Limb-state feedback from ensembles of simultaneously recorded dorsal root ganglion neurons

Neuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications

Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.

Common muscle synergies for control of center of mass and force in nonstepping and stepping postural behaviors

We investigated muscle activity, ground reaction forces, and center of mass (CoM) acceleration in two different postural behaviors for standing balance control in humans to determine whether common neural mechanisms are used in different postural tasks. We compared nonstepping responses, where the base of support is stationary and balance is recovered by returning CoM back to its initial position, with stepping responses, where the base of support is enlarged and balance is recovered by pushing the CoM away from the initial position. In response to perturbations of the same direction, these two postural behaviors resulted in different muscle activity and ground reaction forces. We hypothesized that a common pool of muscle synergies producing consistent task-level biomechanical functions is used to generate different postural behaviors. Two sets of support-surface translations in 12 horizontal-plane directions were presented, first to evoke stepping responses and then to evoke nonstepping responses. Electromyographs in 16 lower back and leg muscles of the stance leg were measured. Initially (∼100-ms latency), electromyographs, CoM acceleration, and forces were similar in nonstepping and stepping responses, but these diverged in later time periods (∼200 ms), when stepping occurred. We identified muscle synergies using non-negative matrix factorization and functional muscle synergies that quantified correlations between muscle synergy recruitment levels and biomechanical outputs. Functional muscle synergies that produce forces to restore CoM position in nonstepping responses were also used to displace the CoM during stepping responses. These results suggest that muscle synergies represent common neural mechanisms for CoM movement control under different dynamic conditions: stepping and nonstepping postural responses.

Future directions for spinal cord injury research: recent developments and model systems contributions

The authors look forward and discuss future directions in spinal cord injury (SCI) from a perspective of biomedical, psychosocial and technologic research. This discussion is based both on recent developments from various fields of knowledge and, more specifically, on SCI Model Systems' research contributions to medical rehabilitation. Biomedical research, as described here, includes (1) the process of moving from the "bench to bedside" and harnessing knowledge from basic science to produce new clinical treatment options for SCI during the life span; (2) the rapid proliferation of clinical trials aimed at neurologic recovery; (3) the growth of new technologies to restore and improve function; and (4) the challenges of developing relevant outcome measures to evaluate efficacy and effectiveness of interventions. Recent progress in psychosocial research has contributed significantly to understanding of the many factors associated with disability during the life course, the importance of quality of life issues, and the value of activity, participation, and the environment in promoting successful rehabilitation outcomes following SCI. Technology and bioengineering advances are discussed in relation to access to high-quality technology; restoration and replacement of movement; and technology to enhance rehabilitation outcomes.

State-space decoding of primary afferent neuron firing rates

Kinematic state feedback is important for neuroprostheses to generate stable and adaptive movements of an extremity. State information, represented in the firing rates of populations of primary afferent (PA) neurons, can be recorded at the level of the dorsal root ganglia (DRG). Previous work in cats showed the feasibility of using DRG recordings to predict the kinematic state of the hind limb using reverse regression. Although accurate decoding results were attained, reverse regression does not make efficient use of the information embedded in the firing rates of the neural population. In this paper, we present decoding results based on state-space modeling, and show that it is a more principled and more efficient method for decoding the firing rates in an ensemble of PA neurons. In particular, we show that we can extract confounded information from neurons that respond to multiple kinematic parameters, and that including velocity components in the firing rate models significantly increases the accuracy of the decoded trajectory. We show that, on average, state-space decoding is twice as efficient as reverse regression for decoding joint and endpoint kinematics.

Preferred locomotor phase of activity of lumbar interneurons during air-stepping in subchronic spinal cats

Spinal locomotor circuits are intrinsically capable of driving a variety of behaviors such as stepping, scratching, and swimming. Based on an observed rostrocaudal wave of activity in the motoneuronal firing during locomotor tasks, the traveling-wave hypothesis proposes that spinal interneuronal firing follows a similar rostrocaudal pattern of activation, suggesting the presence of spatially organized interneuronal modules within the spinal motor system. In this study, we examined if the spatial organization of the lumbar interneuronal activity patterns during locomotor activity in the adult mammalian spinal cord was consistent with a traveling-wave organizational scheme. The activity of spinal interneurons within the lumbar intermediate zone was examined during air-stepping in subchronic spinal cats. The preferred phase of interneuronal activity during a step cycle was determined using circular statistics. We found that the preferred phases of lumbar interneurons from both sides of the cord were evenly distributed over the entire step cycle with no indication of functional groupings. However, when units were subcategorized according to spinal hemicords, the preferred phases of units on each side largely fell around the period of extensor muscle activity on each side. In addition, there was no correlation between the preferred phases of units and their rostrocaudal locations along the spinal cord with preferred phases corresponding to both flexion and extension phases of the step cycle found at every rostrocaudal level of the cord. These results are consistent with the hypothesis that interneurons operate as part of a longitudinally distributed network rather than a rostrocaudally organized traveling-wave network.

