Abstract
A major problem in neurorehabilitation is the lack of objective outcomes to measure movement quality. Movement quality features, such as coordination and stability, are essential for everyday motor actions. These features allow reacting to continuously changing environment or to resist external perturbations. Neurological disorders affect movement quality, leading to functionally impaired movements. Recent findings suggest that the central nervous system organizes motor elements (eg, muscles, joints, fingers) into task-specific ensembles to stabilize motor tasks performance. A method to quantify this feature has been previously developed based on the uncontrolled manifold (UCM) hypothesis. UCM quantifies movement quality in a spatial-temporal domain using intertrial analysis of covariation between motor elements. In this point-of-view article, we first describe major obstacles (eg, the need for group analysis) that interfere with UCM application in clinical settings. Then, we propose a process of quantifying movement quality for a single individual with a novel use of bootstrapping simulations and UCM analysis. Finally, we reanalyze previously published data from individuals with neurological disorders performing a wide range of motor tasks, that is, multi-digit pressing and postural balance tasks. Our method allows one to assess motor quality impairments in a single individual and to detect clinically important motor behavior changes. Our solution may be incorporated into a clinical setting to assess sensorimotor impairments, evaluate the effects of specific neurological treatments, or track movement quality recovery over time. We also recommended the proposed solution to be used jointly with a typical statistical analysis of UCM parameters in cohort studies.
References
| 1. | Stinear, CM, Lang, CE, Zeiler, S, Byblow, WD. Advances and challenges in stroke rehabilitation. Lancet Neurol. 2020;19:348-360. doi:10.1016/S1474-4422(19)30415-6 Google Scholar | Crossref | Medline |
| 2. | Clarke, CE, Patel, S, Ives, N, et al; PD REHAB Collaborative Group . Physiotherapy and occupational therapy vs no therapy in mild to moderate Parkinson disease: a randomized clinical trial. JAMA Neurol. 2016;73:291-299. doi:10.1001/jamaneurol.2015.4452 Google Scholar | Crossref | Medline |
| 3. | French, B, Thomas, LH, Coupe, J, et al. Repetitive task training for improving functional ability after stroke. Cochrane Database Syst Rev. 2016;11:CD006073. doi:10.1002/14651858.CD006073.pub3 Google Scholar | Crossref | Medline |
| 4. | Dobkin, B, Apple, D, Barbeau, H, et al; Spinal Cord Injury Locomotor Trial Group . Weight-supported treadmill vs over-ground training for walking after acute incomplete SCI. Neurology. 2006;66:484-493. doi:10.1212/01.wnl.0000202600.72018.39 Google Scholar | Crossref | Medline | ISI |
| 5. | Piscitelli, D. Motor rehabilitation should be based on knowledge of motor control. Arch Physiother. 2016;6:5. doi:10.1186/s40945-016-0019-z Google Scholar | Crossref | Medline |
| 6. | Stinear, CM. Stroke rehabilitation research needs to be different to make a difference. F1000Res. 2016;5:F1000 Faculty Rev 1467. doi:10.12688/f1000research.8722.1 Google Scholar | Crossref | Medline |
| 7. | Winstein, CJ, Wolf, SL, Dromerick, AW, et al. Effect of a task-oriented rehabilitation program on upper extremity recovery following motor stroke: the ICARE randomized clinical trial. JAMA. 2016;315:571-581. doi:10.1001/jama.2016.0276 Google Scholar | Crossref | Medline | ISI |
