Spatiotemporal characteristics of neural activity in tibial nerves with carbon nanotube yarn electrodes.

BACKGROUND: Reliable interfacing with peripheral nervous system is essential to extract neural signals. Current implantable peripheral nerve electrodes cannot provide long-term reliable interfaces due to their mechanical mismatch with host nerves. Carbon nanotube (CNT) yarns possess excellent mechanical flexibility and electrical conductivity. It is of great necessity to investigate the selectivity of implantable CNT yarn electrodes. NEW METHOD: Neural interfaces were fabricated with CNT yarn electrodes insulated with Parylene-C. Acute recordings were carried out on tibial nerves of rats, and compound nerve action potentials (CNAPs) were electrically evoked by biphasic current stimulation of four toes. Spatiotemporal characteristics of neural activity and spatial selectivity of the electrodes, denoted by selectivity index (SI), were analyzed in detail. RESULTS: Conduction velocities of sensory afferent fibers recorded by CNT yarn electrodes varied between 4.25 m/s and 37.56 m/s. The SI maxima for specific toes were between 0.55 and 0.99 across seven electrodes. SIs for different CNT yarn electrodes are significantly different among varied toes. COMPARISON WITH EXISTING METHODS: Most single CNT yarn electrode with a ∼ 500 µm exposed length can be sensitive to one or two specific toes in rodent animals. While, it is only possible to discriminate two non-adjacent toes by multisite TIME electrodes. CONCLUSION: Single CNT yarn electrode exposed ∼ 500 µm showed SI values for different toes comparable to a multisite TIME electrode, and had high spatial selectivity for one or two specific toes. The electrodes with cross section exposed could intend to be more sensitive to one specific toe.

Bayesian spatial filters for source signal extraction: a study in the peripheral nerve.

The ability to extract physiological source signals to control various prosthetics offer tremendous therapeutic potential to improve the quality of life for patients suffering from motor disabilities. Regardless of the modality, recordings of physiological source signals are contaminated with noise and interference along with crosstalk between the sources. These impediments render the task of isolating potential physiological source signals for control difficult. In this paper, a novel Bayesian Source Filter for signal Extraction (BSFE) algorithm for extracting physiological source signals for control is presented. The BSFE algorithm is based on the source localization method Champagne and constructs spatial filters using Bayesian methods that simultaneously maximize the signal to noise ratio of the recovered source signal of interest while minimizing crosstalk interference between sources. When evaluated over peripheral nerve recordings obtained in vivo, the algorithm achieved the highest signal to noise interference ratio ( 7.00 ±3.45 dB) amongst the group of methodologies compared with average correlation between the extracted source signal and the original source signal R = 0.93. The results support the efficacy of the BSFE algorithm for extracting source signals from the peripheral nerve.

Mechanism of highly synchronized bilateral hippocampal activity.

In vivo studies of epileptiform discharges in the hippocampi of rodents have shown that bilateral seizure activity can sometimes be synchronized with very small delays (<2 ms). This observed small time delay of epileptiform activity between the left and right CA3 regions is unexpected given the physiological propagation time across the hemispheres (>6 ms). The goal of this study is to determine the mechanisms of this tight synchronization with in-vitro electrophysiology techniques and computer simulations. The hypothesis of a common source was first eliminated by using an in-vitro preparation containing both hippocampi with a functional ventral hippocampal commissure (VHC) and no other tissue. Next, the hypothesis that a noisy baseline could mask the underlying synchronous activity between the two hemispheres was ruled out by low noise in-vivo recordings and computer simulation of the noisy environment. Then we built a novel bilateral CA3 model to test the hypothesis that the phenomenon of very small left-to-right propagation delay of seizure activity is a product of epileptic cell network dynamics. We found that the commissural tract connectivity could decrease the delay between seizure events recorded from two sides while the activity propagated longitudinally along the CA3 layer thereby yielding delays much smaller than the propagation time between the two sides. The modeling results indicate that both recurrent and feedforward inhibition were required for shortening the bilateral propagation delay and depended critically on the length of the commissural fiber tract as well as the number of cells involved in seizure generation. These combined modeling/experimental studies indicate that it is possible to explain near perfect synchronization between the two hemispheres by taking into account the structure of the hippocampal network.

Extraction of control signals from a mixture of source activity in the peripheral nerve.

