Advances In Neural Engineering Research Articles

Engineered cortical microcircuits for investigations of neuroplasticity.

Recent advances in neural engineering have opened new ways to investigate the impact of topology on neural network function. Leveraging microfluidic technologies, it is possible to establish modular circuit motifs that promote both segregation and integration of information processing in the engineered neural networks, similar to those observed in vivo. However, the impact of the underlying topologies on network dynamics and response to pathological perturbation remains largely unresolved. In this work, we demonstrate the utilization of microfluidic platforms with 12 interconnected nodes to structure modular, cortical engineered neural networks. By implementing geometrical constraints inspired by a Tesla valve within the connecting microtunnels, we additionally exert control over the direction of axonal outgrowth between the nodes. Interfacing these platforms with nanoporous microelectrode arrays reveals that the resulting laminar cortical networks exhibit pronounced segregated and integrated functional dynamics across layers, mirroring key elements of the feedforward, hierarchical information processing observed in the neocortex. The multi-nodal configuration also facilitates selective perturbation of individual nodes within the networks. To illustrate this, we induced hypoxia, a key factor in the pathogenesis of various neurological disorders, in well-connected nodes within the networks. Our findings demonstrate that such perturbations induce ablation of information flow across the hypoxic node, while enabling the study of plasticity and information processing adaptations in neighboring nodes and neural communication pathways. In summary, our presented model system recapitulates fundamental attributes of the microcircuit organization of neocortical neural networks, rendering it highly pertinent for preclinical neuroscience research. This model system holds promise for yielding new insights into the development, topological organization, and neuroplasticity mechanisms of the neocortex across the micro- and mesoscale level, in both healthy and pathological conditions.

A neuromimetic realization of hippocampal CA1 for theta wave generation

Recent advances in neural engineering allowed the development of neuroprostheses which facilitate functionality in people with neurological problems. In this research, a real-time neuromorphic system is proposed to artificially reproduce the theta wave and firing patterns of different neuronal populations in the CA1, a sub-region of the hippocampus. The hippocampal theta oscillations (4–12 Hz) are an important electrophysiological rhythm that contributes in various cognitive functions, including navigation, memory, and novelty detection. The proposed CA1 neuromimetic circuit includes 100 linearized Pinsky–Rinzel neurons and 668 excitatory and inhibitory synapses on a field programmable gate array (FPGA). The implemented spiking neural network of the CA1 includes the main neuronal populations for the theta rhythm generation: excitatory pyramidal cells, PV+ basket cells, and Oriens Lacunosum-Moleculare (OLM) cells which are inhibitory interneurons. Moreover, the main inputs to the CA1 region from the entorhinal cortex via the perforant pathway, the CA3 via Schaffer collaterals, and the medial septum via fimbria–fornix are also implemented on the FPGA using a bursting leaky-integrate and fire (LIF) neuron model. The results of hardware realization show that the proposed CA1 neuromimetic circuit successfully reconstructs the theta oscillations and functionally illustrates the phase relations between firing responses of the different neuronal populations. It is also evaluated the impact of medial septum elimination on the firing patterns of the CA1 neuronal population and the theta wave’s characteristics. This neuromorphic system can be considered as a potential platform that opens opportunities for neuroprosthetic applications in future works.

Second Harmonic Generation Imaging of Collagen in Chronically Implantable Electrodes in Brain Tissue.

Advances in neural engineering have brought about a number of implantable devices for improved brain stimulation and recording. Unfortunately, many of these micro-implants have not been adopted due to issues of signal loss, deterioration, and host response to the device. While glial scar characterization is critical to better understand the mechanisms that affect device functionality or tissue viability, analysis is frequently hindered by immunohistochemical tissue processing methods that result in device shattering and tissue tearing artifacts. Devices are commonly removed prior to sectioning, which can itself disturb the quality of the study. In this methods implementation study, we use the label free, optical sectioning method of second harmonic generation (SHG) to examine brain slices of various implanted intracortical electrodes and demonstrate collagen fiber distribution not found in normal brain tissue. SHG can easily be used in conjunction with multiphoton microscopy to allow direct intrinsic visualization of collagen-containing glial scars on the surface of cortically implanted electrode probes without imposing the physical strain of tissue sectioning methods required for other high resolution light microscopy modalities. Identification and future measurements of these collagen fibers may be useful in predicting host immune response and device signal fidelity.

Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning

Objective. Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available. Approach. We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings. Main results. We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation. Significance. Our work will enable future advances in neural engineering with simultaneous stimulation and recording.

Neuromodulation methods for animal locomotion control

Recent advances in neural engineering and neuroscience have enabled practical brain computer interfaces (BCIs). This paper reviews the BCI technology narrowed in the methods for controlling animals’ behaviors using invasive neural interface methods. Currently, using stimulation on various brain regions, it has been shown that animal behaviors can be precisely controlled in accordance with the experimenter’s commands. To date, two distinct strategies have been proposed and shown effective. One is to control animal behaviors with reward- or punishment-related neuromodulations based on operant conditioning. The other is to force the actual movement by stimulating motor-related brain region. Mesolimbic pathway, amygdala and somatosensory cortex are a set of target regions for the former approach, while nigrostriatal pathway is the typical target area for the latter one. This review describes the underlying principles of those neuromodulations depending on the different target regions. In addition, electrical and optogenetic modulation methods are also reviewed as valid neuromodulation strategies to manage the animal behaviors.

Beyond ‘communication and control’: towards ethically complete rationales for brain-computer interface research

Rapid advances in neural engineering have led to an increasing use of human subjects in brain-computer interface (BCI) research. Here we ask whether the rationales articulated for this research have kept pace with related ethical requirements. To answer this question, we examined the content of peer-reviewed BCI research publications of studies involving human subjects. We analysed the publications for the rationale expressed for the research against a backdrop of journal type and study design. We discuss the results in the context of strategies that neural engineering researchers can adopt to ensure that the ethical dimensions of human subject research are not lost amid the technological drive for results.

Sound Localization Sensors for Search and Rescue Biobots

Recent advances in neural engineering have enabled direct control of insect locomotion through neural and muscular stimulation. The resulting insect biobots, with a natural ability to crawl through small spaces, offer unique advantages over traditional synthetic robots. A cyberphysical network of such biobots could prove useful for search and rescue applications in uncertain disaster environments. We present a vision-based automated system for an objective assessment of biobotic navigation capability on Madagascar hissing cockroaches. We report the most precise control results obtained with insect biobots so far both manually and autonomously. We also demonstrate autonomous control capability where a low-power insect-mounted array of microphones was used to localize a sound source and guide the biobot toward it. Forming a wireless mobile sensor network with directional and omnidirectional microphones distributed within the structure of a rubble pile could be useful for both environmental mapping and localization of trapped survivors under the rubble.

Advances in Neural Engineering

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Neurosecurity: security and privacy for neural devices

An increasing number of neural implantable devices will become available in the near future due to advances in neural engineering. This discipline holds the potential to improve many patients' lives dramatically by offering improved-and in some cases entirely new-forms of rehabilitation for conditions ranging from missing limbs to degenerative cognitive diseases. The use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals. The authors define "neurosecurity"-a version of computer science security principles and methods applied to neural engineering-and discuss why neurosecurity should be a critical consideration in the design of future neural devices.