A neural network approach to motor-sensory relations during postural disturbance

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Brain Res Bull. 2006 Apr 28;69(4):365-74.

A neural network approach to motor-sensory relations during postural

disturbance.

Wu G, Haugh L, Sarnow M, Hitt J.

Department of Physical Therapy, University of Vermont, Burlington, VT

05405, USA.

This study explored whether artificial neural networks (ANN) can be

used to quantify the motor-sensory relationship during postural

disturbance. An ANN model was constructed with seven mechanical

stimuli to the visual, vestibular and somatosensory systems (i.e.,

head angular and linear accelerations, eye-target distance, ankle

joint rotation and velocity, as well as normal and shear ground

contact forces under the feet) as inputs, and electromyographic

activities of tibialis anterior and gastrocnemius muscles as outputs.

These inputs and outputs were directly measured during a sudden toes-

up-down rotation of the supporting base in two groups of elderly

subjects: people with peripheral neuropathy (NP) who have severe loss

of mechanoreception in the sole of their feet and people without NP.

The products of ANN weights were used in a summary statistic called

the Q-value to estimate the contribution of each mechanical stimulus

to sensory systems in determining each leg muscle activity. It was

found that: (1) the stimuli to the vestibular system and/or ankle

proprioceptors have greater contributions to leg muscle activities,

especially the TA muscle, in people with NP than people without NP;

(2) the stimuli to somatosensory receptors have the greatest

contribution, and the stimuli to the vestibular system have the least

contribution to both muscle activities in both groups. These findings

are supported by previous studies and have demonstrated the potential

of the Q-value concept in the ANN model in studying the motor-sensory

relationship in human postural control.