Spinal Cord Injury: Automatic Scoring of Motor Function

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DESCRIPTION (provided by applicant): Disorders of motor function due to accidental injury, stroke and neurodegenerative disorders, together with disorders of mental health, are the most crippling human ailments. One common result of mechanical insult is spinal cord injury (SCI), in which damage of the spinal cord by contusion, compression or laceration causes loss of sensation, motor and reflex function below the point of injury. Other symptoms of SCI may include bowel and bladder dysfunction, hyperalgesia and sexual dysfunction. Although many SCI patients survive injury, major chronic dysfunction is the most common outcome. There are approximately 10,000 new cases of spinal cord injury (SCI) in the U.S each year. In contrast to the relatively small acute patient population, chronic SCI, involving approximately 200,000-250,000 people in this country, is extremely expensive to society, both in terms of human and economic costs. Because most SCI victims are under age 30 at the time of their injury, and most now live a near-normal life span, direct medical costs average $2 million per patient over a lifetime, and approximately $5 billion total per year in the US only. Therefore, any drug or intervention that can positively improve the quality of life of patients suffering from SCI is likely to have a greater financial impact than would be expected simply from the cost of acute injury each year. Animal models for SCI and other conditions that affect gait and motor coordination are successfully used today to develop new treatments. Assessing the level of motor dysfunction in an animal model is a difficult challenge as most researchers rely on the subjective scoring of symptoms. We propose here to build a system that will allow a more objective, faster and consistent assessment of the level of injury and course of recovery in rat and mouse models of SCI that can be applied in the future to other gait and motor coordination disorders. An artificial intelligent system based on computer vision will be developed to capture and score gait and motor coordination in rodents and will be validated against the standard scores of motor function obtained using the methods of Basso, Beattie, and Bresnahan (1995) for the rat model of SCI.