1,953
Views
5
CrossRef citations to date
0
Altmetric
Survey Paper

Modeling of hyper-adaptability: from motor coordination to rehabilitation

, , ORCID Icon, &
Pages 802-817 | Received 28 Jan 2021, Accepted 07 May 2021, Published online: 24 Jun 2021

References

  • Isa T. Dexterous hand movements and their recovery after central nervous system injury. Annu Rev Neurosci. 2019;42:315–335.
  • Doya K. Complementary roles of basal ganglia and cerebellum in learning and motor control. Curr Opin Neurobiol. 2000;10(6):732–739.
  • Nudo RJ, Milliken GW. Reorganization of movement representations in primary motor cortex following focal ischemic infarcts in adult squirrel monkeys. J Neurophysiol. 1996;75(5):2144–2149.
  • Taub E, Uswatte G, Elbert T. New treatments in neurorehabiliation founded on basic research. Nature Rev Neurosci. 2002;3(3):228–236.
  • Ward NS. Mechanisms underlying recovery of motor function after stroke. Postgrad Med J. 2005;81(958):510–514.
  • Murase N, Duque J, Mazzocchio R, et al. Influence of interhemispheric interactions on motor function in chronic stroke. Ann. Neurol.. 2004;55(3):400–409.
  • Rehme AK, Grefkes C. Cerebral network disorders after stroke: evidence from imaging-based connectivity analyses of active and resting brain states in humans. J Physiol. 2013;591(1):17–31.
  • Reiss AP, Wolf SL, Hammel EA, et al. Constraint-induced movement therapy (CIMT): current perspectives and future directions. Stroke Res Treat. 2012;2012:159391.
  • Liepert J, Bauder H, Miltner WHR, et al. Treatment-induced cortical reorganization after stroke in humans. Stroke. 2000;31(6):1210–1216.
  • Kawahira K, Shimodozono M, Etoh S, et al. Effects of intensive repetition of a new facilitation technique on motor functional recovery of the hemiplegic upper limb and hand. Brain Inj. 2010;24(10):1202–1213.
  • Raine S, Meadows L, Lynch-Ellerington M. Bobath concept theory and clinical practice in neurological rehabilitation. Chichester: Wiley-Blackwell; 2009.
  • Kogami H, An Q, Yang N, et al. Effect of physical therapy on muscle synergy structure during standing-up motion of hemiplegic patients. IEEE Robot Autom Lett. 2018;3(3):2229–2236.
  • Nitsche MA, Paulus W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J Physiol. 2000;527(3):633–639.
  • Pascual-leone A, Valls-solé J, Wassermann EM, et al. Responses to rapid-rate transcranial magnetic stimulation of the human motor cortex. Brain. 1994;117(4):847–858.
  • Sandy MW, Forrester L, Villagra F, et al. Intracortical inhibition and facilitation with unilateral dominant, unilateral nondominant and bilateral movement tasks in left- and right-handed adults. J Neurol Sci. 2008;269(1-2):96–104.
  • Luft AR, McCombe-Waller S, Whitall J, et al. Repetitive bilateral arm training and motor cortex activation in chronic stroke. JAMA. 2004;292(15):1853–1862.
  • Alnajjar F, Oaki K, Itkonen M, et al. Self-support biofeedback training for recovery from motor impairment after stroke. IEEE Access. 2020;8:72138–72157.
  • Jaillard A, Martin CD, Garambois K, et al. Vicarious function within the human primary motor cortex? A longitudinal fMRI stroke study. Brain. 2005;128(5):1122–1138.
  • Miyai I, Yagura H, Oda I, et al. Premotor cortex is involved in restoration of gait in stroke. Ann Neurol. 2002;52(2):188–194.
  • Willer C, Ramsay SC, Wise RJS, et al. Individual patterns of functional reorganization in the human cerebral cortex after capsular infraction. Ann Neurol. 1993;33(2):181–189.
  • Takei T, Confais J, Tomatsu S, et al. Neural basis for hand muscle synergies in the primate spinal cord. Proc Natl Acad Sci. 2017;114(32):8643–8648.
  • Cheung VCK, Piron L, Agostini M, et al. Stability of muscle synergies for voluntary actions after cortical stroke in humans. Proc Natl Acad Sci USA. 2009;106(46):19563–19568.
  • Cheung VCK, Turolla A, Agostini M, et al. Muscle synergy patterns as physiological markers of motor cortical damage. Proc Natl Acad Sci USA. 2012;109(36):14652–14656.
