Microchannel-based technologies for nerve interfacing

Biomedical technologies in neuroprosthetics are made to restore or replace functions from the nervous system, as well as alleviate pathological conditions. Peripheral nerve interfaces use neuromodulatory techniques to inhibit pain or depression, prevent epileptic strokes, or connect amputee patients to an artificial limb. Man-made neural interfaces are, however, far from matching the complexity of the nerves, leaving patients with low-resolution interfaces. To engineer peripheral neuroprostheses, scientists and clinicians investigate neuromodulatory strategies using in vitro neuron culture, explanted nerves, in vivo and in silico experiments using simulation, patch-clamp or extracellular recordings, and neural classification/learning algorithms. However, the available models can not replicate the complex morphology of the nerves, thereby limiting the resolution and selective recording of axonal activity. While nerve fibers respond differently to a neuromodulation paradigm depending on their size and degree of myelination, studies often fail to deal with the whole variety of axon characteristics present in the body. Therefore, in this thesis, I developed a nerve-on-a-chip platform that uniquely integrates the following features: stimulation and recording of single myelinated nerve fibers, controlled single-fiber action potential propagation with respect to electrodes position, and signal amp- lification leading to recorded signals in the 20 - 300 ÎŒV range. Nerve fibers diameter can further be deduced from propagation velocity to include the effect of nerve heterogeneity and function in the implementation of neuromodulatory strategies. I first present a versatile nerve-on-a-chip platform, aiming at offering robust standards to eval- uate neuromodulation strategies and selectivity of neuroprostheses. In order to conduct rapid, high resolution and systematic recording of peripheral nerve activity, microchannel electrodes are used to achieve stimulation and recording of single myelinated nerve fibers ex vivo. I demonstrate the platform versatility through three case studies: (i) Optimising the design of microchannel regenerative implants to detect the velocity and direction of neural signal propagation. I implemented and experimentally validated a mathematical model predicting how the reliability of velocity calculation depends on the implant geometry and neural signal waveform. (ii) Demonstrating how heat-induced neuroinhibition upon P3HT:PCBM (blend polymer) illumination selectively and reversibly silenced thinner fibers, but did not affect larger fibers. (iii) To improve artificial sensory feedback, new patterns of electrical stimulation were investigated in silico. In this study, the platform is used to show that high frequency modulated stimulation elicits more biomimetic nerve activity. This novel nerve-on-a-chip platform enables rapid prototyping using standard microfabrication methods, simultaneous probing of myelinated fibers of various diameters, data library collection for computational modelling and multi-factorial analysis. Subsequently, I have applied the results from (i) to develop a microfabrication process of a microchannel regenerative implant, further exploiting the use of microchannel electrodes to improve neural interfaces. The nerve-on-a-chip platform is an efficient tool for designing and evaluating new implantable neuroprosthetic devices, using time and cost-efficient techniques. We foresee that this simple and broad

Framework for the Development of Neuroprostheses: From Basic Understanding by Sciatic and Median Nerves Models to Bionic Legs and Hands

Francesco Maria Petrini, Stanisa Raspopovic, Giacomo Valle, Marek Zelechowski

Neuroprostheses based on electrical stimulation are becoming a therapeutic reality, dramatically improving the life of disabled people. They are based on neural interfaces that are designed to create an intimate contact with neural cells. These devices speak the language of electron currents, while the human nervous system uses ionic currents to communicate. A deep understanding of the complex interplay between these currents, during the electrical stimulation, is essential for the development of optimized neuroprostheses. Neural electrodes can have different geometries, placement within the nervous system, and the stimulation protocols (paradigms of use). This high-dimensional problem is not tractable by an empiric, brute-force approach and should be tackled by exact computational models, making use of our accumulated knowledge. In pursuit of this goal, a hybrid finite element method-NEURON modeling-is used for a solution of electrical field generated by stimulation, within the different neural structures having anisotropic conductivity, and a corresponding neural response computation. In this work, an important correction of perineurium electrical conductivity is computed. Models of median and sciatic nerves, innervating the hand and foot areas, relevant to the development of bionic hands and legs with sensory feedback, are implemented. The obtained results have the potential to optimize the design of neural interfaces, in terms of shape and number of stimulating contacts. Guidelines for the neurosurgical planning are proposed, by indicating the optimal number of implants for a specific nerve to obtain the best efficacy with the lowest invasiveness. The interpretation is proposed for one of the basic problems of neural interfaces, consisting in the change of the stimulation threshold due to fibrotic reaction of tissue. We show that it is possible to use human microstimulation as an experimental setup for testing of afferent stimulation paradigms, which can be translated to further chronic implants. In the future, models will have a key role in the decision of the most appropriate design of customized neuroprostheses, their optimal modality of use, understanding the effects that occur during their use, and minimizing animal and human experimentation.

