From: Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Model A: Han et al. 2008 [112] | |

Structure: A bilateral limb-use model using a population vector framework and reinforcement and error-based learning. | |

Example Prediction: If spontaneous recovery, motor training, or both, bring function above a certain threshold, then training can be stopped, as the repeated spontaneous arm use provides a form of motor learning that further bootstraps function and spontaneous use (i.e. the “virtuous cycle”) | |

Model B: Reinkensmeyer et al. 2012 [136] | |

Structure: A wrist strength recovery model using a simplified corticospinal neural network and reinforcement learning via stochastic search | |

Example Prediction: Reinforcement learning can explain a broad range of features of stroke recovery, including exponential recovery, residual capacity, and shift of brain activation to secondary motor areas. | |

Model C: Casadio and Sanguineti, 2012 [56] | |

Structure: An arm impairment reduction model using a linear, discrete-time, shift invariant dynamical system driven by data from robotic therapy | |

Example Prediction: A parameter describing retention predicts Fugl-Meyer score 3 months following robotic therapy. | |

Model D: Hidaka et al. 2012 [206] | |

Structure: First order dynamic model that incorporates a modifiable parameter that controls the effect of arm function on use. | |

Example Prediction: Therapy increased the parameter that controls the effect of arm function on use. An increase in this parameter, which can be thought of as the confidence to use the arm for a given level of function, led to an increase in spontaneous use after therapy compared to before therapy. | |

Model E: Reinkensmeyer 2003 [207] | |

Structure: Adaptive Markov model with Hebbian plasticity that maps relationship between normal and abnormal sensory and motor states, allowing for physical assistance from a rehabilitation trainer | |

Example Prediction: Assistance-as-needed can enhance recovery beyond what is possible with unassisted movement practice. | |

Model F: Jarrassé et al. 2012 [210] | |

Structure: Uses a cost function with error and effort terms, generated by both the therapist (or robot) and human trainee, to characterize a broad range of interactive behaviors of two-agent systems. | |

Example prediction: Sensorimotor rehabilitation may be modeled in terms of the cost functions that the trainee and the trainer seek to implement, as well as the algorithms they use to implement those cost functions. |