Making sense of muscle activity in sensorimotor deficits
Lena H. Ting, PhD
If two interventions are shown to have
equivalent clinical outcomes:
•  Are they redundant?
–  e.g. can choose either to same effect?
•  Are they complementary?
–  e.g. doing both is better
•  Is one more effective for some people than others?
•  How can the interventions be improved to optimize
outcomes for an individual?
•  Why do they work?
–  E.g. what neural deficit are improved and what changes
or neuroplasticity is induced?
Progress in rehabilitation depends upon
mechanistic metrics of motor function that can:
•  Identify neural mechanisms underlying motor
deficit or improvement
–  Why do successful rehabilitation interventions work?
Levin et al. 2009, Coote et, al 2009
–  Which subpopulations stand to benefit most? Kwakkel 2009, Dobkin 2011 –  Are improvements in function due to compensation or
restoration (or vicariation) of function?
Metz et al 2005, Raghavhan 2010
•  Predict motor performance in the real world
–  Do clinical score predict mobility and falls risk?
Bowden et al. 2010 EMG is noninvasive, neural organization &
biomechanics…and is difficult to interpret cortex
brainstem
spinal cord
Sensory
receptors
muscles
skeleton
tissues
environment
Tresch commentary on Lockhart and Ting Nature Neuroscience 2007
How can we get the information out?
Framework
Behavioral
experiments
Functional
data analysis
Modeling and
simulations
•  Theoretical framework with key
components for movement
•  Experiments that probe specific
neural mechanisms
•  Computational methods for
understanding complex signals
•  Predictive models of movement
in experiments and disease
Clinical movement disorders
Neurophysiology
Sensorimotor feedback
Biomechanics
Sensorimotor feedback framework for
understanding muscle activity
•  Hierarchal relationship of temporal and spatial
structure underlying muscle activation
–  Temporal structure related to task-level goals
–  Spatial structure related to multi-joint coordination
•  Using models + experiments + computation in
–  Peripheral neuropathy balance
–  Parkinson’s disease balance
–  Stroke walking
•  Advantages of sparse analysis methods
–  Wavelet functional ANOVA for temporal structure
–  NMF based muscle synergies for spatial structure
Sensorimotor feedback framework for
understanding muscle activity
•  Hierarchal relationship of temporal and spatial
structure underlying muscle activation
–  Temporal structure related to task-level goals
–  Spatial structure related to multi-joint coordination
•  Using models + experiments + computation in
–  Peripheral neuropathy balance
–  Parkinson’s disease balance
–  Stroke walking
•  Advantages of sparse analysis methods
–  Wavelet functional ANOVA for temporal structure
–  NMF based muscle synergies for spatial structure
Framework
Temporal recruitment of muscle synergies based on task-level goals
Sensorimotor transformation
Sensory
input
Motor
output
EMG
Temporal &
spatial complexity
Ting and McKay, Current Opinion in Neurobiology 2007; Chiel et al. J Neuorsci 2009 ;Ting et al. IJNMBME 2012
Postural responses to perturbation:
Sensorimotor transformations in action
quiet standing
passive response & sensory encoding
EMG
cortex
brainstem
short-latency (spinal / local variables)
long-latency (brainstem / task variables)
spinal cord
step transition
Sensory
“decision”
receptors
muscles
Force
skeleton
CoM
tissues
50 100 200 ms
environment
perturbation
Functional
data analysis
Spatiotemporal patterns related to a few parameters related to CoM motion
Ting and McKay, Curr Op in Neurobiol 2007; Chiel et al. J Neuorsci 2009 ;Ting et al. Int J Numer Method Biomed Eng 2012 Functional
data analysis
Time course of muscle activity reflects sensed body motion
CoM
backward
CoM
motion
backward
d(t)
CoM motion
time
v
support surface
translation
feedback
gains
time
delay
delayed sum
scaled CoM
a
kd
kv
v
(t
a
time
ka
recorded
TA EMG
Sensory
feedback
Sensory estimates
of center of mass
(CoM) motion
threshold
reconstructed
TA timing
and magnitude
d
d
tibialis
anterior
(TA)
