Patent Description:
Electroceutical devices are medical devices which treat ailments using electrical impulses. Such devices may utilise bioelectric neuromodulation to treat a range of diseases or medical conditions.

One advantage of bioelectric neuromodulation devices, compared to pharmaceutical or biological treatments, is that the level of stimulation may be rapidly adjusted to respond to changing patient needs. This is known as closed-loop control. However, true closed-loop bioelectric neuromodulation requires the ability to chronically stimulate or activate neural activity, inhibit or suppress neural activity, and sense ongoing spontaneous or naturally evoked neural activity.

<CIT>, <CIT>, discloses a peripheral nerve electrode array that includes a first, second and third pair of electrodes spaced from each other along a longitudinal axis of the electrode array, the second pair of electrodes being located between the first and third pairs of electrodes, and a method for treating or preventing a chronic inflammatory condition in a human subject in need thereof, comprising providing to the human subject a therapeutically effective electrical stimulation of the anterior central abdominal vagus nerve or the posterior central abdominal vagus nerve, wherein the electrical stimulation is provided through two or more previously implanted electrodes at a site below the cardiac branches and above the hepatic-celiac branches of the nerve; and whereby the chronic inflammatory condition is prevented or treated in the human subject.

<CIT> relates to an apparatus for measuring non-stimulated activity from a sensory nerve using spaced apart sensors. The received signals may be correlated and summed to improve the signal-to-noise ratio.

Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each of the appended claims.

According to one aspect of the present disclosure there is provided a method of detecting neural activity in a nerve, the method comprising:.

In some embodiments, the first and/or second electrical signal may have a negative signal-to-noise ratio (SNR). That is, a power of a neural signal component may be smaller than a power of a noise signal component of the first and/or second electrical signal. Conventional recording apparatus, suitable for recording evoked neural activity in response to artificial stimulation, is typically unable to record ongoing spontaneous or natural neural activity due to excessive noise in the signal. The disclosed method may provide the ability to sense and extract neural signals which would otherwise be hidden in background noise.

In some embodiments, each of the first and second pairs of electrodes may be located outside a perineurium (nerve sheath) of the nerve. Since a high signal-to noise ratio is not necessarily required, the method may detect spontaneous or natural neural activity without requiring breach or penetration of the perineurium. As such, methods according to the present disclosure may be considered minimally invasive. The electrodes being located outside the perineurium may increase the longevity of devices employing the method, and their suitability for chronic implantation.

Lag time may be understood as a time offset, conduction delay or latency between the first and second electrical signals. In some embodiments, the at least one non-zero lag time may be preselected based on a distance between the first pair of electrodes and the second pair of electrodes. Alternatively, or additionally, the at least one non-zero lag time may be preselected based on a fibre type of the nerve. The lag time may be preselected to substantially coincide with a neural signal conduction time between the first and second pairs of electrodes. For example, for a given distance between the electrode pairs, the lag time may be selected based on an anticipated conduction speed of a fibre type of interest.

An absolute value of the non-zero lag time may be selected to be greater than a threshold value. The threshold value may be set to be sufficient to distinguish signals detected at the non-zero lag time from signals detected at zero lag time. For example, the absolute value of the non-zero lag time may be above <NUM>, <NUM>, <NUM> or otherwise.

In some embodiments, the correlation analysis may be applied for a single non-zero lag time. In other embodiments, the correlation analysis may be applied for a plurality of non-zero lag times. The plurality of non-zero lag times may span a range of lag times. For example, the plurality of non-zero lag times may be set at increments between a maximum and minimum lag time. The plurality of non-zero lag times may include negative and positive sign lag times.

The method may further comprise categorising the neural signal as afferent or efferent based on the sign of the lag time at which the neural signal is detected. That is, the direction of travel of the neural signal in the nerve may be indicated by whether the neural signal is detected at a positive lag time or a negative lag time, depending on which pair of electrodes is first reached by the signal. For example, an neural signal may reach the first pair of electrodes before the second pair of electrodes resulting in the signal being detected at a positive lag time. The neural signal may then be categorised as afferent or efferent depending on the relative positioning of the first and second electrodes along the nerve.

Further, the method may comprise categorising a fibre type of the nerve based on a magnitude of a non-zero lag time at which the neural signal is detected. For example, for a known distance between the first and second pairs of electrodes, the non-zero lag time can be indicative of a conduction speed of the nerve. The conduction speed may then be used to categorise the nerve fibre type based on known characteristics of neural fibres.

