Neuroengineering Applications Research Articles

Comprehensive proteomic analysis of the differential expression of 62 proteins following intracortical microelectrode implantation

Intracortical microelectrodes (IMEs) are devices designed to be implanted into the cerebral cortex for various neuroscience and neuro-engineering applications. A critical feature of IMEs is their ability to detect neural activity from individual neurons. Currently, IMEs are limited by chronic failure, largely considered to be caused by the prolonged neuroinflammatory response to the implanted devices. Over the past few years, the characterization of the neuroinflammatory response has grown in sophistication, with the most recent advances focusing on mRNA expression following IME implantation. While gene expression studies increase our broad understanding of the relationship between IMEs and cortical tissue, advanced proteomic techniques have not been reported. Proteomic evaluation is necessary to describe the diverse changes in protein expression specific to neuroinflammation, neurodegeneration, or tissue and cellular viability, which could lead to the further development of targeted intervention strategies designed to improve IME functionality. In this study, we have characterized the expression of 62 proteins within 180 μm of the IME implant site at 4-, 8-, and 16-weeks post-implantation. We identified potential targets for immunotherapies, as well as key pathways that contribute to neuronal dieback around the IME implant.

A silicon diode–based optoelectronic interface for bidirectional neural modulation

The development of advanced neural modulation techniques is crucial to neuroscience research and neuroengineering applications. Recently, optical-based, nongenetic modulation approaches have been actively investigated to remotely interrogate the nervous system with high precision. Here, we show that a thin-film, silicon (Si)-based diode device is capable to bidirectionally regulate in vitro and in vivo neural activities upon adjusted illumination. When exposed to high-power and short-pulsed light, the Si diode generates photothermal effects, evoking neuron depolarization and enhancing intracellular calcium dynamics. Conversely, low-power and long-pulsed light on the Si diode hyperpolarizes neurons and reduces calcium activities. Furthermore, the Si diode film mounted on the brain of living mice can activate or suppress cortical activities under varied irradiation conditions. The presented material and device strategies reveal an innovated optoelectronic interface for precise neural modulations.

Biomaterial strategies for regulating the neuroinflammatory response.

Injury and disease in the central nervous system (CNS) can result in a dysregulated inflammatory environment that inhibits the repair of functional tissue. Biomaterials present a promising approach to tackle this complex inhibitory environment and modulate the mechanisms involved in neuroinflammation to halt the progression of secondary injury and promote the repair of functional tissue. In this review, we will cover recent advances in biomaterial strategies, including nanoparticles, hydrogels, implantable scaffolds, and neural probe coatings, that have been used to modulate the innate immune response to injury and disease within the CNS. The stages of inflammation following CNS injury and the main inflammatory contributors involved in common neurodegenerative diseases will be discussed, as understanding the inflammatory response to injury and disease is critical for identifying therapeutic targets and designing effective biomaterial-based treatment strategies. Biomaterials and novel composites will then be discussed with an emphasis on strategies that deliver immunomodulatory agents or utilize cell-material interactions to modulate inflammation and promote functional tissue repair. We will explore the application of these biomaterial-based strategies in the context of nanoparticle- and hydrogel-mediated delivery of small molecule drugs and therapeutic proteins to inflamed nervous tissue, implantation of hydrogels and scaffolds to modulate immune cell behavior and guide axon elongation, and neural probe coatings to mitigate glial scarring and enhance signaling at the tissue-device interface. Finally, we will present a future outlook on the growing role of biomaterial-based strategies for immunomodulation in regenerative medicine and neuroengineering applications in the CNS.

Retinal ganglion cells undergo cell type-specific functional changes in a computational model of cone-mediated retinal degeneration.

