Sensorimotor effects of plasticity-inducing percutaneous peripheral nerve stimulation protocols

BACKGROUND

Electrical stimulation of skin afferents can induce somatosensory plasticity in humans. Nevertheless, it is unknown if this is possible to do through percutaneous stimulation of a peripheral nerve, which will allow for regional anaesthesia interventions. Furthermore, potentiation protocols applied over mainly non-nociceptive fibers inhibit nociception in rodents, but this has not been tested in humans.

OBJECTIVE

to determine whether a protocol aiming to depress the nociceptive circuit and another aiming to potentiate non-nociceptive circuits produce regional hypoalgesia and changes in motor function, applied through percutaneous peripheral nerve stimulation (pPNS), and to assess which of them is more promising for pain relief, immediately and 24 hours after intervention.

METHODS

PT-cLF protocol aims to depress the nociceptive pathway through Pain Threshold, continuous Low Frequency stimulation and ST-bHF aims to produce potentiation of the non-nociceptive pathway, through Sensory Threshold burst stimulation at High Frequency. All subjects (n=29) went through both protocols and a control condition in a randomized and blinded crossover design.

RESULTS

Compared to control, ST-bHF induced distal hypoalgesia, towards electrical (p=0.04) and mechanical stimuli (p=0.02) and produced mechanical hypoesthesia (p=0.02). Contrarily, hypoalgesia was not observed after PT-cLF (p>0.05) but increased electrical motor threshold (p=0.04), reduced motor recruitment (p=0.03), and the subjects reported feeling reduced strength (p<0.01).

CONCLUSION

This works provides evidence that is possible to induce antinociceptive plasticity in a wide territory using pPNS. Moreover, it demonstrates for the first time in humans that a protocol aiming to produce long-term potentiation applied predominantly over non-nociceptive afferents induces hypoesthesia and hypoalgesia.

Reorganization of action observation and sensory-motor networks after action observation therapy in children with congenital hemiplegia: A pilot study

New rehabilitation programs based on action observation therapy (AOT) are effective in improving motor function in children with congenital hemiplegia. In this pilot study we tested the potential effects of AOT on the reorganization of the motor system by functional magnetic resonance imaging (fMRI). As part of a randomized trial, eight subjects (age range: 6.2-14.5 years) with congenital hemiplegia were randomly assigned to an experimental (EG) or control (CG) group. All children underwent a clinical and neurophysiological assessment with Assisting Hand Assessment (AHA), MRI, and fMRI at baseline (T0), 1(T1), and 8(T2) weeks after the end of 3-week treatment. For the EG, AOT consisted in the observation of uni/bimanual goal-directed actions followed by their execution. CG watched same-duration computer games and then performed the same actions in the same order used in the EG. fMRI study was carried out using two different paradigms, for exploring sensory-motor network (SMN) localization and action observation network (AON). The pattern of brain activation was generally similar between T0 and T1 for both groups, while it was more widespread at T2, compared to T0 and T1, in the EG. This enlargement was coupled with functional improvement at AHA. Single-subject analysis shows a reduction of lateralization indexes both for the AON and the SMN. This pilot study, despite the small sample, showed the fMRI feasibility for providing relevant biomarkers of brain plasticity for monitoring the AOT response in children with congenital hemiplegia. The study was registered at http://www.clinicaltrials.gov (identifier NCT01016496).

Candidate Interneurons Mediating the Resetting of the Locomotor Rhythm by Extensor Group I Afferents in the Cat

