Operant conditioning of spinal reflexes: from basic science to clinical therapy

New appreciation of the adaptive capabilities of the nervous system, recent recognition that most spinal cord injuries are incomplete, and progress in enabling regeneration are generating growing interest in novel rehabilitation therapies. Here we review the 35-year evolution of one promising new approach, operant conditioning of spinal reflexes. This work began in the late 1970’s as basic science; its purpose was to develop and exploit a uniquely accessible model for studying the acquisition and maintenance of a simple behavior in the mammalian central nervous system (CNS). The model was developed first in monkeys and then in rats, mice, and humans. Studies with it showed that the ostensibly simple behavior (i.e., a larger or smaller reflex) rests on a complex hierarchy of brain and spinal cord plasticity; and current investigations are delineating this plasticity and its interactions with the plasticity that supports other behaviors. In the last decade, the possible therapeutic uses of reflex conditioning have come under study, first in rats and then in humans. The initial results are very exciting, and they are spurring further studies. At the same time, the original basic science purpose and the new clinical purpose are enabling and illuminating each other in unexpected ways. The long course and current state of this work illustrate the practical importance of basic research and the valuable synergy that can develop between basic science questions and clinical needs

Targeting Neuroplasticity to Improve Motor Recovery after Stroke

After neurological injury, people develop abnormal patterns of neural activity that limit motor recovery. Traditional rehabilitation, which concentrates on practicing impaired skills, is seldom fully effective. New targeted neuroplasticity (TNP) protocols interact with the CNS to induce beneficial plasticity in key sites and thereby enable wider beneficial plasticity. They can complement traditional therapy and enhance recovery. However, their development and validation is difficult because many different TNP protocols are conceivable, and evaluating even one of them is lengthy, laborious, and expensive. Computational models can address this problem by triaging numerous candidate protocols rapidly and effectively. Animal and human empirical testing can then concentrate on the most promising ones. Here we simulate a neural network of corticospinal neurons that control motoneurons eliciting unilateral finger extension. We use this network to (1) study the mechanisms and patterns of cortical reorganization after a stroke, and (2) identify and parameterize a TNP protocol that improves recovery of extension force. After a simulated stroke, standard training produced abnormal bilateral cortical activation and suboptimal force recovery. To enhance recovery, we interdigitated standard trials with trials in which the teaching signal came from a targeted population of sub-optimized neurons. Targeting neurons in secondary motor areas on 5-20% of the total trials restored lateralized cortical activation and improved recovery of extension force. The results illuminate mechanisms underlying suboptimal cortical activity post-stroke; they enable identification and parameterization of the most promising TNP protocols. By providing initial guidance, computational models could facilitate and accelerate realization of new therapies that improve motor recovery

Targeting Neuroplasticity to Improve Motor Recovery after Stroke

After neurological injury, people develop abnormal patterns of neural activity that limit motor recovery. Traditional rehabilitation, which concentrates on practicing impaired skills, is seldom fully effective. New targeted neuroplasticity (TNP) protocols interact with the CNS to induce beneficial plasticity in key sites and thereby enable wider beneficial plasticity. They can complement traditional therapy and enhance recovery. However, their development and validation is difficult because many different TNP protocols are conceivable, and evaluating even one of them is lengthy, laborious, and expensive. Computational models can address this problem by triaging numerous candidate protocols rapidly and effectively. Animal and human empirical testing can then concentrate on the most promising ones. Here we simulate a neural network of corticospinal neurons that control motoneurons eliciting unilateral finger extension. We use this network to (1) study the mechanisms and patterns of cortical reorganization after a stroke, and (2) identify and parameterize a TNP protocol that improves recovery of extension force. After a simulated stroke, standard training produced abnormal bilateral cortical activation and suboptimal force recovery. To enhance recovery, we interdigitated standard trials with trials in which the teaching signal came from a targeted population of sub-optimized neurons. Targeting neurons in secondary motor areas on 5-20% of the total trials restored lateralized cortical activation and improved recovery of extension force. The results illuminate mechanisms underlying suboptimal cortical activity post-stroke; they enable identification and parameterization of the most promising TNP protocols. By providing initial guidance, computational models could facilitate and accelerate realization of new therapies that improve motor recovery

