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In August 2020 Elon Musk’s start-up, Neuralink, introduced its next generation neural implant which establishes wireless, bilateral communication between its user (his or her brain to be more precise) and a mobile phone or computer. Neuralink signalled the fact that we are on the verge of the wide commercialisation of brain-computer interfaces (BCIs). Such mind-reading or mind-altering technologies interfere with the human brain and/or the human mind, but this action is not a novelty. The treatment of neurological and mental diseases through the brain has a long history and aims to address certain malfunctions of the organ or to reorient the ways one think and perceives the world. These treatments are based either on physical interference with the brain or with the mind. The latter, which is the main area of interest of this paper, is called psychotherapy. To contribute to the acceptance and development of Brain-Computer Interfaces in Europe, this article provides an overview of the legal and ethical considerations that arise in connection with psychotherapy. The article finds that under specific circumstances, interference with absolute rights such as the freedom of thought or freedom of information is possible. A key to legitimacy is informed consent of the user/patient. Furthermore, psychotherapists are bound by a wide range of ethical principles, which should be applied on a case-by-case basis. As the human brain is unique, developing a standardized technology to interfere therewith is extremely difficult, especially when considering the stringency and personalized application of legal and ethical requirements.

As one of the promising upcoming alternatives to traditional drug therapy in central nervous system disorders and psychiatric disease, medical devices for neuromodulation have received a lot of attention. In addition to the invasive neural implant technologies used for deep-brain stimulation, a range of noninvasive brain stimulation (NIBS) techniques have recently been at the center of interest in research and therapy. Here, we provide an overview of the ever-growing family of NIBS methodologies, their clinical applications, and mechanisms of action involved. We suggest that NIBS technologies can be classified based on (1) the underlying technique (magnetic: transcranial magnetic stimulation, or electrical: transcranial electrical stimulation), (2) the targeted neurobiological process (ongoing processing, excitability/plasticity, oscillatory entrainment), or (3) the clinical domain of application (treatment, diagnosis, or prognosis). The current overview should prove valuable in understanding along which dimensions NIBS can be compared with traditional or alternative upcoming CNS modulation technologies.

Conducting polymers (CPs) consist of appropriate intrinsic monomers intertwined like biomolecules. These stable CPs exhibit enhanced conductivity and sensitivity at the cellular level that finds various applications in the manufacturing of biomedical devices. Indeed, they can be synthesized using different techniques rapidly and cost-effectively with typical biocompatibility and biodegradability behavior. The affinity of CPs towards cells at the polymer-tissue interface extends their applications in the biomedical and clinical fields such as artificial nerves, biosensors, neural implants, drug delivery devices, etc. Thus, taking advantage of the critical role played by the CPs in the biomedical sector, this chapter focuses on the CPs outlook towards tissue engineering aspects with a special emphasis on the synthesis and fine-tuning of the surface properties providing enhanced physicochemical characteristics required in the biomedical field.

Flexible neural implants are extremely favored, as the most successful strategy to promote probe-tissue integration and avoid severe gliosis relies on reducing the mechanical mismatch between probe and brain tissue. But what is a reliastic requirement for achieving chronic recording stability? Which are the critical dimensions and main factors determining device integration? To answer these questions, two types of hair-sized polyimide-based flexible intracortical (PIXI) arrays were fabricated, differing only in cross-section. Chronic tissue reaction to both types was evaluated in rats, in different implantation setups. Interfacial stresses were found to play a critical role in long-term implant tissue integration. Still, all the devices provided high quality chronic recordings of single units and expression of the inflammatory genes was not significantly upregulated for the larger device. Our study points out that the most relevant factor in eliciting FBR is played by probe-tissue mechanical interactions and not by device size alone.

To date, a wide variety of neural tissue implants have been developed for neurophysiology recording from living tissues. An ideal neural implant should minimize the damage to the tissue and perform reliably and accurately for long periods of time. Therefore, the materials utilized to fabricate the neural recording implants become a critical factor. The materials of these devices could be classified into two broad categories: electrode materials as well as packaging and substrate materials. In this review, inorganic (metals and semiconductors), organic (conducting polymers), and carbon-based (graphene and carbon nanostructures) electrode materials are reviewed individually in terms of various neural recording devices that are reported in recent years. Properties of these materials, including electrical properties, mechanical properties, stability, biodegradability/bioresorbability, biocompatibility, and optical properties, and their critical importance to neural recording quality and device capabilities, are discussed. For the packaging and substrate materials, different material properties are desired for the chronic implantation of devices in the complex environment of the body, such as biocompatibility and moisture and gas hermeticity. This review summarizes common solid and soft packaging materials used in a variety of neural interface electrode designs, as well as their packaging performances. Besides, several biopolymers typically applied over the electrode package to reinforce the mechanical rigidity of devices during insertion, or to reduce the immune response and inflammation at the device-tissue interfaces are highlighted. Finally, a benchmark analysis of the discussed materials and an outlook of the future research trends are concluded.

Neural-interfaced prostheses aim to restore sensorimotor limb functions in amputees. They rely on bidirectional neural interfaces, which represent the communication bridge between nervous system and neuroprosthetic device by controlling its movements and evoking sensory feedback. Compared to extraneural electrodes (i.e., epineural and perineural implants), intraneural electrodes, implanted within peripheral nerves, have higher selectivity and specificity of neural signal recording and nerve stimulation. However, being implanted in the nerve, their main limitation is represented by the significant inflammatory response that the body mounts around the probe, known as Foreign Body Reaction (FBR), which may hinder their rapid clinical translation. Furthermore, the mechanical mismatch between the consistency of the device and the surrounding neural tissue may contribute to exacerbate the inflammatory state. The FBR is a non-specific reaction of the host immune system to a foreign material. It is characterized by an early inflammatory phase eventually leading to the formation of a fibrotic capsule around intraneural interfaces, which increases the electrical impedance over time and reduces the chronic interface biocompatibility and functionality. Thus, the future in the reduction and control of the FBR relies on innovative biomedical strategies for the fabrication of next-generation neural interfaces, such as the development of more suitable designs of the device with smaller size, appropriate stiffness and novel conductive and biomimetic coatings for improving their long-term stability and performance. Here, we present and critically discuss the latest biomedical approaches from material chemistry and tissue engineering for controlling and mitigating the FBR in chronic neural implants.

Brain implants are promising instruments for a broad variety of nervous tissue diseases with a wide range of applications, e.g. for stimulation, signal recording or local drug delivery. Recently, graphene-based scaffold materials have emerged as attractive candidates as neural interfaces, 3D scaffolds, or drug delivery systems due to their excellent properties like flexibility, high surface area, conductivity, and lightweight. To date, however, there is a lack of appropriate studies of the foreign body response, especially by glial cells, towards graphene-based materials. In this work, we investigated the effects of macroscopic, highly porous (>99.9%) graphene oxide (GO) and reduced graphene oxide (rGO) (conductivity ∼1 S m−1) scaffolds with tailorable macro- and microstructure on human astrocyte and microglial cell viability and proliferation as well as expression of neuroinflammation and astrogliosis associated genes in an indirect contact approach. In this in vitro model, as well as ex vivo in organotypic murine brain slices, we could demonstrate that both GO and rGO based 3D scaffolds exert slight effects on the glial cell populations which are the key players of glial scar formation. These effects were in most cases completely abolished by curcumin, a known anti-inflammatory and anti-fibrotic drug that could in perspective be applied to brain implants as a protectant.

