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" } ]