In some embodiments, the method may also comprise applying the correlation analysis for a zero lag time to obtain the correlation data. Signals which are received at both electrodes simultaneously will generally correspond to increased correlation in the correlated data at a substantially zero lag time. The method may further comprise detecting, from the correlation data, at least one alternative signal indicative of electrical activity, the alternative signal corresponding to increased correlation between the first and second signals for a substantially zero lag time. The alternative signals may be indicative of movement or evoked neural responses to stimulation.

In some embodiments, the neural signal may correspond to one or more regions of increased correlation between the first and second signals at the at least one non-zero lag time. Similarly, in some embodiments, the alternative signal may correspond to one or more regions of increased correlation between the first and second signals at zero lag time.

In some embodiments, the one or more regions of increased correlation in the correlation data at the at least one non-zero lag time (corresponding to the neural signal) may include one or more peaks in correlation between the first and second signals, the peaks being centred at the at least one non-zero lag time. Described is also that the one or more regions of increased correlation in the correlation data at the at zero lag time (corresponding to an alternative signal) may include one or more peaks in correlation between the first and second signals, the peaks being centred at zero lag time.

In some embodiments, the nerve may be a peripheral nerve. In other embodiments, the nerve may be a central nervous system nerve. In some embodiments, the nerve may be an autonomic nervous system nerve. The ability to detect, monitor and/or record neural activity in the autonomic nervous system may be advantageous, as stimulation of autonomic nerves typically does not produce a conscious percept. In other embodiments, the nerve may be a nerve of the somatic nervous system, for example, a mixed somatosensory nerve. In some embodiments, the nerve may be myelinated. In other embodiments, the nerve may be non-myelinated.

As examples, the nerve may be the pelvic nerve, vagus nerve or sciatic nerve. However, the disclosed method is not limited to these nerves.

The ability to detect or sense neural activity, particularly ongoing spontaneous or natural neural activity may be useful for neuromodulation of peripheral nerves. In particular, the ability to detect or sense ongoing spontaneous neural activity may enable the validation of a number of potential biomarkers useful for closed-loop control of electroceutical devices. For example, the method may be useful for detection of neural activity such as afferent signalling of increasing inflammation in inflammatory bowel disease (IBD), wherein optionally therapeutic treatment is initiated or adapted in response to the detected neural activity. As IBD is a remitting/relapsing condition, there will often be periods where no therapeutic treatment is required. By monitoring afferent activity in the vagus nerve using the presently disclosed method, it may be possible to detect an increase in afferent neural activity associated with a flare (that is, an increase in inflammation) before the patient experiences symptoms of the flare. In such cases, it may be possible to initiate or increase therapeutic treatment (for example, by stimulation of the vagus nerve using an electroceutical device) in direct response to the detected increase in afferent neural activity. Continued monitoring of subsequent afferent activity may then detect a resultant decrease in afferent activity associated with a decrease in inflammation, providing an indication for cessation or reduction of the therapeutic treatment. Adaptation (e.g. initiation, cessation, increase or decrease) of therapeutic treatment in response to detected neural activity may allow for ongoing closed-loop treatment of IBD, without the patient experiencing symptoms of the disease. Such closed-loop treatment may ensure that therapeutic treatment is only applied when required or only applied to a degree that is necessary. This has potential benefits for electroceutical devices in terms of reduced power consumption and/or improved battery life and minimisation of any off-target effects or safety issues.

In other examples, the method may be useful for detection of neural activity such as bladder volume afferent signalling, for example, for closed loop control of bladder prostheses.

According to another aspect of the present disclosure, there is provided processing apparatus configured to carry out the above described method. The processing apparatus may be at least partially implantable. The processing apparatus may be wholly implantable.

The received first and second electrical signals may be amplified, filtered or otherwise processed prior to applying the correlation analysis. Accordingly, the processing apparatus may comprise a signal amplifier, signal filter and/or other types of signal processors. The processing apparatus may comprise at least two recording inputs (or channels) for receiving the first and second electrical signals. The processing apparatus may be configured to receive (and optionally record) the first and second electrical signals at a sample rate of about <NUM> or more, for example, a sample rate of at least <NUM>, <NUM>, <NUM>, <NUM>, <NUM> or more. The processing apparatus may be configured to amplify received signals. For example, the processing apparatus may be configured to provide at least <NUM> times gain to the first and/second electrical signals. The processing apparatus may be configured to provide a band pass filter, for example, at least a <NUM>-<NUM> band pass filter.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable memory medium comprising instructions to cause a processing apparatus to perform the above described method.