brainstem & spinal cord
time
Sensorimotor
Response Model
(SRM)
Motor responses
measured with
electromyogram (EMG)
Functional
data analysis
Agonist (TA) muscles activity reflects sensed backward CoM motion
CoM
backward
CoM
motion
backward
d(t)
CoM motion
time
v
support surface
translation
a
Sensory
encoding
Sensory estimates
of center of mass
(CoM) motion
feedback
gains
time
delay
delayed sum
scaled CoM
threshold
reconstructed
TA timing
and magnitude
d
d
tibialis
anterior
(TA)
brainstem & spinal cord
kd
kv
v
(t
time
a
ka
Conduction & Synaptic &
circuit gain
processing delay
Summation
interneuron or
motor neuron
Sensorimotor
Response Model
(SRM)
recorded
TA EMG
time
Motor responses
measured with
electromyogram (EMG)
Functional
data analysis
Antagonist (MG) muscles reflect sensed forward CoM motion
forward
CoM
forward
motion
CoM motion
CoM
d(t)
kv
delayed
sum
scaled CoM
threshold
Reconstructed MG
timing and magnitude
v
(t
a
ka
a
Sensory
backward
encoding
CoM
motion
backward
CoM motion
d
time
delay
kd
v
support surface
translation
feedback
gains
d
d
medial
gastrocnemius
(MG)
brainstem & spinal cord
Summation
basal& interneuron
Synaptic &
Conduction
inhibit
or
inappropriate
ganglia
program
circuit
gain
processing
delay
motor
neuron
brainstem
& spinal
cord
feedback
gains
time
delay
delayed
sum
scaled CoM
threshold
•  Agonist muscle (TA)d is inhibited and antagonist
k
muscle (MG)
is excited
at the end of the
v
(t
k
perturbation
a
d
v
a
v
ka
Coactivation with threat: Carpenter MG, Frank JS, Adkin AL, Paton A, Allum JH. J Neurophysiol 92: 3255-65, 2004
Behavioral
experiments
Common sensory signals drive reciprocal agonist/antagonist muscle activity
Safavynia and Ting, J Neurophysiol 2013
Behavioral
experiments
CoM kinematics predict EMG
better than joint kinematics in humans
Safavynia, and Ting J Neurophysiology 2013
Delayed, task-level error feedback predicts
temporal recruitment of muscle synergies
Ting and McKay, Curr Op in Neurobiol 2007; Chiel et al. J Neuorsci 2009 ;Ting et al. Int J Numer Method Biomed Eng 2012 Each muscle synergy has a preferred direction
of recruitment
Safavynia and Ting, J Neurophysiol 2013
Time course of muscle synergy recruitment in
preferred direction is described by CoM feedback
Safavynia and Ting, J Neurophysiol 2013
Parameters from preferred direction predict
activity in other directions
•  Based on projection of CoM kinematic error along each
muscle synergy’s preferred direction
Safavynia and Ting, J Neurophysiol 2013
Each functional muscle synergy produces an
consistent endpoint force direction Activation level!
Muscle synergy recruitment, ci
Muscle weights, wi
Synergy vector!
Ting and Macpherson, J Neurophysiology 2005; Torres-Oviedo, Macpherson, Ting, J Neurophysiology 2006
Muscle synergies quantify variability across subjects using a common framework Torres-Oviedo, Macpherson, Ting, J Neurophysiology 2006
Muscle synergies underlie trial by trial variability
•  Ankle and hip
strategy are
implemented by
different synergies
•  Muscle synergy
activations vary
from trial to trial Torres-Oviedo and Ting, J Neurophysiology 2007
•  Muscle activity
variations are not
random but
coupled in muscle
Averaging
synergy patterns
Sensorimotor feedback framework for
understanding muscle activity
•  Hierarchal relationship of temporal and spatial
structure underlying muscle activation
–  Temporal structure related to task-level goals
–  Spatial structure related to multi-joint coordination
•  Using models + experiments + computation in
–  Peripheral neuropathy balance
–  Parkinson’s disease balance
–  Stroke walking
•  Advantages of sparse analysis methods
–  Wavelet functional ANOVA for temporal structure
–  NMF based muscle synergies for spatial structure
Modeling and
simulations
Neuromechanical model can predict optimal solutions (data-free) in cat
Musculoskeletal
system
Muscle
activation
Sensory
feedback
Nervous
system
Lockhart and Ting, Nature Neuroscience 2007, Welch and Ting, J Neurophysiology 2008, 2009, Safavynia and Ting 2011, 2013
Detailed features of muscle activity can be predicted using by only 3 parameters