Extracting physiological signals to control external devices such as prosthetics is a field of research that offers great hope for patients suffering from disabilities. In this paper, we present an algorithm for isolating control signals from peripheral nerve cuff recordings. The algorithm is able to extract individual control signals from a mixture of source signal activity while maximizing SNR and minimizing cross-talk between the control signals. Based on fast independent component analysis FICA and an adaptation of Champagne, the proposed algorithm is tested against previously published results obtained using beamforming techniques in an acute preparation of rabbits. Preliminary results demonstrate an improvement in performance.

Selective recovery of fascicular activity in peripheral nerves.

The peripheral nerves of an amputee's residual limb still carry the information required to provide the robust, natural control signals needed to command a dexterous prosthetic limb. However, these signals are mixed in the volume conductor of the body and extracting them is an unmet challenge. A beamforming algorithm was used to leverage the spatial separation of the fascicular sources, recovering mixed pseudo-spontaneous signals with normalized mean squared error of 0.14 ± 0.10 (n = 12) in an animal model. The method was also applied to a human femoral nerve model using computer simulations and recovered all five fascicular-group signals simultaneously with R(2) = 0.7 ± 0.2 at a signal-to-noise ratio of 0 dB. This technique accurately separated peripheral neural signals, potentially providing the voluntary, natural and robust command signals needed for advanced prosthetic limbs.

Recovery of neural activity from nerve cuff electrodes.

The ability to recover signals from the peripheral nerves would provide natural and physiological signals for controlling artificial limbs and neural prosthetic devices. Current cuff electrode systems can provide multiple channels but the signals have low signal to noise ratio and are difficult to recover. Previous work has shown that beamforming algorithms provide a method to extract such signals from peripheral nerve activiy [1]. This paper describes in-silico and in vivo experiments done to validate that method in a more realistic case. A modified beam forming algorithm capable of significantly decrease cross talk between channels is described and the results of the a 16-channel Flat Interface Nerve Electrode used to recover signals from the sciatic nerve in rabbit while the distal tibial and peroneal branches were stimulated The beamforming spatial filters were able to distinguish which branch was being stimulated, and in many cases how strongly, over a large range of stimulation intensities.

A mechanism to explain zero-delay bilateral seizure synchronization.

Synchronization in bilateral CA3 regions via fimbria-fornix-hippocampal commissures system (FFHC) in rodent hippocampus has revealed that bilateral seizures can sometimes be synchronized with very small delays (< 1 ms). This observed small time delay at the start of afterdischarges between the left and right CA3 regions is unexpected given the propagation time across the hemispheres (> 6 ms). The possibility of a common source was first eliminated by in-vitro brain slices experiments. We then tested the hypothesis that, in the presence of noise, synchronization can take place before the seizure activity is sufficient large to be detected generating an apparent zero-delay between the two sides. This hypothesis was tested with computer simulation with a network of interconnected hippocampal neurons. These results provide an explanation for this aberrant simultaneous seizure detection and indicate the importance of noise in the interpretation of the timing of neuronal events.

Peripheral nerve signal recording and processing for artificial limb control.

In order to take full advantage of modern multiple-degree of freedom prosthetic limbs, robust and natural control signals are needed. Previous work has shown that beamforming provides a method to extract such signals from peripheral nerve activity [1]. This paper describes in vivo experiments done to validate that method in a more realistic case. A 16-channel Flat Interface Nerve Electrode was used to record from the Sciatic nerve in Rabbit, while the distal Tibial and Peroneal branches were stimulated. Beamforming provided R(2)=0.7 ± 0.2, an improvement of 0.12 ± 0.06 over the a posteriori chosen best channels. When more realistic signals were generated using kHz-level stimulation, the beamforming filters were able to distinguish which branch was being stimulated, and in many cases how strongly, over a large range of stimulation intensities.

In vivo localization of fascicular activity.

Users of modern high degree of freedom prosthetics need to provide a large number of natural, intuitive command signals in order to realize the high level of dexterity these devices offer. This level of natural control is beginning to be seen with new technologies like Targeted Muscle Reinnervation; however several serious drawbacks still exist. Flat Interface Nerve Electrode recordings provide a safe and stable means to record natural, intuitive volitional command signals. We investigate the use of Antenna Array techniques to separate command signals from different sources based on their spatial distribution within the nerve. Through a Rabbit sciatic model, it is shown that the system is able to separate compound action potentials elicited from the Tibial and Peroneal branches using 16-channel recordings made on the main sciatic nerve trunk.

Localization and control of activity in peripheral nerves.

