Reduced short term adaptation to robot generated dynamic environment in children affected by Cerebral Palsy

BackgroundIt is known that healthy adults can quickly adapt to a novel dynamic environment, generated by a robotic manipulandum as a structured disturbing force field. We suggest that it may be of clinical interest to evaluate to which extent this kind of motor learning capability is impaired in children affected by cerebal palsy.MethodsWe adapted the protocol already used with adults, which employs a velocity dependant viscous field, and compared the performance of a group of subjects affected by Cerebral Palsy (CP group, 7 subjects) with a Control group of unimpaired age-matched children. The protocol included a familiarization phase (FA), during which no force was applied, a force field adaptation phase (CF), and a wash-out phase (WO) in which the field was removed. During the CF phase the field was shut down in a number of randomly selected "catch" trials, which were used in order to evaluate the "learning index" for each single subject and the two groups. Lateral deviation, speed and acceleration peaks and average speed were evaluated for each trajectory; a directional analysis was performed in order to inspect the role of the limb's inertial anisotropy in the different experimental phases.ResultsDuring the FA phase the movements of the CP subjects were more curved, displaying greater and variable directional error; over the course of the CF phase both groups showed a decreasing trend in the lateral error and an after-effect at the beginning of the wash-out, but the CP group had a non significant adaptation rate and a lower learning index, suggesting that CP subjects have reduced ability to learn to compensate external force. Moreover, a directional analysis of trajectories confirms that the control group is able to better predict the force field by tuning the kinematic features of the movements along different directions in order to account for the inertial anisotropy of arm.ConclusionsSpatial abnormalities in children affected by cerebral palsy may be related not only to disturbance in motor control signals generating weakness and spasticity, but also to an inefficient control strategy which is not based on a robust knowledge of the dynamical features of their upper limb. This lack of information could be related to the congenital nature of the brain damage and may contribute to a better delineation of therapeutic intervention.

[1]  David J Ostry,et al.  Modifiability of generalization in dynamics learning. , 2007, Journal of neurophysiology.

[2]  B. Dan,et al.  Proposed definition and classification of cerebral palsy, April 2005. , 2005, Developmental medicine and child neurology.

[3]  J. Flanagan,et al.  The Inertial Anisotropy of the Arm Is Accurately Predicted during Movement Planning , 2001, The Journal of Neuroscience.

[4]  J. Krakauer,et al.  Error correction, sensory prediction, and adaptation in motor control. , 2010, Annual review of neuroscience.

[5]  Rieko Osu,et al.  Endpoint Stiffness of the Arm Is Directionally Tuned to Instability in the Environment , 2007, The Journal of Neuroscience.

[6]  M. Levin,et al.  Compensatory strategies for reaching in stroke. , 2000, Brain : a journal of neurology.

[7]  Philip N. Sabes,et al.  The planning and control of reaching movements , 2000, Current Opinion in Neurobiology.

[8]  J. Patton,et al.  Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors , 2005, Experimental Brain Research.

[9]  J. Lackner,et al.  Rapid adaptation to Coriolis force perturbations of arm trajectory. , 1994, Journal of neurophysiology.

[10]  D J Ostry,et al.  Compensation for interaction torques during single- and multijoint limb movement. , 1999, Journal of neurophysiology.

[11]  E. Flachs,et al.  Cerebral palsy in eastern Denmark: declining birth prevalence but increasing numbers of unilateral cerebral palsy in birth year period 1986-1998. , 2010, European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society.

[12]  J R Flanagan,et al.  Composition and Decomposition of Internal Models in Motor Learning under Altered Kinematic and Dynamic Environments , 1999, The Journal of Neuroscience.

[13]  Antigone S Papavasiliou,et al.  Management of motor problems in cerebral palsy: a critical update for the clinician. , 2009, European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society.

[14]  R. Shadmehr,et al.  Neural correlates of motor memory consolidation. , 1997, Science.

[15]  D. Reinkensmeyer,et al.  Review of control strategies for robotic movement training after neurologic injury , 2009, Journal of NeuroEngineering and Rehabilitation.

[16]  G. Cioni,et al.  Visual disorders in children with brain lesions: 1. Maturation of visual function in infants with neonatal brain lesions: correlation with neuroimaging. , 2001, European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society.

[17]  Andrew A G Mattar,et al.  Effects of human arm impedance on dynamics learning and generalization. , 2009, Journal of neurophysiology.

[18]  Maura Casadio,et al.  A proof of concept study for the integration of robot therapy with physiotherapy in the treatment of stroke patients , 2009, Clinical rehabilitation.

[19]  D. Wolpert,et al.  Is the cerebellum a smith predictor? , 1993, Journal of motor behavior.

