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Computational Neurorehabilitation

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Neurorehabilitation Technology

Abstract

Computational Neurorehabilitation is an emerging field at the intersection of Neurorehabilitation, Computational Neuroscience, Motor Control and Learning, and Statistical Learning. The overarching goals of Computational Neurorehabilitation are to understand and to further improve motor recovery following neurologic injury by mathematically modeling and simulating the neural processes underlying the change in behavior due to rehabilitation (1). This chapter is organized into three main sections. First, we review the overall framework of Computational Neurorehabilitation and argue that computational neurorehabilitation models belong to the general class of dynamical system models. Second, we discuss the three categories of plastic processes that have been incorporated in previous models: unsupervised, supervised, and reinforcement learning. Third, we discuss the two main types of models in Computational Neurorehabilitation: Qualitative “biological” models whose main goal is to advance our understanding of the neural mechanisms of recovery and Quantitative “predictive” models whose main goal is to predict long-term changes in functional outcomes for individual patients. We illustrate these two types of models by briefly reviewing a number of recent relevant qualitative and quantitative models. We conclude by suggesting future directions for the field.

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References

  1. Reinkensmeyer DJ, Burdet E, Casadio M, Krakauer JW, Kwakkel G, Lang CE, et al. Computational neurorehabilitation: modeling plasticity and learning to predict recovery. J Neuroeng Rehabil. 2016;13(1):42.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Reinkensmeyer DJ, Aoyagi D, Emken JL, Galvez JA, Ichinose W, Kerdanyan G, et al. Tools for understanding and optimizing robotic gait training. J Rehabil Res Dev. 2006;43(5):657–70.

    Article  PubMed  Google Scholar 

  3. Frank MJ. Computational models of motivated action selection in corticostriatal circuits. Curr Opin Neurobiol. 2011;21(3):381–6.

    Article  CAS  PubMed  Google Scholar 

  4. Han CE, Arbib MA, Schweighofer N. Stroke rehabilitation reaches a threshold. PLoS Comput Biol. 2008;4(8): e1000133.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hidaka Y, Han CE, Wolf SL, Winstein CJ, Schweighofer N. Use it and improve it or lose it: interactions between arm function and use in humans post-stroke. PLoS Comput Biol. 2012;8(2): e1002343.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wang C, Winstein C, D’Argenio DZ, Schweighofer N. The efficiency, efficacy, and retention of task practice in chronic stroke. Neurorehabil Neural Repair. 2020;34(10):881–90.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Scheidt RA, Stoeckmann T. Reach adaptation and final position control amid environmental uncertainty after stroke. J Neurophysiol. 2007;97(4):2824–36.

    Article  PubMed  Google Scholar 

  8. Casadio M, Sanguineti V. Learning, retention, and slacking: a model of the dynamics of recovery in robot therapy. IEEE Trans Neural Syst Rehabil Eng. 2012;20(3):286–96.

    Article  PubMed  Google Scholar 

  9. Reinkensmeyer DJ, Guigon E, Maier MA. A computational model of use-dependent motor recovery following a stroke: optimizing corticospinal activations via reinforcement learning can explain residual capacity and other strength recovery dynamics. Neural Netw. 2012;29–30:60–9.

    Article  PubMed  Google Scholar 

  10. Riley JD, Le V, Der-Yeghiaian L, See J, Newton JM, Ward NS, et al. Anatomy of stroke injury predicts gains from therapy. Stroke. 2011;42(2):421–6.

    Google Scholar 

  11. Stinear CM, Barber PA, Petoe M, Anwar S, Byblow WD. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain. 2012;135(Pt 8):2527–35.

    Article  PubMed  Google Scholar 

  12. Kwakkel G, Kollen B. Predicting improvement in the upper paretic limb after stroke: a longitudinal prospective study. Restor Neurol Neurosci. 2007;25(5–6):453–60.

    PubMed  Google Scholar 

  13. Nudo RJ, Wise BM, SiFuentes F, Milliken GW. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science. 1996;272(5269):1791–4.

    Article  CAS  PubMed  Google Scholar 

  14. Schweighofer N, Han CE, Wolf SL, Arbib MA, Winstein CJ. A functional threshold for long-term use of hand and arm function can be determined: predictions from a computational model and supporting data from the extremity constraint-induced therapy evaluation (EXCITE) trial. Phys Ther. 2009;89(12):1327–36.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Duncan PW, Lai SM, Keighley J. Defining post-stroke recovery: implications for design and interpretation of drug trials. Neuropharmacology. 2000;39(5):835–41.

