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We present a high electrode density and high channel count CMOS (complementary metal-oxide-semiconductor) active neural probe containing 1344 neuron sized recording pixels (20 µm × 20 µm) and 12 reference pixels (20 µm × 80 µm), densely packed on a 50 µm thick, 100 µm wide, and 8 mm long shank. The active electrodes or pixels consist of dedicated in-situ circuits for signal source amplification, which are directly located under each electrode. The probe supports the simultaneous recording of all 1356 electrodes with sufficient signal to noise ratio for typical neuroscience applications. For enhanced performance, further noise reduction can be achieved while using half of the electrodes (678). Both of these numbers considerably surpass the state-of-the art active neural probes in both electrode count and number of recording channels. The measured input referred noise in the action potential band is 12.4 µVrms, while using 678 electrodes, with just 3 µW power dissipation per pixel and 45 µW per read-out channel (including data transmission).

Brain-computer interfaces are being developed to restore movement for people living with paralysis due to injury or disease. Although the therapeutic potential is great, long-term stability of the interface is critical for widespread clinical implementation. While many factors can affect recording and stimulation performance including electrode material stability and host tissue reaction, these factors have not been investigated in human implants. In this clinical study, we sought to characterize the material integrity and biological tissue encapsulation via explant analysis in an effort to identify factors that influence electrophysiological performance. We examined a total of six Utah arrays explanted from two human participants involved in intracortical BCI studies. Two platinum (Pt) arrays were implanted for 980 days in one participant (P1) and two Pt and two iridium oxide (IrOx) arrays were implanted for 182 days in the second participant (P2). We observed that the recording quality followed a similar trend in all six arrays with an initial increase in peak-to-peak voltage during the first 30–40 days and gradual decline thereafter in P1. Using optical and two-photon microscopy we observed a higher degree of tissue encapsulation on both arrays implanted for longer durations in participant P1. We then used scanning electron microscopy and energy dispersive X-ray spectroscopy to assess material degradation. All measures of material degradation for the Pt arrays were found to be more prominent in the participant with a longer implantation time. Two IrOx arrays were subjected to brief survey stimulations, and one of these arrays showed loss of iridium from most of the stimulated sites. Recording performance appeared to be unaffected by this loss of iridium, suggesting that the adhesion of IrOx coating may have been compromised by the stimulation, but the metal layer did not detach until or after array removal. In summary, both tissue encapsulation and material degradation were more pronounced in the arrays that were implanted for a longer duration. Additionally, these arrays also had lower signal amplitude and impedance. New biomaterial strategies that minimize fibrotic encapsulation and enhance material stability should be developed to achieve high quality recording and stimulation for longer implantation periods.

Paired associative stimulation is a relatively new non-invasive brain stimulation technique that combines transcranial magnetic stimulation and peripheral nerve stimulation. The effects of paired associative stimulation on the excitability of the cerebral cortex can vary according to the time interval between the transcranial magnetic stimulation and peripheral nerve stimulation. We established a model of cerebral ischemia in rats via transient middle cerebral artery occlusion. We administered paired associative stimulation with a frequency of 0.05 Hz 90 times over 4 weeks. We then evaluated spatial learning and memory using the Morris water maze. Changes in the cerebral ultra-structure and synaptic plasticity were assessed via transmission electron microscopy and a 64-channel multi-electrode array. We measured mRNA and protein expression levels of brain-derived neurotrophic factor and N-methyl-D-aspartate receptor 1 in the hippocampus using a real-time polymerase chain reaction and western blot assay. Paired associative stimulation treatment significantly improved learning and memory in rats subjected to cerebral ischemia. The ultra-structures of synapses in the CA1 area of the hippocampus in rats subjected to cerebral ischemia were restored by paired associative stimulation. Long-term potentiation at synapses in the CA3 and CA1 regions of the hippocampus was enhanced as well. The protein and mRNA expression of brain-derived neurotrophic factor and N-methyl-D-aspartate receptor 1 increased after paired associative stimulation treatment. These data indicate that paired associative stimulation can protect cognition after cerebral ischemia. The observed effect may be mediated by increases in the mRNA and protein expression of brain-derived neurotrophic factor and N-methyl-D-aspartate receptor 1, and by enhanced synaptic plasticity in the CA1 area of the hippocampus. The animal experiments were approved by the Animal Ethics Committee of Tongji Medical College, Huazhong University of Science & Technology, China (approval No. TJ-A20151102) on July 11, 2015.

