Advertisement

Computational models and motor learning paradigms: Could they provide insights for neuroplasticity after stroke? An overview

Published:August 11, 2016DOI:https://doi.org/10.1016/j.jns.2016.08.019

      Highlights

      • Computational models can be used to better understand motor control mechanisms.
      • Neuroplasticity occurs in case of permanent changes of brain structure and function.
      • Neuroplasticity is modulated by administration of drugs.
      • Motor learning is sustained by positive interaction with external environment.
      • Internal models have been described to explain the activation of voluntary movements.

      Abstract

      Computational approaches for modelling the central nervous system (CNS) aim to develop theories on processes occurring in the brain that allow the transformation of all information needed for the execution of motor acts. Computational models have been proposed in several fields, to interpret not only the CNS functioning, but also its efferent behaviour. Computational model theories can provide insights into neuromuscular and brain function allowing us to reach a deeper understanding of neuroplasticity. Neuroplasticity is the process occurring in the CNS that is able to permanently change both structure and function due to interaction with the external environment. To understand such a complex process several paradigms related to motor learning and computational modeling have been put forward. These paradigms have been explained through several internal model concepts, and supported by neurophysiological and neuroimaging studies. Therefore, it has been possible to make theories about the basis of different learning paradigms according to known computational models.
      Here we review the computational models and motor learning paradigms used to describe the CNS and neuromuscular functions, as well as their role in the recovery process. These theories have the potential to provide a way to rigorously explain all the potential of CNS learning, providing a basis for future clinical studies.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of the Neurological Sciences
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Adamovich S.V.
        • Merians A.S.
        • Boian R.
        • Tremaine M.
        • Burdea G.S.
        • Recce M.
        • et al.
        A virtual reality based exercise system for hand rehabilitation post-stroke: transfer to function.
        Conf. Proc. IEEE Eng. Med. Biol. Soc. 2004; 7: 4936-4939
        • Dobkin B.
        Neurologic Rehabilitation.
        FA Davis Publishers, Philadelphia1996
        • Manganotti P.
        • Acler M.
        • Zanette G.P.
        • Smania N.
        • Fiaschi A.
        Motor cortical disinhibition during early and late recovery after stroke.
        Neurorehabil. Neural Repair. 2008; 22: 396-403
        • Wu C.Y.
        • Hsieh Y.W.
        • Lin K.C.
        • Chuang L.L.
        • Chang Y.F.
        • Liu H.L.
        • et al.
        Brain reorganization after bilateral arm training and distributed constraint-induced therapy in stroke patients: a preliminary functional magnetic resonance imaging study.
        Chang Gung Med. J. 2010; 33: 628-638
        • Lucca L.F.
        Virtual reality and motor rehabilitation of the upper limb after stroke: a generation of progress?.
        J. Rehabil. Med. 2009; 41: 1003-1100
        • Hamzei F.
        Przestrojenie mózgu po udarze.
        in: Hamzei F. Neurorehabilitacja oparta na dowodach naukowych. MedPharm Polska, Wrocław2010: 90-97
        • Hamzei F.
        • Dettmers C.
        • Rijntjes M.
        • Glauche V.
        • Kiebel S.
        • Weber B.
        • et al.
        Visuomotor control within a distributed parieto-frontal network.
        Exp. Brain Res. 2002; 146: 273-281
        • Hummel F.C.
        Stymulacja mózgu w neurorehabilitacji.
        in: Hamzei F. Neurorehabilitacja oparta na dowodach naukowych Wroclaw. MedPharm Polska, 2010: 118-140
        • Hamzei F.
        • Rijntjes M.
        • Dettmers C.
        • Glauche V.
        • Weiller C.
        • Buchel C.
        The human action recognition system and its relationship to Broca's area: an fMRI study.
        NeuroImage. 2003; 19: 637-644
        • Kiper P.
        • Baba A.
        • Agostini M.
        • Turolla A.
        Proprioceptive based training for stroke recovery. Proposal of new treatment modality for rehabilitation of upper limb in neurological diseases.
        Arch. Physiother. 2015; 5: 6
        • Ward N.S.
        Neural plasticity and recovery of function.
        Prog. Brain Res. 2005; 150: 527-535
        • Jang S.H.
        • You S.H.
        • Hallett M.
        • Cho Y.W.
