Johan van Doornik

Definition and Classification of Negative Motor Signs in Childhood

In this report we describe the outcome of a consensus meeting that occurred at the National Institutes of Health in Bethesda, Maryland, March 12 through 14, 2005. The meeting brought together 39 specialists from multiple clinical and research disciplines including developmental pediatrics, neurology, neurosurgery, orthopedic surgery, physical therapy, occupational therapy, physical medicine and rehabilitation, neurophysiology, muscle physiology, motor control, and biomechanics. The purpose of the meeting was to establish terminology and definitions for 4 aspects of motor disorders that occur in children: weakness, reduced selective motor control, ataxia, and deficits of praxis. The purpose of the definitions is to assist communication between clinicians, select homogeneous groups of children for clinical research trials, facilitate the development of rating scales to assess improvement or deterioration with time, and eventually to better match individual children with specific therapies. “Weakness” is defined as the inability to generate normal voluntary force in a muscle or normal voluntary torque about a joint. “Reduced selective motor control” is defined as the impaired ability to isolate the activation of muscles in a selected pattern in response to demands of a voluntary posture or movement. “Ataxia” is defined as an inability to generate a normal or expected voluntary movement trajectory that cannot be attributed to weakness or involuntary muscle activity about the affected joints. “Apraxia” is defined as an impairment in the ability to accomplish previously learned and performed complex motor actions that is not explained by ataxia, reduced selective motor control, weakness, or involuntary motor activity. “Developmental dyspraxia” is defined as a failure to have ever acquired the ability to perform age-appropriate complex motor actions that is not explained by the presence of inadequate demonstration or practice, ataxia, reduced selective motor control, weakness, or involuntary motor activity.

Visual Feedback Reduces Co-contraction in Children With Dystonia

Inappropriate muscle activation and co-contraction are important features in childhood dystonia, and clinical interventions are often targeted to reduce the excess muscle activation. Previous research has shown that visual biofeedback of muscle activity can help people to reduce excess muscle activation in a variety of motor disorders. To investigate the effectiveness of similar techniques for dystonia, we had participants perform a tracking task with and without visual feedback of co-contraction. Children with dystonia had greater levels of co-contraction than children without dystonia. Most importantly, individuals were able to reduce their co-contraction significantly when visual biofeedback was provided. These results indicate that children with dystonia are able to control co-contraction, at least to a certain extent, provided attention can be directed to the excess muscle activation. These results also suggest that methods of biofeedback focusing on inappropriate muscle activations might provide a clinical benefit for treatment of children with dystonia.

Finger muscle control in children with dystonia.

Background: Childhood dystonia is a disorder that involves inappropriate muscle activation during attempts at voluntary movement. Few studies have investigated the muscle activity associated with dystonia in children, and none have done so in the hands.

Hypertonia in Childhood Secondary Dystonia Due to Cerebral Palsy Is Associated with Reflex Muscle Activation

It is often assumed that co‐contraction of antagonist muscles is responsible for increased resistance to passive movement in hypertonic dystonia. Although co‐contraction may certainly contribute to hypertonia in some patients, the role of reflex activation has never been investigated. We measured joint torque and surface electromyographic activity during passive flexion and extension movements of the elbow in 8 children with hypertonic arm dystonia due to dyskinetic cerebral palsy. In all cases, we found significant phasic electromyographic activity in the lengthening muscle, consistent with reflex activity. By correlating activation with position or velocity of the limb, we determined that some children exhibit position‐dependent activation, some exhibit velocity‐dependent activation, and some exhibit a mixed pattern of activation. We conclude that involuntary or reflex muscle activation in response to stretch may be a significant contributor to increased tone in hypertonic dystonia, and we conjecture that this activation may be more important than co‐contraction for determining the resistance to passive movement.

Oral Baclofen Increases Maximal Voluntary Neuromuscular Activation of Ankle Plantar Flexors in Children With Spasticity Due to Cerebral Palsy

Although spasticity is a common symptom in children with cerebral palsy, weakness may be a much greater contributor to disability. We explore whether a treatment that reduces spasticity may also have potential benefit for improving strength. Ten children with cerebral palsy and spasticity in the ankle plantar flexor muscles were treated with oral baclofen for 4 weeks. We tested voluntary ability to activate ankle plantar flexor muscles using the ratio of the surface electromyographic signal during isometric maximal voluntary contraction to the M-wave during supramaximal electrical stimulation of the tibial nerve and tested muscle strength using maximal isometric plantar flexion torque. Mean maximal voluntary neuromuscular activation increased from 1.13 ± 1.02 to 1.60 ± 1.30 ( P < .05) after treatment, corresponding to an increase in 9 of 10 subjects. Mean maximal plantar flexion torque did not change. We conjecture that antispasticity agents could facilitate strength training by increasing the ability to voluntarily activate muscle.

Neural Network Robot Control with Noisy Learning

Abstract Neural network based control of a serial-link robotic manipulator is considered subject to a signal dependent noise (SDN) model corrupting the training signal. A radial basis function (RBF) network is utilized in the feedforward control to approximate the unknown inverse dynamics. The weights are adaptively adjusted according to a gradient descent plus a regulation term (Narendras e -modification). A typical quadratic stochastic Lyapunov function is constructed which shows under certain noise models it is not necessary to employ quartic Lyapunov functions as is typically carried out in stochastic adaptive backstepping designs. Bounds on the feedback gains, and learning rate parameters are derived that guarantee the origin of the closed loop system is semi-globally, uniformly bounded in expectation (SGUBE).

Feedback Error Learning with basis function networks

In this paper, we examine the stability properties of Feedback Error Learning, a model for biological control systems. We consider a specific model for cerebellar learning during fast voluntary movements. We assume that the feedforward approximation is represented by a basis function network. We establish local stability regions and compute explicit bounds on the feedback gain matrices.

Uniform Boundedness of Feedback Error Learning for a Class of Stochastic Nonlinear Systems

In this paper we analyze stochastic stability and boundedness of the neurophysiologically inspired feedback error learning (FEL) paradigm, a control algorithm that uses an inverse model of the plant to maximize tracking performance under uncertain conditions. FEL is analyzed in the framework of an adaptive state feedback controller. An inverse model of the plant is adaptively learned by a neural network based on basis functions, while the output of the feedback controller is used as the training signal. The nonlinear plant under consideration is described as a multidimensional SISO stochastic differential equation. The tracking error was shown to be uniformly bounded in the case where the variance of the noise on the parameter update rule was constant and the variance of the noise on the state variables was a function of the tracking error. When the system was allowed to have only noise on the states variables, with variance linear to the tracking error, then FEL was shown to be stochastically stable