This paper presents a low-power, low-noise microsystem for the recording of neural local field potentials or intracranial electroencephalographic signals. It features 32 timemultiplexed channels at the electrode interface and offers the possibility to spatially delta encode data to take advantage of the large correlation of signals captured from nearby channels. The circuit also implements a mixed-signal voltage-triggered autoranging algorithm which allows to attenuate large interferers in digital domain while preserving neural information. This effectively increases the system dynamic range and avoids the onset of saturation. A prototype, fabricated in a standard 180nm CMOS process, has been experimentally verified in-vitro with cellular cultures of primary cortical neurons from mice. The system shows an integrated input-referred noise in the 0.5–200 Hz band of 1.4 Vrms for a spot noise of about 85 nV= p Hz. The system draws 1.5 W per channel from 1.2V supply and obtains 71 dB + 26 dB dynamic range when the artifact-aware autoranging mechanism is enabled, without penalising other critical specifications such as crosstalk between channels or commonmode and power supply rejection ratios.

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson’s disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement kinematics. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.Significance StatementNeurotechnology will revolutionize the treatment of neurological and psychiatric patients, promising novel treatment avenues for previously intractable brain disorders. However, optimal surgical and computational approaches and their interactions with neurological disorders are unknown. How can recent advances in machine learning and connectomics aid the precision and performance of invasive brain signal decoding strategies? Do the brain disorders treated with such approaches have impact on decoding performance? We propose a real time compatible advanced machine learning pipeline for invasively recorded brain signals in Parkinson’s disease (PD) patients. We report optimal movement decoding strategies with respect to signal source, model architecture and connectomic fingerprint and demonstrate that PD pathophysiology significantly and negatively impacts movement decoding. Our study has broad impacts for the development of smart brain implants for the treatment of PD and other brain disorders.

An essential method to investigate neuromodulation effects of an invasive neural interface (INI) is magnetic resonance imaging (MRI). Presently, MRI imaging of patients with neural implants is highly restricted in high field MRI (e.g., 3 T and higher) due to patient safety concerns. This results in lower resolution MRI images and, consequently, degrades the efficacy of MRI imaging for diagnostic purposes in these patients. Cubic silicon carbide (3C-SiC) is a biocompatible wide-band-gap semiconductor with a high thermal conductivity and magnetic susceptibility compatible with brain tissue. It also has modifiable electrical conductivity through doping level control. These properties can improve the MRI compliance of 3C-SiC INIs, specifically in high field MRI scanning. In this work, the MRI compliance of epitaxial SiC films grown on various Si wafers, used to implement a monolithic neural implant (all-SiC), was studied. Via finite element method (FEM) and Fourier-based simulations, the specific absorption rate (SAR), induced heating, and image artifacts caused by the portion of the implant within a brain tissue phantom located in a 7 T small animal MRI machine were estimated and measured. The specific goal was to compare implant materials, thus, the effect of leads outside the tissue was not considered. The results of the simulations were validated via phantom experiments in the same 7 T MRI system. The simulation and experimental results revealed that free-standing 3C-SiC films had little to no image artifacts compared to silicon and platinum reference materials inside the MRI at 7 T. In addition, FEM simulations predicted an ~30% SAR reduction for 3C-SiC compared to Pt. These initial simulations and experiments indicate an all-SiC INI may effectively reduce MRI induced heating and image artifacts in high field MRI. In order to evaluate the MRI safety of a closed-loop, fully functional all-SiC INI as per ISO/TS 10974:2018 standard, additional research and development is being conducted and will be reported at a later date.

The lifetime of neural implants is strongly dependent on packaging due to the aqueous and biochemically aggressive nature of the body. Over the last decade, there has been a drive towards neuromodulatory implants which are wireless and approaching millimeter-scales with increasing electrode count. A so-far unrealized goal for these new types of devices is an in-vivo lifetime comparable to a sizable fraction of a healthy patient’s lifetime (>10–20 years). Existing, approved medical implants commonly encapsulate components in metal enclosures (e.g. titanium) with brazed ceramic inserts for electrode feedthrough. It is unclear how amenable the traditional approach is to the simultaneous goals of miniaturization, increased channel count, and wireless communication. Ceramic materials have also played a significant role in traditional medical implants due to their dielectric properties, corrosion resistance, biocompatibility, and high strength, but are not as commonly used for housing materials due to their brittleness and the difficulty they present in creating complex housing geometries. However, thin-film technology has opened new opportunities for ceramics processing. Thin films derived largely from the semiconductor industry can be deposited and patterned in new ways, have conductivities which can be altered during manufacturing to provide conductors as well as insulators, and can be used to fabricate flexible substrates. In this review, we give an overview of packaging for neural implants, with an emphasis on how ceramic materials have been utilized in medical device packaging, as well as how ceramic thin-film micromachining and processing may be further developed to create truly reliable, miniaturized, neural implants.

Brain implants are increasingly used to treat neurological disorders and diseases. However, the brain foreign body response (FBR) elicited by implants affects neuro-electrical transduction and long-term reliability limiting their clinical adoption. The mismatch in Young’s modulus between silicon implants (∼180 GPa) and brain tissue (∼1-30 kPa) exacerbates the FBR resulting in the development of flexible implants from polymers such as polyimide (∼1.5-2.5 GPa). However, a stiffness mismatch of at least two orders of magnitude remains. Here, we introduce (i) the first mechanically matched brain implant (MMBI) made from silicone (∼20 kPa), (ii) new microfabrication methods, and (iii) a novel dissolvable sugar shuttle to reliably implant MMBIs. MMBIs were fabricated via vacuum-assisted molding using sacrificial sugar molds and were then encased in sugar shuttles that dissolved within 2 min after insertion into rat brains. Sections of rat neocortex implanted with MMBIs, PDMS implants, and silicon implants were analyzed by immunohistochemistry 3 and 9-weeks post-implantation. MMBIs resulted in significantly higher neuronal density and lower FBR within 50 µm of the tissue-implant interface compared to PDMS and silicon implants suggesting that materials mechanically matched to brain further minimize the FBR and could contribute to better implant functionality and long-term reliability.

