Neural plasticity and neurorehabilitation: Teaching the new brain old tricks

School of Biological and Health Sciences Engineering, Arizona State University, Tempe, AZ, USA.
Journal of Communication Disorders (Impact Factor: 1.45). 04/2011; 44(5):521-8. DOI: 10.1016/j.jcomdis.2011.04.006
Source: PubMed


Following brain injury or disease there are widespread biochemical, anatomical and physiological changes that result in what might be considered a new, very different brain. This adapted brain is forced to reacquire behaviors lost as a result of the injury or disease and relies on neural plasticity within the residual neural circuits. The same fundamental neural and behavioral signals driving plasticity during learning in the intact brain are engaged during relearning in the damaged/diseased brain. The field of neurorehabilitation is now beginning to capitalize on this body of work to develop neurobiologically informed therapies focused on key behavioral and neural signals driving neural plasticity. Further, how neural plasticity may act to drive different neural strategies underlying functional improvement after brain injury is being revealed. The understanding of the relationship between these different neural strategies, mechanisms of neural plasticity, and changes in behavior may facilitate the development of novel, more effective rehabilitation interventions for treating brain injury and disease. LEARNING OUTCOMES: Readers will be able to: (a) define neural plasticity, (b) understand how learning in the intact and damaged brain can drive neural plasticity, (c) identify the three basic neural strategies mediating functional improvement, and (d) understand how adjuvant therapies have the potential to upregulate plasticity and enhance functional recovery.

126 Reads
    • "Functional recovery from stroke extends well into the chronic stages and involves (re)learning of new movements [1] [2]. Continued functional improvement can be attributed to adaptive plasticity in the remaining cortical and subcortical brain tissue [3] [4]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Feedback provision is an essential component of motor learning for improving upper limb recovery in people with stroke. Along with sensorimotor impairments, many individuals post-stroke have cognitive deficits that can influence arm motor recovery. However, few studies have identified whether the training environment and presence of cognitive deficits influences the ability to use feedback in individuals post-stroke. We evaluated the influence of the training environment and cognitive impairments on the ability to use feedback to enhance arm motor recovery. Twenty-four subjects with chronic poststroke upper limb hemiparesis were randomized to practice pointing movements in a 3D virtual environment (VE) or a similarly designed physical environment (PE; n=12/group) for 12 sessions (72 trials/session, 3 days/week). All participants were provided with feedback about movement speed (Knowledge of Results) and trunk displacement (Knowledge of Performance). Neurocognitive functioning was assessed only before task practice (PRE), while kinematic assessments were carried out at PRE, immediately after (POST) and 3 months (RET) after task practice. Repeated measures ANOVAs with mixed models assessed the changes in kinematic outcomes. Neurocognitive function was correlated with kinematic outcomes. Those training in the VE had greater endpoint speed and ranges of shoulder horizontal adduction, shoulder flexion and elbow extension. They also tended to use less trunk displacement. Kinematic improvements were related to fewer deficits in memory, problem solving, attention and visuoperception. The presence of cognitive deficits influenced the ability to use feedback in people with chronic stroke for upper limb motor learning and recovery. Information about the presence of these deficits can help in the selection of the most appropriate interventions for maximizing arm motor recovery and motor learning in chronic stroke.
    International Conference on Virtual Rehabilitation, Valencia, Spain; 06/2015
  • Source
    • "All these findings support the hypothesis that neuroplasticity may be enhanced by rehabilitation [12]. In this view, advanced MRI may address knowledge gaps between the observed clinical improvement and the neural mechanisms underlying the improved function after rehabilitation, providing a powerful tool to investigate functional and structural brain changes related to recovery of function [22]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Rehabilitation is recognized to be important in ameliorating motor and cognitive functions, reducing disease burden, and improving quality of life in patients with multiple sclerosis (MS). In this systematic review, we summarize the existing evidences that motor and cognitive rehabilitation may enhance functional and structural brain plasticity in patients with MS, as assessed by means of the most advanced neuroimaging techniques, including diffusion tensor imaging and task-related and resting-state functional magnetic resonance imaging (MRI). In most cases, the rehabilitation program was based on computer-assisted/video game exercises performed in either an outpatient or home setting. Despite their heterogeneity, all the included studies describe changes in white matter microarchitecture, in task-related activation, and/or in functional connectivity following both task-oriented and selective training. When explored, relevant correlation between improved function and MRI-detected brain changes was often found, supporting the hypothesis that training-induced brain plasticity is specifically linked to the trained domain. Small sample sizes, lack of randomization and/or an active control group, as well as missed relationship between MRI-detected changes and clinical performance, are the major drawbacks of the selected studies. Knowledge gaps in this field of research are also discussed to provide a framework for future investigations.
    Neural Plasticity 04/2015; DOI:10.1155/2015/481574 · 3.58 Impact Factor
  • Source
    • "Furthermore, the field of rehabilitation has recognized the importance of individualizing therapy for individual patients. Characterizing the organization and activation patterns of each patient's available motor modules may guide the development of specific therapies that maximize the opportunity for neural plasticity and ultimately enhance functional outcome (Kleim, 2011). "
    [Show abstract] [Hide abstract]
    ABSTRACT: OBJECTIVE: Individual muscle activation patterns may be controlled by motor modules constructed by the central nervous system to simplify motor control. This study compared modular control of gait between persons with Parkinson's disease (PD) and neurologically-healthy older adults (HOA) and investigated relationships between modular organization and gait parameters in persons with PD. METHODS: Fifteen persons with idiopathic PD and fourteen HOA participated. Electromyographic recordings were made from eight leg muscles bilaterally while participants walked at their preferred walking speed for 10min on an instrumented treadmill. Non-negative matrix factorization techniques decomposed the electromyographic signals, identifying the number and nature of modules accounting for 95% of variability in muscle activations during treadmill walking. RESULTS: Generally, fewer modules were required to reconstruct muscle activation patterns during treadmill walking in PD compared to HOA (p<.05). Control of knee flexor and ankle plantar flexor musculature was simplified in PD. Activation timing was altered in PD while muscle weightings were unaffected. Simplified neuromuscular control was related to decreased walking speed in PD. CONCLUSION: Neuromuscular control of gait is simplified in PD and may contribute to gait deficits in this population. SIGNIFICANCE: Future studies of locomotor rehabilitation in PD should consider neuromuscular complexity to maximize intervention effectiveness.
    Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 03/2013; 124(7). DOI:10.1016/j.clinph.2013.02.006 · 3.10 Impact Factor
Show more

Similar Publications