Approximately 1,275,000 people in the US have a spinal cord injury severe enough to cause some paralysis of the arms and/or legs. Epidural stimulation using implanted multi-electrode stimulating arrays over the lumbosacral spinal cord has recently shown promise in assisting individuals with severe spinal cord injuries to stand, walk, and even facilitate voluntary movement. Both animal model and human studies have shown that sub-threshold facilitation of motor recovery gives the best results. The underlying neural mechanisms by which sub-threshold epidural stimulation leads to motor recovery are incompletely known.
\r\n\r\nThis thesis uses computational methods to study the facilitation effect. A neuron is facilitated if a sub-threshold synaptic input can cause a neuronal output under the influence of a stimulating electric field. The analysis in this thesis is based on a computational model of the epidural spinal stimulation process in the rat spinal cord. This model includes a time-domain finite element simulation (using COMSOL\u00ae) of the various tissues in the spinal cord with the appropriate anisotropic and frequency-dependent complex relative permittivities. The voltages obtained from the finite element simulations were used as the extracellular voltage in NEURON simulations.
\r\n\r\nA population of neurons were simulated under a wide variety of conditions. These simulations highlight the effect of neuron orientation, location, and synaptic timing as key parameters which influence facilitation.
\r\n\r\nThis study indicates that regions of the spinal cord that have previously been ignored may be actively involved in motor recovery. These results may also enable the design of specialized epidural electrode arrays and the design of new stimulation protocols.
", "doi": "10.7907/2DVK-G212", "publication_date": "2019", "thesis_type": "phd", "thesis_year": "2019" }, { "id": "thesis:11000", "collection": "thesis", "collection_id": "11000", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:06012018-140912331", "primary_object_url": { "basename": "LukeUrban_Thesis.pdf", "content": "final", "filesize": 43141815, "license": "other", "mime_type": "application/pdf", "url": "/11000/9/LukeUrban_Thesis.pdf", "version": "v5.0.0" }, "type": "thesis", "title": "An Electrophysiological Study Of Voluntary Movement and Spinal Cord Injury", "author": [ { "family_name": "Urban", "given_name": "Luke Stuart", "clpid": "Urban-Luke-Stuart" } ], "thesis_advisor": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "clpid": "Burdick-J-W" } ], "thesis_committee": [ { "family_name": "Perona", "given_name": "Pietro", "clpid": "Perona-P" }, { "family_name": "Abu-Mostafa", "given_name": "Yaser S.", "clpid": "Abu-Mostafa-Y-S" }, { "family_name": "Edgerton", "given_name": "V. Reggie", "clpid": "Edgerton-V-R" }, { "family_name": "Burdick", "given_name": "Joel Wakeman", "clpid": "Burdick-J-W" } ], "local_group": [ { "literal": "div_bbe" } ], "abstract": "Voluntary movement is generated from the interaction between neurons in our brain and the neurons in our spinal cord that engage our muscles. A spinal cord injury destroys the connection between these two regions, but parts of their underlying neural circuits survive. A new class of treatment (the brain-machine interface) takes advantage of this fact by either a) recording neural activity from the brain and predicting the intended movement (neural prosthetics) or b) stimulating neural activity in the spinal cord to facilitate muscle activity (spinal stimulation). This thesis covers new research studying the brain-machine interface and its application for spinal injury.
\r\n\r\nFirst, the electrical properties of the microelectrode (the main tool of the brain-machine interface) are studied during deep brain recording and stimulation. This work shows that the insulation coating the electrode forms a capacitor with the surrounding neural tissue. This capacitance causes large spikes of voltage in the surrounding tissue during deep brain stimulation, which will cause electrical artifacts in neural recordings and may damage the surrounding neurons. This work also shows that a coaxially shielded electrode will block this effect.