According to another aspect of the present disclosure, there is provided a system for detecting neural activity in a nerve, the system comprising:.

The provision of first and second pairs of electrodes does not preclude the provision of third, fourth, fifth or yet further electrode pairs, whether for the purposes of monitoring or applying electrical signals.

In some embodiments, at least one of the first and second electrode pairs may be comprised in an electrode mounting device adapted to mount to the nerve to electrically interface the first and second electrode pairs with the nerve. The first and second pairs of electrodes may be in a substantially fixed relationship. For example, the electrode mounting device may comprise a support which substantially maintains the relative locations and orientations of the electrodes.

In some embodiments, the electrode mounting device may comprise an electrode array, the electrode array comprising the first pair of electrodes and the second pair of electrodes. In this embodiment, the two first electrodes may be positioned proximate each other along the electrode array and the two second electrodes may be positioned proximate each other along the electrode array. The first pair of electrodes may be spaced from the second pair of electrodes along the electrode array. For example, the first and second electrode pairs may be comprised in an electrode array such as that disclosed in PCT application no. <CIT> (<CIT>, <CIT>).

The two first electrodes may be spaced from each other by a distance a1 and the two second electrodes may be spaced from each other by a distance a2. The first and second pairs of electrodes may be spaced from each other by a distance b1. The distances a1 and a2 may be substantially equal, i.e. it may be that a1=a2 or they may be different. In general, the distance b1 may be greater than the distances a1 and a2. For example, the ratio between the distance a1 or distance a2 and the distance b1 may be between <NUM>:<NUM> and <NUM>:<NUM>, between <NUM>:<NUM> and <NUM>:<NUM> or about <NUM>:<NUM>. In another example, the ratio may be about <NUM>:<NUM> or more. For example, the ratio between the distance a1 or distance a2 and the distance b1, may be about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, about <NUM>:<NUM>, or more.

Alternatively, or additionally, the distance b1 may be selected based on a type, or property, of fibre of the nerve in which detection of neural activity is desired. As an example, for a known nerve fibre conduction velocity (V, e.g., <NUM>/s), the distance b1 may be selected to give increased correlation (or, in some embodiments, a region and/or peak in correlation) at a specific latency (L, e.g., <NUM>), for example, using the formula b1 = VL (e.g., <NUM>). The magnitude of the specific latency may be selected to be large enough that the increased correlation is adequately distinguishable from background noise present at or around <NUM>, and/or selected to be small enough to minimise any signal temporal dispersion effects.

By way of example only, embodiments of the present disclosure are now described with reference to the accompanying Figures in which:.

A method of detecting neural activity in a nerve according to an embodiment of the present disclosure is described with reference to flowchart <NUM> of <FIG>. The method comprises receiving a first electrical signal <NUM> and a second electrical signal <NUM>. The received first and second electrical signals <NUM>, <NUM> may be amplified, filtered or otherwise processed. The first and second electrical signals <NUM>, <NUM> are received from respective first and second pairs of electrodes (for example, electrode pairs <NUM>, <NUM> as shown in <FIG>). The first pair of electrodes <NUM> comprises two first electrodes <NUM>, <NUM> located proximate each other along the nerve. Similarly, the second pair of electrodes <NUM> comprises two second electrodes <NUM>, <NUM> located proximate each other along the nerve. As shown in <FIG>, the second pair of electrodes <NUM> is spaced from the first pair of electrodes <NUM> along the nerve. While the vagus nerve is shown in the embodiment of <FIG>, it will be appreciated that the disclosed method may be applied with respect to other nerves.

Referring again to the flowchart <NUM> of <FIG>, at <NUM>, a correlation analysis is applied between the first electrical signal <NUM> and the second electrical signal <NUM> to obtain correlation data. The applying of the correlation analysis <NUM> is performed for one or more lag times, including for at least one non-zero lag time. The lag time may be understood as a time offset, conduction delay or latency between the first and second electrical signals, which may be a function of a distance between the first and second electrode pairs and a conduction speed of a signal.