Interest in the field of the natural control of human limb using physiological signals has risen dramatically in the past 20 years due to the success of the brain machine interface. Cortical signals carry significant information but are difficult to access. The peripheral nerves of the body carry both command and sensory signals and are far more accessible. While numerous studies have documented the selective stimulation properties of, conventionally round, nerve cuff electrodes (i.e., transverse geometry) and even self-sizing electrodes, recording the activity levels from individual fascicles using these electrodes is still an unsolved problem. Moreover, the control algorithms for the control of joint movement with multiple contact electrodes such as the flat interface nerve electrode (FINE) have been difficult to implement. We propose solutions to both these problems by using beam forming techniques to detect the location and the activity in various fascicles. We also developed a control algorithm that separates the dynamic from the passive properties to solve the redundancy problem in multiple joint problems. This techniques could find application in the natural control of artificial limbs from peripheral nerve signals for patients with amputated limbs or to restore function in patients with stroke or paralyzed limbs.

Diffusive coupling can induce synchronized periodic activity in neural networks.

Diffusive coupling also known as nearest-neighbor coupling is a common form of coupling but its role in the behavior of neural circuits is unclear. Previous experimental and theoretical studies have shown that potassium lateral diffusion coupling (i.e., diffusive coupling) was responsible for synchronization of neuronal activity. We tested the hypothesis that potassium lateral diffusion coupling is required to generate periodic epileptiform activity in a zero-Ca(2+) CA1 pyramidal neuron network model. The simulation results show that potassium lateral diffusion coupling is crucial for establishing a periodic synchronized epileptiform activity similar to that observed in experimental preparations. This results suggest that potassium lateral diffusion coupling - a physiological realization of the concept of diffusive coupling - can play a role in network behavior.

Blind source separation of peripheral nerve recordings.

Prosthetic devices can be controlled using signals recorded in parts of the body where sensation and/or voluntary movement have been retained. Although neural prosthetic applications have used single-channel recordings, multiple-channel recordings could provide a significant increase in useable control signals. Multiple control signals can be acquired from recordings of a single implant by using a multi-contact electrode placed over a multi-fasciculated peripheral nerve. These recordings can be separated to recover the individual fascicular signals. Blind source separation (BSS) algorithms have been developed to extract independent source signals from recordings of their mixtures. The hypothesis that BSS algorithms can recover individual fascicular signals from nerve cuff recordings at physiological signal-to-noise ratio (SNR approximately 3-10 dB) was investigated in this study using a finite-element model (FEM) of a beagle hypoglossal nerve with a flattening interface nerve electrode (FINE). Known statistical properties of fascicular signals were used to generate a set of four sources from which the neural signals recorded at the surface of the nerve with a multi-contact FINE were simulated. Independent component analysis (ICA) was then implemented for BSS of the simulated recordings. A novel post-ICA processing algorithm was developed to solve ICA's inherent permutation ambiguities. The similarity between the estimated and original fascicular signals was quantified by calculating their correlation coefficients. The mean values of the correlation coefficients calculated were higher than 0.95 (n = 50). The effects of the geometric layout of the FINE electrode and noise on the separation algorithm were also investigated. The results show that four distinct overlapping fascicular source signals can be simultaneously recovered from neural recordings obtained using a FINE with five or more contacts at SNR levels higher than 8 dB making them available for use as control signals.

Suppression of axonal conduction by sinusoidal stimulation in rat hippocampus in vitro.

Deep brain stimulation (DBS), also known as high frequency stimulation (HFS), is a well-established therapy for Parkinson's disease and essential tremor, and shows promise for the therapeutic control of epilepsy. However, the direct effect of DBS on neural elements close to the stimulating electrode remains an important unanswered question. Computational studies have suggested that HFS has a dual effect on neural elements inhibiting cell bodies, while exciting axons. Prior experiments have shown that sinusoidal HFS (50 Hz) can suppress synaptic and non-synaptic cellular activity in several in vitro epilepsy models, in all layers of the hippocampus. However, the effects of HFS on axons near the electrode are still unclear. In the present study, we tested the hypothesis that HFS suppresses axonal conduction in vitro. Sinusoidal HFS was applied to the alvear axon field of transverse rat hippocampal slices. The results show that HFS suppresses the alvear compound action potential (CAP) as well as the CA1 antidromic evoked potential (AEP). Complete suppression was observed as a 100% reduction in the amplitude of the evoked field potential for the duration of the stimulus. Evoked potential width and latency were not significantly affected by sinusoidal HFS. Suppression was dependent on HFS amplitude and frequency, but independent of stimulus duration and synaptic transmission. The frequency dependence of sinusoidal HFS is similar to that observed in clinical DBS, with maximal suppression between 50 and 200 Hz. HFS produced not only suppression of axonal conduction but also a correlated rise in extracellular potassium. These data provide new insights into the effects of HFS on neuronal elements, and show that HFS can block axonal activity through non-synaptic mechanisms.