Objective. Implanted devices providing real-time neural activity classification and control are increasingly used to treat neurological disorders, such as epilepsy and Parkinson’s disease. Classification performance is critical to identifying brain states appropriate for the therapeutic action (e.g. neural stimulation). However, advanced algorithms that have shown promise in offline studies, in particular deep learning (DL) methods, have not been deployed on resource-restrained neural implants. Here, we designed and optimized three DL models or edge deployment and evaluated their inference performance in a case study of seizure detection. Approach. A deep neural network (DNN), a convolutional neural network (CNN), and a long short-term memory (LSTM) network were designed and trained with TensorFlow to classify ictal, preictal, and interictal phases from the CHB-MIT scalp EEG database. A sliding window based weighted majority voting algorithm was developed to detect seizure events based on each DL model’s classification results. After iterative model compression and coefficient quantization, the algorithms were deployed on a general-purpose, off-the-shelf microcontroller for real-time testing. Inference sensitivity, false positive rate (FPR), execution time, memory size, and power consumption were quantified. Main results. For seizure event detection, the sensitivity and FPR for the DNN, CNN, and LSTM models were 87.36%/0.169 h−1, 96.70%/0.102 h−1, and 97.61%/0.071 h−1, respectively. Predicting seizures for early warnings was also feasible. The LSTM model achieved the best overall performance at the expense of the highest power. The DNN model achieved the shortest execution time. The CNN model showed advantages in balanced performance and power with minimum memory requirement. The implemented model compression and quantization achieved a significant saving of power and memory with an accuracy degradation of less than 0.5%. Significance. Inference with embedded DL models achieved performance comparable to many prior implementations that had no time or computational resource limitations. Generic microcontrollers can provide the required memory and computational resources, while model designs can be migrated to application-specific integrated circuits for further optimization and power saving. The results suggest that edge DL inference is a feasible option for future neural implants to improve classification performance and therapeutic outcomes.

Neural interfaces capable of multi-site electrical recording, on-site signal classification, and closed-loop therapy are critical for the diagnosis and treatment of neurological disorders. However, deploying machine learning algorithms on low-power neural devices is challenging, given the tight constraints on computational and memory resources for such devices. In this paper, we review the recent developments in embedding machine learning in neural interfaces, with a focus on design trade-offs and hardware efficiency. We also present our optimized tree-based model for low-power and memory-efficient classification of neural signal in brain implants. Using energy-aware learning and model compression, we show that the proposed oblique trees can outperform conventional machine learning models in applications such as seizure or tremor detection and motor decoding.

State-of-the-art advances in battery-free and flexible implants require new materials and devices for brain behavior recording and stimulation where the constraints in terms of mechanical and electrical matching are crucial. Wireless Power Transfer based neural implants consist of receiver antenna, power management circuit, and shank for power transmission, recording and stimulation. Here, the implementation of a novel implantable device fabricated on a flexible polyimide substrate and a modular approach to implant device typology is introduced. A flexible substrate of polyimide is chosen to provide better implant-brain tissue mechanical, biocompatibility and biointegration compliance. Both, electrical and mechanical analysis are provided to prove the correct operation of the fabricated device under the modelled environment. COMSOL Multiphysics mechanical modelling in implantation case is carried out to analyze the optimal shape and material choice.

Visual prosthesis requires a subcutaneous control unit to transfer information from an external headset to a brain implant in order to restore sight. Optogenetic prosthesis need high power than their electrical equivalents, but resolution can potentially be higher. Both necessitate dedicated embedded hardware. External system is wirelessly powered and receives a compressed video stream using our zRLE protocol, which needs decompression. Transmission is via Bluetooth communication. The control system is then designed to communicate with custom ASICs which directly control the brain implant. Our architecture is composed of 4 functional modules: wireless power receiving module, power management module, core control module, and wireless data receiving module. All the above modules are integrated on a circular PCB prototype, and the entire control system has to operate efficiently with three local micro-controller compute cores.

Deep brain stimulation (DBS) for Parkinson's disease, essential tremor and epilepsy is an established palliative treatment. DBS uses electrical neuromodulation to suppress symptoms. Most current systems provide a continuous pattern of fixed stimulation, with clinical follow-ups to refine settings constrained to normal office hours. An issue with this management strategy is that the impact of stimulation on circadian, i.e. sleep-wake, rhythms is not fully considered; either in the device design or in the clinical follow-up. Since devices can be implanted in brain targets that couple into the reticular activating network, impact on wakefulness and sleep can be significant. This issue will likely grow as new targets are explored, with the potential to create entraining signals that are uncoupled from environmental influences. To address this issue, we have designed a new brain-machine-interface for DBS that combines a slow-adaptive circadian-based stimulation pattern with a fast-acting pathway for responsive stimulation, demonstrated here for seizure management. In preparation for first-in-human research trials to explore the utility of multi-timescale automated adaptive algorithms, design and prototyping was carried out in line with ISO risk management standards, ensuring patient safety. The ultimate aim is to account for chronobiology within the algorithms embedded in brain-machine-interfaces and in neuromodulation technology more broadly.

This paper explores the prospect of brain implants as related to human activity and functioning. The researchers present information compiled through popular data collection using specific keywords related to brain implantation. The study calls into question and discusses the harm that could result if a negligent populace receives brain implants to “merge” with artificial intelligence through brain computer interfaces. Its intent is to raise awareness of the risks that brain implantation imposes on an individual’s health, wellbeing and livelihood.

Society acts according to the public interest. Products and technologies are integrated into society based on their value in people’s lives. Nueralink is developing an EEG implant that can bring many useful features to the public. This work explores the technological feasibility of a brain implant becoming a mainstream product as accepted by the public, and presents capabilities that are possible with such a device that would benefit people’s daily lives. The paper concludes that a brain implant will likely be adopted into the mainstream in the future due to its many functional benefits like driving assistance, vision enhancements, paralysis cures, and human-computer interaction benefits.

Neural modulation techniques with electricity, light and other forms of energy have enabled the deconstruction of neural circuitry. One major challenge of existing neural modulation techniques is the invasive brain implants and the permanent skull attachment of an optical fiber for modulating neural activity in the deep brain. Here we report an implant-free and tether-free optical neuromodulation technique in deep-brain regions through the intact scalp with brain-penetrant second near-infrared (NIR-II) illumination. Macromolecular infrared nanotransducers for deep-brain stimulation (MINDS) demonstrate exceptional photothermal conversion efficiency of 71% at 1064 nm, the wavelength that minimizes light attenuation by the brain in the entire 400-1700 nm spectrum. Upon widefield 1064-nm illumination >50 cm above the mouse head at a low incident power density of 10 mW/mm2, deep-brain neurons are activated by MINDS-sensitized TRPV1 channels with minimal thermal damage. Our approach could open opportunities for simultaneous neuromodulation of multiple socially interacting animals by remotely irradiating NIR-II light to stimulate each subject individually.

Parylene-C has been used as a substrate and encapsulation material for many implantable medical devices. However, to ensure the flexibility required in some applications, minimize tissue reaction, and protect parylene from degradation in vivo an additional outmost layer of polydimethylsiloxane (PDMS) is desired. In such a scenario, the adhesion of PDMS to parylene is of critical importance to prevent early failure caused by delamination in the harsh environment of the human body. Towards this goal, we propose a method based on creating chemical covalent bonds using intermediate ceramic layers as adhesion promoters between PDMS and parylene.To evaluate our concept, we prepared three different sets of samples with PDMS on parylene without and with oxygen plasma treatment (the most commonly employed method to increase adhesion), and samples with our proposed ceramic intermediate layers of silicon carbide (SiC) and silicon dioxide (SiO2). The samples were soaked in phosphate-buffered saline (PBS) solution at room temperature and were inspected under an optical microscope. To investigate the adhesion property, cross-cut tape tests and peel tests were performed. The results showed a significant improvement of the adhesion and in-soak long-term performance of our proposed encapsulation stack compared with PDMS on parylene and PDMS on plasma-treated parylene. We aim to use the proposed solution to package bare silicon chips on active implants.

