A new method for spike extraction using velocity selective recording demonstrated with physiological ENG in Rat

Highlights

• New methods are developed to extract spike trains based on conduction velocity.

• Histograms describing firing rates of particular neurons are created.

• Methods are applied to data recorded in-vivo from rat.

• Cutaneous skin sensation is detectable from neural recordings.

Abstract

Background

This paper describes a series of experiments designed to verify a new method of electroneurogram (ENG) recording that enables the rate of neural firing within prescribed bands of propagation velocity to be determined in real time. Velocity selective recording (VSR) has been proposed as a solution to the problem of increasing the information available from an implantable neural interface (typically with electrodes in circumferential nerve cuffs) and has been successful in transforming compound action potentials into the velocity domain.

New method

The new method extends VSR to naturally-evoked (physiological) ENG in which the rate of neural firing at particular velocities is required in addition to a knowledge of the velocities present in the recording.

Results

The experiments, carried out in rats required individual spikes to be distinct and non-overlapping, which could be achieved by a microchannel or small-bore cuff. In these experiments, strands of rat nerve were laid on ten hook electrodes in oil to demonstrate the principle.

Comparison with existing method

The new method generates a detailed overview of the firing rates of neurons based on their conduction velocity and direction of propagation. In addition it allows real time working in contrast to existing spike sorting methods using statistical pattern processing techniques.

Conclusions

Results show that by isolating neural activity based purely on conduction velocity it was possible to determine the onset of direct cutaneous stimulation of the L5 dermatome.

Introduction

If it were possible to construct an ideal nerve interface, such a system would allow recording from and stimulation of every single axon in the nerve; it would be stable in time so that each axon, once identified, would have a known function. Every axon would have a physiological label so, for example, some axons in the femoral nerve, after identification, would be labelled vastus lateralis, efferent. However, before these physiological labels could be attached, it would be helpful to know the propagation velocity and direction for every fibre (afferent or efferent) which would greatly reduce its possible range of function. At present of course, no such interface exists. Practical methods start with the axon-specific, such as intra-fascicular devices, fine tungsten needle electrodes (Yoshida et al., 2000) or arrays such as the Utah design (Maynard et al., 1997); these are typically invasive, show poor chronic applicability in peripheral nerves, and give no indication of the axon/action potential characteristics. At the extra-fascicular level, cuffs can be safe for chronic clinical use, but are limited in recording the composite activity from all the axons in the nerve. In this range of types, Longitudinal Intrafasicular Electrode (LIFE) arrays can record from small groups of axons, perhaps within one fascicle, but do not show activity in the whole nerve (Boretius et al., 2010). Micro-channel nerve interfaces have enabled inter-fascicular recording from peripheral nerves with single unit activity resolution, but the number of axons in each micro-channel is quite large (∼100, FitzGerald et al., 2012) and physiological characterisation is still limited. No current interface method allows communication with one axon, or even a group of only a few axons, with physiological labelling.

The method that we call velocity selective recording (VSR) has been applied to cuffs and, by extension, could be applied to micro-channels. By filtering the neural signal in the velocity domain, activity within bands of conduction velocity can be discriminated and, if that band corresponds to a functional group of fibres, such as, for example, the γ efferents which are responsible for muscle spindle contraction, it should be possible to estimate the activity in those fibres. Thus the VSR method should improve both the quantity and quality of the information that can be extracted from the neural signal using practical types of electrode structure. This improvement may yield substantial benefits in a clinical neuroprosthesis if one can provide better resolution at the input. However, in spite of the many potential applications of VSR, to date it has only been demonstrated with electrically-evoked electroneurogram (ENG), i.e. compound action potentials (CAPs) (Schuettler et al., 2011, Schuettler et al., 2013). This is because there are several very significant differences between the requirements of recording electrically-evoked and natural ENG that complicate the process of recording the latter. These differences include much smaller signal amplitudes (typically, using cuffs, 1–10 μV, as opposed to about 100 μV for CAPs) and the need to determine the rate of neural firing in a particular velocity band, rather than the relative amplitudes of activity between bands, which is generally the case for CAPs. This paper presents the first experimental validation of a new VSR-based method using naturally-evoked (physiological) signals. The data were obtained from a strand of intact nerve within a dorsal/sensory root of a rat and a new method was employed that overcomes the difficulties of recording natural ENG and allows neuronal firing rates in specified velocity bands to be computed in real time. We call this the method of velocity spectral density (VSD) (Metcalfe et al., 2014).

The new method was validated by capturing and then manually calculating the propagation velocities of individual spikes (i.e. action potentials-APs) and comparing the firing rates in each of a chosen set of velocity bands with the output from the VSD processor. The recording was made with the nerve resting on hook electrodes immersed in oil. This was convenient for the experiment but we expect that the signals so obtained were similar to the outputs from a row of electrodes in a micro-channel. Both arrangements greatly increase the amplitude of extracellular potentials (V_ex) compared to a nerve of diameter 1 mm or greater placed in a cuff, enabling individual ENG spikes to be distinguished and counted: this is essential for the method and also allows validation by inspection of the electroneurograms. The ten electrodes were connected in separate pairs to form five bipolar recording channels before amplification and band-pass filtering.

There is, of course, a long history of analysing neural recordings from microelectrodes in brain. These methods normally identify spikes by the characteristics of their shape. This spike sorting is generally not done in real time and the methods often use substantial computing power (Gibson et al., 2012). By comparison, the proposed VSD method, in common with other VSR-based approaches, can operate in real time and is relatively economical in terms of computational effort. These features are important in certain neuroprosthetic devices such as the “Bioelectronic Medicines” currently being advocated by GlaxoSmithKline (Famm et al., 2013). This is because the devices must be small and low-powered and the firing rates of fibres that serve different functions must be calculated without significant computation delays.

Section 2 considers the VSR approach in general and the modifications and new methods necessary for it to compute VSDs while Section 3 describes the experimental methods employed. Section 4 describes the experimental results and Section 5 provides discussion and conclusions.