    Article  CAS  PubMed  Google Scholar 

  16. Bains AS, Schweighofer N. Time-sensitive reorganization of the somatosensory cortex post-stroke depends on interaction between Hebbian plasticity and homeoplasticity: a simulation study. J Neurophysiol. 2014;jn 00433 2013.

    Google Scholar 

  17. Murphy TH, Corbett D. Plasticity during stroke recovery: from synapse to behaviour. Nat Rev Neurosci. 2009;10(12):861–72.

    Article  CAS  PubMed  Google Scholar 

  18. Schweighofer N, Lee JY, Goh HT, Choi Y, Kim SS, Stewart JC, et al. Mechanisms of the contextual interference effect in individuals poststroke. J Neurophysiol. 2011;106(5):2632–41.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Smith MA, Ghazizadeh A, Shadmehr R. Interacting adaptive processes with different timescales underlie short-term motor learning. PLoS Biol. 2006;4(6): e179.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kording KP, Tenenbaum JB, Shadmehr R. The dynamics of memory as a consequence of optimal adaptation to a changing body. Nat Neurosci. 2007;10(6):779–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lee JY, Schweighofer N. Dual adaptation supports a parallel architecture of motor memory. J Neurosci. 2009;29(33):10396–404.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kim S, Ogawa K, Lv J, Schweighofer N, Imamizu H. Neural substrates related to motor memory with multiple timescales in sensorimotor adaptation. PLoS Biol. 2015;13(12): e1002312.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Doya K. What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? Neural Netw. 1999;12(7–8):961–74.

    Article  CAS  PubMed  Google Scholar 

  24. Hebb D. The organisation of behaviour. New York: Wiley; 1949.

    Google Scholar 

  25. Goodall S, Reggia JA, Chen Y, Ruppin E, Whitney C. A computational model of acute focal cortical lesions. Stroke. 1997;28(1):101–9.

    Article  CAS  PubMed  Google Scholar 

  26. Goodall S, Reggia JA, Cho S. Modeling brain adaptation to focal damage. In: Proceedings of the annual symposium on computer application [sic] in medical care. 1994:860–4.

    Google Scholar 

  27. Reggia JA. Neurocomputational models of the remote effects of focal brain damage. Med Eng Phys. 2004;26(9):711–22.

    Article  PubMed  Google Scholar 

  28. Varier S, Kaiser M, Forsyth R. Establishing, versus maintaining, brain function: a neuro-computational model of cortical reorganization after injury to the immature brain. J Int Neuropsychol Soc. 2011;17(6):1030–8.

    Article  PubMed  Google Scholar 

  29. Butz M, van Ooyen A, Worgotter F. A model for cortical rewiring following deafferentation and focal stroke. Front Comput Neurosci. 2009;3:10.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kawato M. Internal models for motor control and trajectory planning. Curr Opin Neurobiol. 1999;9(6):718–27.

    Article  CAS  PubMed  Google Scholar 

  31. Izawa J, Shadmehr R. Learning from sensory and reward prediction errors during motor adaptation. PLoS Comput Biol. 2011;7(3): e1002012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Williams RJ. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn. 1992;8(3–4):229–56.

    Article  Google Scholar 

  33. Gullapalli V. A stochastic reinforcement learning algorithm for learning real-valued functions. Neural Netw. 1990;3:671–92.

    Article  Google Scholar 

  34. Schaal S. Is imitation learning the route to humanoid robots? Trends Cogn Sci. 1999;3(6):233–42.

    Article  CAS  PubMed  Google Scholar 

  35. Krakauer JW, Carmichael ST. Broken movement: the neurobiology of motor recovery after stroke. Cambridge: The MIT Press; 2017. xiv, p. 269.

    Google Scholar 

  36. Dromerick AW, Geed S, Barth J, Brady K, Giannetti ML, Mitchell A, et al. Critical Period After Stroke Study (CPASS): a phase II clinical trial testing an optimal time for motor recovery after stroke in humans. Proc Natl Acad Sci USA. 2021;118(39).