Sensory information arising from limb movements controls the spinal locomotor circuitry to adapt the motor pattern to demands of the environment. Stimulation of extensor group (gr) I afferents during fictive locomotion in decerebrate cats prolongs the ongoing extension, and terminates ongoing flexion with an initiation of the subsequent extension, i. e. "resetting to extension". Moreover, instead of the classical Ib non-reciprocal inhibition, stimulation of extensor gr I afferents produces a polysynaptic excitation in extensor motoneurons with latencies (∼3.5-4.0 ms) compatible with 3 interposed interneurons. We assume that some interneurons in this pathway actually belong to the rhythm-generating layer of the locomotor Central Pattern Generator (CPG), since their activity was correlated to a resetting of the rhythm. In the present work fictive locomotion was (mostly) induced by i.v. injection of nialamide followed by l-DOPA in paralyzed cats following decerebration and spinalization at C1 level. In some experiments, we extended previous observations during fictive locomotion on the emergence and locomotor state-dependence of polysynaptic excitatory postsynaptic potentials from extensor gr I afferents to ankle extensor motoneurons. However, the main focus was to record location and properties of interneurons (n = 62) that (i) were active during the extensor phase of fictive locomotion and (ii) received short-latency excitation (mono-, di- or polysynaptic) from extensor gr I afferents. We conclude that the interneurons recorded fulfill the characteristics to belong to the neuronal pathway activated by extensor gr I afferents during locomotion, and may contribute to the 'resetting to extension' as part of the locomotor CPG.

Brain Gray Matter Volume Is Modulated by Visual Input and Overall Learning Success but Not by Time Spent on Learning a Complex Balancing Task

To better understand the process of neuroplasticity, this study assesses brain changes observed by voxel-based morphometry (VBM) in response to two different learning conditions. Twenty-two young, healthy subjects learned slacklining, a complex balancing task, with either their eyes open (EO, n = 11) or their eyes closed (EC, n = 11). The learning took place three times per week for four weeks, with learning periods of 1 hour, providing a total of 12 hours of learning. The scanning and testing protocols were applied at three time-points: (1) immediately before learning (baseline), (2) immediately afterwards (post-test), and (3) two months afterwards (follow-up). The EO group performed better on the task-specific test. Significant group*time interaction effects were found in sensory-motor areas at the post-test, with increases in the EO group only. The results suggest that VBM-observed brain changes in response to learning a complex balancing task vary depending on the learning success and the availability of visual input, and not solely on the amount of time spent on learning. These findings should be taken into account by future studies using similar methodologies.

Convergence of monosynaptic and polysynaptic sensory paths onto common motor outputs in a Drosophila feeding connectome

We reconstructed, from a whole CNS EM volume, the synaptic map of input and output neurons that underlie food intake behavior of Drosophila larvae. Input neurons originate from enteric, pharyngeal and external sensory organs and converge onto seven distinct sensory synaptic compartments within the CNS. Output neurons consist of feeding motor, serotonergic modulatory and neuroendocrine neurons. Monosynaptic connections from a set of sensory synaptic compartments cover the motor, modulatory and neuroendocrine targets in overlapping domains. Polysynaptic routes are superimposed on top of monosynaptic connections, resulting in divergent sensory paths that converge on common outputs. A completely different set of sensory compartments is connected to the mushroom body calyx. The mushroom body output neurons are connected to interneurons that directly target the feeding output neurons. Our results illustrate a circuit architecture in which monosynaptic and multisynaptic connections from sensory inputs traverse onto output neurons via a series of converging paths.

Human-Derived Disturbance Estimation and Compensation (DEC) Method Lends Itself to a Modular Sensorimotor Control in a Humanoid Robot

The high complexity of the human posture and movement control system represents challenges for diagnosis, therapy, and rehabilitation of neurological patients. We envisage that engineering-inspired, model-based approaches will help to deal with the high complexity of the human posture control system. Since the methods of system identification and parameter estimation are limited to systems with only a few DoF, our laboratory proposes a heuristic approach that step-by-step increases complexity when creating a hypothetical human-derived control systems in humanoid robots. This system is then compared with the human control in the same test bed, a posture control laboratory. The human-derived control builds upon the identified disturbance estimation and compensation (DEC) mechanism, whose main principle is to support execution of commanded poses or movements by compensating for external or self-produced disturbances such as gravity effects. In previous robotic implementation, up to 3 interconnected DEC control modules were used in modular control architectures separately for the sagittal plane or the frontal body plane and successfully passed balancing and movement tests. In this study we hypothesized that conflict-free movement coordination between the robot's sagittal and frontal body planes emerges simply from the physical embodiment, not necessarily requiring a full body control. Experiments were performed in the 14 DoF robot Lucy Posturob (i) demonstrating that the mechanical coupling from the robot's body suffices to coordinate the controls in the two planes when the robot produces movements and balancing responses in the intermediate plane, (ii) providing quantitative characterization of the interaction dynamics between body planes including frequency response functions (FRFs), as they are used in human postural control analysis, and (iii) witnessing postural and control stability when all DoFs are challenged together with the emergence of inter-segmental coordination in squatting movements. These findings represent an important step toward controlling in the robot in future more complex sensorimotor functions such as walking.

