Development Of Neuroprostheses Research Articles

Advanced neuroprosthetic electrode design optimized by electromagnetic finite element simulation: innovations and applications

Based on electrophysiological activity, neuroprostheses can effectively monitor and control neural activity. Currently, electrophysiological neuroprostheses are widely utilized in treating neurological disorders, particularly in restoring motor, visual, auditory, and somatosensory functions after nervous system injuries. They also help alleviate inflammation, regulate blood pressure, provide analgesia, and treat conditions such as epilepsy and Alzheimer’s disease, offering significant research, economic, and social value. Enhancing the targeting capabilities of neuroprostheses remains a key objective for researchers. Modeling and simulation techniques facilitate the theoretical analysis of interactions between neuroprostheses and the nervous system, allowing for quantitative assessments of targeting efficiency. Throughout the development of neuroprostheses, these modeling and simulation methods can save time, materials, and labor costs, thereby accelerating the rapid development of highly targeted neuroprostheses. This article introduces the fundamental principles of neuroprosthesis simulation technology and reviews how various simulation techniques assist in the design and performance enhancement of neuroprostheses. Finally, it discusses the limitations of modeling and simulation and outlines future directions for utilizing these approaches to guide neuroprosthesis design.

Revolutionizing Human Augmentation: The Future of Neuroprosthetics and Brain-Computer Interface Technologies

The future of neuroprosthesis and brain-computer interface (BCI) technologies is poised to transform medical treatments, cognitive development, and human-machine interaction. Neuroprosthetics, which integrate artificial devices with the nervous system to restore or enhance sensory and motor functions, have already improved the lives of people with disabilities. Advances in materials science and neural interfacing are taking these technologies even further, enabling more seamless integration and better control of prosthetic limbs and sensory devices. As these technologies improve, they may soon be able to restore complex functions such as speech, vision, and memory. At the same time, BCIs go beyond traditional applications for neurological disorders, opening up possibilities for cognitive enhancement, neural data transfer, and immersive virtual experiences. With advances in machine learning and neuroimaging, BCIs can enhance cognitive abilities, treat neurological diseases, and redefine human-computer interaction. However, these technologies also raise ethical, social, and security concerns. The risk of misuse, such as unauthorized access to neural data, and the potential creation of cognitive hierarchies emphasize the need for robust regulation. Ensuring accessibility and equity is also essential to ensure that the benefits of these advances are shared equitably. The development of neuroprostheses and BCIs has had a positive impact.

The Case for Neurosurgical Intervention in Cancer Neuroscience.

The emerging field of cancer neuroscience reshapes our understanding of the intricate relationship between the nervous system and cancer biology; this new paradigm is likely to fundamentally change and advance neuro-oncological care. The profound interplay between cancers and the nervous system is reciprocal: Cancer growth can be induced and regulated by the nervous system; conversely, tumors can themselves alter the nervous system. Such crosstalk between cancer cells and the nervous system is evident in both the peripheral and central nervous systems. Recent advances have uncovered numerous direct neuron-cancer interactions at glioma-neuronal synapses, paracrine mechanisms within the tumor microenvironment, and indirect neuroimmune interactions. Neurosurgeons have historically played a central role in neuro-oncological care, and as the field of cancer neuroscience is becoming increasingly established, the role of neurosurgical intervention is becoming clearer. Examples include peripheral denervation procedures, delineation of neuron-glioma networks, development of neuroprostheses, neuromodulatory procedures, and advanced local delivery systems. The present review seeks to highlight key cancer neuroscience mechanisms with neurosurgical implications and outline the future role of neurosurgical intervention in cancer neuroscience.

Multistakeholder Perceptions on Lower-Limb Prosthetic User Requirements and the Development of Neuroprostheses: A Contextual Inquiry

ABSTRACT Introduction Neuroprosthetic systems that can work with prosthetic legs are currently being developed to provide individuals with lower-limb amputation with intent control over their device and sensory feedback. No such system is commercially available, and the effects of providing functions that have previously not been available to lower-limb prosthetic users are unclear. Methods Here we present investigations of the perceptions of multiple stakeholders (prosthetic users, physician, psychologist, physiotherapist, prosthetist, and groups of prosthetic designers and engineers) on prosthetic user problems and the development of neuroprosthetics. The investigation entailed semistructured interviews, focus group discussions, and a contextual inquiry. Results Our findings indicate that prosthetic users may face several challenges that can potentially be addressed via neurological interfacing. We further identified criteria perceived as integral for the development of lower-limb neuroprostheses as well as considerations for the actualization of a usable system that reaches end users. Conclusions On the whole, the field of neuroprosthetics has great potential to increase the wellbeing, mobility, and quality of life of persons with lower-limb amputation.

