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

Study design:This is an analytical descriptive study.Objectives:The main goal of this study was to compare the modular organization of bilateral lower limb control in incomplete spinal cord injury (iSCI) patients during overground walking, using muscle synergies analysis. The secondary goal was to determine whether the similarity between the patients and control group correlate with clinical indicators of walking performance.Setting:This study was conducted in National Hospital for Spinal Cord Injury (Toledo, Spain).Methods:Eight iSCI patients and eight healthy subjects completed 10 walking trials at matched speed. For each trial, three-dimensional motion analysis and surface electromyography (sEMG) analysis of seven leg muscles from both limbs were performed. Muscle synergies were extracted from sEMG signals using a non-negative matrix factorization algorithm. The optimal number of synergies has been defined as the minimum number needed to obtain variability accounted for (VAF) ⩾90%.Results:When compared with healthy references, iSCI patients showed fewer muscle synergies in the most affected side and, in both sides, significant differences in the composition of synergy 2. The degree of similarity of these variables with the healthy reference, together with the composition of synergy 3 of the most affected side, presented significant correlations (P<0.05) with walking performance.Conclusion: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.

[1]  M P Kadaba,et al.  Measurement of lower extremity kinematics during level walking , 1990, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[2]  A. J. del-Ama,et al.  Comparative ergonomic assessment of manual wheelchairs by paraplegic users , 2013, Disability and rehabilitation. Assistive technology.

[3]  Arturo Forner-Cordero,et al.  Gait kinematic analysis in patients with a mild form of central cord syndrome , 2010, Journal of NeuroEngineering and Rehabilitation.

[4]  Francesco Lacquaniti,et al.  Patterned control of human locomotion , 2012, The Journal of physiology.

[5]  Steven A. Kautz,et al.  Evaluation of Abnormal Synergy Patterns Poststroke: Relationship of the Fugl-Meyer Assessment to Hemiparetic Locomotion , 2010, Neurorehabilitation and neural repair.

[6]  Ping Wang,et al.  Detection of abnormal muscle activations during walking following spinal cord injury (SCI). , 2013, Research in developmental disabilities.

[7]  Jessica L. Allen,et al.  Neuromechanical Principles Underlying Movement Modularity and Their Implications for Rehabilitation , 2015, Neuron.

[8]  A. Curt,et al.  Diagnostic criteria of traumatic central cord syndrome. Part 2: A Questionnaire Survey among Spine Specialists , 2010, Spinal Cord.

[9]  C. Charalambous Repeatability of Kinematic, Kinetic, and Electromyographic Data in Normal Adult Gait , 2014 .

[10]  F. Lacquaniti,et al.  Coordination of Locomotion with Voluntary Movements in Humans , 2005, The Journal of Neuroscience.

[11]  T. Chuang,et al.  Temporal differences in relative phasing of gait initiation and first step length in patients with cervical and lumbosacral spinal cord injuries , 2004, Spinal Cord.

[12]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[13]  Dario Farina,et al.  Motor modules in robot-aided walking , 2012, Journal of NeuroEngineering and Rehabilitation.

[14]  Stefano Piazza,et al.  Shared muscle synergies in human walking and cycling. , 2014, Journal of neurophysiology.

[15]  Richard R Neptune,et al.  Modular control of human walking: a simulation study. , 2009, Journal of biomechanics.

[16]  Francesco Lacquaniti,et al.  Evolutionary and Developmental Modules , 2013, Front. Comput. Neurosci..

[17]  V. Dietz,et al.  Outcome after incomplete spinal cord injury: central cord versus Brown-Sequard syndrome , 2010, Spinal Cord.

[18]  Francesco Lacquaniti,et al.  Distributed plasticity of locomotor pattern generators in spinal cord injured patients. , 2004, Brain : a journal of neurology.

[19]  Dario Farina,et al.  Impulses of activation but not motor modules are preserved in the locomotion of subacute stroke patients. , 2011, Journal of neurophysiology.

[20]  Matthew C. Tresch,et al.  The number and choice of muscles impact the results of muscle synergy analyses , 2013, Front. Comput. Neurosci..

[21]  F. Lacquaniti,et al.  Temporal components of the motor patterns expressed by the human spinal cord reflect foot kinematics. , 2003, Journal of neurophysiology.

[22]  E. Bizzi,et al.  Muscle synergy patterns as physiological markers of motor cortical damage , 2012, Proceedings of the National Academy of Sciences.

[23]  Stacie A. Chvatal,et al.  Neuromuscular constraints on muscle coordination during overground walking in persons with chronic incomplete spinal cord injury , 2014, Clinical Neurophysiology.

[24]  Benjamin J. Fregly,et al.  Persons with Parkinson’s disease exhibit decreased neuromuscular complexity during gait , 2013, Clinical Neurophysiology.

[25]  M. Pierrynowski Biomechanics of overground vs. treadmill walking in healthy individuals , 2009 .

[26]  F. Lacquaniti,et al.  Distributed neural networks for controlling human locomotion Lessons from normal and SCI subjects , 2009, Brain Research Bulletin.

[27]  F. Lacquaniti,et al.  Motor patterns in human walking and running. , 2006, Journal of neurophysiology.

[28]  Steven A Kautz,et al.  Modular control of varied locomotor tasks in children with incomplete spinal cord injuries. , 2013, Journal of neurophysiology.

[29]  Richard R Neptune,et al.  Three-dimensional modular control of human walking. , 2012, Journal of biomechanics.

[30]  J. Cissik Prediction of high naevus count in a healthy UK population to estimate melanoma risk , 2015, BDJ.

[31]  F. Lacquaniti,et al.  Five basic muscle activation patterns account for muscle activity during human locomotion , 2004, The Journal of physiology.

[32]  Richard R Neptune,et al.  Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke. , 2010, Journal of neurophysiology.

[33]  A Esquenazi,et al.  Temporal-spatial feature of gait after traumatic brain injury. , 1999, The Journal of head trauma rehabilitation.

[34]  Rebecca L. Routson,et al.  Modular organization across changing task demands in healthy and poststroke gait , 2014, Physiological reports.

[35]  M. Popovic,et al.  A randomized trial of functional electrical stimulation for walking in incomplete spinal cord injury: Effects on walking competency , 2014, The journal of spinal cord medicine.

[36]  A. d’Avella,et al.  Locomotor Primitives in Newborn Babies and Their Development , 2011, Science.

[37]  Richard R Neptune,et al.  The influence of locomotor rehabilitation on module quality and post-stroke hemiparetic walking performance. , 2013, Gait & posture.