Neuroscience and Technology

8/22/2019 Neuroscience and Technology

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Upper limb rehabilitation post-stroke: making the science and

technology work for patients

Jane Burridge


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Overview Some very basic neuroscience that underpins

neuroplasticity

Modulation of neuroplasticity

Behavioural influences on neuroplasticity

How neuroscience can be applied to rehabilitation andin particular FES

Potential for combined approaches


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The synapse transmission of a signal between two nerves

Chemical (neurotransmittersreleased on the arrival of anaction potential) or

Electrical (ions flow between

the cells so that they areelectrically coupled)

Excitatory or Inhibitory


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Anterior horn cell connections


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What determines whether a cell fires?

Hebbian learning rule (1949):

Repetitive activation of a presynapticneuron together with simultaneousactivation of a neighbouringpostsynaptic neuron leads to anincrease in synaptic strength betweenthem.

Substantiated by experimentalevidence and underpins LTP and

LTD


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Brain

Pyramidaltracts

AHC

Muscle

Propriospinalconnections

Hebbian learning hypothesis applied to motor learning associated withvoluntary drive and peripheral electrical neuromuscular stimulation


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Brain

Pyramidaltracts

AHC

Muscle


Lesion

Transmission fails

Hebb synapse residualconnectivity reduces

Hebbian learning hypothesis applied to motor learning associated with

voluntary drive and peripheral electrical neuromuscular stimulation


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Brain

Voluntary effort mustcoincide withstimulation

AHC

Muscle


Lesion

Motor axon firesbackwards and forwards

Hebb synapseconductivity increases

Hebbian learning hypothesis applied to motor learning associated with

voluntary drive and peripheral electrical neuromuscular stimulation


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Neuroplasticity Synaptic connections are continually being modified (re-

organisation of circuitry)

In response to demand learning, memory, disuse (learntnon- use)

After damage to the CNS

LTP and LTD: alteration of the structure of the synapse

Cellular level

Increased sensitivity to neural transmitters

Increase number and branches of dendrites

Increase and strengthening of synaptic connections (Hebbe)

Axon sprouting


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Voluntary motor control - classical and current view of motor

connections

A single MI neuron can influence the motor neuron pools of manymuscles

Spinal neuron pools receive input from broad overlapping corticalterritories

Motor cortex does not map area to muscle and may relate more topatterns of movement primitive patterns or laid down through use

The overlapping and flexible structure underpins the ability of thesystem to adapt and therefore potentially recover following damage

A B C

1

X Y Z

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A B C

1

X Y Z

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M1

Spinal motoneuron pools

Muscles


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Cortical maps use it or lose it

Topology of the sensory and motor cortex is not fixed butflexible and adapts to learning and experience (Donoghue

1996).

Areas with more connections (fine motor control or moreacute sensation) have larger representation

Factors that promote change:1. Enriched environment

2. Lack of sensory input (e.g.amputation)

3. Immobilising a limb Increase in size relates to increased

skill


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Flexibility of the motor cortex: implications forrehabilitation

Areas have the ability to adapt their function ratherthan acquiring new functions

Intensive training of one cortical area may be at theexpense of other surrounding areas

Rapid changes in cortical activity intensive vs.

extensive training Identify damaged regions and apply targeted therapy


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Modulation of neuroplasticity


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Changes in excitability in response to electricalnerve stimulation

TMS evokes a motor potential detected by EMG

Relationship between level of TMS and EMG amplitude

Following a period of electrical stimulation therelationship changes i.e. the same Level of TMSresults in a higher EMG amplitude

See: M.C. Ridding et al, Changes in muscle responses to stimulation of the motor cortex induced byperipheral nerve stimulation in human subjects Exp Brain Res (2000) 131:135143


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Paired Associative Stimulation

For a comprehensive review refer to Ziemann et al. (2008)


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Electrical and Magnetic stimulation (cranialand peripheral)

Repetitive transcranial magnetic stimulation (rTMS)

Transcranial direct current stimulation (tDCS)

