This project explores how the brain controls arm movements. This problem is examined through studies of muscle activation during well-defined tasks coupled with the creation of a computer graphics model of the muscles and bones of the arm. Using multi-axial load cells and electromyographic (EMG) recordings, we have obtained detailed information on how individual muscles are used for specific load conditions. With so many muscles crossing each joint, the nervous system has many possible ways to activate the muscles to produce any given load. In order to understand the solution chosen by the nervous system, we have built detailed computer graphics models of the arm based on anatomical measurements from cadavers. These fully articulating biomechanical models estimate possible muscle contribution to joint torque at any joint angle, taking into account changes in muscle moment arm, fiber length, tendon properties, etc. We plan to use EMGs to control the model, thus allowing the user to control the arm on the screen just by thinking about moving his or her own arm. We call this the virtual arm. By connecting a person directly to the computer, we will have a novel way to test models of prosthetic limbs and strategies for functional electrical stimulation. These models will be used to explore the strategies used by the brain to control arm movements in normal and neurologically impaired subjects.
A new approach to in vivo analysis of musculoskeletal dynamics uses cine-phase contrast (cine-PC) magnetic resonance imaging (MRI) to image and track the moving knee. Cine-PC MRI, a non-invasive technique, is capable of measuring 3D muscle fiber and skeletal velocity, in vivo, during dynamic tasks. Through integration, 3D musculoskeletal movement can be tracked. A combination of the use of this new technology and conventional MRI, electromyography, and musculoskeletal modeling provide a unique opportunity to elucidate the compensation strategies employed by patients with anterior cruciate ligament (ACL) injuries.
In addition, I have been collaborating on a project to analyze pathological tissues in the wrist using magnetic resonance imaging data. In diseased tissue, the MR signal is often altered. By using computer graphics imaging processing techniques we can quantify and stage musculoskeletal disorders. Our current applications include osteoarthritis and carpal tunnel syndrome.
In another project, I have been studying mechanisms for maintaining joint stability at the knee. Using EMG studies and biomechanical models, we have examined which load types require ligaments to be loaded (i.e., those for which muscles are insufficient to counter external loads). These models allow us to estimate forces in muscles and ligaments in vivo. To date, we have examined unimparied subjects as well as those with anterior cruciate ligament injuries. We are planning to study patients with osteoarthritis. Additionally, we have examined reflexes following mechanical and electrical stimulation of the collateral ligaments and have evidence which indicates that mechanoreceptors within the ligaments are used in a control loop to stabilize the human knee joint. All of these studies are aimed at understanding neuromechanical causes underlying the acceleration of osteoarthritis.
Finally, I am collaborating with Prof. Synder-Mackler (in Physical Therapy) on a project which looks at ACL deficient subjects. Through her NIH grant, we are examining what makes "copers" and "non-copers"--those who do well with ACL deficiencies and those who don't and will require treatment. This is being done by studying the subjects' kinematics (i.e., gait analysis) and muscle activation patterns (EMGs) during a variety of tasks.