At <NUM>, at least one neural signal indicative of neural activity in the nerve is detected from the correlation data, the neural signal corresponding to increased correlation between the first and second electrical signals at a non-zero lag time.

<FIG> illustrates application of the method to model signal data. Model signals traces were generated, including: 'C-fibre' afferent neural signal (Aff); slow and fast efferent neural signals (Eff); noise signal from electromyographic activity (EMG); and random background noise signal (Noise). The scale of the Aff and Eff signals is <NUM> times smaller than the scale of the EMG and Noise signals. Model first and second electrical signals Rec1 and Rec2 were generated by combining multiple instances of the Aff and Eff signals, and the EMG and Noise signals with appropriate delay to simulate a <NUM> spacing between electrode pairs. As can be appreciated, the EMG and large efferent activity are apparent in the model electrical signals Rec1 and Rec2. However, the small afferent and efferent neural signals of interest are swamped by the EMG and background Noise signals and are not readily detectable in either the Rec1 or Rec2 traces.

In this example, a correlation analysis was applied between the model first and second electrical signals Rec1 and Rec2 to obtain correlation data, according to the disclosed method, as shown in the lower portion of <FIG>. The correlation analysis was applied for a range of non-zero lag times (conduction delays) between approximately -<NUM> to <NUM>, and also at zero lag time. The software used for the correlation analysis was Igor Pro <NUM> and the main function used was 'correlate'. This function performs a linear correlation using the following formula: <MAT>.

The correlation data is presented graphically in the form of an activity 'heat map', in which darker areas indicate increased correlation between the first and second electrical signals and more power for a given time and conduction delay (lag time) combination. The 'heat map' was produced by repeating the correlation on blocks of the recorded signal data. The afferent neural signal (Aff), slow and fast efferent neural signals (Eff) and electromyographic signals (EMG) are each apparent in the correlation data as shown in <FIG>.

Each neural signal may appear in the graphical correlation data as a region of increased correlation between the first and second signals, indicated by a darkened band (or 'hot spot') having a central portion and flanking side portions. The central and side portions represent three peaks in correlation between the first and second electrical signals, for a given time value but corresponding to various lag times. The signal type may be categorised based on the sign of the lag time at which the band is centred. For example, referring to <FIG>, the afferent neural signal (Aff) is detected in the graphical correlation data as the dark band centred at <NUM> lag time (highlighted by the solid circle <NUM>). The slow efferent signal is detected in the graphical correlation data as the dark band centred at -<NUM> lag time (highlighted by the dotted circle <NUM>). The fibre type and size may be categorised based on the magnitude of the lag time at which the band is centred. For example, the fast efferent activity is detected in the graphical correlation data as the dark band centered at a much smaller lag time of approximately -<NUM> (highlighted by the dashed circle <NUM>). The slow efferent signal may be distinguished from the fast efferent signal by the difference in magnitude of the lag times at which the respective signals are centred.

Signals in the graphical correlation data detected as the dark band <NUM> centred at substantially <NUM> (i.e. at zero lag time) are those which are received at both the first and second pair of electrodes substantially simultaneously. Such alternative signals may not be representative of signals conducting up or down the nerve fibre. For example, EMG activity (indicative of muscle activity) is substantially simultaneously recorded on both electrode pairs and appears as a dark band centred at substantially <NUM> lag time.

With reference to <FIG>, in another example, electrode arrays were chronically implanted on the pelvic nerve of normal adult rats, the electrode arrays each including two pairs of electrodes spaced from each other along the pelvic nerve. The rats were instrumented to allow cystometry (measurement of bladder pressure) and controlled filling of the bladder. During awake cystometry sessions, differential recording (100x gain, <NUM>-<NUM> band pass filter; <NUM> or <NUM> Hz sampling) was used to receive first and second electrical signals (N1 and N2) from the pelvic nerve, via the respective pairs of electrodes, during a spontaneous bladder voiding event.