\r\n\r\nSecond, the activity of neurons in the parietal cortex is studied during hand movements, which has applications for neural prosthetics. Prior work suggests that the parietal cortex encodes a state-estimator [1], which combines sensory feedback with the internal efference copy to predict the state of the hand. To test this idea, we used a visual lag to misalign sensory feedback from the efference copy. The expectation was that a state-estimator would unknowingly combine the delayed visual feedback with the current efference information, resulting in incorrect predictions of the hand. Our results show a drop in correlation between neural activity in the parietal cortex and hand movement during a visual lag, supporting the idea that the parietal cortex encodes a state-estimator. This correlation gradually recovers over time, showing that parietal cortex is adaptive to sensory delays.
\r\n\r\nThird, while the intention of spinal stimulation was to interact locally with neural circuits in the spinal cord, results from the clinic show that electrical stimulation of the lumbosacral enlargement enables paraplegic patients to regain voluntary movement of their legs [2]. This means that spinal stimulation facilitates communication across an injury site. To further study this effect, we developed a new behavioral task in the rodent. Rats were trained to kick their right hindlimb in response to an auditory cue. The animals then received a spinal injury that caused paraplegia. After injury, the animals recovered the behavior (they could kick in response to the cue), but only during spinal stimulation. Their recovered behavior was slower and more stereotyped than their pre-injury response. Administering quipazine to these rodents disrupted their ability to respond to the cue, suggesting that serotonin plays an important role in the recovered pathway. This work proves that the new behavioral task is a successful tool for studying the recovery of voluntary movement.
\r\n\r\nFuture work will combine cortical recordings with this behavioral task in the rodent to study plasticity in the nervous system and improve treatment of spinal cord injuries.
\r\n\r\n[1] Mulliken, Grant H., Sam Musallam, and Richard A. Andersen. \"Forward estimation of movement state in posterior parietal cortex.\" Proceedings of the National Academy of Sciences105.24 (2008): 8170-8177.
\r\n\r\n[2] Harkema, Susan, et al. \"Effect of epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study.\" The Lancet 377.9781 (2011): 1938-1947.
", "doi": "10.7907/K6P2-ZH75", "publication_date": "2018", "thesis_type": "phd", "thesis_year": "2018" }, { "id": "thesis:9818", "collection": "thesis", "collection_id": "9818", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05312016-211459301", "primary_object_url": { "basename": "liu_zhao_2016_thesis.pdf", "content": "final", "filesize": 11058862, "license": "other", "mime_type": "application/pdf", "url": "/9818/55/liu_zhao_2016_thesis.pdf", "version": "v5.0.0" }, "type": "thesis", "title": "Electromyographic Signal Processing With Application To Spinal Cord Injury", "author": [ { "family_name": "Liu", "given_name": "Zhao", "clpid": "Liu-Zhao" } ], "thesis_advisor": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "clpid": "Burdick-J-W" } ], "thesis_committee": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "clpid": "Burdick-J-W" }, { "family_name": "Rutledge", "given_name": "David B.", "clpid": "Rutledge-D-B" }, { "family_name": "Yang", "given_name": "Changhuei", "clpid": "Yang-Changhuei" }, { "family_name": "Choo", "given_name": "Hyuck", "clpid": "Choo-Hyuck" }, { "family_name": "Edgerton", "given_name": "V. Reggie", "clpid": "Edgerton-V-R" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "An Electromyogram or Electromyographic (EMG) signal is the recording of the electrical activity produced by muscles. It measures the electric currents generated in muscles during their contraction. The EMG signal provides insight into the neural activation and dynamics of the muscles, and is therefore important for many different applications, such as in clinical investigations that attempt to diagnose neuromuscular deficiencies. In particular, the work in this thesis is motivated by rehabilitation for patients with spinal cord injury. The EMG signal is very important for researchers and practitioners to monitor and evaluate the effect of the rehabilitation training and the condition of muscles, as the EMG signal provides information that helps infer the neural activity in the spinal cord. Before the work in this thesis, EMG analysis required significant amounts of manual labeling of interesting signal features. The motivation of this thesis is to fully automate the EMG analysis tasks and yield accurate, consistent results.