Neural engineering--a new discipline for analyzing and interacting with the nervous system.

OBJECTIVES: The field of neural engineering focuses on an area of research at the interface between neuroscience and engineering. The area of neural engineering was first associated with the brain machine interface but is much broader and encompasses experimental, computational, and theoretical aspects of neural interfacing, neuroelectronics, neuromechanical systems, neuroinformatics, neuroimaging, neural prostheses, artificial and biological neural circuits, neural control, neural tissue regeneration, neural signal processing, neural modelling and neuro-computation. One of the goals of neural engineering is to develop a selective interface for the peripheral nervous system. METHODS: Nerve cuffs electrodes have been developed to either reshape or maintain the nerve into an elongated shape in order to increase the circumference to cross sectional ratio. It is then possible to place many electrodes around the nerve to achieve selectivity. This new cuff (flat interface nerve electrode: FINE) was applied to the hypoglossal nerve and the sciatic nerve in dogs and cats to estimate the selectivity of the interface. RESULTS: By placing many contacts close to the axons, three different types of selectivity were achieved: 1) The FINE could generate a high degree of stimulation selectivity as estimated by the individual fascicle recording. 2) Similarly, recording selectivity was also demonstrated and blind source algorithms were applied to recover the signals. 3) Finally, by placing arrays of electrodes along the nerve, small fiber diameters could be excited before large fibers thereby reversing the recruitment order. CONCLUSION: Taking advantage of the fact that nerves are not round but oblong or flat allows a novel design for selective nerve interface with the peripheral nervous system. This new design has found applications in many disorders of the nervous system such as bladder incontinence, obstructive sleep apnea and stroke.

The recording properties of a multi-contact nerve electrode as predicted by a finite element model of the canine hypoglossal nerve.

Most functional electrical stimulation (FES) systems rely only on unidirectional (i.e., efferent) activation of the target organ to yield therapeutic outcomes. For applications involving multi-fasciculated nerves, however, artificial sensors have exhibited limited results. As such, the flat-interface-nerve-electrode (FINE) is presented as a means of obtaining an effective closed-loop control system. To investigate the ability of this electrode to achieve selective recordings at physiological signal-to-noise ratio (SNR), a finite element model (JFEM) of a beagle hypoglossal nerve with an implanted FINE was constructed. Action potentials (AP) were generated at various SNR levels and the performance of the electrode was assessed with a selectivity index (0 < or = SI < or = 1; ability of the electrode to distinguish two active sources). Computer simulations yielded a selective range (0.05 < or = SI < or = 0.76) that was (1) related to the inter-fiber distance and (2) used to predict the minimum inter-fiber distance (0.23 mm < or = d < or = 1.42 mm) required for selective recording. The results of this study suggest that the FINE can record neural activity from a multi-fasciculated nerve and, more importantly, distinguish neural activity from pairs of fascicles at physiologic SNR.

Blind source separation of nerve cuff recordings.

Electrical stimulation of peripheral nerves can be used to restore partial motor function to individuals with neurological impairment. Previous work from our lab has shown that the flat interface nerve electrode (FINE) can be used to selectively stimulate fiber populations within a nerve trunk. We expect that selective recording of spatially segregated axon populations may also be possible with the FINE. The purpose of this modeling study was to assess the feasibility of using blind source separation (BSS) of the neurograms recorded with the FINE to distinguish signals from independent fascicles. We show that BSS is useful for identifying independent fascicular signals. Further, we introduce a simple post-BSS processing method that resolves the inherent permutation ambiguity of BSS, and allows the BSS-estimated signals to be deterministically related to the appropriate corresponding fascicles.

Electric field effects in hyperexcitable neural tissue: a review.