Lockhart and Ting, Nature Neuroscience 2007
“Bounce” in muscle activity reflects non-ideal
perturbation acceleration, not neural processing
Lockhart and Ting, Nature Neuroscience 2007
Pyridoxine (B6) overdose causes permanent
sensory neuropathy
•  Affects sensory afferents with cell bodies in dorsal
root ganglia; largest neurons first
•  Dosed to target Group I (10-20 um) afferents
–  Muscle spindles
–  Cutaneous afferents
–  Golgi Tendon organs
•  IP injections on 2 consecutive days
•  Ataxia, followed by motor recovery over 2-3
weeks Stapley, Ting, Hulliger, Macpherson, J Neuroscience 2002
Muscle activity reflects loss of acceleration
sensory information
• Optimal muscle pattern in sensory loss differs from intact
• Kinematics are quantitatively but not qualitatively different
Lockhart and Ting, Nature Neuroscience 2007
Sensorimotor feedback framework for
understanding muscle activity
•  Hierarchal relationship of temporal and spatial
structure underlying muscle activation
–  Temporal structure related to task-level goals
–  Spatial structure related to multi-joint coordination
•  Using models + experiments + computation in
–  Peripheral neuropathy balance
–  Parkinson’s disease balance
–  Stroke walking
•  Advantages of sparse analysis methods
–  Wavelet functional ANOVA for temporal structure
–  NMF based muscle synergies for spatial structure
Parkinson’s disease impairs balance and
mobility leading to falls and injury
•  PD is a neurodegenerative disorder affecting the
basal gangia
–  Loss of dopamine production in substantia nigra
•
•
•
•
3-month fall rate ≈ 46%1
6-month fall rate ≈ 5× vs. age-matched adults2 Risk of a hip fracture ≈ 3x vs. age-matched adults3
Among least responsive to dopaminergic
pharmacotherapy2,4
1Pickering
RM et al. Mov Disord 22: 1892-1900, 2007; 2Bloem BR et al. J Neurol 248: 950-958-958, 2001"
3Melton LJ, 3rd et al. Mov Disord 21: 1361-1367, 2006; 4Klawans HL. Mov Disord 1: 187-192, 1986."
Dynamic co-contraction of TA and MG in PD
Pre-AT
Subject 3
H&Y 2.5
kd kv ka
Kp=0.024, Kv=0.054, Ka=0.661,Td=0.100
Kp=0.000, Kv=0.000, Ka=0.000,Td=0.100
CoM
bias=0.093,R2=0.553, VAF=0.759
d(t)
EMG (mean)
EMG (trials)
SRM fit
0.5 nu
TA
Appopriate SRM Component
Inappropriate SRM Component
kd kv ka
Kp=0.000, Kv=0.000, Ka=0.000,Td=0.100
Kp=0.035, Kv=0.036, Ka=0.507,Td=0.100
MG
bias=0.369,R2=0.559, VAF=0.925
0.5 nu
support surface
translation
McKay, Hackney, Ting, in progress
0.0
5 cm
time (sec)
1.0
•  TA and MG both excited
in response to backward
CoM motion and inhibited
in response to forward
CoM motion
•  MG not excited in
response to forward CoM
Antagonist may also co-activate with agonists
for stability, or with threat
forward
CoM
forward
motion
CoM motion
CoM
d(t)
kv
backward
CoM
motion
backward
CoM motion
v
a
delayed
sum
scaled CoM
threshold
v
(t
a
ka
a
d
time
delay
kd
v
support surface
translation
feedback
gains
d
d
medial
gastrocnemius
(MG)
brainstem & spinal cord
basal
ganglia
brainstem & spinal cord
feedback
gains
time
delay
delayed
sum
scaled CoM
d
kd
kv
v
(t
a
inhibit
inappropriate
program
threshold
Biphasic
response
due to
summation
of two
neural
pathways
ka
Coactivation with threat: Carpenter et al. J Neurophysiology 2004
Coactivation with threat: Carpenter MG, Frank JS, Adkin AL, Paton A, Allum JH. J Neurophysiol 92: 3255-65, 2004
Adapted tango (AT) neurorehabilitation
improves balance and mobility in individuals
with PD
•  Promotes adherence →
counters excessive attrition in
PD •  Adapted for PD → safety,
dynamic balance, internal +
external movement cues
•  Effective → gains on Berg
Balance Scale, gait speed, and
other clinical measures1-4
–  Retained for 1 month1
1Hackney
ME, and Earhart GM. Neurorehabil Neural Repair 24: 384-392, 2010; 2Am J Dance Ther 31: 41-45, 2010;
3J Rehabil Med 41: 475-481, 2009; 4Duncan RP, and Earhart GM. Neurorehabil Neural Repair 26: 132-143, 2012
Intensive AT Rehabilitation improved clinical
measures of static and dynamic balance
Pre
Post
P-value (paired t-test)
Unified Parkinson s
Disease Rating Scale
30±5!