\r\n\r\nThe EMG signal contains multiple muscle responses. The difficulty in processing the EMG signal arises from the fact that the transient muscle response is a transient signal with unknown arrival time, unknown duration, and unknown shape. In addition, the EMG signal recorded from patients with spinal cord injury during rehabilitation is very different from the EMG signal of normal healthy people undergoing the same motions. For example, some of the muscle responses are very weak and thus hard to detect. Because of this, general EMG processing tools and methods are either not applicable or insufficient.
\r\n\r\nThe primary contribution of this thesis is the development of a wavelet-based, double-threshold algorithm for the detection of transient peaks in the EMG signal. The application of wavelet transform in the detection of transient signals has been studied extensively and employed successfully. However, most of the theories assume certain knowledge about the shapes of the transient signals, which makes it hard to be generalized to the transient signals with arbitrary shapes. The proposed detection scheme focuses on the more fundamental feature of most transient signals (in particular the EMG signal): peaks, instead of the shapes. The continuous wavelet transform with Mexican Hat wavelet is employed. This thesis theoretically derived a framework for selecting a set of scales based on the frequency domain information. Ridges are identified in the time-scale space to combine the wavelet coefficients from different scales. By imposing two thresholds, one on the wavelet coefficient and one on the ridge length, the proposed detection scheme can achieve both high recall and high precision. A systematic approach for selecting the optimal parameters via simulation is proposed and demonstrated. Comparing with other state-of-the-art detection methods, the proposed method in this thesis yields a better detection performance, especially in the low Signal-to-Noise-Ratio (SNR) environment.
\r\n\r\nBased on the transient peak detection result, the EMG signal is further segmented and classified into various groups of monosynaptic Motor Evoked Potentials (MEPs) and polysynaptic MEPs using techniques stemming from Principal Component Analysis (PCA), hierarchical clustering, and Gaussian mixture model (GMM). A theoretical framework is proposed to segment the EMG signal based on the detected peaks. The scale information of the detected peak is used to derive a measure for its effective support. Several different techniques have been adapted together to solve the clustering problem. An initial hierarchical clustering is first performed to obtain most of the monosynaptic MEPs. PCA is used to reduce the number of features and the effect of the noise. The reduced feature set is then fed to a GMM to further divide the MEPs into different groups of similar shapes. The method of breaking down a segment of multiple consecutive MEPs into individual MEPs is derived.
\r\n\r\nA software with graphic user interface has been implemented in Matlab. The software implements the proposed peak detection algorithm, and enables the physiologists to visualize the detection results and modify them if necessary. The solutions proposed in this thesis are not only helpful to the rehabilitation after spinal cord injury, but applicable to other general processing tasks on transient signals, especially on biological signals.
", "doi": "10.7907/Z9QJ7F99", "publication_date": "2016", "thesis_type": "phd", "thesis_year": "2016" }, { "id": "thesis:8994", "collection": "thesis", "collection_id": "8994", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:06052015-084726649", "primary_object_url": { "basename": "Thesis.pdf", "content": "final", "filesize": 3194374, "license": "other", "mime_type": "application/pdf", "url": "/8994/1/Thesis.pdf", "version": "v2.0.0" }, "type": "thesis", "title": "Microelectrode Implants for Spinal Cord Stimulation in Rats ", "author": [ { "family_name": "Nandra", "given_name": "Mandheerej Singh", "clpid": "Nandra-Mandheerej-Singh" } ], "thesis_advisor": [ { "family_name": "Tai", "given_name": "Yu-Chong", "clpid": "Tai-Yu-Chong" } ], "thesis_committee": [ { "family_name": "Tai", "given_name": "Yu-Chong", "clpid": "Tai-Yu-Chong" }, { "family_name": "Edgerton", "given_name": "V. Reggie", "clpid": "Edgerton-V-R" }, { "family_name": "Burdick", "given_name": "Joel Wakeman", "clpid": "Burdick-J-W" }, { "family_name": "Emami", "given_name": "Azita", "clpid": "Emami-A" }, { "family_name": "Yang", "given_name": "Changhuei", "clpid": "Yang-Changhuei" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "Paralysis is a debilitating condition afflicting millions of people across the globe, and is particularly deleterious to quality of life when motor function of the legs is severely impaired or completely absent. Fortunately, spinal cord stimulation has shown great potential for improving motor function after spinal cord injury and other pathological conditions. Many animal studies have shown stimulation of the neural networks in the spinal cord can improve motor ability so dramatically that the animals can even stand and step after a complete spinal cord transaction.