Brain-machine interfaces (BMIs) record and translate neural activity into a control signal for assistive or other devices. Intracortical microelectrode arrays (MEAs) enable high degree-of-freedom BMI control for complex tasks by providing fine-resolution neural recording. However, chronically implanted MEAs are subject to a dynamic in vivo environment where transient or systematic disruptions can interfere with neural recording and degrade BMI performance. Typically, neural implant failure modes have been categorized as biological, material, or mechanical. While this categorization provides insight into a disruption's causal etiology, it is less helpful for understanding degree of impact on BMI function or possible strategies for compensation. Therefore, we propose a complementary classification framework for intracortical recording disruptions that is based on duration of impact on BMI performance and requirement for and responsiveness to interventions: (1) Transient disruptions interfere with recordings on the time scale of minutes to hours and can resolve spontaneously; (2) Reversible disruptions cause persistent interference in recordings but the root cause can be remedied by an appropriate intervention; (3) Irreversible compensable disruptions cause persistent or progressive decline in signal quality, but their effects on BMI performance can be mitigated algorithmically; and (4) Irreversible non-compensable disruptions cause permanent signal loss that is not amenable to remediation or compensation. This conceptualization of intracortical BMI disruption types is useful for highlighting specific areas for potential hardware improvements and also identifying opportunities for algorithmic interventions. We review recording disruptions that have been reported for MEAs and demonstrate how biological, material, and mechanical mechanisms of disruption can be further categorized according to their impact on signal characteristics. Then we discuss potential compensatory protocols for each of the proposed disruption classes. Specifically, transient disruptions may be minimized by using robust neural decoder features, data augmentation methods, adaptive machine learning models, and specialized signal referencing techniques. Statistical Process Control methods can identify reparable disruptions for rapid intervention. In-vivo diagnostics such as impedance spectroscopy can inform neural feature selection and decoding models to compensate for irreversible disruptions. Additional compensatory strategies for irreversible disruptions include information salvage techniques, data augmentation during decoder training, and adaptive decoding methods to down-weight damaged channels.

Reliable packaging for implantable neural prosthetic devices in body fluids is a long-standing challenge for device’s chronic applications. This work studied the stability of Parylene C (PA), SiO 2 , and Si 3 N 4 packages on tungsten wires using accelerated, reactive aging tests in three solutions: pH 7.4 phosphate-buffered saline (PBS), PBS+30 mM H 2 O 2 , and PBS+150 mM H 2 O 2 . Different combinations of coating thicknesses and deposition methods were studied at 67 °C. Analysis of the preliminary data shows that the pinholes/defects, cracks, and interface delamination are the main attributes of metal erosion and degradation in reactive aging solutions. Failure at the interface of package and metal is the dominating factor in the wire samples with open tips.

Reliable packaging for implantable neural prosthetic devices in body fluids is a long-standing challenge for devices&rsquo, chronic applications. This work studied the stability of Parylene C (PA), SiO2, and Si3N4 packages and coating strategies on tungsten wires using accelerated, reactive aging tests in three solutions: pH 7.4 phosphate-buffered saline (PBS), PBS + 30 mM H2O2, and PBS + 150 mM H2O2. Different combinations of coating thicknesses and deposition methods were studied at various testing temperatures. Analysis of the preliminary data shows that the pinholes/defects, cracks, and interface delamination are the main attributes of metal erosion and degradation in reactive aging solutions. Failure at the interface of package and metal is the dominating factor in the wire samples with open tips.

The methods used to treat various neurological diseases are evolving. The facilities provided by the technology have led to creation of new treatment opportunities. Neuromodulation is one of these important methods. By definition, the neuromodulation is a change in neural activity which occurs by stimulating a specific area of nervous system. The mentioned stimulation can be electrical, magnetic, or chemical. This method is used in various diseases, such as stroke, Parkinson's, Alzheimer's, and amyotrophic lateral sclerosis (ALS). Multiple sclerosis (MS) is no exception in this regard and methods including the neurofeedback and transcranial magnetic stimulation (TMS) are used to treat various complications of the MS. One aspect of neuromodulation is the use of neural implant, which is applied nowadays, especially in the Parkinson's disease, and the use of microchips and prostheses to treat various symptoms in different neurological diseases has received significant attention. Although neural implant has been exploited to improve the symptoms of MS, they appear to have much greater potential to improve the condition of patients with MS. It seems that more attention to the symptoms of MS, on the one hand, and a new approach to the pathogenesis of this disease and considering it as a connectomopathy, on the other hand, can provide new opportunities for application of this method in the treatment of MS.

One of the main challenges of the current ultrasonic transducers for powering brain implants is the complexity of focusing ultrasonic waves in various axial and lateral directions. The available transducers usually use electrically controlled phased array for beamforming the ultrasonic waves, which increases the complexity of the system even further. In this article, we propose a straightforward solution for selective powering of brain implants to remove the complexity of conventional phased arrays. Our approach features a Sectored-Multiring Ultrasonic Transducer (S-MRUT) on a single piezoelectric sheet, specifically designed for powering implantable devices for optogenetics in freely moving animals. The proposed unidirectional S-MRUT is capable of focusing the ultrasonic waves on brain implants located at different depths and regions of the brain. The S-MRUT is designed based on Fresnel Zone Plate (FZP) theory, simulated in COMSOL, and fabricated with the microfabrication process. The acoustic profile of the seven different configurations of the S-MRUT was measured using a hydrophone with the total number of 7436 grid points. The measurements show the ability of the proposed S-MRUT to sweep the focus point of the acoustic waves in the axial direction in depths of 1 – 3 mm, which is suitable for powering implants in the striatum of the mouse. Furthermore, the proposed S-MRUT demonstrates a steering area with an average radius of 0.862 mm and 0.678 mm in experiments and simulations, respectively. The S-MRUT is designed with the size of ${3.8}\times {3.8}\times {0.5}$ mm3 and the weight of ${0.054}{gr}$ , showing that it is compact and light enough to be worn by a mouse. Finally, the S-MRUT was tested in our measurement setup, where it successfully transfers sufficient power to a 2.8-mm3 optogentic stimulator to turn on a micro-LED on the stimulator.