Paired associative stimulation (PAS)

Functional electrical stimulation (FES)


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Evidence for rTMS and tDCS

Up- regulation of excitabilityin the affected hemisphere

Anodal tDCS (1

Hummel) sham vs. tDCS)

Rapid rate TMS (2Khedr)

Down- regulation ofexcitability in the intacthemisphere

Cathodal tDCS (4Fregni)

Low frequency rTMS (1Hz)to M1 (4Schambra;5Mansur)

1 Hummel et al Brain 2Khedr et al neurology 2005 65; 466- 68; 3Fregni et al Neuroreport 2005 16:1551-55; 4Schambra et al, Clin Neurophysiology 2003;114:130- 33 5Mansur et al, Neurology 2005 64:1802- 04


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Paired Associated Stimuli

Pre-measures TMS alone

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Post-measures TMS alone

Intervention peripheral nerve stimulus + TMS


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Influencing neuroplastic changes Anti Nogo A1

Amphetamines and Dopaminergic stimulation

Stem cell therapy

1Weissner & Schwab Journal of cerebral blood flow & metabolism 23: 154- 165 2003

It is likely that if any are effective they will need toaccompanied by intensive physical therapy to drive

appropriate neuroplastic changes


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MCA lesion Anti-Nogo-A antibody infusion at 24 hours

B: baited staircase test rat retrieves pellets with affected forepaw

C: Grasped and eaten pellets as %of pre- lesion

D: Successful attempts (eaten pellets X 100/ eaten pellets + displaced pellets

Statistically significant difference treatment vs. control p+0.05 (both C and D)

A: MRI scan at 24hours and 9 weeks


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Summing-up - neuroplasticity

Changes in CNS structure, excitability and connectivity occur inresponse to environmental and behavioural conditions

Neuroplasticity enables people to recover following lesions and for

healthy people to learn new skills

Interventions may have considerable impact:

Modulating cortical excitability

Enhancing corticospinal plasticity Neuroscience can explain the mechanisms associated with recovery and

potentially drive:

Effective rehabilitation approaches

Identify who will respond best to what approach Evaluate the effect of interventions at the impairment as well as the

functional level


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Behavioural influences on neuroplasticity


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Facilitation of sensorimotor re-learning

Neuromusculoskeletal factors:

Improved motor control Muscle strengthening and increased range of movement

Modulation of spasticity

Sensory input Intrinsic - direct stimulation of sensory fibres or

secondary, tactile / proprioceptive feedback

Extrinsic - feedback from the experience of movement or

the observed achievement of a goal Repetition - goal orientated

Motivation - Increased attention


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Practice -

Simple repetition is not enough

Challenging - at the limit of performance (Nudo RJ, J RehabilMed 2003; 41: 7- 10)

Context: goal orientated, relevant, real vs. imagined(Ching- yi Wu Arch Phys med 2000) or simulated (Hu- ing Ma. Am J OT1999)

Varied - Random vs. block (Hanlon RE, Arch Phys Med Aug 96)

Feedback: encouragement

Ericsson KA et al. The role of deliberate practice in the acquisition of expert

performance. Psych rev 1993 Vol 100; 3; 363- 406


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What can we learn from the principles of

motor learning?

Rehabilitation is boring: how can we motivate patients?

Need for intensity - home use portable

Allow the patient take charge

Encourage patients to try harder practice more - using games orrelevant activities

Can we use technology to adapt the task presented?

Optimise performance and learning

Provide feedback to engage and motivate

Design systems that have a personalised content Measure progress

Can we combine modulation of the CNS with technology based practice?


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How does this relate to

Neurorehabilitation technologies?


