Modeling Nerve Compression in Carpal Tunnel Syndrome

Abstract

Nerve function loss can result from a variety of conditions that are either sudden onset like head and spinal cord trauma or slowly develop from chronic pressure as in the case of carpal tunnel syndrome. In either case we see compression ofthe nerve ultimately resulting in axon demyelination and loss of signal conduction. For chronic conditions such as carpal tunnel syndrome, treatments focus on alleviating symptoms. Some patients undergo surgery which can be successful in relieving pressure on the median nerve by inflamed surrounding tendons. Symptoms of classical carpal tunnel syndrome have been debated and sometimes patients experiencing similar numbness and pain in the hand do not necessarily have the underlying condition of a compressed median nerve. Therefore, better markers are needed for determining true cases of nerve compression as well as clinical measures to indicate the need for surgical treatment. We have demonstrated a correlation between clinically observed nerve compression derived from MRI slides and clinically observed increases in conduction delay. We have done this by computationally modeling a myelinated axon with various levels of compression and finding the increase in conduction delay from a normal control with no compression. We show that conduction delay measurements could be used as a clinical tool to determine the amount of nerve compression present in a patient of mild carpal tunnel syndrome although further data is required to create a fully predictive model.