    Google Scholar 

  37. Page SJ, Gater DR, Bach YRP. Reconsidering the motor recovery plateau in stroke rehabilitation. Arch Phys Med Rehabil. 2004;85(8):1377–81.

    Article  PubMed  Google Scholar 

  38. Brouwer BJ, Schryburt-Brown K. Hand function and motor cortical output poststroke: are they related? Arch Phys Med Rehabil. 2006;87(5):627–34.

    Google Scholar 

  39. Ward NS, Newton JM, Swayne OB, Lee L, Frackowiak RS, Thompson AJ, et al. The relationship between brain activity and peak grip force is modulated by corticospinal system integrity after subcortical stroke. Eur J Neurosci. 2007;25(6):1865–73.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Takiyama K, Okada M. Recovery in stroke rehabilitation through the rotation of preferred directions induced by bimanual movements: a computational study. PLoS ONE. 2012;7(5): e37594.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. MacLellan CL, Keough MB, Granter-Button S, Chernenko GA, Butt S, Corbett D. A critical threshold of rehabilitation involving brain-derived neurotrophic factor is required for poststroke recovery. Neurorehabil Neural Repair. 2011;25(8):740–8.

    Article  PubMed  Google Scholar 

  42. Schwerz de Lucena D, Rowe J, Chan V, Reinkensmeyer DJ. Magnetically counting hand movements: validation of a calibration-free algorithm and application to testing the threshold hypothesis of real-world hand use after stroke. Sensors (Basel). 2021;21(4).

    Google Scholar 

  43. Ballester BR, Nirme J, Duarte E, Cuxart A, Rodriguez S, Verschure P, et al. The visual amplification of goal-oriented movements counteracts acquired non-use in hemiparetic stroke patients. J Neuroeng Rehabil. 2015;12:50.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Burdet E, Li Y, Kager S, Chua KSG, Hussain A, Campolo D. Interactive robot assistance for upper-limb training. Rehabilitation robotics: Academic Press; 2018. p. 137–48

    Google Scholar 

  45. Reinkensmeyer DJ. How to retrain movement after neurologic injury: A computational rationale for incorporating robot (or therapist) assistance. Proc Annu Int IEEE EMBS. 2003;25:1479–82.

    Google Scholar 

  46. Schweighofer N, Wang C, Mottet D, Laffont I, Bakhti K, Reinkensmeyer DJ, et al. Dissociating motor learning from recovery in exoskeleton training post-stroke. J Neuroeng Rehabil. 2018;15(1):89.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Friedman N, Rowe JB, Reinkensmeyer DJ, Bachman M. The manumeter: a wearable device for monitoring daily use of the wrist and fingers. IEEE J Biomed Health Inform. 2014;18(6):1804–12.

    Article  PubMed  Google Scholar 

  48. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging. 2017;30(4):449–59.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Nibras N, Liu C, Mottet D, Wang C, Reinkensmeyer D, Remy-Neris O, et al. Dissociating sensorimotor recovery and compensation during exoskeleton training following stroke. Front Hum Neurosci. 2021;15: 645021.

    Google Scholar 

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Acknowledgements

The author acknowledges grant NIH R21NS120274 and thanks Denis Mottet, Etienne Burdet, Carolee Winstein, Cheol Han, Jim Gordon, Jun Izawa, Michael Arbib, and David Reinkensmeyer for fruitful discussions that have led to the ideas presented in this chapter.

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Authors and Affiliations

  1. Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA

    Nicolas Schweighofer

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  1. Nicolas Schweighofer
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Correspondence to Nicolas Schweighofer .

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Editors and Affiliations

  1. Department of Mechanical and Aerospace Engineering and Department of Anatomy and Neurobiology, University of California, Irvine, CA, USA

    David J. Reinkensmeyer

  2. Delft University of Technology, Delft, The Netherlands

    Laura Marchal-Crespo

  3. Spinal Cord Injury Center, University Hospital Balgrist, Zürich, Switzerland

    Volker Dietz

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Schweighofer, N. (2022). Computational Neurorehabilitation. In: Reinkensmeyer, D.J., Marchal-Crespo, L., Dietz, V. (eds) Neurorehabilitation Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-08995-4_16

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  • DOI: https://doi.org/10.1007/978-3-031-08995-4_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08994-7

  • Online ISBN: 978-3-031-08995-4

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