Maybe there is something in here(13 pages) from 2013. WHAT DID YOUR DOCTOR DO WITH THIS TO CREATE PROTOCOLS FOR YOUR RECOVERY? NOTHING? Then fire him/her and the board of directors of your hospital. Because they are incompetent and not using research to inform new models of recovery.
Neuromotor recovery from stroke: computational models at central, functional, and muscle synergy level
2013, Frontiers in Computational Neuroscience
Maura Casadio , Irene Tamagnone , Susanna Summa and Vittorio Sanguineti *
Department of Informatics, Bioengineering, Robotics and Systems Engineering, Neuroengineering and Neurorobotics Lab (NeuroLAB), University of Genoa, Genoa,Italy
Edited by:
Andrea D’Avella, IRCCS Fondazione Santa Lucia, Italy
Reviewed by:
Gianluigi Mongillo, Paris Descartes University, France David Reinkensmeyer, University of California at Irvine, USA
*Correspondence:
Vittorio Sanguineti, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via all’Opera Pia 13, 16145 Genoa, Italy e-mail: vittorio.sanguineti@unige.it
Department of Informatics, Bioengineering, Robotics and Systems Engineering, Neuroengineering and Neurorobotics Lab (NeuroLAB), University of Genoa, Genoa,Italy
Edited by:
Andrea D’Avella, IRCCS Fondazione Santa Lucia, Italy
Reviewed by:
Gianluigi Mongillo, Paris Descartes University, France David Reinkensmeyer, University of California at Irvine, USA
*Correspondence:
Vittorio Sanguineti, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via all’Opera Pia 13, 16145 Genoa, Italy e-mail: vittorio.sanguineti@unige.it
Computational models of neuromotor recovery after a stroke might help to unveil the underlying physiological mechanisms and might suggest how to make recovery faster and more effective. At least in principle, these models could serve: (i) To provide testable hypotheses on the nature of recovery; (ii) To predict the recovery of individual patients;(iii) To design patient-specific “optimal” therapy, by setting the treatment variables for maximizing the amount of recovery or for achieving a better generalization of the learned abilities across different tasks. Here we review the state of the art of computational models for neuromotor recovery through exercise, and their implications for treatment.We show that to properly account for the computational mechanisms of neuromotor recovery, multiple levels of description need to be taken into account. The review specifically covers models of recovery at central, functional and muscle synergy level.
Keywords:functional recovery,cortical reorganization,motor skill learning,compensation,robot,slacking,muscle synergy
INTRODUCTION
In the nervous system, a cerebrovascular accident (stroke) elicits a complex series of reorganization processes at molecular, cellular, neural population, behavioral (sensorimotor and cognitive)and social interaction levels, with temporal scales that range from hours, to months, to years (Schaechter, 2004; Barbay et al.,2006; Nudo, 2006, 2007). Alterations occurs well beyond the actual lesion, including a low-activity “penumbra” region in the surrounding areas and inter-hemispheric unbalance due to a decreased activity in the ipsilesional side (Hummel and Cohen,2006). Animal models and human studies suggest that functional recovery is mediated by use-dependent reorganization of the preserved neural circuitry. A key to neuromotor recovery, and the basis of neurorehabilitation interventions, is movement associated with a task (Nudo, 2006, 2007) and with volitional effort (Blennerhassett and Dite, 2004; Higgins et al., 2006;Timmermans et al., 2010). This process produces alterations in neuronal excitability (Ward and Cohen, 2004), leading to changes in neural circuitry, with a process resembling that occurring in the developing brain. Redundancy in the musculoskeletal system plays a key role in neuromotor recovery. It has long been suggested (Bernstein, 1967) that the nervous system has a modular control structure to deal with redundancy. According to this view, the nervous system adaptively controls combinations of motor primitives that are the “building blocks” of movement organization. The pressure toward re-gaining functional independence may lead to the development of compensatory strategies that, even when adequate for carrying out activities of daily life(ADLs), may be stereotypical or energetically inefficient so that they may ultimately prevent true recovery (Levin, 1996b; Cirstea and Levin, 2000). For instance, an excess use of the nonparetic limb can have a negative influence on the process of cortical reorganization (Avanzino et al., 2011)(Really, you're missing the research that show using the good side improves recovery of the bad side?) by further reinforcing the imbalance between the two hemispheres. Models of neuromotor recovery that explicitly take modularity into account might be the most appropriate level of description for these phenomena.In summary, neuromotor recovery through exercise is the end result of a complex interplay between activity-dependent reorganization of the brain areas close to the lesion, the recruitment of new neural pathways and the development of novel motor strategies.A deeper understanding of the functional and physiological mechanisms underlying recovery would have strong impact on approaches to neuromotor rehabilitation. Computational motor control and, more in general, computational models may greatly contribute to this understanding (Huang and Krakauer, 2009).Even more importantly, models may be directly incorporated into technological solutions, and can constitute the basis for personalized therapy. In fact, Marchal-Crespo and Reinkensmeyer (2009) pointed out that there is a specific need for “improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.” However, while musculoskeletal models have a long history in the personalization of treatment of movement disorders (Fregly et al., 2012), computational models of neuromotor recovery through exercise are still in their infancy.Here, we review the state of the art of computational models for neuromotor recoveryand their implications for treatment. We then suggest directions for future research.