Research over the past four decades has highlighted the importance of certain brain cells, called glial cells, and has moved the neurocentric vision of structure, function, and pathology of the nervous system toward a more holistic perspective. In this view, the demand for technologies that are able to target and both selectively monitor and control glial cells is emerging as a challenge across neuroscience, engineering, chemistry, and material science. Frequently neglected or marginally considered as a barrier to be overcome between neural implants and neuronal targets, glial cells, and in particular astrocytes, are increasingly considered as active players in determining the outcomes of device implantation. This review provides a concise overview not only of the previously established but also of the emerging physiological and pathological roles of astrocytes. It also critically discusses the most recent advances in biomaterial interfaces and devices that interact with glial cells and thus have enabled scientists to reach unprecedented insights into the role of astroglial cells in brain function and dysfunction. This work proposes glial interfaces and glial engineering as multidisciplinary fields that have the potential to enable significant advancement of knowledge surrounding cognitive function and acute and chronic neuropathologies.

Diamond possesses many favorable properties for biochemical sensors, including biocompatibility, chemical inertness, resistance to biofouling, an extremely wide potential window, and low double-layer capacitance. The hardness of diamond, however, has hindered its applications in neural implants due to the mechanical property mismatch between diamond and soft nervous tissues. Here, we present a flexible, diamond-based microelectrode probe consisting of multichannel boron-doped polycrystalline diamond (BDD) microelectrodes on a soft Parylene C substrate. We developed and optimized a wafer-scale fabrication approach that allows the use of the growth side of the BDD thin film as the sensing surface. Compared to the nucleation surface, the BDD growth side exhibited a rougher morphology, a higher sp3 content, a wider water potential window, and a lower background current. The dopamine (DA) sensing capability of the BDD growth surface electrodes was validated in a 1.0 mM DA solution, which shows better sensitivity and stability than the BDD nucleation surface electrodes. The results of these comparative studies suggest that using the BDD growth surface for making implantable microelectrodes has significant advantages in terms of the sensitivity, selectivity, and stability of a neural implant. Furthermore, we validated the functionality of the BDD growth side electrodes for neural recordings both in vitro and in vivo. The biocompatibility of the microcrystalline diamond film was also assessed in vitro using rat cortical neuron cultures., Biosensors: Flexible diamond-based microelectrodes for neural sensing A flexible, sensitive, stable neural probe consisting of boron-doped polycrystalline diamond (BDD) microelectrodes has been developed, whereby the BDD growth surface is used as the electrode site for neurophysiological and neurochemical sensing. Among carbon materials, BDD has found widespread use in neurotransmitter detection, but the hardness of diamond has impeded application in neural implants. A team headed by Wen Li at Michigan State University, USA, succeeded in developing a process that allows the growth side of the BDD thin film to be used as the sensing surface: they transferred the BDD patterns from a solid silicon substrate onto a flexible polymer substrate. The authors validated their BDD microelectrode technology for neural recordings both in vitro and in vivo, and they believe it has excellent potential for research into various brain disorders, such as Parkinson’s disease.

This chapter focuses on AI-based cognitive and perceptual enhancements. AI-based brain enhancements are already under development, and they may become commonplace over the next 30–50 years. We raise doubts concerning whether radical AI-based enhancements transhumanists advocate will accomplish the transhumanists goals of longevity, human flourishing, and intelligence enhancement. We urge that even if the technologies are medically safe and are not used as tools by surveillance capitalism or an authoritarian dictatorship, these enhancements may still fail to do their job for philosophical reasons. In what follows, we explore one such concern, a problem that involves the nature of the self. We illustrate that the so called transhumanist efforts to “merge oneself with AI” could lead to perverse realizations of AI technology, such as the demise of the person who sought enhancement. And, in a positive vein, we offer ways to avoid this, at least within the context of one theory of the nature of personhood.

Despite the huge complexity of the foreign body reaction, a quantitative assessment over time of the scar tissue thickness around implanted materials is needed to figure out the evolution of neural implants for long times. A data-driven approach, based on phenomenological polynomial functions, is able to reproduce experimental data. Nevertheless, a misuse of this strategy may lead to unsatisfactory results, even if standard indexes are optimized. In this work, an effective in silico procedure was presented to reproduce the scar tissue dynamics around implanted synthetic devices and to predict the capsule thickness for times before and after experimental detections.

Advanced polymer science and design technologies are constantly evolving to meet ever-growing expectations for flexible optical MEMS. In this work, we present design and microfabrication considerations for designed flexible Polymeric Opto-Electro-Mechanical Systems (POEMS). The presented methods integrate waveguide fabrication and laser diode (LD) chip assembly with Lawrence Livermore National Laboratory's (LLNL's) flexible thin-film technology to enable LLNL's first neural optoelectrode that can deliver guided light for neural activation. We support our findings with electrical and optical bench verification tests, present thermal simulation models to analyze heat dissipation of laser light sources on polymer substrates and discuss potential modifications for next generation prototypes. This fully integrated approach will allow spatial precision, scalability and more particularly, longer lifetime, needed to enable chronic studies of brain activities.

Repeated non-diffuse optical imaging of the brain is difficult. This is due to the fact that the cranial bone is highly scattering and thus a strong optical barrier. Repeated craniotomies increase the risk of complications and may disrupt the biological systems being imaged. We previously introduced a potential solution in the form of a transparent ceramic cranial implant called the Window to the Brain (WttB) implant. This implant is made of nanocrystalline Yttria-Stabilized Zirconia (nc-YSZ), which possesses the requisite mechanical strength to serve as a permanent optical access window in human patients. In this present study, we demonstrate repeated brain imaging of n = 5 mice using both OCT and LSI across the WttB implant over 4 weeks. The main objectives are to determine if the WttB implant allows for chronic OCT imaging, and to shed further light on the question of whether optical access provided by the WttB implant remains stable over this duration in the body. The Window to the Brain implant allowed for stable repeated imaging of the mouse brain with Optical Coherence Tomography over 28 days, without loss of signal intensity. Repeated Laser Speckle Imaging was also possible over this timeframe, but signal to noise ratio and the sharpness of vessels in the images decreased with time. This can be partially explained by elevated blood flow during the first imaging session in response to trauma from the surgery, which was also detected by OCT flow imaging. These results are promising for long-term optical access through the WttB implant, making feasible chronic in vivo studies in multiple neurological models of brain disease.

ObjectiveMultisite, silicon-based probes are widely used tools to record the electrical activity of neuronal populations. Several physical features of these devices (e.g. shank thickness, tip geometry) are designed to improve their recording performance. Here, our goal was to investigate whether the position of recording sites on the silicon shank might affect the quality of the recorded neural signal in acute experiments.ApproachNeural recordings obtained with five different types of high-density, single-shank, planar silicon probes from anesthetized rats were analyzed. Wideband data were filtered (500 - 5000 Hz) to extract spiking activity, then various quantitative properties (e.g. amplitude distribution of the filtered potential, single unit yield) of the recorded cortical and thalamic activity were compared between sites located at different positions of the silicon shank, focusing particularly on edge and center sites.Main resultsEdge sites outperformed center sites: mean values of the examined properties of the spiking activity were in most cases higher for edge sites (~94%, 33/35) and a large fraction of these differences were also statistically significant (~45%, 15/33) with effect sizes ranging from small to large. Although the single unit yield was similar between site positions, the difference in signal quality was remarkable in the range corresponding to high-amplitude spikes. Furthermore, the advantage of edge sites slightly decreased for probes having a narrower shank.SignificanceThe better signal quality on edge sites might be the result of the reduced shielding effect of the silicon shank providing a larger field of view for edge sites to detect spikes, or the less tissue damage caused near the edges of the shank. Our results might aid the design of novel neural implants in enhancing their recording performance by identifying more efficient recording site placements.