Switching Adaptability in Human-Inspired Sidesteps: A Minimal Model

Humans can adapt to abruptly changing situations by coordinating redundant components, even in bipedality. Conventional adaptability has been reproduced by various computational approaches, such as optimal control, neural oscillator, and reinforcement learning; however, the adaptability in bipedal locomotion necessary for biological and social activities, such as unpredicted direction change in chase-and-escape, is unknown due to the dynamically unstable multi-link closed-loop system. Here we propose a switching adaptation model for performing bipedal locomotion by improving autonomous distributed control, where autonomous actuators interact without central control and switch the roles for propulsion, balancing, and leg swing. Our switching mobility model achieved direction change at any time using only three actuators, although it showed higher motor costs than comparable models without direction change. Our method of evaluating such adaptation at any time should be utilized as a prerequisite for understanding universal motor control. The proposed algorithm may simply explain and predict the adaptation mechanism in human bipedality to coordinate the actuator functions within and between limbs.

The Functional Integration in the Sensory-Motor System Predicts Aging in Healthy Older Adults

Healthy aging is typically accompanied by a decrease in the motor capacity. Although the disrupted neural representations and performance of movement have been observed in older age in previous studies, the relationship between the functional integration of sensory-motor (SM) system and aging could be further investigated. In this study, we examine the impact of healthy aging on the resting-state functional connectivity (rsFC) of the SM system, and investigate as to how aging is affecting the rsFC in SM network. The SM network was identified and evaluated in 52 healthy older adults and 51 younger adults using two common data analytic approaches: independent component analysis and seed-based functional connectivity (seed at bilateral M1 and S1). We then evaluated whether the altered rsFC of the SM network could delineate trajectories of the age of older adults using a machine learning methodology. Compared with the younger adults, the older demonstrated reduced functional integration with increasing age in the mid-posterior insula of SM network and increased rsFC among the sensorimotor cortex. Moreover, the reduction in the rsFC of mid-posterior insula is associated with the age of older adults. Critically, the analysis based on two-aspect connectivity-based prediction frameworks revealed that the age of older adults could be reliably predicted by this reduced rsFC. These findings further indicated that healthy aging has a marked influence on the SM system that would be associated with a reorganization of SM system with aging. Our findings provide further insight into changes in sensorimotor function in the aging brain.

From self-assessment to frustration, a small step toward autonomy in robotic navigation

Autonomy and self-improvement capabilities are still challenging in the fields of robotics and machine learning. Allowing a robot to autonomously navigate in wide and unknown environments not only requires a repertoire of robust strategies to cope with miscellaneous situations, but also needs mechanisms of self-assessment for guiding learning and for monitoring strategies. Monitoring strategies requires feedbacks on the behavior's quality, from a given fitness system in order to take correct decisions. In this work, we focus on how a second-order controller can be used to (1) manage behaviors according to the situation and (2) seek for human interactions to improve skills. Following an incremental and constructivist approach, we present a generic neural architecture, based on an on-line novelty detection algorithm that may be able to self-evaluate any sensory-motor strategies. This architecture learns contingencies between sensations and actions, giving the expected sensation from the previous perception. Prediction error, coming from surprising events, provides a measure of the quality of the underlying sensory-motor contingencies. We show how a simple second-order controller (emotional system) based on the prediction progress allows the system to regulate its behavior to solve complex navigation tasks and also succeeds in asking for help if it detects dead-lock situations. We propose that this model could be a key structure toward self-assessment and autonomy. We made several experiments that can account for such properties for two different strategies (road following and place cells based navigation) in different situations.

