Modular control of gait after incomplete spinal cord injury: differences between sides

Pérez-Nombela, Soraya; Barroso, Filipe O.; Torricelli, Diego; Reyes-Guzmán, Ana de los; del-Ama, Antonio J.; Gómez-Soriano, Julio; Pons, José L; Gil-Agudo, Ángel

doi:10.1038/sc.2016.99

Cited by 36 publications

(42 citation statements)

References 36 publications

(88 reference statements)

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“…By comparing these muscle synergies, we can assess gait symmetries of patients. Although the usual method for previous studies to assess the performance of stroke patients is to compare them with healthy subjects (Bowden et al, 2010 ; Gizzi et al, 2011 ; Routson et al, 2013 ; Hayes et al, 2014 ; Barroso et al, 2016 ; Pérez-Nombela et al, 2017 ), we show that lateral symmetry can still be used as a relative measure by comparing the sides of the patients. Future considerations would be to analyze healthy subjects to test the accuracy of this method.…”

Section: Discussionmentioning

confidence: 69%

Lateral Symmetry of Synergies in Lower Limb Muscles of Acute Post-stroke Patients After Robotic Intervention

et al. 2018

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Gait disturbance is commonly associated with stroke, which is a serious neurological disease. With current technology, various exoskeletons have been developed to provide therapy, leading to many studies evaluating the use of such exoskeletons as an intervention tool. Although these studies report improvements in patients who had undergone robotic intervention, they are usually reported with clinical assessment, which are unable to characterize how muscle activations change in patients after robotic intervention. We believe that muscle activations can provide an objective view on gait performance of patients. To quantify improvement of lateral symmetry before and after robotic intervention, muscle synergy analysis with Non-Negative Matrix Factorization was used to evaluate patients' EMG data. Eight stroke patients in their acute phase were evaluated before and after a course of robotic intervention with the Hybrid Assistive Limb (HAL), lasting over 3 weeks. We found a significant increase in similarity between lateral synergies of patients after robotic intervention. This is associated with significant improvements in gait measures like walking speed, step cadence, stance duration percentage of gait cycle. Clinical assessments [Functional Independence Measure-Locomotion (FIM-Locomotion), FIM-Motor (General), and Fugl-Meyer Assessment-Lower Extremity (FMA-LE)] showed significant improvements as well. Our study shows that muscle synergy analysis can be a good tool to quantify the change in neuromuscular coordination of lateral symmetry during walking in stroke patients.

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Section: Discussionmentioning

confidence: 69%

Lateral Symmetry of Synergies in Lower Limb Muscles of Acute Post-stroke Patients After Robotic Intervention

et al. 2018

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“…There are several examples of studies employing factorization of EMG activity to study human locomotion. For several reasons, the most widespread locomotion type that has been studied is walking ( Ivanenko et al, 2004 ; Cappellini et al, 2006 ; Courtine et al, 2006 ; Clark et al, 2010 ; McGowan et al, 2010 ; Dominici et al, 2011 ; Allen and Neptune, 2012 ; Bolton and Misiaszek, 2012 ; Chvatal and Ting, 2012 , 2013 ; Lacquaniti et al, 2012 ; Oliveira et al, 2012 ; Rodriguez et al, 2013 ; Barroso et al, 2014 ; Maclellan et al, 2014 ; Routson et al, 2014 ; Coscia et al, 2015 ; Gonzalez-Vargas et al, 2015 ; Hagio et al, 2015 ; Licence et al, 2015 ; Martino et al, 2015 ; Nazifi et al, 2015 ; Tang et al, 2015 ; Buurke et al, 2016 ; Gui and Zhang, 2016 ; Kim et al, 2016 ; Lencioni et al, 2016 ; Meyer et al, 2016 ; Pérez-Nombela et al, 2016 ; Yokoyama et al, 2016 ; Allen et al, 2017 ; Janshen et al, 2017 ; Santuz et al, 2017a ; Shuman et al, 2017 ; Saito et al, 2018 ). Due to the easiness of examining this slow-speed type of locomotion, it is not a surprise that the majority of studies use walking as the main object of investigation.…”

Section: Discussionmentioning

confidence: 99%

“…Whether for computational or neurophysiological reasons, some studies found consistency in the recruitment of the same motor primitives and/or modules across varying velocities ( Ivanenko et al, 2004 ; Cappellini et al, 2006 ; Routson et al, 2014 ; Buurke et al, 2016 ; Gui and Zhang, 2016 ), while others found walking-, running-, and/or velocity-specific sets of motor primitives and/or modules ( Cappellini et al, 2006 ; Routson et al, 2014 ; Coscia et al, 2015 ; Gonzalez-Vargas et al, 2015 ; Yokoyama et al, 2016 ). The role of muscle synergies for locomotion in pathology has been a focus of a few groups in recent years ( Latash and Anson, 2006 ; Clark et al, 2010 ; Giszter and Hart, 2013 ; Rodriguez et al, 2013 ; Routson et al, 2014 ; Coscia et al, 2015 ; Tang et al, 2015 ; Falaki et al, 2016 ; Lencioni et al, 2016 ; Meyer et al, 2016 ; Pérez-Nombela et al, 2016 ; Shuman et al, 2016 , 2017 ; Wenger et al, 2016 ; Allen et al, 2017 ; Banks et al, 2017 ). Given the simplification in presenting the data due to the dimensionality reduction, it is appealing to think to a possible clinical application of the method.…”

