Perspectives on the role of afferent signals in control of motor neuroprostheses

Correlation among proprioception, muscle strength, and balance

[Purpose] To study the correlation among proprioception, muscle strength, and balance. [Subjects and Methods] A balance testing system (Biodex Balance System, BBS) and an isokinetic testing system (Biodex System 4, BS4) were used to test related indexes in 24 healthy young females. [Results] With the knee joint at 15 degree flexion, proprioception was significantly correlated with Limits of Stability-Time values, and was highly significantly correlated with Limits of Stability-Overall and Athlete Single Leg Medial/Lateral values. The sense of force was significantly correlated with Limits of Stability-Overall and Athlete Single Leg-Overall values. Quadriceps strength was significantly associated with Limits of Stability-Overall, Athlete Single Leg Medial/Lateral, and Athlete Double Leg-Overall values. The ratio of Quadriceps to Hamstring strength was significantly correlated with Athlete Single Leg Medial/Lateral, and Athlete Single Leg-Overall values. With the knee joint at 45°, proprioception was highly significantly correlated with dynamic balance, and was significantly correlated with double foot support under static balance; force sense had a high correlation with Limits of Stability-Overall, but no correlation with other indexes. Quadriceps strength had a significant correlation with dynamic and static balance; the ratio of Quadriceps/Hamstring had a highly significant correlation with Limits of Stability-Overall, Athlete Single Leg-Anterior/Posterior and Athlete Single Leg-Overall. [Conclusion] At different knee angles, the correlation differs among proprioception, force sense, quadriceps strength, the Quadriceps/Hamstring ratio, and balance.

Force sensor in simulated skin and neural model mimic tactile SAI afferent spiking response to ramp and hold stimuli

BACKGROUND

The next generation of prosthetic limbs will restore sensory feedback to the nervous system by mimicking how skin mechanoreceptors, innervated by afferents, produce trains of action potentials in response to compressive stimuli. Prior work has addressed building sensors within skin substitutes for robotics, modeling skin mechanics and neural dynamics of mechanotransduction, and predicting response timing of action potentials for vibration. The effort here is unique because it accounts for skin elasticity by measuring force within simulated skin, utilizes few free model parameters for parsimony, and separates parameter fitting and model validation. Additionally, the ramp-and-hold, sustained stimuli used in this work capture the essential features of the everyday task of contacting and holding an object.

METHODS

This systems integration effort computationally replicates the neural firing behavior for a slowly adapting type I (SAI) afferent in its temporally varying response to both intensity and rate of indentation force by combining a physical force sensor, housed in a skin-like substrate, with a mathematical model of neuronal spiking, the leaky integrate-and-fire. Comparison experiments were then conducted using ramp-and-hold stimuli on both the spiking-sensor model and mouse SAI afferents. The model parameters were iteratively fit against recorded SAI interspike intervals (ISI) before validating the model to assess its performance.

RESULTS

Model-predicted spike firing compares favorably with that observed for single SAI afferents. As indentation magnitude increases (1.2, 1.3, to 1.4 mm), mean ISI decreases from 98.81 ± 24.73, 54.52 ± 6.94, to 41.11 ± 6.11 ms. Moreover, as rate of ramp-up increases, ISI during ramp-up decreases from 21.85 ± 5.33, 19.98 ± 3.10, to 15.42 ± 2.41 ms. Considering first spikes, the predicted latencies exhibited a decreasing trend as stimulus rate increased, as is observed in afferent recordings. Finally, the SAI afferent's characteristic response of producing irregular ISIs is shown to be controllable via manipulating the output filtering from the sensor or adding stochastic noise.

CONCLUSIONS

This integrated engineering approach extends prior works focused upon neural dynamics and vibration. Future efforts will perfect measures of performance, such as first spike latency and irregular ISIs, and link the generation of characteristic features within trains of action potentials with current pulse waveforms that stimulate single action potentials at the peripheral afferent.

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.

On the identification of sensory information from mixed nerves by using single-channel cuff electrodes

Background

Several groups have shown that the performance of motor neuroprostheses can be significantly improved by detecting specific sensory events related to the ongoing motor task (e.g., the slippage of an object during grasping). Algorithms have been developed to achieve this goal by processing electroneurographic (ENG) afferent signals recorded by using single-channel cuff electrodes. However, no efforts have been made so far to understand the number and type of detectable sensory events that can be differentiated from whole nerve recordings using this approach.

Methods

To this aim, ENG afferent signals, evoked by different sensory stimuli were recorded using single-channel cuff electrodes placed around the sciatic nerve of anesthetized rats. The ENG signals were digitally processed and several features were extracted and used as inputs for the classification. The work was performed on integral datasets, without eliminating any noisy parts, in order to be as close as possible to real application.

Results

The results obtained showed that single-channel cuff electrodes are able to provide information on two to three different afferent (proprioceptive, mechanical and nociceptive) stimuli, with reasonably good discrimination ability. The classification performances are affected by the SNR of the signal, which in turn is related to the diameter of the fibers encoding a particular type of neurophysiological stimulus.