  • Clark DJ, Ting LH, Zajac FE, et al. Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke. J Neurophysiol. 2010;103(2):844–857.
  • Yang N, An Q, Kogami H, et al. Temporal features of muscle synergies in sit-to-stand motion reflect the motor impairment of post-stroke patients. IEEE Trans Neural Syst Rehabil Eng. 2019;27(10):2118–2127.
  • Stepp N, Turvey MT. On strong anticipation. Cogn Syst Res. 2010;11(2):148–164.
  • Haruno M, Wolpert DM, Kawato M. Mosaic model for sensorimotor learning and control. Neural Comput. 2001;13(10):2201–2220.
  • Todorov E, Jordan MI. Optimal feedback control as a theory of motor coordination. Nat Neurosci. 2002;5:1226–1235.
  • Alvarez-Aguirre A, van de Wouw N, Oguchi T, et al. Predictor-based remote tracking control of a mobile robot. IEEE Trans Control Syst Technol. 2014;22(6):2087–2102.
  • Eberle H, Nasuto SJ, Hayashi Y. Anticipation from sensation: using anticipating synchronization to stabilize a system with inherent sensory delay. R Soc Open Sci. 2018;5(3):171314.
  • Voss HU. Anticipating chaotic synchronization. Phys Rev E. 2000 May;61:5115–5119.
  • Pruszynski JA, Kurtzer I, Scott SH. Rapid motor responses are appropriately tuned to the metrics of a visuospatial task. J Neurophysiol. 2008;100(1):224–238. PMID: 18463184.
  • Cluff T, Scott SH. Apparent and actual trajectory control depend on the behavioral context in upper limb motor tasks. J Neurosci. 2015;35(36):12465–12476.
  • Hayashi Y, Nasuto SJ, Eberle H. Renormalized time scale for anticipating and lagging synchronization. Phys Rev E. 2016;93(5):052229.
  • Dajani HR, Lam JCH. Prediction of pulsatile physiological signals using a negative group delay circuit. In: Proceedings of the 1st WSEAS International Conference on Biomedical Electronics and Biomedical Informatics. World Scientific and Engineering Academy and Society (WSEAS); Rhodes, Greece; 2008. p. 91–96.
  • Voss HU, Stepp N. A negative group delay model for feedback-delayed manual tracking performance. J Comput Neurosci. 2016;41(3):295–304.
  • Dajani HR, Lam JCH. Prediction of pulsatile physiological signals using a negative group delay circuit. In: Proceedings of the 1st WSEAS International Conference on Biomedical Electronics and Biomedical Informatics, BEBI'08. Stevens Point (WI): World Scientific and Engineering Academy and Society (WSEAS); 2008. p. 91–96.
  • Oguchi T, Nijmeijer H. Control of nonlinear systems with time-delay using state predictor based on synchronization. In: Proceedings of ENOC 2005; Eindhoven, Netherlands; 2005. p. 1150–1156.
  • Eberle H, Nasuto SJ, Hayashi Y. Synchronization-based control for a collaborative robot. R Soc Open Sci. 2020;7(12):201267.
  • Stepp N. Anticipation in feedback-delayed manual tracking of a chaotic oscillator. Exp Brain Res. 2009;198:521–525.
  • Takei T, Lomber SG, Cook DJ, et al. Transient deactivation of dorsal premotor cortex or parietal area 5 impairs feedback control of the limb in macaques. Curr Biol. 2021;31(7):1476–1487.
  • Yamins DLK, Hong H, Cadieu CF, et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc Natl Acad Sci. 2014;111(23):8619–8624.
  • Mante V, Sussillo D, Shenoy KV, et al. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature. 2013;503(7474):78–84.
  • Elsayed GF, Lara AH, Kaufman MT, et al. Reorganization between preparatory and movement population responses in motor cortex. Nat Commun. 2016;7:13239.
  • Feulner B, Clopath C. Neural manifold under plasticity in a goal driven learning behaviour. PLoS Comput Biol. 2021;17(2):e1008621.
  • Sussillo D, Churchland MM, Kaufman MT, et al. A neural network that finds a naturalistic solution for the production of muscle activity. Nat Neurosci. 2015;18(7):1025–1033.
  • Scott SH. Inconvenient truths about neural processing in primary motor cortex. J Physiol (Lond). 2008;586(5):1217–1224.
  • Churchland MM, Shenoy KV. Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex. J Neurophysiol. 2007;97(6):4235–4257.
  • Churchland MM, Cunningham JP, Kaufman MT, et al. Neural population dynamics during reaching. Nature. 2012;487(7405):51–56.