High-channel-count neural interfaces are typically packaged by being permanently bonded to their packaged electronics followed by encapsulation. Such interfaces are often intimately integrated into neural tissue, their removal to replace the battery or upgrade electronics is not undesirable. Gaskets are widely used to provide liquid/electrical isolation and to seal the connection between two or more mating parts. Pressure-driven microgaskets are well established in the field of microfluidics. Although rematable microgaskets for fluidic interconnects exist, the use of microgaskets for electrical isolation have not been demonstrated. Our approach is to electrically isolate 2-D arrays of contact pads using a compressible silicone microgasket. Electrochemical impedance spectroscopy (EIS) was used to quantify the electrical isolation of the microgasket on contact pads, which were formed in a polyimide flex circuit, as a function of frequency after being soaked in saline. Experiments have shown that the compressed sub-millimeter PDMSe microgasket can provide excellent isolation (i.e., >30 MΩ at 1 KHz) that is comparable to the other more conventional packaging methods, such as encapsulation in polydimethylsiloxane elastomer (PDMSe) or parylene-C. Our microgasket-based approach should be scalable to high channel counts and high channel densities enabling much smaller and higher-performance neural implants.

Artificial electrical stimulation of peripheral nerves for sensory feedback restoration can greatly benefit from computational models for simulation-based neural implant design in order to reduce the trial-and-error approach usually taken, thus potentially significantly reducing research and development costs and time. To this end, we built a computational model of a peripheral nerve trunk in which the interstitial space between the fibers and the tissues was modelled using a resistor network, thus enabling distance-dependent ephaptic coupling between myelinated axons and between fascicles as well. We used the model to simulate a) the stimulation of a nerve trunk model with a cuff electrode, and b) the propagation of action potentials along the axons. Results were used to investigate the effect of ephaptic interactions on recruitment and selectivity stemming from artificial (i.e., neural implant) stimulation and on the relative timing between action potentials during propagation. Ephaptic coupling was found to increase the number of fibers that are activated by artificial stimulation, thus reducing the artificial currents required for axonal recruitment, and it was found to reduce and shift the range of optimal stimulation amplitudes for maximum inter-fascicular selectivity. During propagation, while fibers of similar diameters tended to lock their action potentials and reduce their conduction velocities, as expected from previous knowledge on bundles of identical axons, the presence of many other fibers of different diameters was found to make their interactions weaker and unstable., Author summary The design of neural interfaces for artificial electrical stimulation in prostheses can greatly benefit from simulations using electrode-nerve interface models. Studies on electrical stimulation of nerves generally neglect the effects of ephaptic coupling (i.e., coupling between axon fibers and bundles due to their local electric fields) on axon responses to stimulation as ephaptic coupling is normally assumed to play no significant role in bundles of myelinated axons. However, studies have shown that ephaptic effects are non negligible in mammalian peripheral nerves. As such, we have built a resistor network model of a small peripheral nerve trunk for simulation of ephaptic interactions between axons under artificial electrical stimulation. Results show that ephaptic coupling clearly increases axon recruitment and, hence, influences selectivity. On the other hand, the ephaptic coupling effects during action potential propagation observed in this work are more complex than the known effects for ideal, homogeneous bundles. In this work, heterogeneity and variability of fiber diameters in a bundle were found to greatly diminish the strength of ephaptic interactions to the point where these interactions do not play a relevant role on action potential propagation.

Neural implants with large numbers of electrodes have become an important tool for examining brain functions. However, these devices typically displace a large intracranial volume compared to the neurons they record. This large size limits the density of implants, provokes tissue reactions that degrade chronic performance, and impedes the ability to accurately visualize recording sites within intact circuits. Here we report next-generation silicon-based neural probes at cellular-scale (5×10µm cross-section), with ultra-high-density packing (as little as 66µm between shanks) and 64 or 256 closely-spaced recording sites per probe. We show that these probes can be inserted into superficial or deep brain structures and maintain high-quality spike recordings in freely behaving rats for many weeks. Finally, we demonstrate a slice-in-place approach for the precise registration of recording sites relative to nearby neurons and anatomical features, including striatal µ-opioid receptor patches. This scalable technology provides a valuable tool for examining information processing within neural circuits, and potentially for human brain-machine interfaces.

There is growing evidence supporting the use of percutaneous tibial nerve stimulation to manage lower urinary tract symptoms (LUTS) such as urgency, frequency and urge incontinence, in a non-pharmacologic, minimally invasive approach. Given this, there is now an impetus to move this technology forward from an interval (i.e., weekly and/or monthly) toward a continuous dosing, using implantable devices. This review article focuses on the newest implantable devices and the most current data demonstrating safety and efficacy in the management of refractory overactive bladder. There are new studies showing that continuous (or even semi-continuous) stimulation of the tibial nerve can be of similar efficacy as other chronic neural implant devices, such as sacral neuromodulation. This includes the Blue Wind Renova, StimGuard, eCoin, and Bioness Stimrouter. While the data on these devices are still short-term, implantable tibial nerve stimulation holds promise in the field of managing LUTS and pelvic floor disorders. Durability and minimizing migration remain challenging.

Progress has been made in the field of neural interfacing using both mouse and rat models, yet standardization of these models' interchangeability has yet to be established. The mouse model allows for transgenic, optogenetic, and advanced imaging modalities which can be used to examine the biological impact and failure mechanisms associated with the neural implant itself. The ability to directly compare electrophysiological data between mouse and rat models is crucial for the development and assessment of neural interfaces. The most obvious difference in the two rodent models is size, which raises concern for the role of device-induced tissue strain. Strain exerted on brain tissue by implanted microelectrode arrays is hypothesized to affect long-term recording performance. Therefore, understanding any potential differences in tissue strain caused by differences in the implant to tissue size ratio is crucial for validating the interchangeability of rat and mouse models. Hence, this study is aimed at investigating the electrophysiological variances and predictive device-induced tissue strain. Rat and mouse electrophysiological recordings were collected from implanted animals for eight weeks. A finite element model was utilized to assess the tissue strain from implanted intracortical microelectrodes, taking into account the differences in the depth within the cortex, implantation depth, and electrode geometry between the two models. The rat model demonstrated a larger percentage of channels recording single unit activity and number of units recorded per channel at acute but not chronic time points, relative to the mouse model Additionally, the finite element models also revealed no predictive differences in tissue strain between the two rodent models. Collectively our results show that these two models are comparable after taking into consideration some recommendations to maintain uniform conditions for future studies where direct comparisons of electrophysiological and tissue strain data between the two animal models will be required.

