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High density electrodes are a new frontier for biomedical implants. Increasing the density and the number of electrodes used for the stimulation of retinal ganglion cells is one possible strategy for enhancing the quality of vision experienced by patients using retinal prostheses. The present work presents an integration strategy for a diamond based, high density, stimulating electrode array with a purpose built application specific integrated circuit (ASIC). The strategy is centered on flip-chip bonding of indium bumps to create high count and density vertical interconnects between the stimulator ASIC and an array of diamond neural stimulating electrodes. The use of polydimethylsiloxane (PDMS) housing prevents cross-contamination of the biocompatible diamond electrode with non-biocompatible materials, such as indium, used in the microfabrication process. Micro-imprint lithography allowed edge-to-edge micro-scale pattering of the indium bumps on non-coplanar substrates that have a form factor that can conform to body organs and thus are ideally suited for biomedical applications. Furthermore, micro-imprint lithography ensures the compatibility of lithography with the silicon ASIC and aluminum contact pads. Although this work focuses on 256 stimulating diamond electrode arrays with a pitch of 150 μm, the use of indium bump bonding technology and vertical interconnects facilitates implants with tens of thousands electrodes with a pitch as low as 10 μm, thus ensuring validity of the strategy for future high acuity retinal prostheses, and bionic implants in general.

Microelectrode arrays and microprobes have been widely utilized to measure neuronal activity, both in vitro and in vivo. The key advantage is the capability to record and stimulate neurons at multiple sites simultaneously. However, unlike the single-cell or single-channel resolution of intracellular recording, microelectrodes detect signals from all possible sources around every sensor. Here, we review the current understanding of microelectrode signals and the techniques for analyzing them. We introduce the ongoing advancements in microelectrode technology, with focus on achieving higher resolution and quality of recordings by means of monolithic integration with on-chip circuitry. We show how recent advanced microelectrode array measurement methods facilitate the understanding of single neurons as well as network function.

Pesticides are unique environmental contaminants that are specifically introduced into the environment to control pests, often by killing them. Although pesticide application serves many important purposes, including protection against crop loss and against vector-borne diseases, there are significant concerns over the potential toxic effects of pesticides to non-target organisms, including humans. In many cases, the molecular target of a pesticide is shared by non-target species, leading to the potential for untoward effects. Here, we review the history of pesticide usage and the neurotoxicity of selected classes of pesticides, including insecticides, herbicides, and fungicides, to humans and experimental animals. Specific emphasis is given to linkages between exposure to pesticides and risk of neurological disease and dysfunction in humans coupled with mechanistic findings in humans and animal models. Finally, we discuss emerging techniques and strategies to improve translation from animal models to humans.

The electrical excitability of neural networks is influenced by different environmental factors. Effective and simple methods are required to objectively and quantitatively evaluate the influence of such factors, including variations in temperature and pharmaceutical dosage. The aim of this paper was to introduce 'the voltage threshold measurement method', which is a new method using microelectrode arrays that can quantitatively evaluate the influence of different factors on the electrical excitability of neural networks. We sought to verify the feasibility and efficacy of the method by studying the effects of acetylcholine, ethanol, and temperature on hippocampal neuronal networks and hippocampal brain slices. First, we determined the voltage of the stimulation pulse signal that elicited action potentials in the two types of neural networks under normal conditions. Second, we obtained the voltage thresholds for the two types of neural networks under different concentrations of acetylcholine, ethanol, and different temperatures. Finally, we obtained the relationship between voltage threshold and the three influential factors. Our results indicated that the normal voltage thresholds of the hippocampal neuronal network and hippocampal slice preparation were 56 and 31 mV, respectively. The voltage thresholds of the two types of neural networks were inversely proportional to acetylcholine concentration, and had an exponential dependency on ethanol concentration. The curves of the voltage threshold and the temperature of the medium for the two types of neural networks were U-shaped. The hippocampal neuronal network and hippocampal slice preparations lost their excitability when the temperature of the medium decreased below 34 and 33°C or increased above 42 and 43°C, respectively. These results demonstrate that the voltage threshold measurement method is effective and simple for examining the performance/excitability of neuronal networks.