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The potential influence of rehabilitation technology onperformance

RepetitionGoal orientated practice

Feedback fromsuccessful performance

RepetitionGoal orientated practice

Feedback fromsuccessful performance

Robotics orFunctionalElectricalStimulation

Movement & Sensory inputStiffness / ROM

SpasticityMuscle strength

Movement & Sensory inputStiffness / ROM

SpasticityMuscle strength

ImprovedPerformance

ImprovedPerformance

NeuroplasticityMotor Learning

NeuroplasticityMotor Learning

Varied repetitionat limit of performanceFeedback fromsuccessful performance

Varied repetitionat limit of performanceFeedback fromsuccessful performance

Reducesupport

Unable toperform

task


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Inertial sensor triggered stimulation for reachand grasp

Voluntary drive - attention

Paired Associated Stimuli

Goal orientated and functional

Feedback of performance

Muscle strengthening

Possible reduction inspasticity


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Implanted microstimulators (Bions) for closed

loop upper limb rehabilitation post-stroke

Microstimulators implanted into

elbow, wrist and finger/ thumbextensors

Independent control of each device

Sensors initiate stimulation andtransfer between activity sequences

Stimulation is responsive to

participants speed of movement

Therapeutic effect of 12 weekshome exercise and 12 weekfollow- up


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Robot Therapy


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Theoretical benefit of Rehabilitation Robots

Robot will allow the patient to achieve a task

Repetitive goal orientated practice requiring attention

Tasks can be adjusted to provide success at the limit ofperformance

Motivating and varied VR / games

Allows intensive and safe training could be used in conjunctionwith FES or CIMT (shaping) therapy at home

Appropriate for all levels of ability


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Evidence for Robot Therapy

Strong evidence for improved motor control (impairment) and someevidence for improved function [Kwakkel 2008, EBRSR & Prange 2006]

Proximal training = proximal benefit

Possibly people with moderate impairment respond better

Better understanding of how therapy should be applied dose,activities, bilateral, resisted / assisted

Include hand and wrist

Potential for combining functional training with robot training

Potential for combining with modulation of neuroplasticity


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Systematic review Kwakkel (2008)


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Designing a Smart Armeo

Including the wrist and hand

Initial work to model normal hand opening in the specific tasks

Mechanical opening

ES to open the hand

Providing feedback


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Using iterative learning control to modulate electricalstimulation (ES) in a robot workstation tracking task

2D pursuit tracking task

Using ES rather than mechanical error correction ILC to ensure that minimal ES is applied to correct

tracking error

Iterative Learning Control mediated by FES with chronic


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Iterative Learning Control mediated by FES with chronicstroke subjects - Workstation

Elliptical projected

trajectory

Learning Control (ILC) using a Robot & FES - ILC algorithm


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Learning Control (ILC) using a Robot & FES - ILC algorithmapplies during extension phase only


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Tracking results

Fig 1 shows the UNSTIMULTED error aseach session for each subject

Fig 2a shows the mean corrected errorin one task at each session for all

subjects

Fig 2b shows the %max stimulationused

Fig 1 Fig 2

CIMT FUT and the Southampton Mitt


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CIMT, FUT and the Southampton Mitt

Learnt non- use supported by neurophysiologyand animal studies

Inhibition of the unaffected hemisphere excitationof the contralateral (affected) hemisphere?

Large and growing body of literature multiplemethodologies

Beneficial in early recovery for patients withproximal control and some wrist and hand functionparticularly those with neglect


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CIMT: outcome of the EXCITE trial

Single- blind multi- centre RCT

3- 9 months post- stroke (all had >10 active wrist extension)

Compared two- weeks CIMT with conventional care (TreatmentN=106: Control N=116)

CIMT: Constraint of the non-affected hand for 90% of the waking dayAND received task training (shaping) for up to 6 hours/ day

Primary outcome measures: WMFT and MAL

Between baseline and Post- treatment assessments there was agreater improvement in the CIMT group compared with controls

which was statistically significant (p


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Summing-up Without neuroplasticity we would not be able to learn

or to recover from CNS lesions

Many factors influence neuroplasticity that can bedivided into:

Neurophysiological factors influencing theexcitability of the CNS or the release ofneurotransmitters

Behavioural factors

Rehabilitation technologies can be designed tocapitalise on this knowledge

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Neuroscience and Technology