Restoring Upper Extremity Mobility through Functional Neuromuscular Stimulation using Macro Sieve Electrodes

The last decade has seen the advent of brain computer interfaces able to extract precise motor intentions from cortical activity of human subjects. It is possible to convert captured motor intentions into movement through coordinated, artificially induced, neuromuscular stimulation using peripheral nerve interfaces. Our lab has developed and tested a new type of peripheral nerve electrode called the Macro-Sieve electrode which exhibits excellent chronic stability and recruitment selectivity. Work presented in this thesis uses computational modeling to study the interaction between Macro-Sieve electrodes and regenerated peripheral nerves. It provides a detailed understanding of how regenerated fibers, both on an individual level and on a population level respond differently to functional electrical stimulation compared to non-disrupted axons. Despite significant efforts devoted to developing novel regenerative peripheral interfaces, the degree of spatial clustering between functionally related fibers in regenerated nerves is poorly understood. In this thesis, bioelectrical modeling is also used to predict the degree of topographical organization in regenerated nerve trunks. In addition, theoretical limits of the recruitment selectivity of the device is explored and a set of optimal stimulation paradigms used to selectively activate fibers in different regions of the nerve are determined. Finally, the bioelectrical model of the interface/nerve is integrated with a biomechanical model of the macaque upper limb to study the feasibility of using macro-sieve electrodes to achieve upper limb mobilization

Ventral root or dorsal root ganglion microstimulation to evoke hindlimb motor responses

Functional electrical stimulation is an important therapeutic tool for improving the quality of life of patients following spinal cord injury. Investigators have developed neural interfaces of varying invasiveness and implant location to stimulate neurons and evoke motor responses. Here we present an alternative interface with the ventral roots (VR) or dorsal root ganglia (DRG). We designed preliminary electrophysiology experiments to evaluate the performance of these interfaces, wherein we stimulated lumbar VR or DRG through a penetrating single-wire microelectrode while recording fixed endpoint force and bipolar electromyograms of hindlimb muscles. Data from rat experiments provided evidence for selectivity for target muscles, graded force recruitment, and nontrivial force magnitudes of up to 1 N. Electrophysiology experiments in cats produced similar results to those in rats. In addition, we developed a computational model to estimate the size and quantity of fibers recruited as a function of stimulus amplitude. This model confirmed electrophysiology results showing differences in the thresholds to detect activity in response to VR versus DRG stimulation. The model also provided insights into the mechanisms by which DRG stimulation is more likely to recruit smaller fibers than larger fibers. Finally, we discuss further work to develop and evaluate these potential interfaces

The future of upper extremity rehabilitation robotics: research and practice

The loss of upper limb motor function can have a devastating effect on people’s lives. To restore upper limb control and functionality, researchers and clinicians have developed interfaces to interact directly with the human body’s motor system. In this invited review, we aim to provide details on the peripheral nerve interfaces and brain‐machine interfaces that have been developed in the past 30 years for upper extremity control, and we highlight the challenges that still remain to transition the technology into the clinical market. The findings show that peripheral nerve interfaces and brain‐machine interfaces have many similar characteristics that enable them to be concurrently developed. Decoding neural information from both interfaces may lead to novel physiological models that may one day fully restore upper limb motor function for a growing patient population.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155489/1/mus26860_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155489/2/mus26860.pd

Modelling the effects of ephaptic coupling on selectivity and response patterns during artificial stimulation of peripheral nerves

Artificial electrical stimulation of peripheral nerves for sensory feedback restoration can greatly benefit from computational models for simulation-based neural implant design in order to reduce the trial-and-error approach usually taken, thus potentially significantly reducing research and development costs and time. To this end, we built a computational model of a peripheral nerve trunk in which the interstitial space between the fibers and the tissues was modelled using a resistor network, thus enabling distance-dependent ephaptic coupling between myelinated axons and between fascicles as well. We used the model to simulate a) the stimulation of a nerve trunk model with a cuff electrode, and b) the propagation of action potentials along the axons. Results were used to investigate the effect of ephaptic interactions on recruitment and selectivity stemming from artificial (i.e., neural implant) stimulation and on the relative timing between action potentials during propagation. Ephaptic coupling was found to increase the number of fibers that are activated by artificial stimulation, thus reducing the artificial currents required for axonal recruitment, and it was found to reduce and shift the range of optimal stimulation amplitudes for maximum inter-fascicular selectivity. During propagation, while fibers of similar diameters tended to lock their action potentials and reduce their conduction velocities, as expected from previous knowledge on bundles of identical axons, the presence of many other fibers of different diameters was found to make their interactions weaker and unstable

MATHEMATICAL AND EXPERIMENTAL MODELS FOR STUDYING SOMATOSENSORY FEEDBACK VIA PRIMARY AFFERENT MICROSTIMULATION

A significant problem with current prostheses is the challenge of controlling the device without being able to ‘feel’ it. Without somatosensory feedback, cognitively demanding visual and attentional processes must be relied upon. However, restoring somatosensory feedback offers the possibility of changing a prosthetic from an extracorporeal tool into a part of the user’s body, while enabling a far more natural control scheme. Although artificial somatosensory feedback of prostheses has been attempted since the 1960s, clinical implementations are lacking. This thesis will focus on the use of electrical stimulation to artificially activate the nervous system to restore feedback. With recent advances in electrode design, the possibility of implanting hundreds of electrodes into the nervous system is becoming a reality. However, current stimulation protocols are oriented towards using only a few electrodes. It is likely that new stimulation paradigms will be needed in order to fully take advantage of multichannel microelectrode arrays. This dissertation examines new methods for studying somatosensory feedback. Dorsal root ganglia microstimulation with concurrent nerve-cuff recordings is used to evaluate stimulation thresholds and the types of neurons first recruited. Computational models are developed to explore recruitment beyond threshold as well as the impact of simultaneous stimulation on changing neural recruitment. Finally, an experimental model is developed that uses the cortical response to primary afferent stimulation to assess information transfer to the brain. Together, these models offer new approaches for improving somatosensory feedback stimulation paradigms