  • Michaels JA, Dann B, Scherberger H. Neural population dynamics during reaching are better explained by a dynamical system than representational tuning. PLoS Comput Biol. 2016;12(11):e1005175.
  • Kaufman MT, Churchland MM, Ryu SI, et al. Cortical activity in the null space: permitting preparation without movement. Nat Neurosci. 2014;17(3):440–448.
  • Gallego JA, Perich MG, Naufel SN, et al. Cortical population activity within a preserved neural manifold underlies multiple motor behaviors. Nat Commun. 2018;9:4233.
  • Gallego JA, Perich MG, Chowdhury RH, et al. Long-term stability of cortical population dynamics underlying consistent behavior. Nat Neurosci. 2020;23(2):260–270.
  • Perich MG, Gallego JA, Miller LE. A neural population mechanism for rapid learning. Neuron. 2018;100(4):964–976.
  • Sadtler PT, Quick KM, Golub MD, et al. Neural constraints on learning. Nature. 2014;512(7515):423–426.
  • Takei T, Confais J, Tomatsu S, et al. Neural basis for hand muscle synergies in the primate spinal cord. Proc Natl Acad Sci USA. 2017;114(32):8643–8648.
  • Takei T, Seki K. Spinal interneurons facilitate coactivation of hand muscles during a precision grip task in monkeys. J Neurosci Official J Soc Neurosci. 2010;30(50):17041–17050.
  • Takei T, Seki K. Spinal premotor interneurons mediate dynamic and static motor commands for precision grip in monkeys. J Neurosci. 2013;33(20):8850–8860.
  • Takei T, Seki K. Synaptic and functional linkages between spinal premotor interneurons and hand-muscle activity during precision grip. Front Comput Neurosci. 2013;7:40.
  • Ishida A, Isa K, Umeda T, et al. Causal link between the cortico-rubral pathway and functional recovery through forced impaired limb use in rats with stroke. J Neurosci. 2016;36(2):455–467.
  • Ishida A, Kobayashi K, Ueda Y, Dynamic interaction between cortico-brainstem pathways during training-induced recovery in stroke model rats. J Neurosci. 2019;39(37):7306–7320.
  • Tohyama T, Kinoshita M, Kobayashi K, et al. Contribution of propriospinal neurons to recovery of hand dexterity after corticospinal tract lesions in monkeys. Proc Natl Acad Sci USA. 2017;114(3):604–609.
  • Chao ZC, Sawada M, Isa T, et al. Dynamic reorganization of motor networks during recovery from partial spinal cord injury in monkeys. Cerebral Cortex. 2018;30:7528–15.
  • Nishimura Y, Onoe H, Morichika Y, et al. Time-dependent central compensatory mechanisms of finger dexterity after spinal cord injury. Science. 2007;318(5853):1150–1155.
  • Edelman GM. Neural darwinism: selection and reentrant signaling in higher brain function. Neuron. 1993;10(2):115–125.
  • Perich MG, Rajan K. Rethinking brain-wide interactions through multi-region ‘network of networks’ models. Curr Opin Neurobiol. 2020;65:146–151.
  • Perich MG, Arlt C, Soares S, et al. Inferring brain-wide interactions using data-constrained recurrent neural network models. bioRxiv, page 2020.12.18.423348; 2020.
  • Andalman AS, Burns VM, Lovett-Barron M, et al. Neuronal dynamics regulating brain and behavioral state transitions. Cell. 2019;177(4):970–985.
  • Michaels JA, Schaffelhofer S, Agudelo-Toro A, et al. A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping. Proc Natl Acad Sci USA. 2020;73:202005087.
  • Mathis MW, Mathis A, Uchida N. Somatosensory cortex plays an essential role in forelimb motor adaptation in mice. Neuron. 2017;93(6):1493–1503.
  • Takei T, Lomber SG, Cook DJ, et al. Transient deactivation of dorsal premotor cortex or parietal area 5 impairs feedback control of the limb in macaques. Curr Biol. 2021;31(7):1476–1487.
  • Giszter SF. Motor primitives – new data and future questions. Curr Opin Neurobiol. 2015;33:156–165.
  • Sussillo D, Barak O. Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput. 2013;25(3):626–649.
  • Sussillo D, Abbott LF. Transferring learning from external to internal weights in echo-state networks with sparse connectivity. PLoS One. 2012;7(5):e37372.
  • Michaels AJ, Scherberger H. HebbRNN: a reward-modulated hebbian learning rule for recurrent neural networks. J Open Source Softw. 2016;1(5):60.