Understanding the retina in health and disease is a key issue for neuroscience and neuroengineering applications such as retinal prostheses. During degeneration, the retinal network undergoes complex and multi-stage neuroanatomical alterations, which drastically impact the retinal ganglion cell (RGC) response and are of clinical importance. Here we present a biophysically detailed in silico model of the cone pathway in the retina that simulates the network-level response to both light and electrical stimulation. The model included 11, 138 cells belonging to nine different cell types (cone photoreceptors, horizontal cells, ON/OFF bipolar cells, ON/OFF amacrine cells, and ON/OFF ganglion cells) confined to a 300 × 300 × 210μm patch of the parafoveal retina. After verifying that the model reproduced seminal findings about the light response of retinal ganglion cells (RGCs), we systematically introduced anatomical and neurophysiological changes (e.g., reduced light sensitivity of photoreceptor, cell death, cell migration) to the network and studied their effect on network activity. The model was not only able to reproduce common findings about RGC activity in the degenerated retina, such as hyperactivity and increased electrical thresholds, but also offers testable predictions about the underlying neuroanatomical mechanisms. Overall, our findings demonstrate how biophysical changes typified by cone-mediated retinal degeneration may impact retinal responses to light and electrical stimulation. These insights may further our understanding of retinal processing and inform the design of retinal prostheses.

A nanoscale inorganic coating strategy for stabilizing hydrogel neural probes in vivo.

Hydrogels with adaptable optical and mechanical characteristics show considerable promise for light delivery in vivo with neuroengineering applications. However, the unlinked amorphous polymer chains within hydrogels can cause volumetric swelling after water absorption under physiological conditions over time. Chemically cross-linked poly(vinyl alcohol) (PVA) hydrogels showcase fatigue-resistant attributes and promising biocompatibility for the manufacture of soft neural probes. However, possible swelling of the PVA hydrogel matrix could impact the structural stability of hydrogel-based bioelectronics and their long-term in vivo functionality. In this study, we utilized an atomic layer deposition (ALD) technique to generate an inorganic, silicon dioxide (SiO2) coating layer on chemically cross-linked PVA hydrogel fibers. To evaluate the stability of SiO2-coated PVA hydrogel fibers mimicking the in vivo environment, we conducted accelerated stability tests. SiO2-coated PVA hydrogel fibers showed improved stability over a one-week incubation period under a harsh environment, preventing swelling and preserving their mechanical and optical properties compared to uncoated fibers. These SiO2-coated PVA hydrogel fibers demonstrated nanoscale polymeric crystalline domains (6.5 ± 0.1 nm), an elastic modulus of 73.7 ± 31.7 MPa, a maximum elongation of 113.6 ± 24.2%, and minimal light transmission loss (1.9 ± 0.2 dB cm-1). Lastly, we applied these SiO2-coated PVA hydrogel fibers in vivo to optically activate the motor cortex of transgenic Thy1::ChR2 mice during locomotor behavioral tests. This mouse cohort was genetically modified to express the light-sensitive ion channel, channelrhodopsin-2 (ChR2), and implanted with hydrogel fibers to deliver light to the motor cortex area (M2). Light stimulation via hydrogel fibers resulted in optogenetically modulated mouse locomotor behaviors, including increased contralateral rotation, mobility speeds, and travel distances.

Motion Artifacts Suppression From EEG Signals Using an Adaptive Signal Denoising Method

Motion artifacts are one of the most challenging non-physiological noise sources present in the biomedical signal, which can hinder the true performance of EEG-based neuro-engineering applications. Therefore, motion artifact removal from EEG signals can be potentially the utmost protuberant research topic. To solve this issue, a hybrid signal denoising framework which comprises modified empirical mode decomposition (EMD) and an optimized Laplacian of Gaussian (LoG) filter is proposed for the suppression of motion artifacts from EEG signals. The modified-EMD decomposes the single-channel noisy EEG signal into a set of the optimal number of intrinsic mode functions (IMFs). Furthermore, the optimized LoG filter has been applied to the motion artifact intermixed EEG signal which is considered as low-frequency noise likely to present at low-frequency IMFs. This filter performs smoothing of the signal and removes background noises or artifacts from the EEG signal. The denoised signal is reconstructed by adding filtered output signal and high-frequency IMFs. The robustness of the proposed method is demonstrated through simulated and real EEG data with artifacts. The experimental values of the different sets of performance assessment metrics reveal that the proposed algorithm outperforms the existing state-of-the-art EEG signal denoising algorithms for the suppression of motion artifacts from EEG signals.

Driver Drowsiness Detection: An Approach Based on Intelligent Brain–Computer Interfaces

Estimating reaction times (RTs) and drowsiness states from brain signals is a notable step in creating passive brain–computer interfaces (BCIs). Prior to the deep learning era, estimating RTs and drowsiness from electroencephalogram (EEG) signals was feasible only with moderate accuracy, which led to unreliability for neuro-engineering applications. However, recent developments in machine learning algorithms, notably stationarity-based approaches and deep convolutional neural networks (CNNs), have demonstrated promising results for a class of BCI systems, e.g., motor imagery BCIs, and affective state classification. These methods have not been systematically analyzed for EEG-based driver drowsiness detection and RT prediction.

