscopy measurement at 1 kHz were evaluated and analyzed. The laser input voltage and traverse speed have demonstrated to be critical for the sharp tip, short stripped length, and low electrical impedance of the carbon fiber electrode for brain recording MEAs. A carbon fiber MEA with custom electrode lengths was fabricated to validate the laser-based approach.

Next-Generation Bioelectric Medicine: Harnessing the Therapeutic Potential of Neural Implants

Bioelectric medicine leverages natural signaling pathways in the nervous system to counteract organ dysfunction. This novel approach has potential to address conditions with unmet needs, including heart failure, hypertension, inflammation, arthritis, asthma, Alzheimer's disease, and diabetes. Neural therapies, which target the brain, spinal cord, or peripheral nerves, are already being applied to conditions such as epilepsy, Parkinson's, and chronic pain. While today's therapies have made exciting advancements, their open-loop design-where stimulation is administered without collecting feedback-means that results can be variable and devices do not work for everyone. Stimulation effects are sensitive to changes in neural tissue, nerve excitability, patient position, and more. Closing the loop by providing neural or non-neural biomarkers to the system can guide therapy by providing additional insights into stimulation effects and overall patient condition. Devices currently on the market use recorded biomarkers to close the loop and improve therapy. The future of bioelectric medicine is more holistically personalized. Collected data will be used for increasingly precise application of neural stimulations to achieve therapeutic effects. To achieve this future, advances are needed in device design, implanted and computational technologies, and scientific/medical interpretation of neural activity. Research and commercial devices are enabling the development of multiple levels of responsiveness to neural, physiological, and environmental changes. This includes developing suitable implanted technologies for high bandwidth brain/machine interfaces and addressing the challenge of neural or state biomarker decoding. Consistent progress is being made in these challenges toward the long-term vision of automatically and holistically personalized care for chronic health conditions.

Stimulus Feature-Specific Information Flow Along the Columnar Cortical Microcircuit Revealed by Multivariate Laminar Spiking Analysis

Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

Recent advances in bioelectronics chemistry

Research in bioelectronics is highly interdisciplinary, with many new developments being based on techniques from across the physical and life sciences. Advances in our understanding of the fundamental chemistry underlying the materials used in bioelectronic applications have been a crucial component of many recent discoveries. In this review, we highlight ways in which a chemistry-oriented perspective may facilitate novel and deep insights into both the fundamental scientific understanding and the design of materials, which can in turn tune the functionality and biocompatibility of bioelectronic devices. We provide an in-depth examination of several developments in the field, organized by the chemical properties of the materials. We conclude by surveying how some of the latest major topics of chemical research may be further integrated with bioelectronics.

Recent advances in bioelectronics chemistry

Research in bioelectronics is highly interdisciplinary, with many new developments being based on techniques from across the physical and life sciences. Advances in our understanding of the fundamental chemistry underlying the materials used in bioelectronic applications have been a crucial component of many recent discoveries. In this review, we highlight ways in which a chemistry-oriented perspective may facilitate novel and deep insights into both the fundamental scientific understanding and the design of materials, which can in turn tune the functionality and biocompatibility of bioelectronic devices. We provide an in-depth examination of several developments in the field, organized by the chemical properties of the materials. We conclude by surveying how some of the latest major topics of chemical research may be further integrated with bioelectronics.

Recent advances in neurotechnologies with broad potential for neuroscience research

Interest in deciphering the fundamental mechanisms and processes of the human mind represents a central driving force in modern neuroscience research. Activities in support of this goal rely on advanced methodologies and engineering systems that are capable of interrogating and stimulating neural pathways, from single cells in small networks to interconnections that span the entire brain. Recent research establishes the foundations for a broad range of creative neurotechnologies that enable unique modes of operation in this context. This review focuses on those systems with proven utility in animal model studies and with levels of technical maturity that suggest a potential for broad deployment to the neuroscience community in the relatively near future. We include a brief summary of existing and emerging neuroscience techniques, as background for a primary focus on device technologies that address associated opportunities in electrical, optical and microfluidic neural interfaces, some with multimodal capabilities. Examples of the use of these technologies in recent neuroscience studies illustrate their practical value. The vibrancy of the engineering science associated with these platforms, the interdisciplinary nature of this field of research and its relevance to grand challenges in the treatment of neurological disorders motivate continued growth of this area of study.