Section: Discussionmentioning

confidence: 99%

“…Given the simplification in presenting the data due to the dimensionality reduction, it is appealing to think to a possible clinical application of the method. There have been comparisons between healthy and Parkinson’s disease ( Rodriguez et al, 2013 ; Falaki et al, 2016 ; Allen et al, 2017 ), multiple sclerosis patients ( Lencioni et al, 2016 ), spinal cord injury ( Giszter and Hart, 2013 ; Pérez-Nombela et al, 2016 ; Wenger et al, 2016 ), cerebral palsy ( Li et al, 2013 ; Steele et al, 2015 ; Tang et al, 2015 ; Shuman et al, 2016 , 2017 ), and post-stroke ( Clark et al, 2010 ; Routson et al, 2014 ; Coscia et al, 2015 ; Meyer et al, 2016 ; Banks et al, 2017 ) patients. However, as for the studies on the influence of velocity on the modular organization of motion, also in pathology studies results are often difficult to interpret and require careful analysis.…”

Section: Discussionmentioning

confidence: 99%

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Modular Control of Human Movement During Running: An Open Access Data Set

et al. 2018

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The human body is an outstandingly complex machine including around 1000 muscles and joints acting synergistically. Yet, the coordination of the enormous amount of degrees of freedom needed for movement is mastered by our one brain and spinal cord. The idea that some synergistic neural components of movement exist was already suggested at the beginning of the 20th century. Since then, it has been widely accepted that the central nervous system might simplify the production of movement by avoiding the control of each muscle individually. Instead, it might be controlling muscles in common patterns that have been called muscle synergies. Only with the advent of modern computational methods and hardware it has been possible to numerically extract synergies from electromyography (EMG) signals. However, typical experimental setups do not include a big number of individuals, with common sample sizes of 5 to 20 participants. With this study, we make publicly available a set of EMG activities recorded during treadmill running from the right lower limb of 135 healthy and young adults (78 males and 57 females). Moreover, we include in this open access data set the code used to extract synergies from EMG data using non-negative matrix factorization (NMF) and the relative outcomes. Muscle synergies, containing the time-invariant muscle weightings (motor modules) and the time-dependent activation coefficients (motor primitives), were extracted from 13 ipsilateral EMG activities using NMF. Four synergies were enough to describe as many gait cycle phases during running: weight acceptance, propulsion, early swing, and late swing. We foresee many possible applications of our data that we can summarize in three key points. First, it can be a prime source for broadening the representation of human motor control due to the big sample size. Second, it could serve as a benchmark for scientists from multiple disciplines such as musculoskeletal modeling, robotics, clinical neuroscience, sport science, etc. Third, the data set could be used both to train students or to support established scientists in the perfection of current muscle synergies extraction methods. All the data is available at Zenodo (doi: 10.5281/zenodo.1254380).

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“…For instance, abnormal muscle synergy patterns have been widely reported in post-stroke survivors during locomotion [18], arm isometric contractions [19], and voluntary reaching movements [20]- [23]. Muscle synergies have also been explored in subjects with spinal cord injury, revealing substantial differences to healthy individuals during different motor tasks: hand grip function [24], different locomotor tasks in children [25], postural balance [26], [27], gait [28], and standing protocols under spinal cord stimulation [29]. All this evidence supports that muscle synergies can be a powerful tool for characterizing motor behavior and providing physiological markers to assess motor function in injured patients.…”

Section: Introductionmentioning

confidence: 99%

Identifying Muscle Synergies From Reaching and Grasping Movements in Rats

et al. 2020

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Reaching and grasping (R&G) is a skilled voluntary movement which is critical for animals. In this work, we aim to identify muscle synergy patterns from R&G movements in rats and show how these patterns can be used to characterize such movements and investigate their consistency and repeatability. For that purpose, we analyzed the electromyographic (EMG) activity of five forelimb muscles recorded while the animals were engaged in R&G tasks. Our dataset included 200 R&G attempts from three different rats. Non-negative matrix factorization was used to decompose EMG signals and extract muscle synergies. We compared all pairs of attempts and created cross-validated models to study intra-and inter-subject variability. We found that three synergies were enough to accurately reconstruct the EMG envelopes. These muscle synergies and their corresponding activation coefficients were very similar for all the attempts in the database, providing a general pattern to describe the movement. Results suggested that the movement strategy adopted by an individual in its different attempts was highly repetitive, but also resembled the strategies adopted by the other animals. Inter-subject variability was not much higher than intra-subject variability. This study is a proof-of-concept, but the proposed approaches can help to establish whether there is a stereotyped pattern of neuromuscular activity in R&G movement in healthy rats, and the changes that occur in animal models of acute neurological injuries. Research on muscle synergies could elucidate motor control mechanisms, and lead to quantitative tools for evaluating upper limb motor impairment after an injury. INDEX TERMS Electromyography, motor control, muscle synergies, reaching and grasping, upper limb.

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Modular control of gait after incomplete spinal cord injury: differences between sides

Abstract: The analysis of muscle synergies shows potential to detect differences between the two sides in patients with iSCI. Specifically, the VAF may constitute a new neurophysiological metric to assess and monitor patients' condition throughout the gait recovery process.

Cited by 36 publications

References 36 publications

Lateral Symmetry of Synergies in Lower Limb Muscles of Acute Post-stroke Patients After Robotic Intervention

Lateral Symmetry of Synergies in Lower Limb Muscles of Acute Post-stroke Patients After Robotic Intervention

Modular Control of Human Movement During Running: An Open Access Data Set

Identifying Muscle Synergies From Reaching and Grasping Movements in Rats

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