A convolutional neural-network framework for modelling auditory sensory cells and synapses

In classical computational neuroscience, analytical model descriptions are derived from neuronal recordings to mimic the underlying biological system. These neuronal models are typically slow to compute and cannot be integrated within large-scale neuronal simulation frameworks. We present a hybrid, machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Our DNN-model architecture comprises parallel and differentiable equations that can be used for backpropagation in neuro-engineering applications, and offers a simulation run-time improvement factor of 70 and 280 on CPU or GPU systems respectively. We focussed our development on auditory neurons and synapses, and show that our DNN-model architecture can be extended to a variety of existing analytical models. We describe how our approach for auditory models can be applied to other neuron and synapse types to help accelerate the development of large-scale brain networks and DNN-based treatments of the pathological system.

EEG Signal denoising using hybrid approach of Variational Mode Decomposition and wavelets for depression

BackgroundArtifact contamination reduces the accuracy of various EEG based neuroengineering applications. With time, biomedical signal denoising has been the utmost protuberant research area. So, the noise-reducing algorithm should be carefully deployed since artifacts result in degraded performance. MotivationArtifact reduction or denoising in degraded EEG signals requires a lot of improvement. The main aim of this paper is to present the investigation carried out to suppress the noise found in EEG signals of depression. MethodThe focus is to compare the effectiveness of the physiological signal denoising approaches based on discrete wavelet transform (DWT) and wavelet packet transform (WPT) combined with VMD (variational mode decomposition), namely VMD-DWT and VMD-WPT, with other approaches. In these approaches, the detrended fluctuation analysis (DFA) will be used to define the mode selection criteria. First of all, VMD will decompose the signal into various components, then DWT and WPT will be used to denoise the artifactual components rather than completely rejecting these with DFA as the mode selection basis. Simulations have been carried out on artificially contaminated and real databases of depression to demonstrate the effectiveness of the proposed technique using the performance parameters such as SNR, PSNR, and MSE. ContributionIt can be said that sufficient removal of artifacts is gained by VMD- DFA-WPT and VMD-DFA-DWT though VMD-DFA-WPT outperforms VMD- DFA-DWT and others. Such an artifact removal system may offer an effective solution for clinicians as a crucial stage of pre-processing and may prevent delay in diagnosis for depression signals.

Fluorescent Mantle Carbon Coated Core-Shell SPIONs for Neuroengineering Applications.

Superparamagnetic iron oxide nanoparticles (SPIONs) have been used for a variety of biomedical applications, from multimodal imaging to the mechanical activity of cells and tissues. Herein, we present fluorescently mantled carbon coated core-shell superparamagnetic iron oxide nanoparticles (FC-SPIONs) as an excellent material to promote the neuronal differentiation and neuronal network outgrowth in neural tissue engineering applications. Morphological, structural, and functional group characterizations were systematically investigated. FC-SPIONs showed superior magnetic and inherent fluorescence characteristic properties. Furthermore, FC-SPIONs interactions against neuronal PC12 cells showed promising results and deliberate their potential for significant applications in neuroengineering. Interestingly, FC-SPIONs were assessed as biocompatible and promoted the neuronal PC12 cell differentiation process. Accompanied by these results, network outgrowth and branching patterns of neuronal processes can be regulated using FC-SPIONs. Importantly, FC-SPIONs are promising due to their biocompatibility and selective affinity toward neuronal cells, paving the way for neuronal differentiation and outgrowth and neuronal therapeutics in neuroengineering applications.