Using primary afferent neural activity for predicting limb kinematics in cat

Kinematic state feedback is important for neuroprostheses to generate stable and adaptive movements of an extremity. State information, represented in the firing rates of populations of primary afferent neurons, can be recorded at the level of the dorsal root ganglia (DRG). Previous work in cats showed the feasibility of using DRG recordings to predict the kinematic state of the hind limb using reverse regression. Although accurate decoding results were attained, these methods did not make efficient use of the information embedded in the firing rates of the neural population. This dissertation proposes new methods for decoding limb kinematics from primary afferent firing rates. We present decoding results based on state-space modeling, and show that it is a more principled and more efficient method for decoding the firing rates in an ensemble of primary afferent neurons. In particular, we show that we can extract confounded information from neurons that respond to multiple kinematic parameters, and that including velocity components in the firing rate models significantly increases the accuracy of the decoded trajectory. This thesis further explores the feasibility of decoding primary afferent firing rates in the presence of stimulation artifact generated during functional electrical stimulation. We show that kinematic information extracted from the firing rates of primary afferent neurons can be used in a 'real-time' application as a feedback for control of FES in a neuroprostheses. It provides methods for decoding primary afferent neurons and sets a foundation for further development of closed loop FES control of paralyzed extremities. Although a complete closed loop neuroprosthesis for natural behavior seems far away, the premise of this work argues that an interface at the dorsal root ganglia should be considered as a viable option

Low-dimensional representations of neural time-series data with applications to peripheral nerve decoding

Bioelectronic medicines, implanted devices that influence physiological states by peripheral neuromodulation, have promise as a new way of treating diverse conditions from rheumatism to diabetes. We here explore ways of creating nerve-based feedback for the implanted systems to act in a dynamically adapting closed loop. In a first empirical component, we carried out decoding studies on in vivo recordings of cat and rat bladder afferents. In a low-resolution data-set, we selected informative frequency bands of the neural activity using information theory to then relate to bladder pressure. In a second high-resolution dataset, we analysed the population code for bladder pressure, again using information theory, and proposed an informed decoding approach that promises enhanced robustness and automatic re-calibration by creating a low-dimensional population vector. Coming from a different direction of more general time-series analysis, we embedded a set of peripheral nerve recordings in a space of main firing characteristics by dimensionality reduction in a high-dimensional feature-space and automatically proposed single efficiently implementable estimators for each identified characteristic. For bioelectronic medicines, this feature-based pre-processing method enables an online signal characterisation of low-resolution data where spike sorting is impossible but simple power-measures discard informative structure. Analyses were based on surrogate data from a self-developed and flexibly adaptable computer model that we made publicly available. The wider utility of two feature-based analysis methods developed in this work was demonstrated on a variety of datasets from across science and industry. (1) Our feature-based generation of interpretable low-dimensional embeddings for unknown time-series datasets answers a need for simplifying and harvesting the growing body of sequential data that characterises modern science. (2) We propose an additional, supervised pipeline to tailor feature subsets to collections of classification problems. On a literature standard library of time-series classification tasks, we distilled 22 generically useful estimators and made them easily accessible.Open Acces

Modelling Artificial Stimulation and Response in Peripheral Nerves Including Ephaptic Interactions

This research aims to (1) extend our knowledge on the response of peripheral nerves to artificial stimulation for sensory feedback provision from neural interfaces, and (2) create a computational tool to facilitate this study. We were interested in studying how ephaptic coupling between myelinated fibers influences activity in nerve trunks under artificial stimulation and during action potential propagation. Ephaptic interaction simulations in nerve trunks were performed to quantify this influence. For this, we created peripheral nerve models containing electrodes for electrical stimulation and recording within a tool that can be further used in electrode design optimisation and neural activity research. The created model can use a self-contained or a hybrid field-neuron method. The self-contained method uses a resistor network that electrically couples all axons, tissues, electrodes, and surrounding medium, and is solved by the NEURON simulation environment. The resistor network uses weighted Voronoi tessellations in the Laguerre geometry to define the electrical connections between all nerve elements given any cross-sectional anatomy. The hybrid field-neuron approach also uses the resistor network to compute the fields, but uses them stimulate fiber in a separate simulation. The self-contained model was designed so that it could simulate artificial stimulation, neural activity with ephaptic coupling and electrode recordings simultaneously. Researchers often assume ephaptic coupling is weak among myelinated axons, and therefore, tend to ignore it. Simulations carried out in this work, however, show that ephaptic coupling increases axon recruitment during artificial stimulation. This effect should be taken into account in further research. On the other hand, ephaptic coupling during propagation in realistic bundles with large numbers of heterogeneous myelinated fibers is weaker, unstable, and more complex than what is known from previous studies on bundles of few homogeneous fibers. This research provides detailed results and insights on these aspects of peripheral neural activity