| 8. | Piscitelli, D. Neurorehabilitation: bridging neurophysiology and clinical practice. Neurol Sci. 2019;40:2209-2211. doi:10.1007/s10072-019-03969-2 Google Scholar | Crossref | Medline |
| 9. | Latash, ML, Huang, X. Neural control of movement stability: Lessons from studies of neurological patients. Neuroscience. 2015;301:39-48. doi:10.1016/j.neuroscience.2015.05.075 Google Scholar | Crossref | Medline |
| 10. | Kleim, JA, Jones, TA. Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. J Speech Lang Hear Res. 2008;51(1):S225-S239. doi:10.1044/1092-4388(2008/018) Google Scholar | Crossref | Medline | ISI |
| 11. | Winstein, CJ, Wing, AM, Whitall, J. Motor control and learning principles for rehabilitation of upper limb movements after brain injury. In: Grafman, J, Robertson, LH, eds. Handbook of Neuropsychology. 2nd ed. Vol 9. Elsevier; 2003:79-138. Google Scholar |
| 12. | Krakauer, JW, Carmichael, ST, Corbett, D, Wittenberg, GF. Getting neurorehabilitation right: what can be learned from animal models? Neurorehabil Neural Repair. 2012;26:923-931. doi:10.1177/1545968312440745 Google Scholar | SAGE Journals | ISI |
| 13. | Schmidt, RA, Lee, TD, Winstein, CJ, Wulf, G, Zelaznik, HN. Motor Control and Learning: A Behavioral Emphasis. 6th ed. Human Kinetics; 2019. Google Scholar |
| 14. | Tomita, Y, Rodrigues, MRM, Levin, MF. Upper limb coordination in individuals with stroke: poorly defined and poorly quantified. Neurorehabil Neural Repair. 2017;31:885-897. doi:10.1177/1545968317739998 Google Scholar | SAGE Journals | ISI |
| 15. | Levin, MF, Kleim, JA, Wolf, SL. What do motor “recovery” and “compensation” mean in patients following stroke? Neurorehabil Neural Repair. 2009;23:313-319. doi:10.1177/1545968308328727 Google Scholar | SAGE Journals | ISI |
| 16. | Demers, M, Levin, MF. Do activity level outcome measures commonly used in neurological practice assess upper-limb movement quality? Neurorehabil Neural Repair. 2017;31:623-637. doi:10.1177/1545968317714576 Google Scholar | SAGE Journals | ISI |
| 17. | Tomita, Y, Mullick, AA, Levin, MF. Reduced kinematic redundancy and motor equivalence during whole-body reaching in individuals with chronic stroke. Neurorehabil Neural Repair. 2018;32:175-186. doi:10.1177/1545968318760725 Google Scholar | SAGE Journals | ISI |
| 18. | Kwakkel, G, van Wegen, EEH, Burridge, JH, et al; ADVISORY Group . Standardized measurement of quality of upper limb movement after stroke: consensus-based core recommendations from the second stroke recovery and rehabilitation roundtable. Neurorehabil Neural Repair. 2019;33:951-958. doi:10.1177/1545968319886477 Google Scholar | SAGE Journals | ISI |
| 19. | Asakawa, T, Fang, H, Sugiyama, K, et al. Human behavioral assessments in current research of Parkinson’s disease. Neurosci Biobehav Rev. 2016;68:741-772. doi:10.1016/j.neubiorev.2016.06.036 Google Scholar | Crossref | Medline |
| 20. | Latash, ML, Scholz, JP, Schoner, G. Toward a new theory of motor synergies. Motor Control. 2007;11:276-308. doi:10.1123/mcj.11.3.276 Google Scholar | Crossref | Medline | ISI |
| 21. | Latash, ML. Towards physics of neural processes and behavior. Neurosci Biobehav Rev. 2016;69:136-146. doi:10.1016/j.neubiorev.2016.08.005 Google Scholar | Crossref | Medline |
| 22. | d’Avella, A, Saltiel, P, Bizzi, E. Combinations of muscle synergies in the construction of a natural motor behavior. Nat Neurosci. 2003;6:300-308. doi:10.1038/nn1010 Google Scholar | Crossref | Medline |