Since the 1940s electrocorticography (ECoG) devices and, more recently, in the last decade, micro-electrocorticography (µECoG) cortical electrode arrays were used for a wide set of experimental and clinical applications, such as epilepsy localization and brain–computer interface (BCI) technologies. Miniaturized implantable µECoG devices have the advantage of providing greater-density neural signal acquisition and stimulation capabilities in a minimally invasive fashion. An increased spatial resolution of the µECoG array will be useful for greater specificity diagnosis and treatment of neuronal diseases and the advancement of basic neuroscience and BCI research. In this review, recent achievements of ECoG and µECoG are discussed. The electrode configurations and varying material choices used to design µECoG arrays are discussed, including advantages and disadvantages of µECoG technology compared to electroencephalography (EEG), ECoG, and intracortical electrode arrays. Electrode materials that are the primary focus include platinum, iridium oxide, poly(3,4-ethylenedioxythiophene) (PEDOT), indium tin oxide (ITO), and graphene. We discuss the biological immune response to µECoG devices compared to other electrode array types, the role of µECoG in clinical pathology, and brain–computer interface technology. The information presented in this review will be helpful to understand the current status, organize available knowledge, and guide future clinical and research applications of µECoG technologies.

Soft neural electrode arrays that are mechanically matched between neural tissues and electrodes offer valuable opportunities for the development of disease diagnose and brain computer interface systems. Here, a thermal release transfer printing method for fabrication of stretchable bioelectronics, such as soft neural electrode arrays, is presented. Due to the large, switchable and irreversible change in adhesion strength of thermal release tape, a low‐cost, easy‐to‐operate, and temperature‐controlled transfer printing process can be achieved. The mechanism of this method is analyzed by experiments and fracture‐mechanics models. Using the thermal release transfer printing method, a stretchable neural electrode array is fabricated by a sacrificial‐layer‐free process. The ability of the as‐fabricated electrode array to conform different curvilinear surfaces is confirmed by experimental and theoretical studies. High‐quality electrocorticography signals of anesthetized rat are collected with the as‐fabricated electrode array, which proves good conformal interface between the electrodes and dura mater. The application of the as‐fabricated electrode array on detecting the steady‐state visual evoked potentials research is also demonstrated by in vivo experiments and the results are compared with those detected by stainless‐steel screw electrodes. A low‐cost, easy‐to‐operate, and temperature‐controlled thermal release transfer printing method is successfully realized to form stretchable bioelectronics for medical systems. Using this method, a stretchable neural electrode array with metal/polyimide structure is fabricated by a sacrificial‐layer‐free process and is successfully used for detecting high‐fidelity electrocorticography signals from the dura mater of anesthetized rat.

The temporal expression of microRNA after spinal cord ischemia/reperfusion injury is not yet fully understood. In the present study, we established a model of spinal cord ischemia in Sprague-Dawley rats by clamping the abdominal aorta for 90 minutes, before allowing reperfusion for 24 or 48 hours. A sham-operated group underwent surgery but the aorta was not clamped. The damaged spinal cord was removed for hematoxylin-eosin staining and RNA extraction. Neuronal degeneration and tissue edema were the most severe in the 24- hour reperfusion group, and milder in the 48-hour reperfusion group. RNA amplification, labeling, and hybridization were used to obtain the microRNA expression profiles of each group. Bioinformatics analysis confirmed tour differentially expressed microRNAs （miR-22-3p, miR-743b-3p, miR-201-5p and miR-144-5p） and their common target genes （Tmem69 and Cxcll0）. Compared with the sham group, miR- 22-3p was continuously upregulated in all three ischemia groups but was highest in the group with 11o reperfusion, whereas miR-743b-3p, miR-201-5p and miR-144-5p were downregulated in the three ischemia groups. We have successfully identified the key genes expressed at different stages of spinal cord ischemia/reperfusion injury, which provide a reference for future investigations into the mechanism of spinal cord injury.

Partial shading conditions (PSC) have negative effects on the operation of photovoltaic (PV) systems. In this paper, a PV array reconfiguration method is developed to minimize power losses of PV arrays under partial shading conditions. The proposed reconfiguration method is based on equalizing the reduction of the short-circuit current of the PV modules in the PV array. Eight state-of-the-art Convolutional Neural Network models are employed to estimate the effect of shading on the short-circuit current of a PV module. These models include LeNet-5, AlexNet, VGG 11, VGG 19, Inception V3, ResNet 18, ResNet 34, and ResNet 50. Among eight models, the VGG 19 achieves the best accuracy on 1842 sample images. Therefore, this model is used to estimate the ratio of the actual short-circuit current and the estimated short-circuit current in four studied shading scenarios. This ratio decides the switching rule between PV modules throughout the PV array under PSC. A 2×2 experimental PV array shows that the proposed reconfiguration method improves the output power from 5.81% to 25.19% in four shading patterns. Accordingly, the power losses are reduced from 1.32% to 13.75%. The power improvement and the reduction of power losses of the proposed dynamic PV array reconfiguration system under four case studies demonstrates its effectiveness in addressing the effects of PSC on the PV array.