Trace P of <FIG> shows the bladder pressure cystometry recording over the voiding event. A gradual increase in bladder pressure can be observed, followed by a steeper rise in bladder pressure resulting from contractions of the bladder wall with an initially closed bladder sphincter and, finally, a rapid decrease in bladder pressure as the result of a bladder voiding event. The corresponding first and second electrical signal recordings from the pelvic nerve (N1 and N2) contain a signal with positive SNR during the early rise in pressure. However, the autonomic afferent and efferent neural signals of interest are not readily detectable from the recorded first and second electrical signals N1 and N2, as the signals of interest have a negative signal-to-noise ratio.

<FIG> shows a graphical representation of correlation data obtained by applying a correlation analysis between the first and second electrical signals N1 and N2 of <FIG>. In this example, the correlation analysis was applied for a range of non-zero lag times (conduction delays), from -<NUM> to <NUM>, and also at zero lag time. In the graph of <FIG>, darker portions indicate greater activity, or increased correlation between the first and second signals. A detected first neural signal is apparent, corresponding to the peak in correlation centred at -<NUM>, indicated by the solid circle <NUM>. The first neural signal is categorised in this particular arrangement as afferent based on the negative sign of the lag time at which the peak is centred. The absolute magnitude (<NUM>) of the lag time (conduction delay) indicates that the nerve fibre type is small autonomic (based on a known distance between the electrode pairs and an inferred conduction speed of the signal). Similarly, a second peak in correlation centred at +<NUM>, corresponding to a second neural signal, is indicated by the dotted ellipse <NUM>. The second neural signal is categorised in this particular arrangement as efferent based on the positive sign of the lag time at which the peak is centred. The absolute magnitude (<NUM>) of the lag time indicates that the nerve fibre type is small autonomic, based on a known distance between the electrode pairs. A third neural signal is also apparent in <FIG>, corresponding to a peak in correlation as indicated by the arrow <NUM>. The peak indicated by the arrow <NUM> is centred at a negative lag time of smaller magnitude than the peaks of the first and second neural signals and, as such, can be categorised as fast afferent activity in the nerve. Individual signal traces were extracted from the correlation data and are shown beneath the heat map for each of the slow afferent (SA), fast afferent (FA) and efferent (E) signals. The signal extraction was performed by taking the appropriate row from the correlation data based on the lag time at which the relevant signal was detected.

Other embodiments may apply a correlation analysis over a narrower or wider range of lag times. Alternatively or additionally, a correlation analysis may be applied between the first and second signals for a single lag time of interest (or multiple discrete lag times of interest), for example, to isolate neural responses of one or more conduction speeds of interest.

In this example, the applying a correlation analysis between the first and second electrical signals according to the method enabled the detection of neural signals which would otherwise be hidden in background noise due to a negative signal-to-noise ratio. Further, in this example, the application of the correlation analysis for a non-zero lag time according to the method provided the ability to distinguish between and categorise the detected neural signals.

<FIG> shows another example of data obtained using the experimental setup described above, including in which first and second electrical signal recordings are made from respective pairs of electrodes along the nerve during a bladder voiding event. <FIG>, shows the bladder pressure cystometry recording (panel A) over the voiding event, a graphical representation of the output from a correlation analysis between the two recorded signals, with areas of stronger correlation indicated in lighter shades (panel B), and an extracted trace from correlation data at <NUM> conduction delay (lag time), indicative of afferent activity in the nerve (panel C). An increase in afferent activity corresponding to the second pressure increase in the bladder can be observed, before the signal is swamped by larger activity during the main pressure peak.

<FIG> shows a bladder pressure cystometry trace (panel A, <NUM> sample rate), a corresponding graphical representation of correlation data (panel B, lighter colour indicates a stronger correlation) and an extracted trace from the correlation data of the <NUM> conduction delay efferent activity signal (panel C). Periodic fluctuations are evident in the pressure trace. These fluctuations in pressure are matched by modulations in the efferent activity trace.

<FIG>, shows the pressure fluctuations of <FIG> in greater detail (panel A), a corresponding detail from the correlation analysis heat map (panel B) and a trace extracted from the correlation heat map indicative of efferent activity at -<NUM> conduction delay (panel C), and a trace extracted from the correlation heat mapindicative of afferent activity at <NUM> conduction delay (panel D). Both the efferent and afferent traces exhibit modulations which match the periodic pressure changes. Methods according to the present disclosure thus allow afferent and efferent neural signals to be detected simultaneously, such that any patterns or relationships between the afferent and efferent activity may be identified.