Keywords:functional recovery,cortical reorganization,motor skill learning,compensation,robot,slacking,muscle synergy
INTRODUCTION
In the nervous system, a cerebrovascular accident (stroke) elicits a complex series of reorganization processes at molecular, cellular, neural population, behavioral (sensorimotor and cognitive)and social interaction levels, with temporal scales that range from hours, to months, to years (Schaechter, 2004; Barbay et al.,2006; Nudo, 2006, 2007). Alterations occurs well beyond the actual lesion, including a low-activity “penumbra” region in the surrounding areas and inter-hemispheric unbalance due to a decreased activity in the ipsilesional side (Hummel and Cohen,2006). Animal models and human studies suggest that functional recovery is mediated by use-dependent reorganization of the preserved neural circuitry. A key to neuromotor recovery, and the basis of neurorehabilitation interventions, is movement associated with a task (Nudo, 2006, 2007) and with volitional effort (Blennerhassett and Dite, 2004; Higgins et al., 2006;Timmermans et al., 2010). This process produces alterations in neuronal excitability (Ward and Cohen, 2004), leading to changes in neural circuitry, with a process resembling that occurring in the developing brain. Redundancy in the musculoskeletal system plays a key role in neuromotor recovery. It has long been suggested (Bernstein, 1967) that the nervous system has a modular control structure to deal with redundancy. According to this view, the nervous system adaptively controls combinations of motor primitives that are the “building blocks” of movement organization. The pressure toward re-gaining functional independence may lead to the development of compensatory strategies that, even when adequate for carrying out activities of daily life(ADLs), may be stereotypical or energetically inefficient so that they may ultimately prevent true recovery (Levin, 1996b; Cirstea and Levin, 2000). For instance, an excess use of the nonparetic limb can have a negative influence on the process of cortical reorganization (Avanzino et al., 2011)(Really, you're missing the research that show using the good side improves recovery of the bad side?) by further reinforcing the imbalance between the two hemispheres. Models of neuromotor recovery that explicitly take modularity into account might be the most appropriate level of description for these phenomena.In summary, neuromotor recovery through exercise is the end result of a complex interplay between activity-dependent reorganization of the brain areas close to the lesion, the recruitment of new neural pathways and the development of novel motor strategies.A deeper understanding of the functional and physiological mechanisms underlying recovery would have strong impact on approaches to neuromotor rehabilitation. Computational motor control and, more in general, computational models may greatly contribute to this understanding (Huang and Krakauer, 2009).Even more importantly, models may be directly incorporated into technological solutions, and can constitute the basis for personalized therapy. In fact, Marchal-Crespo and Reinkensmeyer (2009) pointed out that there is a specific need for “improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.” However, while musculoskeletal models have a long history in the personalization of treatment of movement disorders (Fregly et al., 2012), computational models of neuromotor recovery through exercise are still in their infancy.Here, we review the state of the art of computational models for neuromotor recoveryand their implications for treatment. We then suggest directions for future research.
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