\r\n\r\nThis thesis presents work to successfully provide a chronically implantable device for rats that greatly enhances the ability to control the site of spinal cord stimulation. This is achieved through the use of a parylene-C based microelectrode array, which enables a density of stimulation sites unattainable with conventional wire electrodes. While many microelectrode devices have been proposed in the past, the spinal cord is a particularly challenging environment due to the bending and movement it undergoes in a live animal. The developed microelectrode array is the first to have been implanted in vivo while retaining functionality for over a month. In doing so, different neural pathways can be selectively activated to facilitate standing and stepping in spinalized rats using various electrode combinations, and important differences in responses are observed.
\r\n\r\nAn engineering challenge for the usability of any high density electrode array is connecting the numerous electrodes to a stimulation source. This thesis develops several technologies to address this challenge, beginning with a fully passive implant that uses one wire per electrode to connect to an external stimulation source. The number of wires passing through the body and the skin proved to be a hazard for the health of the animal, so a multiplexed implant was devised in which active electronics reduce the number of wires. Finally, a fully wireless implant was developed. As these implants are tested in vivo, encapsulation is of critical importance to retain functionality in a chronic experiment, especially for the active implants, and it was achieved without the use of costly ceramic or metallic hermetic packaging. Active implants were built that retained functionality 8 weeks after implantation, and achieved stepping in spinalized rats after just 8-10 days, which is far sooner than wire-based electrical stimulation has achieved in prior work.
", "doi": "10.7907/Z9930R3G", "publication_date": "2015", "thesis_type": "phd", "thesis_year": "2015" }, { "id": "thesis:7918", "collection": "thesis", "collection_id": "7918", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:07252013-120308708", "primary_object_url": { "basename": "Desautels-Thomas-2014.pdf", "content": "final", "filesize": 13206664, "license": "other", "mime_type": "application/pdf", "url": "/7918/1/Desautels-Thomas-2014.pdf", "version": "v2.0.0" }, "type": "thesis", "title": "Spinal Cord Injury Therapy through Active Learning", "author": [ { "family_name": "Desautels", "given_name": "Thomas Anthony", "clpid": "Desautels-Thomas-Anthony" } ], "thesis_advisor": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "clpid": "Burdick-J-W" } ], "thesis_committee": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "clpid": "Burdick-J-W" }, { "family_name": "Tai", "given_name": "Yu-Chong", "clpid": "Tai-Yu-Chong" }, { "family_name": "Beck", "given_name": "James L.", "clpid": "Beck-J-L" }, { "family_name": "Edgerton", "given_name": "V. Reggie", "clpid": "Edgerton-V-R" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "Therapy employing epidural electrostimulation holds great potential for improving therapy for patients with spinal cord injury (SCI) (Harkema et al., 2011). Further promising results from combined therapies using electrostimulation have also been recently obtained (e.g., van den Brand et al., 2012). The devices being developed to deliver the stimulation are highly flexible, capable of delivering any individual stimulus among a combinatorially large set of stimuli (Gad et al., 2013). While this extreme flexibility is very useful for ensuring that the device can deliver an appropriate stimulus, the challenge of choosing good stimuli is quite substantial, even for expert human experimenters. To develop a fully implantable, autonomous device which can provide useful therapy, it is necessary to design an algorithmic method for choosing the stimulus parameters. Such a method can be used in a clinical setting, by caregivers who are not experts in the neurostimulator's use, and to allow the system to adapt autonomously between visits to the clinic. To create such an algorithm, this dissertation pursues the general class of active learning algorithms that includes Gaussian Process Upper Confidence