Subthalamic deep brain stimulation modulates small fiber-dependent sensory thresholds in Parkinson's disease

The effects of deep brain stimulation of the subthalamic nucleus on nonmotor symptoms of Parkinson's disease (PD) rarely have been investigated. Among these, sensory disturbances, including chronic pain (CP), are frequent in these patients. The aim of this study was to evaluate the changes induced by deep brain stimulation in the perception of sensory stimuli, either noxious or innocuous, mediated by small or large nerve fibers. Sensory detection and pain thresholds were assessed in 25 PD patients all in the off-medication condition with the stimulator turned on or off (on- and off-stimulation conditions, respectively). The relationship between the changes induced by surgery on quantitative sensory testing, spontaneous CP, and motor abilities were studied. Quantitative sensory test results obtained in PD patients were compared with those of age-matched healthy subjects. Chronic pain was present in 72% of patients before vs 36% after surgery (P = .019). Compared with healthy subjects, PD patients had an increased sensitivity to innocuous thermal stimuli and mechanical pain, but a reduced sensitivity to innocuous mechanical stimuli. In addition, they had an increased pain rating when painful thermal stimuli were applied, particularly in the off-stimulation condition. In the on-stimulation condition, there was an increased sensitivity to innocuous thermal stimuli but a reduced sensitivity to mechanical or thermal pain. Pain provoked by thermal stimuli was reduced when the stimulator was turned on. Motor improvement positively correlated with changes in warm detection and heat pain thresholds. Subthalamic nucleus deep brain stimulation contributes to relieve pain associated with PD and specifically modulates small fiber-mediated sensations. (C) 2012 International Association for the Study of Pain. Published by Elsevier B. V. All rights reserved.

Detection of gait perturbations based on proprioceptive information. Application to Limit Cycle Walkers

Walking on irregular surfaces and in the presence of unexpected events is a challenging problem for bipedal machines. Up to date, their ability to cope with gait disturbances is far less successful than humans': Neither trajectory controlled robots, nor dynamic walking machines (Limit CycleWalkers) are able to handle them satisfactorily. On the contrary, humans reject gait perturbations naturally and efficiently relying on their sensory organs that, if needed, elicit a recovery action. A similar approach may be envisioned for bipedal robots and exoskeletons: An algorithm continuously observes the state of the walker and, if an unexpected event happens, triggers an adequate reaction. This paper presents a monitoring algorithm that provides immediate detection of any type of perturbation based solely on a phase representation of the normal walking of the robot. The proposed method was evaluated in a Limit Cycle Walker prototype that suffered push and trip perturbations at different moments of the gait cycle, providing 100% successful detections for the current experimental apparatus and adequately tuned parameters, with no false positives when the robot is walking unperturbed.

Emergence of sensory selection mechanisms in Artificial Life simulations

Background The evolutionary advantages of selective attention are unclear. Since the study of selective attention began, it has been suggested that the nervous system only processes the most relevant stimuli because of its limited capacity [1]. An alternative proposal is that action planning requires the inhibition of irrelevant stimuli, which forces the nervous system to limit its processing [2]. An evolutionary approach might provide additional clues to clarify the role of selective attention. Methods We developed Artificial Life simulations wherein animals were repeatedly presented two objects, "left" and "right", each of which could be "food" or "non-food." The animals' neural networks (multilayer perceptrons) had two input nodes, one for each object, and two output nodes to determine if the animal ate each of the objects. The neural networks also had a variable number of hidden nodes, which determined whether or not it had enough capacity to process both stimuli (Table 1). The evolutionary relevance of the left and the right food objects could also vary depending on how much the animal's fitness was increased when ingesting them (Table 1). We compared sensory processing in animals with or without limited capacity, which evolved in simulations in which the objects had the same or different relevances. Table 1. Nine sets of simulations were performed, varying the values of food objects and the number of hidden nodes in the neural networks. The values of left and right food were swapped during the second half of the simulations. Non-food objects were always worth -3. The evolution of neural networks was simulated by a simple genetic algorithm. Fitness was a function of the number of food and non-food objects each animal ate and the chromosomes determined the node biases and synaptic weights. During each simulation, 10 populations of 20 individuals each evolved in parallel for 20,000 generations, then the relevance of food objects was swapped and the simulation was run again for another 20,000 generations. The neural networks were evaluated by their ability to identify the two objects correctly. The detectability (d') for the left and the right objects was calculated using Signal Detection Theory [3]. Results and conclusion When both stimuli were equally relevant, networks with two hidden nodes only processed one stimulus and ignored the other. With four or eight hidden nodes, they could correctly identify both stimuli. When the stimuli had different relevances, the d' for the most relevant stimulus was higher than the d' for the least relevant stimulus, even when the networks had four or eight hidden nodes. We conclude that selection mechanisms arose in our simulations depending not only on the size of the neuron networks but also on the stimuli's relevance for action.