28±4!
0.189*!
Berg Balance Scale
50±7!
54±4!
0.028*!
Fullerton Advanced
Balance Scale
27±8!
31±6!
0.004*!
Functional Reach (cm)
30±8!
33±6!
0.061!
Dynamic Gait Index
19±4!
21±3!
0.090*!
6-Minute Walk Test (m)
406.1±100.7!
419.6±103.8!
0.721*!
Decreased forward CoM motion in perturbations
McKay, Hackney, Ting, in prep
Muscle activity + computational analysis +
multiple experiments reveals:
•  A common sensorimotor transformation
governing healthy and impaired movements
•  A specific channel of lost sensory information not
evident in kinematics
•  Impaired and improved motor program selection
in PD Supports (Mink 1996)
•  Improved involuntary brainstem sensorimotor
response after exercise-based neurorehabilitation
Sensorimotor feedback framework for
understanding muscle activity
•  Hierarchal relationship of temporal and spatial
structure underlying muscle activation
–  Temporal structure related to task-level goals
–  Spatial structure related to multi-joint coordination
•  Using models + experiments + computation in
–  Peripheral neuropathy balance
–  Parkinson’s disease balance
–  Stroke walking
•  Advantages of sparse analysis methods
–  Wavelet functional ANOVA for temporal structure
–  NMF based muscle synergies for spatial structure
If you don’t have a model, data discovery is also
important but curves are hard to compare
Statistics based
on user-selected
features, e.g. peak
or time bins are
biased
• A lot of
information is not
used, particularly
concerning the
shape of the
curves
•
Data from Welch and Ting J Neurophysiology 2009
Functional
data analysis
Timepoint analysis lack statistical power
• ANOVA on individual
timepoints only identifies
discrete regions with large
differences
• Reduces power due to
multiple comparisons
• But observations are not
independent
McKay, Welch,Vidakovic, Ting J Neurophysiology 2013
ANOVA in wavelet domain produces smooth
difference curves over time; increased power
McKay, Welch,Vidakovic, Ting J Neurophysiology 2013
Wavelets produce sparse representations
using a few time-localized functions
McKay, Welch,Vidakovic, Ting J Neurophysiology 2013
Difference curves for 3 factor functional
ANOVA allow for data discovery
subject x
acceleration x
velocity
contrast
•wfANOVA
contrasts
ignore high
variability
areas
•
Sensorimotor feedback framework for
understanding muscle activity
•  Hierarchal relationship of temporal and spatial
structure underlying muscle activation
–  Temporal structure related to task-level goals
–  Spatial structure related to multi-joint coordination
•  Using models + experiments + computation in
–  Peripheral neuropathy balance
–  Parkinson’s disease balance
–  Stroke walking
•  Advantages of sparse analysis methods
–  Wavelet functional ANOVA for temporal structure
–  NMF based muscle synergies for spatial structure
Muscle synergies can account for cycle-bycycle variations in walking
Clark, Ting, Neptune, Zajac, Kautz J Neurophysiology, 2010
McGowan, Neptune, Clark, Kautz, J Biomechanics , 2010
NMF produces sparse representations that
represent features or events in the data
Sparse =
A few muscles in each synergy
A few synergies
at a time
A few synergies contribute
to a muscle
PCA uses all elements all the time
All muscles in each synergy
All synergies
at a time
Most synergies contribute
to a muscle
Chronic post-stroke hemiplegia
NMF identifies muscle synergies that relate to
motor function
Merged proximal and distal
extensors, PA=0.27±0.16
Independent proximal and
distal extensors, PA=0.52±0.32
•  Preserved spinal structure, reduced corticospinal drive?