Biophysics of Temporal Interference Stimulation

Temporal interference (TI) is a non-invasive neurostimulation technique that utilizes high-frequency external electric fields to stimulate deep neuronal structures without affecting superficial, off-target structures. TI represents a potential breakthrough for treating conditions, such as Parkinson's disease and chronic pain. However, early clinical work on TI stimulation was met with mixed outcomes challenging its fundamental mechanisms and applications. Here, we apply established physics to study the mechanisms of TI with the goal of optimizing it for clinical use. We argue that TI stimulation cannot work via passive membrane filtering, as previously hypothesized. Instead, TI stimulation requires an ion-channel mediated signal rectification process. Unfortunately, this mechanism is also responsible for high-frequency conduction block in off-target tissues, thus challenging clinical applications of TI. In consequence, we propose a set of experimental controls that should be performed in future experiments to refine our understanding and practice of TI stimulation. A record of this paper's transparent peer review process is included in the Supplemental Information.

Recent Advances in Electrical Neural Interface Engineering: Minimal Invasiveness, Longevity, and Scalability

Electrical neural interfaces serve as direct communication pathways that connect the nervous system with the external world. Technological advances in this domain are providing increasingly more powerful tools to study, restore, and augment neural functions. Yet, the complexities of the nervous system give rise to substantial challenges in the design, fabrication, and system-level integration of these functional devices. In this review, we present snapshots of the latest progresses in electrical neural interfaces, with an emphasis on advances that expand the spatiotemporal resolution and extent of mapping and manipulating brain circuits. We include discussions of large-scale, long-lasting neural recording; wireless, miniaturized implants; signal transmission, amplification, and processing; as well as the integration of interfaces with optical modalities. We outline the background and rationale of these developments and share insights into the future directions and new opportunities they enable.

Brain-Machine Interfaces as Commodities: Exchanging Mind for Matter

Brain-machine interfaces (BMIs), which enable a two-way flow of signals, information, and directions between human neurons and computerized machines, offer spectacular opportunities for therapeutic and consumer applications, but they also present unique dangers to the safety, privacy, psychological health, and spiritual well-being of their users. The sale of these devices as commodities for profit exacerbates such issues and may subject the user to an unequal exchange with corporations. Catholic healthcare professionals and bioethicists should be especially concerned about the implications for the essential dignity of the persons using the new BMIs.

Summary

The commercial sale of brain-machine interfaces (BMIs) generates and exacerbates problems for end-users' safety, psychological health, and spiritual well-being.

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

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

A soft and stretchable bilayer electrode array with independent functional layers for the next generation of brain machine interfaces

OBJECTIVE

Brain-Machine Interfaces (BMIs) hold great promises for advancing neuroprosthetics, robotics, and for providing treatment options for severe neurological diseases. The objective of this work is the development and in vivo evaluation of electrodes for BMIs that meet the needs to record brain activity at sub-millimeter resolution over a large area of the cortex while being soft and electromechanically robust (i.e. stretchable).

APPROACH

Current electrodes require a trade-off between high spatiotemporal resolution and cortical coverage area. To address the needs for simultaneous high resolution and large cortical coverage, the prototype electrode array developed in this study employs a novel bilayer routing of soft and stretchable lead wires from the recording sites on the surface of the brain (electrocorticography, ECoG) to the data acquisition system.

MAIN RESULTS

To validate the recording characteristics, the array was implanted in healthy felines for up to 5 months. Neural signals recorded from both layers of the device showed elevated mid-frequency structures typical of local field potential (LFP) signals that were stable in amplitude over implant duration, and also exhibited consistent frequency-dependent modulation after anesthesia induction by Telazol.

SIGNIFICANCE

The successful development of a soft and stretchable large-area, high resolution micro ECoG electrode array (lahrμECoG) is an important step to meet the neurotechnological needs of advanced BMI applications.

