Point-process analysis of neural spiking activity of muscle spindles recorded from thin-film longitudinal intrafascicular electrodes

Recordings from thin-film Longitudinal Intra-Fascicular Electrodes (tfLIFE) together with a wavelet-based de-noising and a correlation-based spike sorting algorithm, give access to firing patterns of muscle spindle afferents. In this study we use a point process probability structure to assess mechanical stimulus-response characteristics of muscle spindle spike trains. We assume that the stimulus intensity is primarily a linear combination of the spontaneous firing rate, the muscle extension, and the stretch velocity. By using the ability of the point process framework to provide an objective goodness of fit analysis, we were able to distinguish two classes of spike clusters with different statistical structure. We found that spike clusters with higher SNR have a temporal structure that can be fitted by an inverse Gaussian distribution while lower SNR clusters follow a Poisson-like distribution. The point process algorithm is further able to provide the instantaneous intensity function associated with the stimulus-response model with the best goodness of fit. This important result is a first step towards a point process decoding algorithm to estimate the muscle length and possibly provide closed loop Functional Electrical Stimulation (FES) systems with natural sensory feedback information.National Institutes of Health (U.S.) (Grant R01-HL084502)National Institutes of Health (U.S.) (Grant DP1-OD003646

Spike Sorting of Muscle Spindle Afferent Nerve Activity Recorded with Thin-Film Intrafascicular Electrodes

Afferent muscle spindle activity in response to passive muscle stretch was recorded in vivo using thin-film longitudinal intrafascicular electrodes. A neural spike detection and classification scheme was developed for the purpose of separating activity of primary and secondary muscle spindle afferents. The algorithm is based on the multiscale continuous wavelet transform using complex wavelets. The detection scheme outperforms the commonly used threshold detection, especially with recordings having low signal-to-noise ratio. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards online model-based estimation of muscle length that can be used in a closed-loop functional electrical stimulation system with natural sensory feedback

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

Real-Time neural signal decoding on heterogeneous MPSocs based on VLIW ASIPs

An important research problem, at the basis of the development of embedded systems for neuroprosthetic applications, is the development of algorithms and platforms able to extract the patient's motion intention by decoding the information encoded in neural signals. At the state of the art, no portable and reliable integrated solutions implementing such a decoding task have been identified. To this aim, in this paper, we investigate the possibility of using the MPSoC paradigm in this application domain. We perform a design space exploration that compares different custom MPSoC embedded architectures, implementing two versions of a on-line neural signal decoding algorithm, respectively targeting decoding of single and multiple acquisition channels. Each considered design points features a different application configuration, with a specific partitioning and mapping of parallel software tasks, executed on customized VLIW ASIP processing cores. Experimental results, obtained by means of FPGA-based prototyping and post-floorplanning power evaluation on a 40nm technology library, assess the performance and hardware-related costs of the considered configurations. The reported power figures demonstrate the usability of the MPSoC paradigm within the processing of bio-electrical signals and show the benefits achievable by the exploitation of the instruction-level parallelism within tasks

Neural Interfacing with Dorsal Root Ganglia: Anatomical Characterization and Electrophysiological Recordings with Novel Electrode Arrays