Conclusions

Our findings indicate that signals of acceptable SNR and corresponding to different physiological modalities (e.g. mediated by different types of nerve fibers) may be distinguished.

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

Functional electrical stimulation (FES) holds great potential for restoring motor functions after brain and spinal cord injury. Currently, most FES systems are under simple finite state control, using external sensors which tend to be bulky, uncomfortable and prone to failure. Sensory nerve signals offer an interesting alternative, with the possibility of continuous feedback control. To test feasibility, we recorded from ensembles of sensory neurons with microelectrode arrays implanted in the dorsal root ganglion (DRG) of walking cats. Limb position and velocity variables were estimated accurately (average R2 values >0.5) over a range of walking speeds (0.1-0.5 m s(-1)) using a linear combination of firing rates from 10 or more neurons. We tested the feasibility of sensory control of intraspinal FES by recording from DRG neurons during hindlimb movements evoked by intraspinal microstimulation of the lumbar spinal cord in an anesthetized cat. Although electrical stimulation generated artifacts, this problem was overcome by detecting and eliminating events that occurred synchronously across the array of microelectrodes. The sensory responses to limb movement could then be measured and decoded to generate an accurate estimate of the limb state. Multichannel afferent recordings may thus provide FES systems with the feedback needed for adaptive control and perturbation compensation, though long-term stability remains a challenge.

A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems

Considerable scientific and technological efforts have been devoted to develop neuroprostheses and hybrid bionic systems that link the human nervous system with electronic or robotic prostheses, with the main aim of restoring motor and sensory functions in disabled patients. A number of neuroprostheses use interfaces with peripheral nerves or muscles for neuromuscular stimulation and signal recording. Herein, we provide a critical overview of the peripheral interfaces available and trace their use from research to clinical application in controlling artificial and robotic prostheses. The first section reviews the different types of non-invasive and invasive electrodes, which include surface and muscular electrodes that can record EMG signals from and stimulate the underlying or implanted muscles. Extraneural electrodes, such as cuff and epineurial electrodes, provide simultaneous interface with many axons in the nerve, whereas intrafascicular, penetrating, and regenerative electrodes may contact small groups of axons within a nerve fascicle. Biological, technological, and material science issues are also reviewed relative to the problems of electrode design and tissue injury. The last section reviews different strategies for the use of information recorded from peripheral interfaces and the current state of control neuroprostheses and hybrid bionic systems.

Relationship between physiological response type (RA and SA) and vibrissal receptive field of neurons within the rat trigeminal ganglion

Cells within the trigeminal ganglion (Vg) encode all the information necessary for the rat to differentiate tactile stimuli, yet it is the least-studied component in the rodent trigeminal somatosensory system. For example, extensive anatomical and electrophysiological investigations have shown clear somatotopic organization in the higher levels of this system, including VPM thalamus and SI cortex, yet whether this conserved schemata exists in the Vg is unknown. Moreover although there is recent interest in recording from vibrissae-responsive cells in the Vg, it is surprising to note that the locations of these cells have not even been clearly demarcated. To address this, we recorded extracellularly from 350 sensory-responsive Vg neurons in 35 Long-Evans rats. First, we determined three-dimensional locations of these cells and found a finer detail of somatotopy than previously reported. Cells innervating dorsal facial features, even within the whisker region, were more dorsal than midline and ventral features. We also show more cells with caudal than rostral whisker receptive fields (RF), similar to that found in VPM and SI. Next, for each vibrissal cell we determined its response type classified as either rapidly (RA) or slowly (SA) adapting. We examined the relationship between vibrissal RF and response type and demonstrate similar proportions of RA and SA cells responding to any whisker. These results suggest that if RA and SA cells encode distinct features of stimuli, as previously suggested, then at the basic physiological level each whisker has similar abilities to encode for such features.

Digital signal processing algorithms for the detection of afferent nerve activity recorded from cuff electrodes

Due to the very poor signal-to-noise ratios (SNR's) usually encountered with whole nerve-cuff signals, the processing method typically applied, rectification and windowed (bin)-integration (RBI), can have serious shortcomings in extracting reliable information. In order to improve detection accuracy, these signals were further analyzed using statistical signal detection algorithms based on their second and higher order spectra (HOS). A comparison with both analog and digital RBI processing suggests that the statistical methods, due to their ability to separate the signal and noise subspaces, are superior. It was determined that the noise typically encountered with nerve-cuff electrode signals is normally (Gaussian) distributed. Therefore, third-order statistics can be applied to, ideally, completely reject the noise component. When cutaneous nerve recordings from the calcaneal nerve (innervating the heel area) were used in a drop-foot correction neural prosthesis, the detection percentage and the insensitivity to algorithm parameters were increased through the use of these statistical methods as to warrant their real-time implementation, and the inherent additional processing hardware that entails.