Models of learning and behavioral modification in rehabilitation

Author(s): Prateek Grover, MD

Originally published:08/04/2017

Last updated:

1. Introduction

Rehabilitation medicine utilizes the World Health Organization Functioning Model to effect positive changes in health, addressing not only body structure/ function, activity and participation, but also the contextual factors of environment and the person.1 Rehabilitation team members quantify functioning limitations, define targets, and implement interventions to bridge this gap. The past century has seen significant changes in these clinical strategies to improve functioning, reflecting a change in the underlying models of learning and behavior modification, in line with the practice of evidence-based-medicine.

“Learning” includes a) skill and knowledge acquisition as well as b) behavior modification.

Learning is broadly classified into2

  • Explicit or Declarative – active process of learning
  • Implicit – mechanisms include
    • Practice resulting in automatic task performance (Procedural learning)
    • The result of stimuli application
      • Repeatedly (Associative: e.g. Habituation, Sensitization)
      • Associated with another stimulus or result (Non-Associative: e.g. Classical and Operant Conditioning).

Overall, learning and behavior modification models attempt to explain improvement in functioning/ skill acquisition, either by recovery (of prior mechanism) or compensation (switch to alternative mechanism).2

2. Relevance to Clinical practice

Motor learning principles underlie rehabilitation strategies used today, and extensive ongoing research for structural (anatomic correlates) and functional (functional connectivity techniques) validation represents a strong move toward evidence-based medicine.

From the medicine perspective, the motor system is comprised of the pyramidal and extrapyramidal systems. Consistent with the functioning approach to health is a relatively new concept (from the physical therapy perspective) that integrates the medical motor system with other anatomic/ physiologic systems. The movement system integrates multiple body structure /function systems, including the Neuro-musculo-skeletal, Cardio-pulmonary, Endocrine, and Integumentary, to enable mobility, ADL, cognition and communication activities, which in turn facilitate participation.3

The following sections briefly present basics of how the motor system is controlled, how the motor system learns, and current rehabilitation approaches of task specific training and neuroplasticity.

MOTOR CONTROL

Motor control theories attempt to explain how the motor system is controlled.

Broadly defined, control systems relate input to output in varying configurations.

  • Input system signals for the movement system can include vision, hearing, sensation, smell, and taste.
  • The output is motor task execution by the motor system.
  • The control systems can be thought of as the anatomic-physiologic systems that drive the he intermediate steps in this process, using a combination of feedback (loop) and feed-forward (internal prediction) mechanisms.2
  • Intermediate steps conceptualized from input (sensory) to output (motor) could include
    • perception / association (sensory cortex and association areas)
    • conceptualization (prefrontal cortex and association area)
    • plan / program (supplemental motor cortex, basal ganglia, cerebellum)
    • activation (primary motor cortex).

A combination of these anatomic structures and physiologic functions driving the motor or movement system can be thought of as a Learning Network.4 Multiple non-mutually-exclusive learning networks have been proposed for the distinct types of learning mentioned in the next section.5

The older Neurofacilitation approach in rehabilitation is based upon older theories of motor control.2

  • Reflex Theory by Sherrington
  • Hierarchical Theory – top-down control in the nervous system
  • Motor Program Theory – the central pattern generator helps in movement

The current Task-Oriented Approach has principles rooted in the newer theories, which include the role of external forces in driving control.2

  • Systems Theory – movement optimization in response to a combination of external forces, internal state and redundancy
  • Ecological theory – perceptual information guides goal directed action

MOTOR LEARNING

Motor learning theories attempt to explain processes underlying movement being learnt.

Older theories include2

  • Closed-loop theory (sensory feedback guides skill acquisition)
  • Schema theory (open-loop process that allows improved learning with a variety of practice settings by formation of a generalized motor program).

Newer theories include2

  • Ecological theory (optimization based upon perceptual as well as motor cues)
  • Stages of Learning theories

The process of learning, or skill acquisition can be conceptualized as multiple sequential stages2,5

  • Initial understanding and trying multiple strategies,
  • Identifying and consistently utilizing the optimal or efficient strategy (Adaptation),
  • Developing lasting Proficiency and/or Automaticity via motor programs.