Clark, Ting, Neptune, Zajac, Kautz J Neurophysiology, 2010
PCA is descriptive, NMF is prescriptive, e.g. constrains the set of possible patterns
Ting and Chvatal 2010, Motor Control, chapter and tutorial available for download
PCA is descriptive, NMF is prescriptive, e.g. constrains the set of possible patterns
Ting and Chvatal 2010, Motor Control, chapter and tutorial available for download
Fewer muscle synergies in the paretic leg in
chronic post-stroke hemiplegia
Low Moderate High
Level of locomotor complexity
•  These were what they used, but not necessarily what
they could use (learned non-use?) àneed experiment
•  We don’t know if these are feedforward or feedback
mediated à need experiments
Clark, Ting, Neptune, Zajac, Kautz J Neurophysiology, 2010
Number of muscle synergies predicts
functional locomotor performance
•  High complexity subjects could walk faster than
moderate, but chose not to à need to find constraints
•  Subjects with the same number of synergies as healthy
controls walked slower à need temporal analysis
Clark, Ting, Neptune, Zajac, Kautz J Neurophysiology, 2010
Number of muscle synergies predicts motor
function similar better than Fugl-Myer score
•  Balance and propulsive scores are predicted by number
of muscle synergies à generalized motor capacity?
Bowden, Clark, Kautz, Neurorehabilitation and Neural Repair, 2010
Muscle synergies are both a useful tool and a
theory of the brain, but use wisely
Framework
Behavioral
experiments
Functional
data analysis
Modeling and
simulations
•  Theoretical framework with key
components for movement
•  Experiments that probe specific
neural mechanisms
•  Computational methods for
understanding complex signals
•  Predictive models of movement
in experiments and disease
Clinical movement disorders
Neurophysiology
Sensorimotor feedback
Biomechanics
Can structure and organization of EMG
output during movement:
•  Differentiate individuals across motor function?
–  Can it do as well as a clinical test?
–  How does muscle activity differ in impairment? •  Identify mechanisms of deficit and recovery?
–  Can it predict more than a clinical test?
–  Differentiate compensatory vs restorative mechanisms?
–  Do they change after successful rehabilitation?
–  What are “good” and “bad” patterns of muscle activity?
•  Predict behavior important in the real world?
–  Does the organization of activity generalize across
behaviors?
Sensorimotor feedback framework for
understanding muscle activity
•  Hierarchal relationship of temporal and spatial
structure underlying muscle activation
Delis et al J Neurophysiology 2013
McCrea &Rybak Brain Research Reviews 2007, Progress in Brain Research 2007
•  Using models + experiments + computation to
–  Identify neural substrates; feedforward vs feedback;
restoration vs substitution –  Predict current and potential motor ability
Allen, Kautz, Neptune Clinical Biomechanics 2013; Walter … Fregly Journal of
Biomechanical Engineering 2014
•  Advantages of sparse analysis methods
–  Sparse coding predicts sensory representations and
could be a general principle for sensing and movement Olshausen and Field Current Opinion in Neurobiology 2004; Ting and McKay
Current Opinion in Neurobiology 2007; Poggio and Bizzi Nature 2004
Ting
Neuromechanics Lab
Current: Jessica Allen, PhD
Harrison Bartlett
Kyle Blum, BS
Kim Lang, BS
Lauren Levey, BS
Lucas McKay, PhD
Andrew Sawers, PhD, MSPO Hongchul Sohn, MS
NIH R01 HD046922, NS053822,
NIH R21 HD075612
NSF EFRI 1137229 Some Alumni:
Jeff Bingham, PhD
Stacie Chvatal, PhD
Nate Bunderson, PhD
Julia Choi, PhD
Gelsy Torres-Oviedo, PhD
Seyed Safavynia, PhD
Torrence Welch PhD
Some Collaborators:
Tom Burkholder
Young-Hui Chang
Charlie Kemp
Madeleine Hackney
Karen Liu
Jane Macpherson
Richard Nichols
Randy Trumbower