<FIG> shows data obtained during another bladder voiding event in a rat including bladder pressure cystometry trace during the voiding event (panel A) a graphical representation of correlation data, where a lighter shade indicates a stronger correlation (panel B), and respective fast afferent and efferent signal traces extracted from the correlation data (panels C and D). From these traces, the relative timing of different neural signals during a typical bladder voiding event can be observed.

With reference to <FIG>, in another example, an electrode array was implanted on the sciatic nerve of a rat. The rat's ankle was manipulated to change the angle of the joint in a <NUM> periodic stretching motion (white trace, top of <FIG>). A correlation analysis was performed on the signals received at the two pairs of electrodes. A graphical representation of this analysis is shown in the lower portion of <FIG>, where a lighter shade indicates a stronger correlation. As seen in the area indicated by the solid ellipse, periods of afferent neural activity were detected, corresponding in frequency to the period of the ankle stretching motion. The conduction speed of the nerve fibre was calculated at around <NUM>/ms, based on the distance between the electrodes and the conduction delay (non-zero lag time), indicating that the nerve fibres conducting the detected neural signal were type A-alpha.

A system for detecting neural activity in a nerve according to an embodiment of the present disclosure is illustrated by system diagram <NUM> in <FIG>. The system includes a first pair of electrodes <NUM>, a second pair of electrodes <NUM> and processing apparatus <NUM>.

The processing apparatus <NUM> may be configured to perform the method disclosed above with reference to <FIG>, for example, or otherwise.

<FIG> illustrates an electrode array <NUM> including first and second surface electrode pairs <NUM>', <NUM>' according to an embodiment of the present disclosure. The first pair of surface electrodes <NUM>' comprises two first electrodes <NUM>', <NUM>' positionable proximate each other along the nerve, and the second pair of electrodes <NUM>' comprises two second electrodes <NUM>', <NUM>' positionable proximate each other along the nerve. The second pair of electrodes <NUM>' is configured to be spaced from the first pair of electrodes <NUM>' along a nerve.

The electrode pairs <NUM>', <NUM>' are embedded or otherwise located in an electrode mounting device <NUM> of the array, which is adapted to electrically interface the first and second electrode pairs <NUM>', <NUM>' with the nerve. The electrode mounting device <NUM> comprises a support <NUM> that substantially maintains the relative orientation and location of the pairs of electrodes <NUM>', <NUM>' with respect to each other. As such, in this embodiment, the spacing between the electrodes <NUM>', <NUM>', <NUM>', <NUM>' is substantially pre-defined and fixed.

An alternative embodiment is illustrated in <FIG>. In this embodiment, electrode array <NUM> includes a lead <NUM> that comprises electrode pairs for detecting neural activity at the nerve. The lead <NUM> divides into three separate branches, each branch comprising a separate electrode mounting device <NUM>, <NUM>, <NUM>. Each electrode mounting device <NUM>, <NUM>, <NUM> comprises a respective pair of electrodes <NUM>", <NUM>", <NUM>". In particular: the first electrode mounting device <NUM> comprises a first pair of electrodes <NUM>", the first pair of electrodes comprising two first electrodes <NUM>", <NUM>" located proximate each other along a longitudinal direction L of the electrode array; the second electrode mounting device <NUM> comprises a second pair of electrodes <NUM>", the second pair of electrodes comprising two second electrodes <NUM>", <NUM>" located proximate each other in the longitudinal direction L of the electrode array. In this embodiment, optionally a third electrode mounting device <NUM> is provided comprising a third pair of electrodes <NUM>". The third pair of electrodes may, for example, comprise two third electrodes <NUM>", <NUM>" located proximate each in the longitudinal axis L of the electrode array and may be for the purposes of detecting, recording, monitoring or applying electrical signals.

It will be appreciated that other embodiments may have four, five or more pairs of electrodes provided for various purposes. Additionally, the first and second pair of electrodes need not be adjacent each other on the array, and may be separated by one or more other pairs of electrodes.