Uniform electric fields applied to neural tissue can modulate neuronal excitability with a threshold value of about 1 mV mm(-1) in normal physiological conditions. However, electric fields could have a lower threshold in conditions where field sensitivity is enhanced, such as those simulating epilepsy. Uniform electrical fields were applied to hippocampal brain slices exposed to picrotoxin, high potassium or low calcium solutions. The results in the low calcium medium show that neuronal activity can be completely blocked in 10% of the 30 slices tested with a field amplitude of 1 mV mm(-1). These results suggest that the threshold for this effect is clearly smaller than 1 mV mm(-1). The hypothesis that the extracellular resistance could affect the sensitivity to the electrical fields was tested by measuring the effect of the osmolarity of the extracellular solution on the efficacy of the field. A 10% decrease in osmolarity resulted in a 56% decrease (n = 4) in the minimum field required for full suppression. A 14% in osmolarity produced an 81% increase in the minimum field required for full suppression. These results show that the extracellular volume can modulate the efficacy of the field and could lower the threshold field amplitudes to values lower than approximately 1 mmV mm(-1).

Propagation of non-synaptic epileptiform activity across a lesion in rat hippocampal slices.

1. Spontaneous non-synaptic epileptiform activity was induced by bathing rat hippocampal slices in low-Ca(2+) solution. Extracellular recordings from electrodes placed on both sides of a complete cut showed that non-synaptic activity was synchronized across the lesion. 2. Ion-selective electrode recordings showed that each event was accompanied by a transient increase in extracellular potassium that diffused across the lesion. The synchrony was destroyed when a thin film was inserted into the lesion site. 3. Local pressure ejection of KCl evoked an event that subsequently propagated across the lesion. 4. After a complete lesion was made, afterdischarges evoked on one half of a slice were not detected on the other half. 5. Voltage-sensitive dye imaging methods showed that epileptic activity propagated across the mechanical lesion without significant attenuation or additional delays. The velocity of the activity was consistent with that of the slow diffusion of a potassium wave. 6. Since field effects were significantly attenuated across the lesion and all gap junctions and cell processes across the lesion would be cut, these data show that extracellular diffusion, most probably potassium, is sufficient to synchronize populations of neurons and propagate slow frequency epileptiform activity.

A 3-D differential coil design for localized magnetic stimulation.

A novel three-dimensional (3-D) differential coil has been designed for improving the localization of magnetic stimulation. This new coil design consists of a butterfly coil with two additional wing units and an extra bottom unit, both perpendicular to the plane of the butterfly coil. The wing units produce opposite fields to restrict the spread of induced fields while the bottom unit enhances the induced fields at the excitation site. The peak induced field generated by this new design is located at the center of the coil, providing an easy identification of the excitation site. The field localization of the new coil is comparable with that of much smaller coils but with an inductance compatible to current magnetic stimulators. Numerical computations based on the principles of electromagnetic induction and using a human nerve model were performed to analyze the induced fields and the stimulation thresholds of new coil designs. The localization of the coil design was assessed by a half power region (HPR), within which the magnitude of the normalized induced field is greater than 1/square root of 2. The HPR for a 3-D differential coil built is improved (decreased) by a factor of three compared with a standard butterfly coil. Induced fields by this new coil were measured and in agreement with theoretical calculations.

Synaptic noise improves detection of subthreshold signals in hippocampal CA1 neurons.

Stochastic resonance (SR) is a phenomenon whereby the detection of a low-level signal is enhanced in a nonlinear system by the introduction of noise. Studies of the effects of SR in neurons have suggested that noise could play a prominent role in improving detection of small signals. Most experimental SR research has focused on the role of noise in sensory neurons using physiological stimuli. Computer simulations show that signal detection in hippocampal neurons is improved by the addition of physiological levels of noise applied extracellularly to synaptic inputs. These results were confirmed experimentally. We now report that endogenous noise sources can also improve signal detection. The noise source was generated by modulating the random synaptic activity on the apical dendrites of CA1 cells in rat hippocampal slices using subthreshold cathodic current. Intracellular recordings of CA1 cells showed that even small increases of synaptic noise are able to greatly improve the detection of an independent, synaptic, subthreshold stimulus as predicted by the simulations. The noise variance in the CA1 cell was compared with the resting variance and with variance changes caused by several endogenous noise sources. In all cases, the increased noise variance was well within the physiological range. These results were supplemented and analyzed with a CA1 computer model. The improved signal detection with small amounts of endogenous noise suggests that the diverse inputs to CA1 are able to improve detection of subthreshold synaptic signals and could provide a means to modulate detection of specific inputs in the hippocampus.