The nervous system maps high-dimension sensory inflow to low-dimension motor outputs during postural responses

J. Lucas McKay1 and Lena H. Ting2

1Electrical and Computer Engineering, 2The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology / Emory University

IntroductionMultiple sources of sensory information are used in patterning appropriate postural responses (Peterka, 2002). Despite this rich sensory inflow, muscle activity during the automatic postural response (APR) in cats is composed of a small number of underlying muscle synergies (Torres-Oviedo et al., 2006). This suggests that the nervous system may map high-dimension sensory information to low-dimension motor outputs during the patterning of the postural response. Such a noninvertible sensorimotor transformation would be consistent with sparse coding schemes observed in sensory processing (Olshausen and Field, 2004). However, it is also possible that sensory information is low-dimension, resulting in similarly low-dimension motor outputs.

SensoryInput

Low-Dimension Sensory InputLimits Motor Output

MotorOutput

Dimension

SensoryInput

Sensorimotor TransformationLimits Motor Output

MotorOutput

Dimension

We hypothesized that muscle synergies during postural responses •to perturbations arise from neural constraints rather than the low dimension of the available sensory information. Somatosensory information from muscles throughout the body is necessary • and sufficient for the generation of appropriate postural responses (Stapley et al., 2002). Visual and vestibular information are used to modulate responses (Inglis and Macpherson 1995). We therefore estimated and compared the dimension of somatosensory • sensory information and motor responses during postural tasks in cats.We predicted that sensorimotor transformations during postural responses • would reduce high sensory input dimension to low motor output dimension.

Methods1 Postural perturbation paradigm temporally

dissociates sensory inputs from motor outputs.Motor outputs follow sensory inputs by a long delay• 16 directions• in the horizontal plane; 15 cm/s vel., 5 cm amp. 3 healthy, unrestrained cats - 365 previously collected trials total•

0-30 ms 120-200 ms

PerturbationOnset

60-140 ms

ProcessingDelay

MechanicalDelay

Disturbance EMG ResponsePerturbation

SOMATOSENSORYINPUT DIMENSION

MOTOR OUTPUTDIMENSION

2DSTIMULUS

DELAY

SOMATOSENSORY INPUTS (0-30 ms)Biomechanical disturbance = 32 Joint Angles• from across the body, 32 Joint Angular Velocities, 12 Ground-reaction forces at the feet

MOTOR OUTPUTS (60-120 ms, 120-180 ms)Neuromotor response (60-120 ms): 16 left hindlimb EMGS• Biomechanical response (120-180 ms) = • Joint angles, Joint angular velocities, forces.Neural delay (60 ms) + electromechanical delay (60 ms) = Biomechanical • response delay (120)

2 Perturbations cause complex joint angle changes in different directions.

Diagonal perturbations are not a superposition of rightwards and • forwards perturbations.

0

200

400

600Time (ms) MCP

Wrist

Elbow

Shoulder

Scapula

Pelvis

Hip

Knee

Ankle

MTP

60°Perturbation

0°Perturbation

2D STIMULUS

0°

90°

270°

180°

3 Input and output time windows were examined.Biomechanical variables: input (0-30 ms) and output (120-200 ms) during • each trial.EMGs: output (initial burst of the APR, 60-120 ms; Ting and Macpherson • 2004) during each trial.

2.5 cm

RFEM

MTPAnkleKnee

Hip

MTPAnkleKnee

Hip

FxFyFz

SEMP

PlatformPosition

JointAngles

JointAngular

Velocities

GroundReaction

Forces

5°

50 °/sec

2.5 N

-250 0 500 1000 -250 0 500Time (ms) Time (ms)

1000

60°Perturbation

0°Perturbation

SENSORYINPUT

MOTOROUTPUT

4 Data Dimension was estimated with PCA.Mean values for each trial were assembled into matrices•

LimbForces

Sensory Input,Motor Output

EMGs

Motor Output

Joint Angles,Joint Velocities

Sensory Input,Motor Output

TrialsVariables

TrialsVariables

TrialsVariables

Dimension of each matrix was estimated as the number of singular values • of the correlation matrix ≥ 0.95.Criterion using R• 2 yields very high (>20) numbers of components, likely due to the large number of experimental variables.

Results5 Both sensory information and motor outputs exhibit significant correlation structure when

compared to shuffled data.

