A prerequisite for rehabilitation is that patients preserve their ability to adapt to novel dynamic environments, an ability that has been associated with the cerebellar system. In this study, we use a robot manipulandum to assess the ability of multiple sclerosis (MS) subjects in the early phase of the disease to adapt to a speed-dependent force field. Their performance is compared with an equal number of age-matched controls. We found that MS subjects display subtle in-coordination problems but do not significantly di fer from controls in their ability to adapt to the force field. These findings are discussed in terms of the possible benefits that MS subjects might receive from robot-assisted therapy that is specifically aimed at impaired visuomotor coordination.
Bastian, A.J. et al. (1996). Cerebellar ataxia: abnormal control of interaction torques across multiple joints. Journal ofNeurophysiology , 76(1): 492—509.
2.
Burdet, E. et al. (2006). Stability and motor adaptation in human arm movements. BiologicalCybernetics, 94(1): 20— 32.
3.
Casadio, M. et al. (2006). Impedance controlled, minimally assistive robotic training of severely impaired hemiparetic patients. Proceedings of the 1st IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, Pisa, Italy.
4.
Casadio, M. et al. (2006). Braccio di Ferro: a new haptic workstation for neuromotor rehabilitation. Technology andHealth Care , 13: 1—20.
5.
Criscimagna-Hemminger, S.E. et al. (2003). Learned dynamics of reaching movements generalize from dominant to nondominant arm. Journal ofNeurophysiology , 89(1): 168— 176.
6.
Donchin, O. et al. (2003). Quantifying generalization from trial-by-trial behavior of adaptive systems that learn with basis functions: theory and experiments in human motor control. Journal of Neuroscience, 23(27): 9032—9045.
7.
Freeman, J.A. et al. (1997). The impact of inpatient rehabilitation on progressive multiple sclerosis. Annals of Neurology, 42(2): 236—244.
8.
Gomi, H. and Kawato, M. (1996). Equilibrium-point control hypothesis examined by measured arm stiffness during multijoint movement. Science , 272(5258): 117—120.
9.
Gordon, J. et al. (1994). Accuracy of planar reaching movements. II. Systematic extent errors resulting from inertial anisotropy. Experimental Brain Research, 99(1): 112—130.
10.
Krebs, H.I. et al. (1998). Robot-aided neurorehabilitation. IEEE Transactions on Rehabilitation Engineering, 6(1): 75—87.
11.
Krebs, H.I. et al. (1999). Overview of clinical trials with MIT-MANUS: a robot-aided neuro-rehabilitation facility. Technology andHealth Care, 7(6): 419—423.
12.
Liu, C. et al. (2003). Does neurorehabilitation have a role in relapsing—remitting multiple sclerosis? Journal ofNeurology, 250(10): 1214—1218.
13.
Maschke, M. et al. (2004). Hereditary cerebellar ataxia progressively impairs force adaptation during goal-directed arm movements. Journal ofNeurophysiology, 91(1): 230— 238.
14.
Mussa-Ivaldi, F.A. et al. (1985). Neural, mechanical, and geometric factors subserving arm posture in humans. Journal of Neuroscience , 5(10): 2732—2743.
15.
Patton, J.L. and Mussa-Ivaldi, F.A. (2004). Robot-assisted adaptive training: custom force fields for teaching movement patterns. IEEE Transactions on Biomedical Engineering, 51(4): 636—646.
16.
Patton, J.L. et al. (2006). Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors. Experimental Brain Research, 168(3): 368—383.
17.
Poser, C.M. et al. (1983). New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Annals of Neurology, 13(3): 227—231
18.
Prange, G.B. et al. (2006). Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. Journal of RehabilitationResearch and Development, 43(2): 171—184.
19.
Rasova, K. et al. (2005). Is it possible to actively and purposely make use of plasticity and adaptability in the neurorehabilitation treatment of multiple sclerosis patients? A pilot project. Clinical Rehabilitation, 19(2): 170—181.
20.
Rocca, M.A. et al. (2002). Adaptive functional changes in the cerebral cortex of patients with nondisabling multiple sclerosis correlate with the extent of brain structural damage. Annals of Neurology, 51(3): 330—339.
21.
Rocca, M.A. et al. (2003). Functional cortical changes in patients with multiple sclerosis and nonspecific findings on conventional magnetic resonance imaging scans of the brain. Neuroimage, 19(3): 826—836.
22.
Rocca, M.A. et al. (2005). Cortical adaptation in patients with MS: a cross-sectional functional MRI study of disease phenotypes. The Lancet Neurology, 4(10): 618—626.
23.
Sanguineti, V. et al. (2003). Cerebellar ataxia: quantitative assessment and cybernetic interpretation. Human MovementScience, 22(2): 189—205.
24.
Scheidt, R.A. et al. (2001). Learning to move amid uncertainty. Journal ofNeurophysiology, 86(2): 971—985.
25.
Shadmehr, R. and Mussa-Ivaldi, F.A. (1994). Adaptive representation of dynamics during learning of a motor task. Journal of Neuroscience , 14(5Pt2): 3208—3224.
26.
Shadmehr, R. and Wise, S.P. (2005). The Computational Neurobiology of Reaching and Pointing: a Foundation for Motor Learning. Cambridge, MA , MIT Press.
27.
Sipe, J.C. et al. (1984). A neurologic rating scale (NRS) for use in multiple sclerosis. Neurology, 34(10): 1368—1372.
28.
Smith, M.A. et al. (2000). Motor disorder in Huntington's disease begins as a dysfunction in error feedback control. Nature , 403(6769): 544—549.
29.
Smith, M.A. and Shadmehr, R. (2005). Intact ability to learn internal models of arm dynamics in Huntington's disease but not cerebellar degeneration. Journal ofNeurophysiology, 93(5): 2809—2821.
30.
Solaro, C. et al. (2007). Subtle upper limb impairment in asymptomatic multiple sclerosis subjects. Multiple Sclerosis13(3): 428—432.
31.
Stocco, L. et al. (1998). Past constrained global minimax optimization of robot parameters. Robotica, 16: 595—605.
32.
Takahashi, C.D. and Reinkensmeyer, D.J. (2003). Hemiparetic stroke impairs anticipatory control of arm movement. Experimental Brain Research , 149(2): 131—140.
33.
Takahashi, C.D. et al. (2001). Impedance control and internal model formation when reaching in a randomly varying dynamical environment. Journal ofNeurophysiology, 86(2): 1047—1051.
34.
Teulings, H.L. et al. (1997). Parkinsonism reduces coordination of finger, wrist, and arm fine motor control. ExperimentalNeurology , 146: 159—170.
35.
Tremblay, S. et al. (2003). Somatosensory basis of speech production . Nature423(6942): 866—869.
36.
Volpe, B.T. et al. (1999). Robot training enhanced motor outcome in patients with stroke maintained over 3 years. Neurology53(8): 1874—1876.
