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Parkinson’s Disease
Volume 2015 (2015), Article ID 497825, 8 pages
Clinical Study

Identification of Changing Lower Limb Neuromuscular Activation in Parkinson’s Disease during Treadmill Gait with and without Levodopa Using a Nonlinear Analysis Index

1Center for Neuromotor and Biomechanics Research (CNBR), Health and Human Performance Department (HHP), University of Houston, 3855 Holman Street, Room 104 Garrison, Houston, TX 77204-6015, USA
2Memorial Bone & Joint Research Foundation, 1140 Business Center Drive, Suite 101, Houston, TX 77043, USA

Received 30 September 2014; Revised 19 December 2014; Accepted 29 December 2014

Academic Editor: Marjan Jahanshahi

Copyright © 2015 Amir Pourmoghaddam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Analysis of electromyographic (EMG) data is a cornerstone of research related to motor control in Parkinson’s disease. Nonlinear EMG analysis tools have shown to be valuable, but analysis is often complex and interpretation of the data may be difficult. A previously introduced algorithm (SYNERGOS) that provides a single index value based on simultaneous multiple muscle activations (MMA) has been shown to be effective in detecting changes in EMG activation due to modifications of walking speeds in healthy adults. In this study, we investigated if SYNERGOS detects MMA changes associated with both different walking speeds and levodopa intake. Nine male Parkinsonian patients walked on a treadmill with increasing speed while on or off medication. We collected EMG data and computed SYNERGOS indices and employed a restricted maximum likelihood linear mixed model to the values. SYNERGOS was sensitive to neuromuscular modifications due to both alterations of gait speed and intake of levodopa. We believe that the current experiment provides evidence for the potential value of SYNERGOS as a nonlinear tool in clinical settings, by providing a single value index of MMA. This could help clinicians to evaluate the efficacy of interventions and treatments in Parkinson’s disease in a simple manner.