[20]  John C Rothwell,et al.  Differential Modulation of Motor Cortical Plasticity and Excitability in Early and Late Phases of Human Motor Learning , 2007, The Journal of Neuroscience.

[21]  F A Mussa-Ivaldi,et al.  Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[22]  K. Himmelmann,et al.  Bilateral spastic cerebral palsy--prevalence through four decades, motor function and growth. , 2007, European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society.

[23]  J. Dewald,et al.  Task-dependent weakness at the elbow in patients with hemiparesis. , 1999, Archives of physical medicine and rehabilitation.

[24]  F. Mussa-Ivaldi,et al.  The motor system does not learn the dynamics of the arm by rote memorization of past experience. , 1997, Journal of neurophysiology.

[25]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[26]  Dan Nemet,et al.  Neuromotor noise limits motor performance, but not motor adaptation, in children. , 2003, Journal of neurophysiology.

[27]  Derek G. Kamper,et al.  Modeling Reaching Impairment After Stroke Using a Population Vector Model of Movement Control That Incorporates Neural Firing-Rate Variability , 2003, Neural Computation.

[28]  R. Shadmehr,et al.  A Gain-Field Encoding of Limb Position and Velocity in the Internal Model of Arm Dynamics , 2003, PLoS biology.

[29]  E Bizzi,et al.  Motor learning by field approximation. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Michael I. Jordan,et al.  An internal model for sensorimotor integration. , 1995, Science.

[31]  Jairo Nunes,et al.  Control as Movement , 2010 .

[32]  D. Wolpert,et al.  Internal models in the cerebellum , 1998, Trends in Cognitive Sciences.

[33]  Hooshang Hemami,et al.  A Qualitative Discussion of Mechanisms of Feedback and Feedforward in the Control of Locomotion , 1983, IEEE Transactions on Biomedical Engineering.

[34]  Hiroshi Imamizu,et al.  Human cerebellar activity reflecting an acquired internal model of a new tool , 2000, Nature.

[35]  H. Buchtel,et al.  Neuronal plasticity: historical roots and evolution of meaning , 2008, Experimental Brain Research.

[36]  A. Mallick,et al.  The epidemiology of childhood stroke. , 2010, European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society.

[37]  H. Topka,et al.  Deficits in phasic muscle force generation explain insufficient compensation for interaction torque in cerebellar patients , 1999, Neuroscience Letters.

[38]  Michael A. Arbib,et al.  Stroke Rehabilitation Reaches a Threshold , 2008, PLoS Comput. Biol..

[39]  David J Ostry,et al.  Transfer of Motor Learning across Arm Configurations , 2002, The Journal of Neuroscience.

[40]  N. Hogan,et al.  Procedural motor learning in Parkinson's disease , 2001, Experimental Brain Research.

[41]  R. Scheidt,et al.  Reach adaptation and final position control amid environmental uncertainty after stroke. , 2007, Journal of neurophysiology.

[42]  L M Harrison,et al.  Plasticity of central motor pathways in children with hemiplegic cerebral palsy , 1991, Neurology.

[43]  R. Seitz,et al.  Congenitally altered motor experience alters somatotopic organization of human primary motor cortex , 2009, Proceedings of the National Academy of Sciences.

[44]  John M. Hollerbach,et al.  Dynamic interactions between limb segments during planar arm movement , 1982, Biological Cybernetics.

[45]  O. Bock Load compensation in human goal-directed arm movements , 1990, Behavioural Brain Research.

[46]  N. Hogan,et al.  The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke. , 1997, Archives of neurology.

[47]  W. Rymer,et al.  Deficits in the coordination of multijoint arm movements in patients with hemiparesis: evidence for disturbed control of limb dynamics , 2000, Experimental Brain Research.

[48]  G. Cioni,et al.  Visual disorders in children with brain lesions: 2. Visual impairment associated with cerebral palsy. , 2001, European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society.

[49]  Vincent S. Huang,et al.  Robotic neurorehabilitation: a computational motor learning perspective , 2009, Journal of NeuroEngineering and Rehabilitation.

[50]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[51]  Maura Casadio,et al.  Abnormal sensorimotor control, but intact force field adaptation, in multiple sclerosis subjects with no clinical disability , 2008, Multiple sclerosis.

[52]  J. Eng,et al.  Biomechanics of reaching: clinical implications for individuals with acquired brain injury , 2002, Disability and rehabilitation.

[53]  Reza Shadmehr,et al.  Evidence for a Forward Dynamics Model in Human Adaptive Motor Control , 1998, NIPS.

[54]  S. P. Evseev,et al.  The Control of Movement , 1996 .

[55]  John W. Krakauer,et al.  Independent learning of internal models for kinematic and dynamic control of reaching , 1999, Nature Neuroscience.