Targeted Muscle Reinnervation and the Effects on Prosthesis Control

             One of the biggest breakthroughs in the work done to help target and stop phantom limb pain caused by the formation of neuromas in amputee patients was done by Dr. Gregory A. Dumanian at Northwestern Feinberg School of Medicine in the plastic surgery department. Dr. Dumanian was able to develop a method of taking the motor nerve that had formed a neuroma at the sight of amputation and excise to healthy fascicle tissue. Then innervate the motor nerve into neighboring muscle tissue that contained no motor control following the amputation of the limb. Then the major mixed nerve that was transected by the amputation was taken and surgically divided into distal segments of motor nerve tissue. When sensory nerves are located they are treated in a similar manner to the motor nerves. This method exists on the principle that nerves that do not have any purpose tend to produce pain responses due to long periods of inaction. By giving the nerve a muscle to innervate the nerve no longer sends a pain response.   This also has the added benefit due to targeted muscle reinnervation (TMR) of being able to better interface with upper limb prosthetics.

            Based off the work done by Dr. Dumanian advancements in the interface between amputees and their prosthesis was able to increase sensitivity and accuracy in hand movement and motor control tests. In research done by Dr. Levi J. Hargrove at the Center for Bionic Medicine, Shirley Ryan Ability Lab the use of myoelectric pattern recognition was able to outperform previous methods of communication between patients and their prosthesis. Prior to the use of myoelectric pattern recognition the method of direct control was used to allow interface between patients with transhumeral amputations and their prosthesis. TMR acts as a biological amplifier of the motor nerve commands that would originally go to the muscles below the site of amputation. Thus, providing physiologically appropriate electromyographic (EMG) control signal which allow more intuitive control over the prosthesis. The conventional amplitude coders that were used on prosthetics were unable to extract the intrinsic information for muscle movement. Pattern recognition measures EMG signals coupled with the use of machine learning algorithms were able to learn the patterns for physiologically appropriate muscle contractions. With the development of TMR as a surgical technique and the advancement of AI learning methods, the field of prosthetic development is further returning normal abilities to those that suffered limb amputation. This allows for better development of limb prosthesis and access to fine motor controls that did not exist in previous methods of prosthesis interface.

            As one field of science advances others soon follow. The development of TMR by Dr. Dumanian and the use of this technique by Dr. Hargrove to increase the effectiveness and accuracy of prosthesis, shows the power of scientific advancement for the greater good of humanity. As more advancements in prosthesis continue to develop and even more effective surgical techniques are created.  The future for those effected by limb amputation may one day see a time when very little lifestyle change is observed from life after the amputation to the lifestyle before.

Works Cited

Dumanian, Gregory A., et al. “Targeted Muscle Reinnervation Treats Neuroma and Phantom Pain in Major Limb Amputees.” Annals of Surgery, vol. 270, no. 2, Aug. 2019, pp. 238–246., https://doi.org/10.1097/sla.0000000000003088. 

Hargrove, Levi J., et al. “Myoelectric Pattern Recognition Outperforms Direct Control for Transhumeral Amputees with Targeted Muscle Reinnervation: A Randomized Clinical Trial.” Scientific Reports, vol. 7, no. 1, 2017, https://doi.org/10.1038/s41598-017-14386-w.