Cognitive control is the ability to withhold a default, prepotent response in favor of a more adaptive choice. Control deficits are common across mental disorders, including depression, anxiety, and addiction. Thus, a method for improving cognitive control could be broadly useful in disorders with few effective treatments. Here, we demonstrate closed-loop enhancement of one aspect of cognitive control by direct brain stimulation in humans. We stimulated internal capsule/striatum in participants undergoing intracranial epilepsy monitoring as they performed a cognitive control/conflict task. Stimulation enhanced performance, with the strongest effects from dorsal capsule/striatum stimulation. We then developed a framework to detect control lapses and stimulate in response. This closed-loop approach produced larger behavioral changes than open-loop stimulation, with a slight improvement in performance change per unit of energy delivered. Finally, we decoded task performance directly from activity on a small number of electrodes, using features compatible with existing closed-loop brain implants. Our findings are proof of concept for a new approach to treating severe mental disorders, based on directly remediating underlying cognitive deficits.

Electrophysiology provides a direct readout of neuronal activity at a temporal precision only limited by the sampling rate. However, interrogating deep brain structures, implanting multiple targets or aiming at unusual angles still poses significant challenges even for expert operators, and errors are only discovered by post-hoc histological reconstruction. Here, we propose a method combining the high-resolution information about bone landmarks provided by micro-CT scanning with the soft tissue contrast of the MRI, which allowed us to precisely localize electrodes and optic fibers in mice in vivo. This enables arbitrating the success of implantation directly after surgery with a precision comparable to the gold standard histological reconstruction. Adjustment of the recording depth with electrode microdrives or early termination of unsuccessful experiments saves many working hours, while fast 3-dimensional feedback helps surgeons to avoid systematic errors. Increased aiming precision will allow more precise targeting of small or deep brain nuclei and multiple targeting of specific cortical layers.

Objective Carbon fiber electrodes may enable better long-term brain implants, minimizing the tissue response commonly seen with silicon-based electrodes. The small diameter fiber may enable high-channel count brain-machine interfaces capable of reproducing dexterous movements. Past carbon fiber electrodes exhibited both high fidelity single unit recordings and a healthy neuronal population immediately adjacent to the recording site. However, the recording yield of our carbon fiber arrays chronically implanted in the brain typically hovered around 30%, for previously unknown reasons. In this paper we investigated fabrication process modifications aimed at increasing recording yield and longevity. Approach We tested a new cutting method using a 532nm laser against traditional scissor methods for the creation of the electrode recording site. We verified the efficacy of improved recording sites with impedance measurements and in vivo array recording yield. Additionally, we tested potentially longer-lasting coating alternatives to PEDOT:pTS, including PtIr and oxygen plasma etching. New coatings were evaluated with accelerated soak testing and acute recording. Main results We found that the laser created a consistent, sustainable 257 ± 13.8 µm2 electrode with low 1 kHz impedance (19 ± 4 kΩ with PEDOT:pTS) and low fiber-to-fiber variability. The PEDOT:pTS coated laser cut fibers were found to have high recording yield in acute (97% > 100 µV pp , N = 34 fibers) and chronic (84% > 100 µV pp , day 7; 71% > 100 µV pp , day 63, N = 45 fibers) settings. The laser cut recording sites were good platforms for the PtIr coating and oxygen plasma etching, slowing the increase in 1 kHz impedance compared to PEDOT:pTS in an accelerated soak test. Significance We have found that laser cut carbon fibers have a high recording yield that can be maintained for over two months in vivo and that alternative coatings perform better than PEDOT:pTS in accelerated aging tests. This work provides evidence to support carbon fiber arrays as a viable approach to high-density, clinically-feasible brain-machine interfaces.

Spinal Cord Injury (SCI) is a severe condition that can result in loss of motor and sensory functions by disrupting communication among neurons, i.e., neuro-spike communication. Future information and communication technology (ICT) based treatment techniques for SCI are expected to rely on nano networks, deployed inside the body. In this respect, modeling neuro-spike communication channels in the spinal cord and revealing the relationship between channel metrics and SCI are required to realize these treatment techniques and diagnosis tools such as replacement neural implants, high-performance diagnosis tools, which are based on ICT metrics instead of large medical data. Therefore, in this study, we focus on a spinal cord network, namely the descending spinal cord pathway, which is responsible for the transmission of brain motor signals to the spinal cord. We aim to analyze the rate of motor information flow to the corresponding muscle. To this end, we model the spinal cord motor network as a layered network consisting of a cascade of two independent neuro-spike channels, which are brain-spinal cord network and spinal cord interneuron-spinal cord motoneuron network. We derive upper and lower bounds for the total rate across the brain-spinal cord network and interneuron-spinal cord network. Our evaluations demonstrate that the total rate in the case of upper motor neuron syndrome (UMNS), which manifests itself with muscle weakness, approaches zero, where the brain-spinal cord network becomes a bottleneck. In lower motor neuron syndrome (LMNS), which results in muscle atrophy, the total rate again approaches zero with the loss of spinal cord motoneurons (MN).

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

Objective. Microfabricated neuroprosthetic devices have made possible important observations on neuron activity; however, long-term high-fidelity recording performance of these devices has yet to be realized. Tissue-device interactions appear to be a primary source of lost recording performance. The current state of the art for visualizing the tissue response surrounding brain implants in animals is immunohistochemistry + confocal microscopy, which is mainly performed after sacrificing the animal. Monitoring the tissue response as it develops could reveal important features of the response which may inform improvements in electrode design. Approach. Optical coherence tomography (OCT), an imaging technique commonly used in ophthalmology, has already been adapted for imaging of brain tissue. Here, we use OCT to achieve real-time, in vivo monitoring of the tissue response surrounding chronically implanted neural devices. The employed tissue-response-provoking implants are coated with a plasma-deposited nanofilm, which has been demonstrated as a biocompatible and anti-inflammatory interface for indwelling devices. We evaluate the method by comparing the OCT results to traditional histology qualitatively and quantitatively. Main results. The differences in OCT signal across the implantation period between the plasma group and the control reveal that the plasma-type coating of otherwise rigid brain probes (glass) only slightly improve the glial encapsulation in the brain parenchyma indicating that geometrical or mechanical influences are dominating the encapsulation process. Significance. Our approach can long-term monitor and compare the tissue-response to chronically-implanted neural probes with and withour plasma coating in living animal models. Our findings provide valuable insigh to the well acknowledged yet not solved challenge.

Light in the range of 700-1100 nm has many proven diagnostics and therapeutic medical applications. In this study, we demonstrate another potential application of light. We implemented a testbed to transmit data wireleßly through a human skull and biological phantoms mimicking skin tißues properties using near-infrared (NIR) light. Currently, radio frequency (RF) is widely used to transmit data wireleßly through biological tißues, example includes implants communications. However, RF communications contain interference and security challenges. In this study, we present a novel use of NIR light for wireleß data transfer, for which a phantom is exposed to an externally modulated light source and the transmitted light through phantom is captured with a receiver. Initially, a high-resolution picture is succeßfully transmitted through skin phantom of 2 cm in depth. In the second phase, we succeßfully communicated through a human skull. Due to hardware limitations, we are able to achieve data rates in tens of kilobits per second. The minimum power applied to the phantoms measured during the data transmißion from 810 nm light-emitting-diode (LED) is 0.098 W/cm2. The measured optical power values are within the safe limit set by ANSI.Z136.1-2007 standard on laser safety, which is 2 W/cm2 for 1 sec exposure of 830 nm wavelength. Initial results are encouraging and there are many poßible applications for a system using light to transmit data wireleßly through biological tißues, including communication with brain implants and performing brain therapies.