| 23. | Sawner, KA, LaVigne, JM, Brunnstrom, S. Brunnstrom’s Movement Therapy in Hemiplegia: A Neurophysiological Approach. 2nd ed. Lippincott; 1992. Google Scholar |
| 24. | Dewald, JP, Pope, PS, Given, JD, Buchanan, TS, Rymer, WZ. Abnormal muscle coactivation patterns during isometric torque generation at the elbow and shoulder in hemiparetic subjects. Brain. 1995;118 (pt 2):495-510. doi:10.1093/brain/118.2.495 Google Scholar | Crossref | Medline |
| 25. | Latash, ML, Zatsiorsky, VM. Biomechanics and Motor Control: Defining Central Concepts. Elsevier/Academic Press; 2016. Google Scholar |
| 26. | Levin, MF, Nichols, TR, Jaric, S. Progress in Motor Control VI conference. Motor Control. 2007;11(suppl). Google Scholar |
| 27. | Scholz, JP, Schoner, G. The uncontrolled manifold concept: identifying control variables for a functional task. Exp Brain Res. 1999;126:289-306. doi:10.1007/s002210050738 Google Scholar | Crossref | Medline | ISI |
| 28. | Olafsdottir, H, Yoshida, N, Zatsiorsky, VM, Latash, ML. Anticipatory covariation of finger forces during self-paced and reaction time force production. Neurosci Lett. 2005;381:92-96. doi:10.1016/j.neulet.2005.02.003 Google Scholar | Crossref | Medline | ISI |
| 29. | Klous, M, Mikulic, P, Latash, ML. Two aspects of feedforward postural control: anticipatory postural adjustments and anticipatory synergy adjustments. J Neurophysiol. 2011;105:2275-2288. doi:10.1152/jn.00665.2010 Google Scholar | Crossref | Medline |
| 30. | Piscitelli, D, Falaki, A, Solnik, S, Latash, ML. Anticipatory postural adjustments and anticipatory synergy adjustments: preparing to a postural perturbation with predictable and unpredictable direction. Exp Brain Res. 2017;235:713-730. doi:10.1007/s00221-016-4835-x Google Scholar | Crossref | Medline |
| 31. | Vaz, DV, Pinto, VA, Junior, RRS, Mattos, DJS, Mitra, S. Coordination in adults with neurological impairment—a systematic review of uncontrolled manifold studies. Gait Posture. 2019;69:66-78. doi:10.1016/j.gaitpost.2019.01.003 Google Scholar | Crossref | Medline |
| 32. | Lewis, MM, Lee, EY, Jo, HJ, et al. Synergy as a new and sensitive marker of basal ganglia dysfunction: a study of asymptomatic welders. Neurotoxicology. 2016;56:76-85. doi:10.1016/j.neuro.2016.06.016 Google Scholar | Crossref | Medline |
| 33. | Park, J, Lewis, MM, Huang, X, Latash, ML. Effects of olivo-ponto-cerebellar atrophy (OPCA) on finger interaction and coordination. Clin Neurophysiol. 2013;124:991-998. doi:10.1016/j.clinph.2012.10.021 Google Scholar | Crossref | Medline | ISI |
| 34. | Park, J, Wu, YH, Lewis, MM, Huang, X, Latash, ML. Changes in multifinger interaction and coordination in Parkinson’s disease. J Neurophysiol. 2012;108:915-924. doi:10.1152/jn.00043.2012 Google Scholar | Crossref | Medline | ISI |
| 35. | Falaki, A, Huang, X, Lewis, MM, Latash, ML. Dopaminergic modulation of multi-muscle synergies in postural tasks performed by patients with Parkinson’s disease. J Electromyogr Kinesiol. 2017;33:20-26. doi:10.1016/j.jelekin.2017.01.002 Google Scholar | Crossref | Medline |
| 36. | Cuadra, C, Falaki, A, Sainburg, R, Sarlegna, FR, Latash, ML. Case Studies in Neuroscience: The central and somatosensory contributions to finger interdependence and coordination: lessons from a study of a “deafferented person. ” J Neurophysiol. 2019;121:2083-2087. doi:10.1152/jn.00153.2019 Google Scholar | Crossref | Medline |
| 37. | Reisman, DS, Scholz, JP. Workspace location influences joint coordination during reaching in post-stroke hemiparesis. Exp Brain Res. 2006;170:265-276. doi:10.1007/s00221-005-0209-5 Google Scholar | Crossref | Medline | ISI |
