Chronically paralyzed muscle requires extensive training before it can deliver a therapeutic dose of repetitive stress to the musculoskeletal system. Neuromuscular electrical stimulation, under feedback control, may subvert the effects of fatigue, yielding more rapid and extensive adaptations to training. The purposes of this investigation were to 1) compare the effectiveness of torque feedback-controlled (FDBCK) electrical stimulation with classic open-loop constant-frequency (CONST) stimulation, and 2) ascertain which of three stimulation strategies best maintains soleus torque during repetitive stimulation. When torque declined by 10%, the FDBCK protocol modulated the base stimulation frequency in three ways: by a fixed increase, by a paired pulse (doublet) at the beginning of the stimulation train, and by a fixed decrease. The stimulation strategy that most effectively restored torque continued for successive contractions. This process repeated each time torque declined by 10%. In fresh muscle, FDBCK stimulation offered minimal advantage in maintaining peak torque or mean torque over CONST stimulation. As long-duration fatigue developed in subsequent bouts, FDBCK stimulation became most effective ( approximately 40% higher final normalized torque than CONST). The high-frequency strategy was selected approximately 90% of the time, supporting that excitation-contraction coupling compromise and not neuromuscular transmission failure contributed to fatigue of paralyzed muscle. Ideal stimulation strategies may vary according to the site of fatigue; this stimulation approach offered the advantage of online modulation of stimulation strategies in response to fatigue conditions. Based on stress-adaptation principles, FDBCK-controlled stimulation may enhance training effects in chronically paralyzed muscle.
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http://dx.doi.org/10.1152/japplphysiol.00385.2006 | DOI Listing |
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Beijing Key Laboratory of Central Nervous System Injury, Beijing Neurosurgical Institute, Capital Medical University, Beijing 10070, China; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 10070, China; U1195, Inserm et Universite Paris-Saclay, 94276 Le Kremlin-Bicetre, France. Electronic address:
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Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.
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Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China. Electronic address:
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