Ieee-Inst Electrical Electronics Engineers Inc2017

Electronic dura mater soft, multimodal neural interfaces

Arthur Edouard Hirsch

Neuroprosthetic devices are engineered to study, support or replace impaired functions of the nervous system. The neural interface is an essential element of neuroprosthetic systems as it allows for transduction of signals and stimuli of desired functions (recording, stimulation, neuromodulation). A persistent challenge for translating neuroprosthetics from the laboratory to the clinic is the lack of long-term biointegration of neural interfaces. This thesis aims at improving biointegration of neural interfaces by reducing the mechanical mismatch between implant and neural tissue. In this thesis, the design, fabrication and characterization of soft surface neural interfaces is described. These soft neural interfaces, termed electronic dura mater or e-dura, were designed to mimic the mechanical properties of dura mater. In contrast with conventional neural technologies, e-dura neural interfaces were made of soft and compliant materials. They conform to the circumvolutions of the brain and spinal cord and follow their dynamic deformation without damaging the surrounding neural tissues. These soft multimodal neural interfaces were fabricated on silicone substrates using techniques imported from the microfabrication industry and incorporate compliant electrodes, stretchable electrical interconnects and a micro-catheter for drug delivery. Evaluation of the e-dura biointegration with spinal tissues demonstrated reduced foreign body reaction, compared to stiff polyimide based implants. Additionally, mechanical tests on an in-vitro spinal surrogate provided insights on the complex biomechanical coupling between implants and neural tissue. E-dura interfaces, implanted in rodents, maintained their functionality over extended periods and provided high-resolution neuronal recordings and concurrent delivery of electrical and chemical neuromodulation. Eventually, the use of gallium thin films was explored to create highly conductive and stretchable interconnects for integration of active electronic components in e-dura neural interfaces.

EPFL2018

Tutorial: a computational framework for the design and optimization of peripheral neural interfaces

Silvestro Micera, Simone Romeni, Giacomo Valle

Peripheral neural interfaces have been successfully used in the recent past to restore sensory-motor functions in disabled subjects and for the neuromodulation of the autonomic nervous system. The optimization of these neural interfaces is crucial for ethical, clinical and economic reasons. In particular, hybrid models (HMs) constitute an effective framework to simulate direct nerve stimulation and optimize virtually every aspect of implantable electrode design: the type of electrode (for example, intrafascicular versus extrafascicular), their insertion position and the used stimulation routines. They are based on the combined use of finite element methods (to calculate the voltage distribution inside the nerve due to the electrical stimulation) and computational frameworks such as NEURON () to determine the effects of the electric field generated on the neural structures. They have already provided useful results for different applications, but the overall usability of this powerful approach is still limited by the intrinsic complexity of the procedure. Here, we illustrate a general, modular and expandable framework for the application of HMs to peripheral neural interfaces, in which the correct degree of approximation required to answer different kinds of research questions can be readily determined and implemented. The HM workflow is divided into the following tasks: identify and characterize the fiber subpopulations inside the fascicles of a given nerve section, determine different degrees of approximation for fascicular geometries, locate the fibers inside these geometries and parametrize electrode geometries and the geometry of the nerve-electrode interface. These tasks are examined in turn, and solutions to the most relevant issues regarding their implementation are described. Finally, some examples related to the simulation of common peripheral neural interfaces are provided. Neural interfaces with implantable electrodes are used to modulate and restore function to the peripheral nervous system. Hybrid modeling described in this protocol is used to optimize each aspect of the implantable electrode design and operation.

NATURE RESEARCH2020