| 38. | Falaki, A, Jo, HJ, Lewis, MM, et al. Systemic effects of deep brain stimulation on synergic control in Parkinson’s disease. Clin Neurophysiol. 2018;129:1320-1332. doi:10.1016/j.clinph.2018.02.126 Google Scholar | Crossref | Medline |
| 39. | Mattos, DJ, Latash, ML, Park, E, Kuhl, J, Scholz, JP. Unpredictable elbow joint perturbation during reaching results in multijoint motor equivalence. J Neurophysiol. 2011;106:1424-1436. doi:10.1152/jn.00163.2011 Google Scholar | Crossref | Medline |
| 40. | Mattos, D, Kuhl, J, Scholz, JP, Latash, ML. Motor equivalence (ME) during reaching: is ME observable at the muscle level? Motor Control. 2013;17:145-175. doi:10.1123/mcj.17.2.145 Google Scholar | Crossref | Medline |
| 41. | Solnik, S, Pazin, N, Coelho, CJ, et al. End-state comfort and joint configuration variance during reaching. Exp Brain Res. 2013;225:431-442. doi:10.1007/s00221-012-3383-2 Google Scholar | Crossref | Medline |
| 42. | Solnik, S, Reschechtko, S, Wu, YH, Zatsiorsky, VM, Latash, ML. Interpersonal synergies: static prehension tasks performed by two actors. Exp Brain Res. 2016;234:2267-2282. doi:10.1007/s00221-016-4632-6 Google Scholar | Crossref | Medline |
| 43. | Furmanek, MP, Solnik, S, Piscitelli, D, Rasouli, O, Falaki, A, Latash, ML. Synergies and motor equivalence in voluntary sway tasks: the effects of visual and mechanical constraints. J Mot Behav. 2018;50:492-509. doi:10.1080/00222895.2017.1367642 Google Scholar | Crossref | Medline |
| 44. | Papi, E, Rowe, PJ, Pomeroy, VM. Analysis of gait within the uncontrolled manifold hypothesis: stabilisation of the centre of mass during gait. J Biomech. 2015;48:324-331. doi:10.1016/j.jbiomech.2014.11.024 Google Scholar | Crossref | Medline |
| 45. | Rosenblatt, NJ, Hurt, CP. Recommendation for the minimum number of steps to analyze when performing the uncontrolled manifold analysis on walking data. J Biomech. 2019;85:218-223. doi:10.1016/j.jbiomech.2019.01.018 Google Scholar | Crossref | Medline |
| 46. | Gajdosik, RL, Bohannon, RW. Clinical measurement of range of motion. Review of goniometry emphasizing reliability and validity. Phys Ther. 1987;67:1867-1872. doi:10.1093/ptj/67.12.1867 Google Scholar | Crossref | Medline | ISI |
| 47. | Efron, B, Tibshirani, R. An Introduction to the Bootstrap. Chapman & Hall; 1993. Google Scholar | Crossref |
| 48. | Efron, B. Better bootstrap confidence intervals. J Am Stat Assoc. 1987;82:171-185. doi:10.1080/01621459.1987.10478410 Google Scholar | Crossref | ISI |
| 49. | Poitras, I, Dupuis, F, Bielmann, M, et al. Validity and reliability of wearable sensors for joint angle estimation: a systematic review. Sensors (Basel). 2019;19:1555. doi:10.3390/s19071555 Google Scholar | Crossref |
| 50. | Heywood, S, Pua, YH, McClelland, J, et al. Low-cost electromyography—validation against a commercial system using both manual and automated activation timing thresholds. J Electromyogr Kinesiol. 2018;42:74-80. doi:10.1016/j.jelekin.2018.05.010 Google Scholar | Crossref | Medline |
| 51. | Supuk, TG, Skelin, AK, Cic, M. Design, development and testing of a low-cost sEMG system and its use in recording muscle activity in human gait. Sensors (Basel). 2014;14:8235-8258. doi:10.3390/s140508235 Google Scholar | Crossref | Medline |
| 52. | Gorter, R, Fox, JP, Apeldoorn, A, Twisk, J. Measurement model choice influenced randomized controlled trial results. J Clin Epidemiol. 2016;79:140-149. doi:10.1016/j.jclinepi.2016.06.011 Google Scholar | Crossref | Medline |