Dorsal root ganglia (DRG), the hubs of neurons conducting sensory information into the spinal cord, are promising targets for clinical and investigative neural interface technologies. DRG stimulation is currently a tertiary therapy for chronic pain patients, which has an estimated prevalence of 11-40% of adults in the United States. In pre-clinical studies, combined neural recording and stimulation at DRG has been used as part of closed-loop systems to drive activity of the limbs and the urinary system. This suggests a role for clinical DRG interfaces to assist, among other patient groups, the nearly 300,000 spinal cord injured patients in the United States. To maximize the utility of DRG interfaces, however, there remains a strong need to improve our understanding of DRG structure. Neural interface technologies for both stimulation and recording rely heavily on the spatial organization of their neural targets. To record high-fidelity neural signals, a microelectrode must be placed within about 200 µm of a neural cell body. Likewise, effective neural stimulation is believed to act on a subset of DRG axons based on their size and target. The spatial organization of DRG, however, has not been previously quantified. In this thesis, I demonstrate a novel algorithm to transform histological cross-sections of DRG to a normalized circular region for quantifying trends across many samples. Using this algorithm on 26 lumbosacral DRG from felines, a common preclinical DRG model, I found that the highest density of neural cell bodies was in the outer 24% radially, primarily at the dorsal aspect. I extended this analysis to a semi-automated cross-DRG analysis in 33 lower lumbar DRG from 10 human donors. I found that the organization of human DRG was similar to felines, with the highest density of cell bodies found in the outer 20-25% of the DRG, depending on spinal level. I also found a trend toward lower small-axon density at the dorsal aspect of L5 DRG, a key region for stimulation applications. To take advantage of this quantitative knowledge of DRG organization, future neural interfaces with DRG will require more advanced technologies. Standard silicon-based electrode arrays, while useful for short-term DRG recordings, ultimately fail in chronic use after several weeks as a result of mechanical mismatch with neural tissue. In this thesis, I demonstrate sensory recording from the surface and interior of sacral DRG during acute surgery using a variety of flexible polyimide microelectrode arrays 4-μm thick and minimum site separation 25 to 40 μm. Using these arrays, I recorded from bladder and somatic afferents with high fidelity. The high density of sites allowed for neural source localization from surface recordings to depths 25 to 107 µm. This finding supports the anatomical analysis suggesting a high density of cell bodies in the dorsal surface region where the surface array was applied. The high site density also allowed for the use of advanced signal processing to decrease analysis time and track neural sources during movement of the array which may occur during future behavioral experiments. This thesis represents significant advances in our understanding of DRG and how to interface with them, particularly related to the way anatomy can inform development of future technologies. Going forward, it will be important to expand the anatomical maps based on organ function and to test the novel flexible arrays in chronic implant experiments.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153477/1/zsperry_1.pd

A ventral root interface for neuroprosthetic control of locomotion

Recent advances in state of the art prosthetic limbs have demonstrated unprecedented levels of dexterity and control within the constraints of an anthropomorphic structure. Unfortunately, patients still struggle to naturally control and rely upon relatively simpler lower limb devices with just one or two joints. For patients living with the loss of a limb, functional motor circuitry is still intact through the spinal cord and into the peripheral nerves, transforming higher level control signals into discrete muscle activations. An interface at the spinal roots can take advantage of this final output of the nervous system to control the device, completely avoiding some of the context sensitivity issues in higher level structures. Further, the anatomical separation of motor and sensory signals into distinct ventral and dorsal components and the relative stability of the spinal column provide a path towards a targeted neuroprosthetic interface. This dissertation develops and validates methods to target motor axons in the ventral roots with multielectrode arrays. We demonstrate the ability to chronically record well-isolated signals from diverse populations of motor axons and develop techniques to identify the muscles they innervate. We subsequently use these motor signals to estimate kinematics during locomotion as accurately as estimations from simultaneously recorded muscle activity in the intact limb, demonstrating that a ventral root prosthetic interface is possible for patients living with loss of limb

Investigation into the control of an upper-limb myoelectric prosthesis

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN053608 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Wavelet-Based Spike Sorting of Muscle Spindle Afferent Nerve Activity Recorded With Thin-Film Intrafascicular Electrodes

International audienceThe continuous complex wavelet transform offers a convenient framework for neural spike sorting. Results show that wavelet-based neural spike detection outperforms simple threshold detection, especially with signals with low signal to noise ratio. Classification of action potentials using their signatures in wavelet space performed as well as a classifier based upon principal components analysis, and better than a classifier based upon template matching. Applied on experimental intrafascicular recordings of muscle spindle afferent nerve response to passive muscle stretch, the spike sorting algorithm manages to isolate afferent activity of units having a linear relationship between neural firing rate and muscle length, an important step towards a model-based estimator of muscle length