        • Park C.M.
        • Cho S.H.
        • et al.
        Cortical reorganization and associated functional motor recovery after virtual reality in patients with chronic stroke: an experimenter-blind preliminary study.
        Arch. Phys. Med. Rehabil. 2005; 86: 2218-2223
        • Johansson B.B.
        Current trends in stroke rehabilitation. A review with focus on brain plasticity.
        Acta Neurol. Scand. 2011; 123: 147-159
        • Ipek M.
        • Hilal H.
        • Nese T.
        • Aynur M.
        • Gazanfer E.
        Neuronal plasticity in a case with total hemispheric lesion.
        J. Med. Life. 2011; 4: 291-294
        • Nudo R.J.
        • Wise B.M.
        • SiFuentes F.
        • Milliken G.W.
        Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct.
        Science. 1996; 272: 1791-1794
        • Bolognini N.
        • Pascual-Leone A.
        • Fregni F.
        Using non-invasive brain stimulation to augment motor training-induced plasticity.
        J. Neuroeng. Rehabil. 2009; 6: 8
        • Nudo R.J.
        Plasticity.
        NeuroRx. 2006; 3: 420-427
        • Buschfort R.
        • Brocke J.
        • Hess A.
        • Werner C.
        • Waldner A.
        • Hesse S.
        Arm studio to intensify the upper limb rehabilitation after stroke: concept, acceptance, utilization and preliminary clinical results.
        J. Rehabil. Med. 2010; 42: 310-314
        • Rossini P.M.
        • Pauri F.
        Neuromagnetic integrated methods tracking human brain mechanisms of sensorimotor areas 'plastic' reorganisation.
        Brain Res. Brain Res. Rev. 2000; 33: 131-154
        • Poldrack R.A.
        Imaging brain plasticity: conceptual and methodological issues–a theoretical review.
        NeuroImage. 2000; 12: 1-13
        • Lin K.C.
        • Chen Y.A.
        • Chen C.L.
        • Wu C.Y.
        • Chang Y.F.
        The effects of bilateral arm training on motor control and functional performance in chronic stroke: a randomized controlled study.
        Neurorehabil. Neural Repair. 2010; 24: 42-51
        • Cachia D.
        • Swearer J.
        • Ferguson W.
        • Moonis M.
        Selective cognitive patterns resulting from bilateral hippocampal ischemia.
        Arch. Med. Sci. 2011; 7: 168-172
        • Stam C.J.
        • van Straaten E.C.
        • Van Dellen E.
        • Tewarie P.
        • Gong G.
        • Hillebrand A.
        • et al.
        The relation between structural and functional connectivity patterns in complex brain networks.
        Int. J. Psychophysiol. 2015;
        • Zheng X.
        • Sun L.
        • Yin D.
        • Jia J.
        • Zhao Z.
        • Jiang Y.
        • et al.
        The plasticity of intrinsic functional connectivity patterns associated with rehabilitation intervention in chronic stroke patients.
        Neuroradiology. 2016; 58: 417-427
        • Formaggio E.
        • Storti S.F.
        • Boscolo Galazzo I.
        • Gandolfi M.
        • Geroin C.
        • Smania N.
        • et al.
        Time-frequency modulation of ERD and EEG coherence in robot-assisted hand performance.
        Brain Topogr. 2015; 28: 352-363
        • Sacchet M.D.
        • Mellinger J.
        • Sitaram R.
        • Braun C.
        • Birbaumer N.
        • Fetz E.
        Volitional control of neuromagnetic coherence.
        Front. Neurosci. 2012; 6: 189
        • Hall E.L.
        • Robson S.E.
        • Morris P.G.
        • Brookes M.J.
        The relationship between MEG and fMRI.
        NeuroImage. 2014; 102: 80-91
        • Richards L.G.
        • Stewart K.C.
        • Woodbury M.L.
        • Senesac C.
        • Cauraugh J.H.
        Movement-dependent stroke recovery: a systematic review and meta-analysis of TMS and fMRI evidence.
        Neuropsychologia. 2008; 46: 3-11
        • Waldowski K.
        • Seniow J.
        • Bilik M.
        • Czlonkowska A.
        Transcranial magnetic stimulation in the therapy of selected post-stroke cognitive deficits: aphasia and visuospatial hemineglect.