The foreign body reaction to implanted electrodes in the brain has long been recognized as a major challenge impacting the performance and reliability of indwelling neurotechnologies. Spatially resolved transcriptomic approaches have enabled high-resolution mapping of cellular and molecular dynamics at the device-tissue interface, yielding novel insight into both acute and chronic tissue responses. Recent whole-transcriptome profiling methods generate exceptionally dense gene expression datasets from individual samples, offering unprecedented resolution and analytical power. Yet, limited studies have explored aggregated results from larger datasets and sample-to-sample variation within an implanted cohort using such techniques due to high costs and complicated downstream analyses. In this work, we provide a comprehensive report of spatial transcriptomics data collected from an expanded cohort of rats (n = 14 rats) implanted with silicon microelectrode arrays in the motor cortices for 1 week (acute) and 6 weeks (chronic). This larger dataset enabled us to explore the variation in results across samples, assess outliers, and examine potential batch effects. We employed differential expression analysis to identify top differentially expressed genes (DEGs) in spatially defined regions at the device-tissue interface to reveal novel biomarkers in the aggregated dataset. We assessed sample-to-sample variabilities, and applied a factorization strategy to identify prominent cell-type contributors of the top DEGs. Using network-based co-expression analysis, we identified gene modules, hub genes, and central regulatory processes governing the device-tissue interface. Our results show: (a) greater variation of top DEGs across samples at the 1-week time point with notable microglial and astroglial cell-type contributors, (b) lower variation of top DEGs across samples and a shift to prominent astroglial cell-type contributors at the 6-week time point, and (c) novel biomarkers that suggest major macrophage- and microglial mediated processes and homeostasis events at the 1-week time point, and greater tissue remodeling, apoptotic and synaptic changes at the 6-week time point. These findings support previous ideas on the evolving tissue response to implanted devices, and present novel details on biomarkers, biological processes and sample variation. Additionally, this study provides a framework for assessing larger datasets employing high-dimensional spatial transcriptomics and highlight key considerations related to across-sample variability and batch effects.

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

[This corrects the article DOI: 10.3389/fncel.2023.1287089.].

Advances in microfabrication technology have enabled the production of devices containing arrays of thousands of closely spaced recording electrodes, which afford subcellular resolution of electrical signals in neurons and neuronal networks. Rationalizing the electrode size and configuration in such arrays demands consideration of application-specific requirements and inherent features of the electrodes. Tradeoffs among size, spatial density, sensitivity, noise, attenuation, and other factors are inevitable. Although recording extracellular signals from neurons with planar metal electrodes is fairly well established, the effects of the electrode characteristics on the quality and utility of recorded signals, especially for small, densely packed electrodes, have yet to be fully characterized. Here, we present a combined experimental and computational approach to elucidating how electrode size, and size-dependent parameters, such as impedance, baseline noise, and transmission characteristics, influence recorded neuronal signals. Using arrays containing platinum electrodes of different sizes, we experimentally evaluated the electrode performance in the recording of local field potentials (LFPs) and extracellular action potentials (EAPs) from the following cell preparations: acute brain slices, dissociated cell cultures, and organotypic slice cultures. Moreover, we simulated the potential spatial decay of point-current sources to investigate signal averaging using known signal sources. We demonstrated that the noise and signal attenuation depend more on the electrode impedance than on electrode size, , especially for electrodes <10 μm in width or diameter to achieve high-spatial-resolution readout. By minimizing electrode impedance of small electrodes (<10 μm) via surface modification, we could maximize the signal-to-noise ratio to electrically visualize the propagation of axonal EAPs and to isolate single-unit spikes. Due to the large amplitude of LFP signals, recording quality was high and nearly independent of electrode size. These findings should be of value in configuring and microelectrode arrays for extracellular recordings with high spatial resolution in various applications.

Brain‒machine interface (BMI) is a promising technology that looks set to contribute to the development of artificial limbs and new input devices by integrating various recent technological advances, including neural electrodes, wireless communication, signal analysis, and robot control. Neural electrodes are a key technological component of BMI, as they can record the rapid and numerous signals emitted by neurons. To receive stable, consistent, and accurate signals, electrodes are designed in accordance with various templates using diverse materials. With the development of microelectromechanical systems (MEMS) technology, electrodes have become more integrated, and their performance has gradually evolved through surface modification and advances in biotechnology. In this paper, we review the development of the extracellular/intracellular type of in vitro microelectrode array (MEA) to investigate neural interface technology and the penetrating/surface (non-penetrating) type of in vivo electrodes. We briefly examine the history and study the recently developed shapes and various uses of the electrode. Also, electrode materials and surface modification techniques are reviewed to measure high-quality neural signals that can be used in BMI.

The amyloid peptides Aβ 40 and Aβ 42 of Alzheimer's disease are thought to contribute differentially to the disease process. Although Aβ 42 seems more pathogenic than Aβ 40 , the reason for this is not well understood. We show here that small alterations in the Aβ 42 :Aβ 40 ratio dramatically affect the biophysical and biological properties of the Aβ mixtures reflected in their aggregation kinetics, the morphology of the resulting amyloid fibrils and synaptic function tested in vitro and in vivo . A minor increase in the Aβ 42 :Aβ 40 ratio stabilizes toxic oligomeric species with intermediate conformations. The initial toxic impact of these Aβ species is synaptic in nature, but this can spread into the cells leading to neuronal cell death. The fact that the relative ratio of Aβ peptides is more crucial than the absolute amounts of peptides for the induction of neurotoxic conformations has important implications for anti‐amyloid therapy. Our work also suggests the dynamic nature of the equilibrium between toxic and non‐toxic intermediates. The amyloid peptides Aβ 40 and Aβ 42 of Alzheimer's disease contribute differentially to the disease process. The findings show here that small changes in the Aβ 42 / 40 ratio have significant effects on the neurotoxicity of Aβ mixtures.