Classification of evoked potentials by Pearson's correlation in a brain-computer interface

International audienceIn this paper, we describe and evaluate the performance of a linear classifier learning technique for use in a brain-computer interface. Electroencephalogram (EEG) signals acquired from individual subjets are analyzed with this technique in order to detect responses to visual stimuli. Signal processing and classification are used for implementing a palliative communication system which allows the individual to spell words. Performance with this technique is evaluated on data collected from eight individuals

Classification des potentiels évoqués par corrélation de Pearson dans une interface cerveau-ordinateur

National audienceDans cette communication, nous décrivons et évaluons les performances d'une technique d'apprentissage des coefficients d'un classifieur linéaire utilisé dans une interface cerveau-ordinateur. Les signaux de l'électroencéphalogramme d'un individu sont analysés au moyen de cette technique afin de mettre en évidence les réponses de ce dernier à des stimuli visuels. Le traitement et la classification des signaux sont utilisés afin d'implanter un système de communication palliative permettant à l'individu d'épeler des mots. Les performances de la méthode de classification ont été évaluées par une expérimentation sur huit personnes

A comparison of classification techniques for the P300 speller

International audienceThis study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data

H-reflex modulation in the human medial and lateral gastrocnemii during standing and walking

The soleus H-reflex is dynamically modulated during walking. However, modulation of the gastrocnemii H-reflexes has not been studied systematically

Effects of Sensorimotor Rhythm Modulation on the Human Flexor Carpi Radialis H-Reflex

People can learn over training sessions to increase or decrease sensorimotor rhythms (SMRs) in the electroencephalogram (EEG). Activity-dependent brain plasticity is thought to guide spinal plasticity during motor skill learning; thus, SMR training may affect spinal reflexes and thereby influence motor control. To test this hypothesis, we investigated the effects of learned mu (8–13 Hz) SMR modulation on the flexor carpi radialis (FCR) H-reflex in 6 subjects with no known neurological conditions and 2 subjects with chronic incomplete spinal cord injury (SCI). All subjects had learned and practiced over more than 10 < 30-min training sessions to increase (SMR-up trials) and decrease (SMR-down trials) mu-rhythm amplitude over the hand/arm area of left sensorimotor cortex with ≥80% accuracy. Right FCR H-reflexes were elicited at random times during SMR-up and SMR-down trials, and in between trials. SMR modulation affected H-reflex size. In all the neurologically normal subjects, the H-reflex was significantly larger [116% ± 6 (mean ± SE)] during SMR-up trials than between trials, and significantly smaller (92% ± 1) during SMR-down trials than between trials (p < 0.05 for both, paired t-test). One subject with SCI showed similar H-reflex size dependence (high for SMR-up trials, low for SMR-down trials): the other subject with SCI showed no dependence. These results support the hypothesis that SMR modulation has predictable effects on spinal reflex excitability in people who are neurologically normal; they also suggest that it might be used to enhance therapies that seek to improve functional recovery in some individuals with SCI or other CNS disorders

Reflex conditioning: a new strategy for improving motor function after spinal cord injury: Chen et al.

Spinal reflex conditioning changes reflex size, induces spinal cord plasticity, and modifies locomotion. Appropriate reflex conditioning can improve walking in rats after spinal cord injury (SCI). Reflex conditioning offers a new therapeutic strategy for restoring function in people with SCI. This approach can address the specific deficits of individuals with SCI by targeting specific reflex pathways for increased or decreased responsiveness. In addition, once clinically significant regeneration can be achieved, reflex conditioning could provide a means of re-educating the newly (and probably imperfectly) reconnected spinal cord

Brain-computer interface use is a skill that user and system acquire together.

A brain-computer interface (BCI) is a computer-based system that acquires, analyzes, and translates brain signals into output commands in real time. Perdikis and colleagues demonstrate superior performance in a Cybathlon BCI race using a system based on "three pillars": machine learning, user training, and application. These results highlight the fact that BCI use is a learned skill and not simply a matter of "mind reading.