Training of manual actions improves language understanding of semantically related action sentences

Conceptual knowledge accessed by language may involve the reactivation of the associated primary sensory-motor processes. Whether these embodied representations are indeed constitutive to conceptual knowledge is hotly debated, particularly since direct evidence that sensory-motor expertise can improve conceptual processing is scarce. In this study, we sought for this crucial piece of evidence, by training naive healthy subjects to perform complex manual actions and by measuring, before and after training, their performance in a semantic language task. Nineteen participants engaged in 3 weeks of motor training. Each participant was trained in three complex manual actions (e.g., origami). Before and after the training period, each subject underwent a series of manual dexterity tests and a semantic language task. The latter consisted of a sentence-picture semantic congruency judgment task, with 6 target congruent sentence-picture pairs (semantically related to the trained manual actions), 6 non-target congruent pairs (semantically unrelated), and 12 filler incongruent pairs. Manual action training induced a significant improvement in all manual dexterity tests, demonstrating the successful acquisition of sensory-motor expertise. In the semantic language task, the reaction times (RTs) to both target and non-target congruent sentence-picture pairs decreased after action training, indicating a more efficient conceptual-semantic processing. Noteworthy, the RTs for target pairs decreased more than those for non-target pairs, as indicated by the 2 × 2 interaction. These results were confirmed when controlling for the potential bias of increased frequency of use of target lexical items during manual training. The results of the present study suggest that sensory-motor expertise gained by training of specific manual actions can lead to an improvement of cognitive-linguistic skills related to the specific conceptual-semantic domain associated to the trained actions.

Self-organized critical noise amplification in human closed loop control

When humans perform closed loop control tasks like in upright standing or while balancing a stick, their behavior exhibits non-Gaussian fluctuations with long-tailed distributions. The origin of these fluctuations is not known. Here, we investigate if they are caused by self-organized critical noise amplification which emerges in control systems when an unstable dynamics becomes stabilized by an adaptive controller that has finite memory. Starting from this theory, we formulate a realistic model of adaptive closed loop control by including constraints on memory and delays. To test this model, we performed psychophysical experiments where humans balanced an unstable target on a screen. It turned out that the model reproduces the long tails of the distributions together with other characteristic features of the human control dynamics. Fine-tuning the model to match the experimental dynamics identifies parameters characterizing a subject's control system which can be independently tested. Our results suggest that the nervous system involved in closed loop motor control nearly optimally estimates system parameters on-line from very short epochs of past observations.

Neonatal intrahippocampal gp120 injection: an examination early in development

The presence of human immunodeficiency virus type 1 (HIV-1) in the brain is believed to be responsible for mediating the pathogenesis of neurological abnormalities through the viral toxins gp120 and Tat. Numerous studies indicate neurotoxic effects of the HIV-1-protein Tat, with demonstrated neurobehavioral and cognitive alterations. However, less clear is the neurotoxic effect of gp120 on neurobehavior. This study was designed to characterize the potential deficits in sensory-motor and preattentive functions, following intrahippocampal administration of gp120. Using a randomized-block design, male and female pups of eight Sprague-Dawley litters were injected bilaterally with either vehicle (VEH) (1 microl volume) or one of the three gp120 doses (1.29, 12.9, or 129 ng/microl) at postnatal day (P)1. Sensory-motor functions were assessed at P3, as measured by the righting reflex and at P8, as measured by negative geotaxis. At P24 animals were tested on preattentive processes, as indexed by sensorimotor gating. Sensorimotor gating was measured by prepulse inhibition (PPI) of the auditory startle response (ASR) (ISIs of 0, 8, 40, 80, 120, and 4000 ms, six trial blocks, Latin-square design). Results indicated gp120-induced neurotoxicity on the righting reflex but not negative geotaxis. For sensorimotor gating, the PPI test demonstrated a reduced inhibition response on peak ASR latency as the dose of gp120 increased. No effect was noted for response inhibition on peak ASR amplitude. These data suggest that intrahippocampal injection of gp120 (0, 1.29, 12.9, or 129 ng/microl) had transient neurotoxic effects on sensory-motor function and limited effects on preattentive processes early in development.