Types of learning, described below, often overlap5

  • Fast learning for routine tasks
  • Slow learning for more skilled tasks such as mastering an instrument
  • Offline consolidation for explicit learning
  • Long term retention of skills

From a neuromechanical perspective, for a given task many functionally equivalent motor behaviors (Motor abundance), and many movements by multiple combinations of muscles (Multifunctionality) are possible. However, specific motor patterns, also referred to as Motor modules develops for that activity for that individual. This determination is guided by biomechanical task relevance (Motor structure), preciseness of movement required (Motor variability) and by Individuality.6 At the cerebral level, as skill is acquired, the activation of the Learning Network fades, except for the cerebellum. It is interesting to note that activation of multiple areas reemerges during stroke recovery.4

Examples of motor learning mechanisms and anatomic correlates of common task-oriented rehabilitation techniques are presented below

  • ADL training is accomplished by Learning through instruction and is hypothesized to be mediated by the prefrontal cortex.7
  • Constraint induced movement therapy is based upon Reinforcement by reward or failure and is proposed to activate the cerebrum and basal ganglia.7
  • Split-belt treadmill training is accomplished by Error-based adaptation and is considered to be mediated by the cerebellum and parietal cortex.7, 8

NEUROPLASTICTY

Use dependent plasticity or Neuroplasticity, often portrayed as a mechanism of learning, is proposed to actually be a consequence of learning and not vice-versa.7

Neuroplasticity is defined as the nervous system’s ability to change.

This can occur at multiple levels8

  • Gene expression (e.g. Immediate Early Genes, IEGs in the motor M1 area)
  • Cellular plasticity (synaptic long-term potentiation and depression, or LTP and LTD, respectively)
  • Organ system level in the cortex, subcortical structures (e.g. cerebellum) and the spinal cord.

Major principles of neurorehabilitation proposed to promote neuroplasticity include9

  • repetition to maintain and/or improve function,
  • incorporating individual characteristics such as subject age and underlying stage of recovery, ensuring relevance and specificity of the therapy to the impairment
  • increasing the challenge level iteratively,
  • utilizing therapy analogous to medicine in terms of optimal intensity, duration and frequency.
  • Carryover of learning from one activity to another can be generalized or inhibited, referred to as transference and impairment respectively.

Current neurorehabilitation strategies including task specific practice as well as the corresponding neural mechanisms can be broadly defined as compensatory or restorative.10 Techniques in neurorehabilitation for selected individual impairments are mentioned below

  • For aphasia, the principle of distributed training or spacing effect is considered superior to massed training,11
  • For paresis, approaches include priming the central system, or augmenting the peripheral system, or a combination.12

Examples of priming include Sensory Stimulation including tactile, visual and kinesthetic, Guided Motor and Visual Imagery, and Action Observation techniques such as mirror therapy.

CNS stimulation techniques, including DCS (Transcranial Direct Current Stimulation) and TMS (Repetitive transcranial magnetic stimulation), as well as pharmacologic agents such as Amantadine and Methylphenidate can also be used to directly prime the nervous system.

Examples of the augmentation include Functional electrical stimulation of nerves (TENS) and muscles with and without biofeedback.

  • For apraxia, techniques to induce learning include internal (describing to self) / external (pictures) strategies, Errorless Completion (subject imitates examiner activity), and Action Observation and Execution.12
  • Constraint Induced Motor Therapy (CIMT) is proposed to help relearn from impairments related with Motor neglect.12
  • For impaired mobility, as in spinal cord injury, neurorehabilitation techniques such as body-weight supported treadmill training (BWSTT) have targeted the proposed Locomotor or Central Pattern Generator (CPG), based primarily upon animal experiments such as Sherrington’s cat. However, much of the literature today shows a move beyond the CPG toward incorporating kinesthetic feedback, lower limb kinetics and kinematics in the form of ankle robots for training and even on-the-ground training,8 and increasing spinal circuit excitability by methods such as transcutaneous spinal direct current stimulation.13
  • Learning principles have been applied to musculoskeletal rehabilitation as well, with focus on goal-direct skilled training as opposed to strength training. As expected and evidenced by reduced cortical excitability, pain reduces skill acquisition. Interestingly, in animal models, the reverse has also been seen, i.e., skilled training reduces pain associated spinal cord Implication, if true in humans, relate to using skilled training for preventing chronic pain.14