Increasing requirements for neural implantation are helping to expand our understanding of nervous systems and generate new developmental approaches. It is thanks to advanced semiconductor technologies that we can achieve the high-density complementary metal-oxide-semiconductor electrode array for the improvement of the quantity and quality of neural recordings. Although the microfabricated neural implantable device holds much promise in the biosensing field, there are some significant technological challenges. The most advanced neural implantable device relies on complex semiconductor manufacturing processes, which are required for the use of expensive masks and specific clean room facilities. In addition, these processes based on a conventional photolithography technique are suitable for mass production, which is not applicable for custom-made manufacturing in response to individual experimental requirements. The microfabricated complexity of the implantable neural device is increasing, as is the associated energy consumption, and corresponding emissions of carbon dioxide and other greenhouse gases, resulting in environmental deterioration. Herein, we developed a fabless fabricated process for a neural electrode array that was simple, fast, sustainable, and customizable. An effective strategy to produce conductive patterns as the redistribution layers (RDLs) includes implementing microelectrodes, traces, and bonding pads onto the polyimide (PI) substrate by laser micromachining techniques combined with the drop coating of the silver glue to stack the laser grooving lines. The process of electroplating platinum on the RDLs was performed to increase corresponding conductivity. Sequentially, Parylene C was deposited onto the PI substrate to form the insulation layer for the protection of inner RDLs. Following the deposition of Parylene C, the via holes over microelectrodes and the corresponding probe shape of the neural electrode array was also etched by laser micromachining. To increase the neural recording capability, three-dimensional microelectrodes with a high surface area were formed by electroplating gold. Our eco-electrode array showed reliable electrical characteristics of impedance under harsh cyclic bending conditions of over 90 degrees. For in vivo application, our flexible neural electrode array demonstrated more stable and higher neural recording quality and better biocompatibility as well during the 2-week implantation compared with those of the silicon-based neural electrode array. In this study, our proposed eco-manufacturing process for fabricating the neural electrode array reduced 63 times of carbon emissions compared to the traditional semiconductor manufacturing process and provided freedom in the customized design of the implantable electronic devices as well.

Patients with paralysis, spinal cord injury, or amputated limbs could benefit from using brain–machine interface technology for communication and neurorehabilitation. In this study, a 32-channel three-dimensional (3D) multielectrode probe array was developed for the neural interface system of a brain–machine interface to monitor neural activity. A novel microassembly technique involving lead transfer was used to prevent misalignment in the bonding plane during the orthogonal assembly of the 3D multielectrode probe array. Standard microassembly and biopackaging processes were utilized to implement the proposed lead transfer technique. The maximum profile of the integrated 3D neural device was set to 0.50 mm above the pia mater to reduce trauma to brain cells. Benchtop tests characterized the electrical impedance of the neural device. A characterization test revealed that the impedance of the 3D multielectrode probe array was on average approximately 0.55 MΩ at a frequency of 1 KHz. Moreover, in vitro cytotoxicity tests verified the biocompatibility of the device. Subsequently, 3D multielectrode probe arrays were implanted in rats and exhibited the capability to record local field potentials and spike signals.

Understanding the mechanical behavior of implanted neural electrode arrays is crucial for BCI development, which is the foundation for ensuring surgical safety, implantation precision, and evaluating electrode efficacy and long-term stability. Therefore, a reliable FE models are effective in reducing animal experiments and are essential for a deeper understanding of the mechanics of the implantation process. This study established a novel finite element model to simulate neural electrode implantation into brain tissue, specifically characterizing the nonlinear mechanical responses of brain tissue. Synchronized electrode implantation experiments were conducted using ex vivo porcine brain tissue. The results demonstrate that the model accurately reproduces the dynamics of the electrode implantation process. Quantitative analysis reveals that the implantation force exhibits a positive correlation with insertion depth, the average implantation force per electrode within a multi-electrode array decreases with increasing electrode number, and elevation in electrode size, shank spacing, and insertion speed each contribute to a systematic increase in insertion force. This study provides a reliable simulation tool and in-depth mechanistic analysis for predicting the implantation forces of high-density neural electrode arrays and offer theoretical guidance for optimizing BCI implantation device design.