Exploring polymeric biomaterials in developing neural prostheses

Neuroprosthetics, with a range of applications such as cognitive, auditory, pain relief, recording, motor, and visual prosthetics have emerged as a promising field in recent years. However, poor electrical conductivity, a high disparity between tissue and interfaces and the onset of reactive gliosis post-implantation remains major challenges in the development of neuroprostheses. The choice of biomaterials in designing the neural interfaces’ in neuroprosthetic applications is of high importance, as the overall sustained performance of neuroprosthetic devices is based on the features of materials used for the neural interfaces. Numerous biomaterials, such as metals and carbon-based materials, have been used in neuroprosthetics thus far. Nonetheless, neuroprosthetics made from polymeric biomaterials are in high demand due to their high biocompatibility, conductivity, and biostability. Furthermore, polymeric biomaterials can be used as a hybrid design to overcome the limitations of other co-biomaterials. This article makes an attempt to review the polymeric biomaterials involved in this cutting-edge technology utilized for different purposes such as substrates, coatings, and miniaturization of electrodes, that might help in enriching our understanding on neuroprosthetics.

Block Phenomena During Electric Micro-Stimulation of Pyramidal Cells and Retinal Ganglion Cells.

Electric micro-stimulation of the nervous system is a means to restore various body functions. The stimulus amplitude necessary to generate action potentials, the lower threshold (LT), is well characterized for many neuronal populations. However, electric overstimulation above an upper threshold (UT) prevents action potential generation and therefore hinders optimal neuro-rehabilitation. Previous studies demonstrated the impact of the UT in micro-stimulation of retinal ganglion cells (RGCs). The observed phenomenon is mostly explained by (i) reversed sodium ion flow in the soma membrane, and (ii) anodal surround block that hinders spike conduction in strongly hyperpolarized regions of the axon at high stimulus intensities. However, up to now, no detailed study of the nature of these phenomena has been presented, particularly for different cell types. Here, we present computational analyses of LT and UT for layer 5 pyramidal cells (PCs) as well as alpha RGCs. Model neurons were stimulated in close vicinity to the cell body and LTs and UTs as well as the ratio UT/LT were compared. Aside from a simple point source electrode and monophasic stimuli also realistic electrode and pulse configurations were examined. The analysis showed: (i) in RGCs, the soma contributed to action potential initiation and block for small electrode distances, whereas in PCs the soma played no role in LTs or UTs. (ii) In both cell types, action potential always initiated within the axon initial segment at LT. (iii) In contrast to a complete block of spike conductance at UT that occurred in RGCs, an incomplete block of spiking appeared in PC axon collaterals. (iv) PC axon collateral arrangement influenced UTs but had small impact on LTs. (v) Population responses of RGCs change from circular regions of activation to ring-shaped patterns for increasing stimulus amplitude. A better understanding of the stimulation window that can reliably activate target neurons will benefit the future development of neuroprostheses.

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.

Neuro-fuzzy models for hand movements induced by functional electrical stimulation in able-bodied and hemiplegic subjects

Functional Electrical Stimulation (FES) may be effective as a therapeutic treatment for improving functional reaching and grasping. Upper-limb FES models for predicting joint torques/angles from stimulation parameters can be useful to support the iterative design and development of neuroprostheses. Most such models focused on shoulder or elbow joints and were defined for fixed electrode configurations. This work proposes the use of a Recurrent Fuzzy Neural Network (RFNN) for modeling FES induced wrist, thumb, and finger movements based on surface multi-field electrodes and kinematic data from able-bodied and neurologically impaired subjects. Different combinations of structure parameters comprising fuzzy term numbers and feedback approaches were tested and analyzed in order to see their effect on the model performance for six subjects. The results showed mean success rates in the range from 60% to 99% and best success rates in the range from 78% to 100% on test data for all subjects. No common trend was found across subjects regarding structure parameters. The model showed the ability to successfully reproduce the response to FES for both able-bodied and hemiplegic subjects at least with one of the tested combinations.

A Cerebellar Neuroprosthetic System: Computational Architecture and in vivo Test.

Emulating the input–output functions performed by a brain structure opens the possibility for developing neuroprosthetic systems that replace damaged neuronal circuits. Here, we demonstrate the feasibility of this approach by replacing the cerebellar circuit responsible for the acquisition and extinction of motor memories. Specifically, we show that a rat can undergo acquisition, retention, and extinction of the eye-blink reflex even though the biological circuit responsible for this task has been chemically inactivated via anesthesia. This is achieved by first developing a computational model of the cerebellar microcircuit involved in the acquisition of conditioned reflexes and training it with synthetic data generated based on physiological recordings. Secondly, the cerebellar model is interfaced with the brain of an anesthetized rat, connecting the model’s inputs and outputs to afferent and efferent cerebellar structures. As a result, we show that the anesthetized rat, equipped with our neuroprosthetic system, can be classically conditioned to the acquisition of an eye-blink response. However, non-stationarities in the recorded biological signals limit the performance of the cerebellar model. Thus, we introduce an updated cerebellar model and validate it with physiological recordings showing that learning becomes stable and reliable. The resulting system represents an important step toward replacing lost functions of the central nervous system via neuroprosthetics, obtained by integrating a synthetic circuit with the afferent and efferent pathways of a damaged brain region. These results also embody an early example of science-based medicine, where on the one hand the neuroprosthetic system directly validates a theory of cerebellar learning that informed the design of the system, and on the other one it takes a step toward the development of neuro-prostheses that could recover lost learning functions in animals and, in the longer term, humans.