Background Neural interfaces often elicit inflammatory responses and neuronal loss in the surrounding tissue which adversely affect the function and longevity of the implanted device. Minocycline, an anti-inflammatory pharmaceutics with neuroprotective properties, may be used for reducing the acute brain tissue responses after implantation. However, conventional administration routes require high doses which can cause adverse systemic side effects. Therefore, the aim of this study was to develop and evaluate a new drug-delivery-system for local and sustained administration of minocycline in the brain. Methods Stainless steel needles insulated with Parylene-C were dip-coated with non-crosslinked gelatin and minocycline-loaded PLGA nanoparticles (MC-NPs) were incorporated into the gelatin-coatings by an absorption method and subsequently trapped by drying the gelatin. Parylene-C insulated needles coated only with gelatin were used as controls. The expression of markers for activated microglia (CD68), all microglia (CX3CR1-GFP), reactive astrocytes (GFAP), neurons (NeuN) and all cell nuclei (DAPI) surrounding the implantation sites were quantified at 3 and 7 days after implantation in mice. Results MC-NPs were successfully incorporated into gelatin-coatings of neural implants by an absorption method suitable for thermosensitive drug-loads. Immunohistochemical analysis of the in vivo brain tissue responses, showed that MC-NPs significantly attenuate the activation of microglial cells without effecting the overall population of microglial cells around the implantation sites. A delayed but significant reduction of the astrocytic response was also found in comparison to control implants. No effect on neurons or total cell count was found which may suggest that the MC-NPs are non-toxic to the central nervous system. Conclusions A novel drug-nanoparticle-delivery-system was developed for neural interfaces and thermosensitive drug-loads. The local delivery of MC-NPs was shown to attenuate the acute brain tissue responses nearby an implant and therefore may be useful for improving biocompatibility of implanted neuro-electronic interfaces. The developed drug-delivery-system may potentially also be used for other pharmaceutics to provide highly localized and therefore more specific effects as compared to systemic administration.

Modulating the electrical activity in the nervous system has shown great potential for neuroscience research and clinical therapies. To reduce risks of infection and restrictions in subject mobility, neuromodulators must be miniaturized and untethered. Safe and reliable wireless power transfer and data delivery with the required size and power constraints is still one of the fundamental challenges in developing miniature neural interfaces. A few wireless neural implants powered by RF, inductive coupling [1]–[3], ultrasound [4], and optics [5] have been reported; however, existing solutions cannot simultaneously achieve implant miniaturization, system portability, functional flexibility, and subject mobility, while avoiding tissue heating due to body absorption of high-frequency EM waves [6], attenuation of mechanical waves crossing different mediums [7], limited penetration depth of light [5], or lack of controllability with magnetothermal nanoparticles [8]. In comparison, magnetoelectric (ME) transducers, which convert low-frequency (100kHz to 10MHz) AC magnetic fields into electrical energy via mechanical coupling between magnetostrictive and piezoelectric films (Fig. 34.3.1, top), are promising for powering biomedical implants by offering all the desired properties summarized in Fig. 34.3.1. At resonance, lead zirconate titanate (PZT)/Metglas-based ME films generate high output voltage $(> 7\mathrm{V}_{\mathrm{pp}})$ with low resistive source impedance (~8000), under a magnetic field of less than 1mT, making it suitable for efficient energy harvesting. In this paper, we present MagNI (Magnetoelectric Neural Implant), the first untethered and programmable neural implant exploiting ME effects, which integrates a 1.5mm2 180nm CMOS SoC, an in-house built 4mmx2mm ME film, a single energy storage capacitor, and on-board electrodes onto a flexible polyimide substrate. It features: (1) a miniature physical dimension of 8.2mm3 and 28mg with only two discrete components added to the SoC (2) adaptive system control and data transfer mechanisms robust under source amplitude variations (1V variation tolerance and 0.2V modulation depth), (3) a best-in-class 90% chip efficiency due to its low static power down to 23.7µW, and (4) the capability to perform fully programmable bi-phasic current stimulation covering 0.05 to 1.5mA amplitude, 64 to $512\mu \mathrm{s}$ pulse width, and 0 to 200Hz frequency ranges, suitable for a variety of clinical targets such as the spinal cord, the vagus nerve and the brain. The concept of a spinal cord stimulating system enabled by MagNI with a battery powered magnetic transmitter assembled on a wearable belt is shown in Fig. 34.3.1 (bottom left).

Up to the present, there have been a variety of attempts to both monitor and control the signals in the nerve systems to investigate the brain functions as well as to treat the neurological diseases. Although electrical methods have been widely employed for both the detection of neural activity and the stimulation of the nerves, various other methods have also been proposed, which include optical, mechanical, thermal, chemical, and magnetic neural stimulation and detection. Optogenetics is a field of research that studies the control and probing of neural activities using light stimulation, which gained research momentum in the early 2000s. Among the various techniques for neural modulation, optogenetics has become one of the most powerful research tools to selectively control or modulate the activity of specific types of neurons, which has been impossible for conventional electrical methods. Due to the unique and powerful characteristics such as cell-type selectivity and biosafety, optogenetics and optical neural interfaces have gained increasing interest as a potential solution for brain studies and human neurological diseases, with its application expanding to a greater diversity of areas. In this chapter, we review various optical neural interfaces for implantable systems by considering the device configurations such as type of light source, presence of waveguides, and optical characteristics such as light power and coupling efficiency. Considering the interest for an untethered implantable neural interfaces and recent trend of utilizing LED (light-emitting diode) as an alternative light source in place of laser, two different types of LED-based systems are compared. Systems which use LED for direct illumination of the nerve cells and systems using LED coupled with waveguides are those. Several different approaches are compared in terms of efficiency, fabrication complexity, multifunctionality, and biosafety. In addition to current relevant technologies, existing challenges are discussed to achieve the ideal implantable optical system for practical purposes.

Over the past 30 years, stem cell technologies matured from an attractive option to investigate neurodegenerative diseases to a possible paradigm shift in their treatment through the development of cell-based regenerative medicine (CRM). Implantable cell replacement therapies promise to completely restore function of neural structures possibly changing how we currently perceive the onset of these conditions. One of the major clinical hurdles facing the routine implementation of stem cell therapy is the limited and inconsistent benefit observed thus far. While unclear, numerous pre-clinical and a handful of clinical cell fate imaging studies point to poor cell retention and survival. Coupling the need to better understand these mechanisms while providing scalable approaches to monitor these treatments in both pre-clinical and clinical scenarios, we show a proof of concept bioimpedance electronic platform for the Agile development of smart and mobile biomedical applications like neural implants or highly portable monitoring devices.