Bound (GP-UCB, Srinivas et al., 2010), developing the Gaussian Process Batch Upper Confidence Bound (GP-BUCB, Desautels et al., 2012) and Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) algorithms. This dissertation develops new theoretical bounds for the performance of these and similar algorithms, empirically assesses these algorithms against a number of competitors in simulation, and applies a variant of the GP-BUCB algorithm in closed-loop to control SCI therapy via epidural electrostimulation in four live rats. The algorithm was tasked with maximizing the amplitude of evoked potentials in the rats' left tibialis anterior muscle. These experiments show that the algorithm is capable of directing these experiments sensibly, finding effective stimuli in all four animals. Further, in direct competition with an expert human experimenter, the algorithm produced superior performance in terms of average reward and comparable or superior performance in terms of maximum reward. These results indicate that variants of GP-BUCB may be suitable for autonomously directing SCI therapy.", "doi": "10.7907/X5M7-EC09", "publication_date": "2014", "thesis_type": "phd", "thesis_year": "2014" }, { "id": "thesis:3666", "collection": "thesis", "collection_id": "3666", "cite_using_url": "https://resolver.caltech.edu/CaltechETD:etd-09202007-135027", "primary_object_url": { "basename": "final_thesis.pdf", "content": "final", "filesize": 3271723, "license": "other", "mime_type": "application/pdf", "url": "/3666/1/final_thesis.pdf", "version": "v3.0.0" }, "type": "thesis", "title": "Robotic Training for Motor Rehabilitation after Complete Spinal Cord Injury", "author": [ { "family_name": "Liang", "given_name": "Yongqiang", "clpid": "Liang-Yongqiang" } ], "thesis_advisor": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "clpid": "Burdick-J-W" }, { "family_name": "Edgerton", "given_name": "V. Reggie", "clpid": "Edgerton-V-R" } ], "thesis_committee": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "clpid": "Burdick-J-W" }, { "family_name": "Antonsson", "given_name": "Erik K.", "clpid": "Antonsson-E-K" }, { "family_name": "Hunt", "given_name": "Melany L.", "clpid": "Hunt-M-L" }, { "family_name": "Murray", "given_name": "Richard M.", "clpid": "Murray-R-M" }, { "family_name": "Edgerton", "given_name": "V. Reggie", "clpid": "Edgerton-V-R" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "The spinal cord circuits have a great degree of automaticity and plasticity. They are able to generate complex locomotor patterns such as stepping and scratching even without input from supraspinal nervous systems. When provided with ensembles of afferent sensory information input associated with a specific motor task, e.g., stepping, the spinal cord can \"learn\" to perform that task even if it is isolated from the supraspinal nervous systems.
\r\n\r\nThe plasticity of the spinal cord led researchers to study the use of physical locomotor training, e.g., treadmill step training with body weight support, to rehabilitate locomotor function after spinal cord injury (SCI). With intensive training, the spinal-cord-injured subject can recover some level of stepping ability. Explorations were made in this thesis to find an optimal training paradigm. Novel assist-as-needed paradigms were developed to allow variability during training since it is an intrinsic feature of normal stepping. Comparative experiments were conducted against fixed-trajectory training. Results demonstrated that variability is an important factor to induce more improvement in step training.
\r\n\r\nStanding is another important function in one's daily life, though it received less research attention than stepping. A prototype stand platform with 6 degrees of freedom was developed as an experimental tool for stand and postural study. Analogous to step training, we tested the effect of daily training on extensor responses in the hind limbs of complete spinal rats. The results showed no significant effect of the training. This led to the conclusion that without tonic input, the spinal cord has very limited ability to generate enough extensor muscle tone and to respond to postural disturbance. Further studies in standing rehabilitation should combine other methods to provide tonic inputs to the spinal cord.