# Components

Sing

ular

Val

ues

0 32

0.95Threshold

0

3

6

# Components

Sensory InputDimension = 8

Sensory InputDimension = 3

NNMFDimension = 4Sensory Input

Dimension = 11

Motor OutputDimension = 8

Motor OutputDimension = 2

Motor OutputDimension = 3

Motor OutputDimension = 5

Sing

ular

Val

ues

Sensory

0 320

3

6

# Components

Sing

ular

Val

ues

0 120

3

6

# Components

Joint Angles Joint Angular Velocities Forces EMG

Sing

ular

Val

ues

NN

MF

VAF

(%)

0 16

3 100

50

6

Motor

Sensory (Shuffled)

Motor (Shuffled)

81.7

AcknowledgmentsWe thank Jane Macpherson and the other researchers responsible for collecting the experimental data used in this retrospective study. Supported by NIH Grant HD46922 to LHT.

ConclusionsPlanar translation perturbations during standing balance are made more complex due to the effects of gravity, introducing 3D joint disturbances throughout the body.

Translation perturbations to standing balance are not equivalent to planar • reaching tasks, or other tasks where 2D motion is imposed by the experimental apparatus (e.g., Kurtzer et al., 2006).

The nervous system maps high-dimension somatosensory information to lower-dimension motor responses during translation perturbations.

Reduced dimension in sensory information due to musculoskeletal dynamics • is further reduced by the sensorimotor transformation during the postural response.

Dimension estimates were pooled across cats and subjected to three-factor ANOVA.

Epoch: Input vs. Output•Data Type: Joint angle, Joint •angular velocity, Force, EMGAnimal•

01

5

10

1 2 3 4 5

PCA Reconstruction

Cor

rela

tion

Mat

rixS

ingu

lar

Valu

es

# Components

ExcludeComponents

< 0.95

6 Sensory inputs are > 2D, although perturbations are 2D by construction.

7.4 (0.2) *ns

5.3 (1.0)3.3 (0.6)

Forces

10.3 (0.6)‡

JointAngles

SOMATOSENSORY INPUTS MOTOR OUTPUTS

Dimension

STIMULUSDIMENSION = 2

8.7 (1.2)

‡

Joint AngularVelocities

3.7 (1.2)

EMG

2.3 (0.6)

Forces

6.7 (1.5)

JointAngles

8.7 (1.2)

Joint AngularVelocities

3.7 (1.2)

PCA

3.7 (0.6)

NNMF0

2

10

* ANOVA, F (1,14) = 8.0; p < 0.013‡ t-test, H0: mean = 2; Bonferroni correction, p < 0.00125

EMGDimension

0

6

t-test; p >> 0.05. (cf. Torres-Oviedo et al., 2006)

8 EMG dimension estimates using PCA are consistent with previous results using NNMF.

7 Motor outputs are lower-dimensional than sensory inputs.

Neural representation and control

Biomechanical interactions with environment

motorbinding

motoneurons

sensorybinding

sensory receptors

estimatedsensory events

desired motoroutputs

hierarchal selection and modulation

sparsesensory and motor

representations

Chiel, Ting, Ekeberg, and Hartmann, 2009.

Symposium:

The Brain in Its Body: Motor Control and Sensing in a

Biomechanical Context.

Wednesday, 1:30-4:00 PM, S100B

ReferencesChiel HJ, Ting LH, Ekeberg O, and Hartmann MJZ. The Brain in Its Body: Motor Control and Sensing in a Biomechanical Context. J Neurosci 29: 12807-12814, 2009.

Inglis JT, and Macpherson JM. Bilateral labyrinthectomy in the cat: effects on the postural response to translation. J Neurophysiol 73: 1181-1191, 1995.

Kurtzer I, Pruszynski JA, Herter TM, and Scott SH. Primate Upper Limb Muscles Exhibit Activity Patterns That Differ From Their Anatomical Action During a Postural

Task. J Neurophysiol 95: 493-504, 2006.

Olshausen BA, and Field DJ. Sparse coding of sensory inputs. Curr Opin Neurobiol 14: 481-487, 2004.

Peterka RJ. Sensorimotor integration in human postural control. J Neurophysiol 88: 1097-1118, 2002.

Stapley PJ, Ting LH, Hulliger M, and Macpherson JM. Automatic postural responses are delayed by pyridoxine-induced somatosensory loss. J Neurosci 22: 5803-5807,

2002.

Ting LH, and Macpherson JM. Ratio of shear to load ground-reaction force may underlie the directional tuning of the automatic postural response to rotation and

translation. J Neurophysiol 92: 808-823, 2004.

Torres-Oviedo G, Macpherson JM, and Ting LH. Muscle synergy organization is robust across a variety of postural perturbations. J Neurophysiol 96: 1530-1546, 2006.