Abstract #15: MazeSuite 3: Design, presentation and analysis platform for spatial navigation, cognitive neuroscience and neuroengineering applications

Abstract #15: MazeSuite 3: Design, presentation and analysis platform for spatial navigation, cognitive neuroscience and neuroengineering applications

Modified cable equation incorporating transverse polarization of neuronal membranes for accurate coupling of electric fields

Objective. We present a theory and computational methods to incorporate transverse polarization of neuronal membranes into the cable equation to account for the secondary electric field generated by the membrane in response to transverse electric fields. The effect of transverse polarization on nonlinear neuronal activation thresholds is quantified and discussed in the context of previous studies using linear membrane models. Approach. The response of neuronal membranes to applied electric fields is derived under two time scales and a unified solution of transverse polarization is given for spherical and cylindrical cell geometries. The solution is incorporated into the cable equation re-derived using an asymptotic model that separates the longitudinal and transverse dimensions. Two numerical methods are proposed to implement the modified cable equation. Several common neural stimulation scenarios are tested using two nonlinear membrane models to compare thresholds of the conventional and modified cable equations. Main results. The implementations of the modified cable equation incorporating transverse polarization are validated against previous results in the literature. The test cases show that transverse polarization has limited effect on activation thresholds. The transverse field only affects thresholds of unmyelinated axons for short pulses and in low-gradient field distributions, whereas myelinated axons are mostly unaffected. Significance. The modified cable equation captures the membrane’s behavior on different time scales and models more accurately the coupling between electric fields and neurons. It addresses the limitations of the conventional cable equation and allows sound theoretical interpretations. The implementation provides simple methods that are compatible with current simulation approaches to study the effect of transverse polarization on nonlinear membranes. The minimal influence by transverse polarization on axonal activation thresholds for the nonlinear membrane models indicates that predictions of stronger effects in linear membrane models with a fixed activation threshold are inaccurate. Thus, the conventional cable equation works well for most neuroengineering applications, and the presented modeling approach is well suited to address the exceptions.

MazeSuite 3: A design, presentation and analysis platform for spatial navigation, cognitive neuroscience and neuroengineering applications

MazeSuite 3: A design, presentation and analysis platform for spatial navigation, cognitive neuroscience and neuroengineering applications

Neurons Internalize Functionalized Micron-Sized Silicon Dioxide Microspheres.

Microparticles have potential as neuron-specific delivery platforms and devices with many applications in neuroscience, pharmacology, and biomedicine. To date, most literature suggests that neurons are not phagocytic cells capable of internalizing microparticles larger than 0.5μm. We report that neurons transport fluorescently labeled silica microspheres with diameters of 1-2μm into neurons in vitro and in rat brain without having overt effects on cell viability. Using flow cytometry, fluorescence-activated cell sorting, and confocal and electron microscopy, we first found that SH-SY5Y human neuroblastoma cells internalized 1-μm silicon microspheres with surface charges of -70mV (hydroxyl and carboxyl), -30mV (amino), and +40mV (ammonio). Uptake was rapid, within 2-4h, and did not affect cell viability 48h later. Flow cytometry assays indicate that SH-SY5Y cells internalize 1- and 1.5-μm microspheres at the same rate over a 24-h incubation period. Electron microscopy confirms that SH-SY5Y cells internalize 1-, 1.5-, and 2-μm microspheres. Confocal microscopy demonstrated that primary cortical neurons also internalized 1-, 1.5-, and 2-μm amino microspheres within 4h. Finally, we injected 1-μm amino microspheres into rat striatum and found microspheres inside neurons. Overall, neurons can internalize microspheres up to 2μm in diameter with a range of surface chemical groups and charges. These findings allow a host of neuroscience and neuroengineering applications including intracellular microdevices within neurons.

The use of intracranial recordings to decode human language: Challenges and opportunities

Decoding speech from intracranial recordings serves two main purposes: understanding the neural correlates of speech processing and decoding speech features for targeting speech neuroprosthetic devices. Intracranial recordings have high spatial and temporal resolution, and thus offer a unique opportunity to investigate and decode the electrophysiological dynamics underlying speech processing. In this review article, we describe current approaches to decoding different features of speech perception and production – such as spectrotemporal, phonetic, phonotactic, semantic, and articulatory components – using intracranial recordings. A specific section is devoted to the decoding of imagined speech, and potential applications to speech prosthetic devices. We outline the challenges in decoding human language, as well as the opportunities in scientific and neuroengineering applications.

Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex

Intracortical microstimulation (ICMS) is a powerful tool to investigate the functional role of neural circuits and may provide a means to restore sensation for patients for whom peripheral stimulation is not an option. In a series of psychophysical experiments with nonhuman primates, we investigate how stimulation parameters affect behavioral sensitivity to ICMS. Specifically, we deliver ICMS to primary somatosensory cortex through chronically implanted electrode arrays across a wide range of stimulation regimes. First, we investigate how the detectability of ICMS depends on stimulation parameters, including pulse width, frequency, amplitude, and pulse train duration. Then, we characterize the degree to which ICMS pulse trains that differ in amplitude lead to discriminable percepts across the range of perceptible and safe amplitudes. We also investigate how discriminability of pulse amplitude is modulated by other stimulation parameters-namely, frequency and duration. Perceptual judgments obtained across these various conditions will inform the design of stimulation regimes for neuroscience and neuroengineering applications.

From 2D to 3D: novel nanostructured scaffolds to investigate signalling in reconstructed neuronal networks.

To recreate in vitro 3D neuronal circuits will ultimately increase the relevance of results from cultured to whole-brain networks and will promote enabling technologies for neuro-engineering applications. Here we fabricate novel elastomeric scaffolds able to instruct 3D growth of living primary neurons. Such systems allow investigating the emerging activity, in terms of calcium signals, of small clusters of neurons as a function of the interplay between the 2D or 3D architectures and network dynamics. We report the ability of 3D geometry to improve functional organization and synchronization in small neuronal assemblies. We propose a mathematical modelling of network dynamics that supports such a result. Entrapping carbon nanotubes in the scaffolds remarkably boosted synaptic activity, thus allowing for the first time to exploit nanomaterial/cell interfacing in 3D growth support. Our 3D system represents a simple and reliable construct, able to improve the complexity of current tissue culture models.

Poly(2-oxazoline)-Based Microgel Particles for Neuronal Cell Culture.

An increasing number of in vivo and in vitro neuro-engineering applications are making use of colloidal particles as neuronal cell carriers. Recent studies highlight the shortcomings of commercial glass particles and stress the benefit of using soft microgel particles (MGPs) instead. This study describes first the fabrication of MGPs from telechelic poly(2-methyl-2-oxazoline)s (PMeOx) cross-linkers and hydrophilic neutral (hydroxyethyl)methacrylate (HEMA) or charged 2-methacryloxyethyltrimethylammonium (METAC) monomers by emulsion polymerization, and it discusses their ability to support cell growth. It establishes that uncharged copolymers lead to MGPs with nonfouling properties inappropriate for cell culture, and it provides a protocol to amend their surface properties to enable cell adhesion. Finally, it demonstrates that the introduction of positive charges by METAC is necessary to obtain surface properties suitable for neuronal cell development. Through the optimization of the PMeOx30 MGP properties, this work provides general guidelines to evaluate and tune MGP chemistry to obtain microcarriers for neuro-engineering applications.

An Implantable Neural Sensing Microsystem with Fiber-Optic Data Transmission and Power Delivery

We have developed a prototype cortical neural sensing microsystem for brain implantable neuroengineering applications. Its key feature is that both the transmission of broadband, multichannel neural data and power required for the embedded microelectronics are provided by optical fiber access. The fiber-optic system is aimed at enabling neural recording from rodents and primates by converting cortical signals to a digital stream of infrared light pulses. In the full microsystem whose performance is summarized in this paper, an analog-to-digital converter and a low power digital controller IC have been integrated with a low threshold, semiconductor laser to extract the digitized neural signals optically from the implantable unit. The microsystem also acquires electrical power and synchronization clocks via optical fibers from an external laser by using a highly efficient photovoltaic cell on board. The implantable unit employs a flexible polymer substrate to integrate analog and digital microelectronics and on-chip optoelectronic components, while adapting to the anatomical and physiological constraints of the environment. A low power analog CMOS chip, which includes preamplifier and multiplexing circuitry, is directly flip-chip bonded to the microelectrode array to form the cortical neurosensor device.

Three-dimensional multiwaveguide probe array for light delivery to distributed brain circuits

To deliver light to the brain for neuroscientific and neuroengineering applications like optogenetics, in which light is used to activate or silence neurons expressing specific photosensitive proteins, optical fibers are commonly used. However, an optical fiber is limited to delivering light to a single target within the 3D structure of the brain. Here, we describe the design and fabrication of an array of thin microwaveguides, which terminates at a three-dimensionally distributed set of points, appropriate for delivering light to targets distributed in a 3D pattern throughout the brain.