Predicting Any Arm Movement Feedback to Induce Three-Dimensional Illusory Movements in Humans

Our sense of body posture and movement is mainly mediated by densely packed populations of tiny mechanoreceptors present in the muscles. Signals triggered in muscle spindles by our own actions contribute crucially to our consciousness of positions and movements by continuously feeding and updating dynamic sensorimotor maps. Deciphering the coding rules whereby the nervous system integrates this proprioceptive information perceptually could help to elucidate the mechanisms underlying kinesthesia. The aim of the present study was to test the validity of a “propriomimetic method” of predicting the proprioceptive streams emitted by each of the muscles involved in two- (2D) and three-dimensional (3D) arm movements. This method was based on the functional properties of muscle spindle populations previously recorded microneurographically in behaving humans. Ia afferent patterns mimicking those evoked when the “arm–forearm” ensemble is drawing straight lines, graphic symbols, and complex 3D figures were calculated. These simulated patterns were then delivered to the main elbow and shoulder muscle tendons of motionless volunteers via a set of vibrators. Results show that the simulated proprioceptive patterns applied induced, in passive subjects, illusory 2D and 3D arm movements, the trajectories of which were very similar to the expected ones. These simulated patterns can therefore be said to be a substitute for the Ia proprioceptive feedback evoked by any human arm movement and this method can certainly be extended to other musculoskeletal ensembles. The illusory movements induced when these proprioceptive patterns are applied to muscle groups via sets of vibrators may provide useful tools for sensorimotor rehabilitation purposes.

The role of assistive robotics in the lives of persons with disability

Robotic assistive devices are used increasingly to improve the independence and quality of life of persons with disabilities. Devices as varied as robotic feeders, smart-powered wheelchairs, independent mobile robots, and socially assistive robots are becoming more clinically relevant. There is a growing importance for the rehabilitation professional to be aware of available systems and ongoing research efforts. The aim of this article is to describe the advances in assistive robotics that are relevant to professionals serving persons with disabilities. This review breaks down relevant advances into categories of Assistive Robotic Systems, User Interfaces and Control Systems, Sensory and Feedback Systems, and User Perspectives. An understanding of the direction that assistive robotics is taking is important for the clinician and researcher alike; this review is intended to address this need.

Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity

This article reviews neural interface technology and its relationship with neuroplasticity. Two types of neural interface technology are reviewed, highlighting specific technologies that the authors directly work with: (1) neural interface technology for neural recording, such as the micro-ECoG BCI system for hand prosthesis control, and the comprehensive rehabilitation paradigm combining MEG-BCI, action observation, and motor imagery training; (2) neural interface technology for functional neural stimulation, such as somatosensory neural stimulation for restoring somatosensation, and non-invasive cortical stimulation using rTMS and tDCS for modulating cortical excitability and stroke rehabilitation. The close interaction between neural interface devices and neuroplasticity leads to increased efficacy of neural interface devices and improved functional recovery of the nervous system. This symbiotic relationship between neural interface technology and the nervous system is expected to maximize functional gain for individuals with various sensory, motor, and cognitive impairments, eventually leading to better quality of life.

Development of closed-loop neural interface technology in a rat model: combining motor cortex operant conditioning with visual cortex microstimulation

Closed-loop neural interface technology that combines neural ensemble decoding with simultaneous electrical microstimulation feedback is hypothesized to improve deep brain stimulation techniques, neuromotor prosthetic applications, and epilepsy treatment. Here we describe our iterative results in a rat model of a sensory and motor neurophysiological feedback control system. Three rats were chronically implanted with microelectrode arrays in both the motor and visual cortices. The rats were subsequently trained over a period of weeks to modulate their motor cortex ensemble unit activity upon delivery of intra-cortical microstimulation (ICMS) of the visual cortex in order to receive a food reward. Rats were given continuous feedback via visual cortex ICMS during the response periods that was representative of the motor cortex ensemble dynamics. Analysis revealed that the feedback provided the animals with indicators of the behavioral trials. At the hardware level, this preparation provides a tractable test model for improving the technology of closed-loop neural devices.