", "doi": "10.7907/T01R-P904", "publication_date": "2008", "thesis_type": "phd", "thesis_year": "2008" }, { "id": "thesis:3117", "collection": "thesis", "collection_id": "3117", "cite_using_url": "https://resolver.caltech.edu/CaltechETD:etd-08142006-165844", "primary_object_url": { "basename": "Thesis.pdf", "content": "final", "filesize": 3982199, "license": "other", "mime_type": "application/pdf", "url": "/3117/1/Thesis.pdf", "version": "v1.0.0" }, "type": "thesis", "title": "Robotics Training Algorithms for Optimizing Motor Learning in Spinal Cord Injured Subjects", "author": [ { "family_name": "Cai", "given_name": "Lance Lin-Lan", "clpid": "Cai-Lance-Lin-Lan" } ], "thesis_advisor": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "orcid": "0000-0002-3091-540X", "clpid": "Burdick-J-W" }, { "family_name": "Edgerton", "given_name": "V. Reggie", "clpid": "Edgerton-V-R" } ], "thesis_committee": [ { "family_name": "Burdick", "given_name": "Joel Wakeman", "orcid": "0000-0002-3091-540X", "clpid": "Burdick-J-W" }, { "family_name": "Gharib", "given_name": "Morteza", "orcid": "0000-0003-0754-4193", "clpid": "Gharib-M" }, { "family_name": "Andersen", "given_name": "Richard A.", "orcid": "0000-0002-7947-0472", "clpid": "Andersen-R-A" }, { "family_name": "Edgerton", "given_name": "V. Reggie", "clpid": "Edgerton-V-R" }, { "family_name": "Abu-Mostafa", "given_name": "Yaser S.", "clpid": "Abu-Mostafa-Y-S" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "The circuitries within the spinal cord are remarkably robust and plastic. Even in the absence of supraspinal control, such circuitries are capable of generating functional movements and changing their level of excitability based on a specific combination of properceptive inputs going into the spinal cord. This has led to an increase in locomotor training, such as Body Weight Support Treadmill training (BWST) for spinal cord injured (SCI) patients. However, today, little is known about the underlying physiological mechanisms responsible for the locomotor recovery achieved with this type of rehabilitative training, and the optimal rehabilitative strategy is still unknown.
\r\n\r\nThis thesis describes a mouse model to study the effect of rehabilitative training on SCI. Using this model, the effects of locomotor recovery on adult spinal mice following complete spinal cord transaction is examined. Results that indicate adult spinal mice can be robotically trained to step, and when combined with the administration of quipazine (a broad serotonin agonist), there is an interaction and retention effect. Results also demonstrate that the training paradigm can be optimized in using \u201cAssisted-as-Needed\u201d (AAN) training. To find the optimal AAN training parameters, a learning model is developed to test the effect of various parameters of the AAN training algorithm. Simulation results from our model show that learning is training-dependent. In addition, the model predicts that improved motor learning can improve post-SCI by making the AAN training more adaptable.
\r\n\r\nThe primary contributions of this thesis are twofold, in biology and engineering. We develop a mouse model using novel robotic devices and controls that can be used to study SCI and other locomotor disorders in the future by taking advantage of the many different strains of transgenic mice that are commercially available. We also further confirm that sensory integration responsible for motor control is distributed throughout the hierarchy of the neuromuscular system and can be achieved within the isolated spinal cord. Lastly, by developing a learning model, we can start looking into how variability plays a role in motor learning, the understanding of which will have profound implications in neurophysiology, machine learning and adaptive optimal controls research.
", "doi": "10.7907/EH12-WD80", "publication_date": "2007", "thesis_type": "phd", "thesis_year": "2007" } ]