        Neurol. Neurochir. Pol. 2009; 43: 460-469
        • Hummel F.C.
        • Cohen L.G.
        Non-invasive brain stimulation: a new strategy to improve neurorehabilitation after stroke?.
        Lancet Neurol. 2006; 5: 708-712
        • Martin P.I.
        • Naeser M.A.
        • Theoret H.
        • Tormos J.M.
        • Nicholas M.
        • Kurland J.
        • et al.
        Transcranial magnetic stimulation as a complementary treatment for aphasia.
        Semin. Speech Lang. 2004; 25: 181-191
        • Oliveri M.
        • Bisiach E.
        • Brighina F.
        • Piazza A.
        • La Bua V.
        • Buffa D.
        • et al.
        rTMS of the unaffected hemisphere transiently reduces contralesional visuospatial hemineglect.
        Neurology. 2001; 57: 1338-1340
        • Avanzino L.
        • Bassolino M.
        • Pozzo T.
        • Bove M.
        Use-dependent hemispheric balance.
        J. Neurosci. 2011; 31: 3423-3428
        • Straudi S.
        • Benedetti M.G.
        • Bonato P.
        Neuroplasticità e motor learning: nuove strategie nella riabilitazione dell'arto superiore nel paziente con ictus cerebrale.
        Sci. Riabil. 2011; 13: 5-11
        • Duque J.
        • Murase N.
        • Celnik P.
        • Hummel F.
        • Harris-Love M.
        • Mazzocchio R.
        • et al.
        Intermanual differences in movement-related interhemispheric inhibition.
        J. Cogn. Neurosci. 2007; 19: 204-213
        • Murase N.
        • Duque J.
        • Mazzocchio R.
        • Cohen L.G.
        Influence of interhemispheric Interactions on motor function in chronic stroke.
        Ann. Neurol. 2004; 55: 400-409
        • Lloyd-Jones D.
        • Adams R.
        • Carnethon M.
        • De Simone G.
        • Ferguson T.B.
        • Flegal K.
        • et al.
        Heart disease and stroke statistics–2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee.
        Circulation. 2009; 119: 480-486
        • Dimyan M.A.
        • Cohen L.G.
        Contribution of transcranial magnetic stimulation to the understanding of functional recovery mechanisms after stroke.
        Neurorehabil. Neural Repair. 2010; 24: 125-135
        • Krakauer J.W.
        Motor learning: its relevance to stroke recovery and neurorehabilitation.
        Curr. Opin. Neurol. 2006; 19: 84-90
        • Floel A.
        • Cohen L.G.
        Recovery of function in humans: cortical stimulation and pharmacological treatments after stroke.
        Neurobiol. Dis. 2010; 37: 243-251
        • Newton J.M.
        • Ward N.S.
        • Parker G.J.
        • Deichmann R.
        • Alexander D.C.
        • Friston K.J.
        • et al.
        Non-invasive mapping of corticofugal fibres from multiple motor areas–relevance to stroke recovery.
        Brain. 2006; 129: 1844-1858
        • Sawaki L.
        • Cohen L.G.
        • Classen J.
        • Davis B.C.
        • Butefisch C.M.
        Enhancement of use-dependent plasticity by d-amphetamine.
        Neurology. 2002; 59: 1262-1264
        • Zorowitz R.D.
        Road to recovery: drugs used in stroke rehabilitation.
        Expert. Rev. Neurother. 2004; 4: 219-231
        • Mortensen J.K.
        • Andersen G.
        Safety of selective serotonin reuptake inhibitor treatment in recovering stroke patients.
        Expert Opin. Drug Saf. 2015; 14: 911-919
        • Walker-Batson D.
        • Curtis S.
        • Natarajan R.
        • Ford J.
        • Dronkers N.
        • Salmeron E.
        • et al.
        A double-blind, placebo-controlled study of the use of amphetamine in the treatment of aphasia.
        Stroke. 2001; 32: 2093-2098
        • Scheidtmann K.
        • Fries W.
        • Muller F.
        • Koenig E.
        Effect of levodopa in combination with physiotherapy on functional motor recovery after stroke: a prospective, randomised, double-blind study.
        Lancet. 2001; 358: 787-790
        • Conforto A.B.
        • Ferreiro K.N.
        • Tomasi C.
        • dos Santos R.L.
        • Moreira V.L.