A simple experimentally based model using proprioceptive regulation of motor primitives captures adjusted trajectory formation in spinal frogs

Spinal circuits may organize trajectories using pattern generators and synergies. In frogs, prior work supports fixed-duration pulses of fixed composition synergies, forming primitives. In wiping behaviors, spinal frogs adjust their motor activity according to the starting limb position and generate fairly straight and accurate isochronous trajectories across the workspace. To test whether a compact description using primitives modulated by proprioceptive feedback could reproduce such trajectory formation, we built a biomechanical model based on physiological data. We recorded from hindlimb muscle spindles to evaluate possible proprioceptive input. As movement was initiated, early skeletofusimotor activity enhanced many muscle spindles firing rates. Before movement began, a rapid estimate of the limb position from simple combinations of spindle rates was possible. Three primitives were used in the model with muscle compositions based on those observed in frogs. Our simulations showed that simple gain and phase shifts of primitives based on published feedback mechanisms could generate accurate isochronous trajectories and motor patterns that matched those observed. Although on-line feedback effects were omitted from the model after movement onset, our primitive-based model reproduced the wiping behavior across a range of starting positions. Without modifications from proprioceptive feedback, the model behaviors missed the target in a manner similar to that in deafferented frogs. These data show how early proprioception might be used to make a simple estimate initial limb state and to implicitly plan a movement using observed spinal motor primitives. Simulations showed that choice of synergy composition played a role in this simplicity. To generate froglike trajectories, a hip flexor synergy without sartorius required motor patterns with more proprioceptive knee flexor control than did patterns built with a more natural synergy including sartorius. Such synergy choices and control strategies may simplify the circuitry required for reflex trajectory construction and adaptation.

Improved decoding of limb-state feedback from natural sensors

Limb state feedback is of great importance for achieving stable and adaptive control of FES neuroprostheses. A natural way to determine limb state is to measure and decode the activity of primary afferent neurons in the limb. The feasibility of doing so has been demonstrated by [1] and [2]. Despite positive results, some drawbacks in these works are associated with the application of reverse regression techniques for decoding the afferent neuronal signals. Decoding methods that are based on direct regression are now favored over reverse regression for decoding neural responses in higher regions in the central nervous system [3]. In this paper, we apply a direct regression approach to decode the movement of the hind limb of a cat from a population of primary afferent neurons. We show that this approach is more principled, more efficient, and more generalizable than reverse regression.

Microstimulation of primary afferent neurons in the L7 dorsal root ganglia using multielectrode arrays in anesthetized cats: thresholds and recruitment properties

Current research in motor neural prosthetics has focused primarily on issues related to the extraction of motor command signals from the brain (e.g. brain-machine interfaces) to direct the motion of prosthetic limbs. Patients using these types of systems could benefit from a somatosensory neural interface that conveys natural tactile and kinesthetic sensations for the prosthesis. Electrical microstimulation within the dorsal root ganglia (DRG) has been proposed as one method to accomplish this, yet little is known about the recruitment properties of electrical microstimulation in activating nerve fibers in this structure. Current-controlled microstimulation pulses in the range of 1-15 microA (200 micros, leading cathodic pulse) were delivered to the L7 DRG in four anesthetized cats using penetrating microelectrode arrays. Evoked responses and their corresponding conduction velocities (CVs) were measured in the sciatic nerve with a 5-pole nerve cuff electrode arranged as two adjacent tripoles. It was found that in 76% of the 69 electrodes tested, the stimulus threshold was less than or equal to 3 microA, with the lowest recorded threshold being 1.1 microA. The CVs of afferents recruited at threshold had a bimodal distribution with peaks at 70 m s(-1) and 85 m s(-1). In 53% of cases, the CV of the response at threshold was slower (i.e. smaller diameter fiber) than the CVs of responses observed at increasing stimulation amplitudes. In summary, we found that microstimulation applied through penetrating microelectrodes in the DRG provides selective recruitment of afferent fibers from a range of sensory modalities (as identified by CVs) at very low stimulation intensities. We conclude that the DRG may serve as an attractive location from which to introduce surrogate somatosensory feedback into the nervous system.

Multiple types of movement-related information encoded in hindlimb/trunk cortex in rats and potentially available for brain-machine interface controls

Brain-machine interface (BMI) systems hold the potential to return lost functions to patients with motor disorders. To date, most efforts in BMI have concentrated on decoding neural activity from forearm areas of cortex to operate a robotic arm or perform other manipulation tasks. Efforts have neglected the locomotion functions of hindlimb/trunk cortex. However, the role of cortex in hindlimb locomotion of intact rats, which are often model systems for BMI testing, is usually considered to be small. Thus, the quality of representations of locomotion available in this area was uncertain. We designed a new rodent BMI system, and tested decoding of the kinematics of trunk and hindlimbs during locomotion using linear regression. Recordings were made from the motor cortex of the hindlimb/trunk area in rats using arrays of six tetrodes (24 channels total). We found that multiple movement-related variables could be decoded simultaneously during locomotion, ranging from the proximal robot/pelvis attachment point, and the distal toe position, through hindlimb joint angles and limb endpoint in a polar coordinate system. Remarkably, the best reconstructed motion parameters were the more proximal kinematics, which might relate to global task variables. The pelvis motion was significantly better reconstructed than any other motion features.