Bioinspired Materials for In Vivo Bioelectronic Neural Interfaces

The success of in vivo neural interfaces relies on their long-term stability and large scale in interrogating and manipulating neural activity after implantation. Conventional neural probes, owing to their limited spatiotemporal resolution and scale, face challenges for studying the massive, interconnected neural network in its native state. In this review, we argue that taking inspiration from biology will unlock the next generation of in vivo bioelectronic neural interfaces. Reducing the feature sizes of bioelectronic neural interfaces to mimic those of neurons enables high spatial resolution and multiplexity. Additionally, chronic stability at the device-tissue interface is realized by matching the mechanical properties of bioelectronic neural interfaces to those of the endogenous tissue. Further, modeling the design of neural interfaces after the endogenous topology of the neural circuitry enables new insights into the connectivity and dynamics of the brain. Lastly, functionalization of neural probe surfaces with coatings inspired by biology leads to enhanced tissue acceptance over extended timescales. Bioinspired neural interfaces will facilitate future developments in neuroscience studies and neurological treatments by leveraging bidirectional information transfer and integrating neuromorphic computing elements.

A 512-Channel Multi-Layer Polymer-Based Neural Probe Array

We present for the first time the design, fabrication, and preliminary bench-top characterization of a high-density, polymer-based penetrating microelectrode array, developed for chronic, large-scale recording in the cortices and hippocampi of behaving rats. We present two architectures for these targeted brain regions, both featuring 512 Pt recording electrodes patterned front-and-back on micromachined eight-shank arrays of thin-film Parylene C. These devices represent an order of magnitude improvement in both number and density of recording electrodes compared with prior work on polymer-based microelectrode arrays. We present enabling advances in polymer micro-machining related to lithographic resolution and a new method for back-side patterning of electrodes. In vitro electrochemical data verifies suitable electrode function and surface properties. Finally, we describe next steps toward the implementation of these arrays in chronic, large-scale recording studies in free-moving animal models.

Protonic conductor: better understanding neural resting and action potential

With the employment of the transmembrane electrostatic proton localization theory with a new membrane potential equation, neural resting and action potential is now much better understood as the voltage contributed by the localized protons/cations at a neural liquid- membrane interface. Accordingly, the neural resting/action potential is essentially a protonic/cationic membrane capacitor behavior. It is now understood with a newly formulated action potential equation: when action potential is <0 (negative number), the localized protons/cations charge density at the liquid-membrane interface along the periplasmic side is >0 (positive number); when the action potential is >0, the concentration of the localized protons and localized nonproton cations is <0, indicating a "depolarization" state. The nonlinear curve of the localized protons/cations charge density in the real-time domain of an action potential spike appears as an inverse mirror image to the action potential. The newly formulated action potential equation provides biophysical insights for neuron electrophysiology, which may represent a complementary development to the classic Goldman-Hodgkin-Katz equation. With the use of the action potential equation, the biological significance of axon myelination is now also elucidated as to provide protonic insulation and prevent any ions both inside and outside of the neuron from interfering with the action potential signal, so that the action potential can quickly propagate along the axon with minimal (e.g., 40 times less) energy requirement.NEW & NOTEWORTHY The newly formulated action potential equation provides biophysical insights for neuron electrophysiology, which may represent a complementary development to the classic Goldman-Hodgkin-Katz equation. The nonlinear curve of the localized protons/cations charge density in the real-time domain of an action potential spike appears as an inverse mirror image to the action potential. The biological significance of axon myelination is now elucidated as to provide protonic insulation and prevent any ions from interfering with action potential signal.

Multichannel parallel processing of neural signals in memristor arrays

Fully implantable neural interfaces with massive recording channels bring the gospel to patients with motor or speech function loss. As the number of recording channels rapidly increases, conventional complementary metal-oxide semiconductor (CMOS) chips for neural signal processing face severe challenges on parallelism scalability, computational cost, and power consumption. In this work, we propose a previously unexplored approach for parallel processing of multichannel neural signals in memristor arrays, taking advantage of their rich dynamic characteristics. The critical information of neural signal waveform is extracted and encoded in the memristor conductance modulation. A signal segmentation scheme is developed to adapt to device variations. To verify the fidelity of the processed results, seizure prediction is further demonstrated, with high accuracy above 95% and also more than 1000× improvement in power efficiency compared with CMOS counterparts. This work suggests that memristor arrays could be a promising multichannel signal processing module for future implantable neural interfaces.