4. Cutting Edge/Unique Concepts/Emerging Issues

Functional MRI has greatly improved our understanding of learning mechanisms and its anatomic correlates.  Another development that is becoming instrumental is the field of Computational Neurorehabilitation. Along with rehabilitation robotics, computational modeling of motor tasks is also helping in development of evidence-based rehabilitation approaches and tools. Such computational models apply quantitative activity data, collected using sensors and rehabilitation session data collected from rehabilitation robots or practice sessions, to validated physiologic motor-sensory internal state conditions, in order to predict functional outcomes in terms of movement and forces (kinematics and kinetics) over time.15

5. Gaps in Knowledge/Evidence Base:

It is important to acknowledge that the many models of learning and behavior modification are just that – models. They approximate our understanding of recovery from pathology such as stroke and SCI, and form the basis of current rehabilitation.

Newer technology such as functional MRIs, computational modeling and rehabilitation robotics are helping to refine these models, optimize rehabilitation approaches, and develop novel rehabilitation tools to improve functioning and quality of life. At the same time, there is no substitute for motivation, and subject engagement is crucial to recovery. Overall, multidimensional approaches such as the Accelerated Skills Recovery Program (ASAP) that combines capacity, skill and motivation could be utilized to optimize learning.16

References

  1. Towards a Common Language for Functioning, Disability and Health – The International Classification of Functioning, Disability and Health, World Health Organization, Geneva, 2002.
  2. Motor Control – Translating Research into Clinical Practice, 4th, Editors Shumway-Cook A, Woollacott M.H., © Lippincott Williams and Wilkins 2012.
  3. Sahrmann SA, The human movement system: our professional identity, Phys Ther. 2014 Jul;94(7):1034-42.
  4. Ween J.E., Functional Imaging of stroke recovery: An Ecological Review from a Neural Network Perspective with an Emphasis on Motor Systems, J Neuroimaging 2008;18:227-236.
  5. Dayan E., Cohen L.G., Neuroplasticity subserving motor skill learning, 2011 November 3; 72(3): 443–454.
  6. Ting H, Chiel H.J., Trumbower R.D., Allen J.L., McKay J.L., Hackney M.E., Kesar T.M., Neuromechanical principles underlying movement modularity and their implications for rehabilitation, Neuron. 2015 April 8; 86(1): 38–54.
  7. Oxford Textbook of Neurorehabilitation, Editors Dietz V and Ward N, © Oxford University Press 2015.
  8. Neurorehabilitation Technology, 2nd, Editors Reinkensmeyer D.J. and Dietz V, © Springer International Publication 2016.
  9. Kleim JA, Jones TA., Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. J Speech Lang Hear Res. 2008 Feb;51(1): S225-39.
  10. Kleim J.A. Neural plasticity and neurorehabilitation: Teaching the new brain old tricks Journal of Communication Disorders 44 (2011) 521–528.
  11. Dignam JK, Rodriguez AD, Copland DA, Evidence for Intensive Aphasia Therapy: Consideration of Theories from Neuroscience and Cognitive Psychology. PM R. 2016 Mar;8(3):254-67.
  12. Pomeroy V, Aglioti SM, Mark VW, McFarland D, Stinear C, Wolf SL, Corbetta M, Fitzpatrick SM. Neurological principles and rehabilitation of action disorders: rehabilitation interventions. Neurorehabil Neural Repair. 2011 Jun;25(5 Suppl):33S-43S.
  13. Hubli M, Dietz V, The physiological basis of neurorehabilitation -locomotor training after spinal cord injury, Journal of NeuroEngineering and Rehabilitation 2013, 10:5.
  14. Boudreau SA, Farina D, Falla D., The role of motor learning and neuroplasticity in designing rehabilitation approaches for musculoskeletal pain disorders. Man Ther. 2010 Oct;15(5):410-4.
  15. Reinkensmeyer DJ, Burdet E, Casadio M, Krakauer JW, Kwakkel G, Lang CE, Swinnen SP, Ward NS, Schweighofer N, Computational neurorehabilitation: modeling plasticity and learning to predict recovery. J Neuroeng Rehabil. 2016 Apr 30;13(1):42.
  16. Winstein CJ, Kay DB, Translating the science into practice: shaping rehabilitation practice to enhance recovery after brain damage. Prog Brain Res. 2015; 218:331-60.

Author Disclosures

Prateek Grover, MD
Nothing to Disclose

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