        • Marie S.K.
        • et al.
        Effects of somatosensory stimulation on motor function after subacute stroke.
        Neurorehabil. Neural Repair. 2010; 24: 263-272
        • Duque J.
        • Hummel F.
        • Celnik P.
        • Murase N.
        • Mazzocchio R.
        • Cohen L.G.
        Transcallosal inhibition in chronic subcortical stroke.
        NeuroImage. 2005; 28: 940-946
        • Freyer F.
        • Reinacher M.
        • Nolte G.
        • Dinse H.R.
        • Ritter P.
        Repetitive tactile stimulation changes resting-state functional connectivity-implications for treatment of sensorimotor decline.
        Front. Hum. Neurosci. 2012; 6: 144
        • Wolpert D.M.
        • Ghahramani Z.
        Computational principles of movement neuroscience.
        Nat. Neurosci. 2000; 3 (Suppl.): 1212-1217
        • Mussa-Ivaldi F.A.
        Modular features of motor control and learning.
        Curr. Opin. Neurobiol. 1999; 9: 713-717
        • Cano-de-la-Cuerda R.
        • Molero-Sanchez A.
        • Carratala-Tejada M.
        • Alguacil-Diego I.M.
        • Molina-Rueda F.
        • Miangolarra-Page J.C.
        • et al.
        Theories and control models and motor learning: clinical applications in neuro-rehabilitation.
        Neurologia. 2012;
        • Kawato M.
        Internal models for motor control and trajectory planning.
        Curr. Opin. Neurobiol. 1999; 9: 718-727
        • Kiper P.
        • Turolla A.
        • Piron L.
        • Agostini M.
        • Baba A.
        • Rossi S.
        • et al.
        Virtual reality for stroke rehabilitation: assessment, training and the effect of virtual therapy.
        Med. Rehabil. 2010; 14: 15-23
        • Flanagan J.R.
        • Wing A.M.
        The role of internal models in motion planning and control: evidence from grip force adjustments during movements of hand-held loads.
        J. Neurosci. 1997; 17: 1519-1528
        • Tamada T.
        • Miyauchi S.
        • Imamizu H.
        • Yoshioka T.
        • Kawato M.
        Activation of the cerebellum in grip force and load force coordination: an fMRI study.
        in: Seitz R.J. Fifth International Conference on Functional Mapping of the Human Brain 1999: Neuroimage. 1999: 492
        • Bizzi E.
        • Tresch M.C.
        • Saltiel P.
        • d'Avella A.
        New perspectives on spinal motor systems.
        Nat. Rev. Neurosci. 2000; 1: 101-108
        • Takahashi C.D.
        • Reinkensmeyer D.J.
        Hemiparetic stroke impairs anticipatory control of arm movement.
        Exp. Brain Res. 2003; 149: 131-140
        • Wolpert D.M.
        • Ghahramani Z.
        • Flanagan J.R.
        Perspectives and problems in motor learning.
        Trends Cogn. Sci. 2001; 5: 487-494
        • Lonini L.
        • Dipietro L.
        • Zollo L.
        • Guglielmelli E.
        • Krebs H.I.
        An internal model for acquisition and retention of motor learning during arm reaching.
        Neural Comput. 2009; 21: 2009-2027
        • Shidara M.
        • Kawano K.
        • Gomi H.
        • Kawato M.
        Inverse-dynamics model eye movement control by Purkinje cells in the cerebellum.
        Nature. 1993; 365: 50-52
        • Criscimagna-Hemminger S.E.
        • Bastian A.J.
        • Shadmehr R.
        Size of error affects cerebellar contributions to motor learning.
        J. Neurophysiol. 2010; 103: 2275-2284
        • Therrien A.S.
        • Bastian A.J.
        Cerebellar damage impairs internal predictions for sensory and motor function.
        Curr. Opin. Neurobiol. 2015; 33: 127-133
        • Laver K.E.
        • George S.
        • Thomas S.
        • Deutsch J.E.
        • Crotty M.
        Virtual reality for stroke rehabilitation.
        Cochrane Database Syst. Rev. 2015; 2CD008349
        • Kiper P.
        • Agostini M.
        • Luque-Moreno C.
        • Tonin P.
        • Turolla A.
        Reinforced feedback in virtual environment for rehabilitation of upper extremity dysfunction after stroke: preliminary data from a randomized controlled trial.
        Biomed. Res. Int. 2014; 2014: 752128
        • Luque-Moreno C.
        • Oliva-Pascual-Vaca A.
        • Kiper P.
        • Rodriguez-Blanco C.
        • Agostini M.
        • Turolla A.
        Virtual reality to assess and treat lower extremity disorders in post-stroke patients.
        Methods Inf. Med. 2016; 55: 89-92
        • Kiper P.
        • Piron L.
        • Turolla A.
        • Stozek J.
        • Tonin P.
        The effectiveness of reinforced feedback in virtual environment in the first 12 months after stroke.
        Neurol. Neurochir. Pol. 2011; 45: 436-444
        • Luque-Moreno C.
        • Ferragut-Garcias A.
        • Rodriguez-Blanco C.
        • Heredia-Rizo A.M.
        • Oliva-Pascual-Vaca J.
        • Kiper P.
        • et al.
        A decade of progress using virtual reality for poststroke lower extremity rehabilitation: systematic review of the intervention methods.
        Biomed. Res. Int. 2015; 2015: 342529
        • Viau A.
        • Feldman A.G.
        • McFadyen B.J.
        • Levin M.F.
        Reaching in reality and virtual reality: a comparison of movement kinematics in healthy subjects and in adults with hemiparesis.
        J. Neuroeng. Rehabil. 2004; 1: 11
        • Sveistrup H.
        Motor rehabilitation using virtual reality.
        J. Neuroeng. Rehabil. 2004; 1: 10
        • McPhee J.
        • Kovecses J.
        • Reinbolt J.A.
        • Seth A.
        • Delp S.L.
        IUTAM Symposium on Human Body DynamicsSimulation of human movement: applications using OpenSim.
        Procedia IUTAM. 2011; 2: 186-198
        • An K.N.
        • Chao E.Y.
        • Cooney W.P.
        • Linscheid R.L.
        Forces in the normal and abnormal hand.
        J. Orthop. Res. 1985; 3: 202-211
        • Harding D.C.
        • Brandt K.D.
        • Hillberry B.M.
        Finger joint force minimization in pianists using optimization techniques.
        J. Biomech. 1993; 26: 1403-1412
        • Fuglevand A.J.
        • Winter D.A.
        • Patla A.E.
        Models of recruitment and rate coding organization in motor-unit pools.
        J. Neurophysiol. 1993; 70: 2470-2488
        • Cheng E.J.
        • Brown I.E.
        • Loeb G.E.
        Virtual muscle: a computational approach to understanding the effects of muscle properties on motor control.
        J. Neurosci. Methods. 2000; 101: 117-130
        • Valero-Cuevas F.J.
        • Hoffmann H.
        • Kurse M.U.
        • Kutch J.J.
        • Theodorou E.A.
        Computational models for neuromuscular function.
        IEEE Rev. Biomed. Eng. 2009; 2: 110-135
        • Li W.
        • Todorov E.
        Iterative linear quadratic regulator design for nonlinear biological movement system.
        in: 1st International Conference on Informatics in Control. Automation and Robotics, Portugal2004: 8
        • Doya K.
        What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?.
        Neural Netw. 1999; 12: 961-974
        • Doya K.
        Complementary roles of basal ganglia and cerebellum in learning and motor control.
        Curr. Opin. Neurobiol. 2000; 10: 732-739
        • Piron L.
        • Tonin P.
        • Piccione F.
        • Iaia V.
        • Trivello E.
        • Dam M.
        Virtual einvironment training therapy for arm motor rehabilitation.
        Presence. 2005; 14
        • Padoa-Schioppa C.
        • Li C.S.
        • Bizzi E.
        Neuronal activity in the supplementary motor area of monkeys adapting to a new dynamic environment.
        J. Neurophysiol. 2004; 91: 449-473
        • Shelton F.N.
        • Reding M.J.
        Effect of lesion location on upper limb motor recovery after stroke.
        Stroke. 2001; 32: 107-112
        • Huang V.S.
        • Krakauer J.W.
        Robotic neurorehabilitation: a computational motor learning perspective.
        J. Neuroeng. Rehabil. 2009; 6: 5
        • Poggio T.
        • Bizzi E.
        Generalization in vision and motor control.
        Nature. 2004; 431: 768-774