As represented in <FIG>, the first, second and third mounting devices <NUM>, <NUM>, <NUM> are spaced from each other in the longitudinal direction L of the electrode array <NUM>. As such, the first, second and third pairs of electrodes <NUM>", <NUM>" are correspondingly spaced from each other in the longitudinal direction L of the electrode array. The first electrodes <NUM>", <NUM>" are spaced from each other by a distance a1 and the second electrodes <NUM>" <NUM>" are spaced from each other by a distance a2, the distances a1 and a2 being in the longitudinal direction of the electrode array and generally from centre-to-centre of the respective electrodes. As also represented in <FIG>, the first and second pairs of electrodes <NUM>", <NUM>" are spaced from each other by a distance b1, the distance b1 being in the longitudinal direction of the electrode array and generally from centre-to-centre of the closest electrodes of the adjacent pairs of electrodes. In the illustrated embodiment, the distances between the electrodes within each pair of electrodes <NUM>", <NUM>" is substantially the same, i.e. a1=a2. In this embodiment, the distance b1, between the first and second pairs of electrodes <NUM>", <NUM>" is greater than the distances a1 and a2 between the electrodes within each pair of electrodes <NUM>", <NUM>". The distance b1 is greater than the distance a1 and the distance a2. The ratio between the distances a1 and a2 and the distance b1 is between <NUM>: <NUM> and <NUM>:<NUM>, and more specifically about <NUM>:<NUM> in this embodiment. In alternative embodiments, the distances a1 and a2 between the first and second pairs of electrodes may not be equal. In some instances, such an asymmetric arrangement of electrodes may be desirable in view of anatomical and/or physiological conditions.

For example, when detecting activity in the rat pelvic nerve in the examples discussed above, the electrode pairs were spaced along the nerve with a distance b1 of approximately <NUM> from each other, resulting in the slow afferent and slow efferent neural signals being detectable at lag times of approximately +/-<NUM>. However, in other embodiments, (e.g., when detecting signals travelling along myelinated nerve fibres) the conduction of signals between the electrode pairs may be much faster. As a result, the lag time across small distances may be very low, such that neural signals are obscured by the background noise present at and around <NUM> lag time. In such embodiments, the distance b1 between the electrode pairs may be increased accordingly, thereby to increase the lag time, such that the neural signal is more clearly distinguishable from the background noise present at <NUM> lag time. For example, when detecting fast afferent activity in the rat sciatic nerve (as shown in <FIG>) the electrodes pairs were spaced along the nerve at a distance b1 of approximately <NUM> from each other. Conversely, in some embodiments, the distance b1 between the electrode pairs may be decreased to avoid temporal dispersion effects. The distance b1 between the electrode pairs may be selected based on an anticipated conduction speed to provide a desired lag time, while minimising temporal dispersion.

In some embodiments, detecting or sensing of neural activity, e.g. in accordance with methods and apparatus described above, particularly ongoing spontaneous or natural neural activity, may be used in conjunction with neuromodulation of peripheral nerves, e.g. as part of closed-loop control of electroceutical devices. Referring for example to <FIG>, the apparatus may be configured generally in accordance with the apparatus described above with reference to <FIG>, but may additionally include therapy electrodes <NUM> configured to apply therapeutic electrical treatment to a nerve, based on the detected neural activity. The processing apparatus <NUM> may therefore detect neural activity and control therapy on the basis of the detected neural therapy. In <FIG>, while therapy electrodes <NUM> are illustrated as being separate from the first and second pairs of electrodes <NUM>, <NUM>, and may be in the form of a third pair of electrodes or otherwise, in other embodiments the first and/or second pairs of electrodes may be selectively operable as therapy electrodes.

The apparatus described with reference to <FIG> may be useful for detection of neural activity such as afferent signalling of increasing inflammation in inflammatory bowel disease (IBD), wherein therapeutic treatment is initiated or adapted in response to the detected neural activity. As IBD is a remitting/relapsing condition, there will often be periods where no therapeutic treatment is required. By monitoring afferent activity in the vagus nerve in the present manner, it may be possible to detect an increase in afferent neural activity associated with a flare (that is, an increase in inflammation) before the patient experiences symptoms of the flare. In such cases, it may be possible to initiate or increase therapeutic treatment (for example, by stimulation of the vagus nerve using an electroceutical device) in direct response to the detected increase in afferent neural activity. Continued monitoring of subsequent afferent activity may then detect a resultant decrease in afferent activity associated with a decrease in inflammation, providing an indication for cessation or reduction of the therapeutic treatment. Adaptation (e.g. initiation, cessation, increase or decrease) of therapeutic treatment in response to detected neural activity may allow for ongoing closed-loop treatment of IBD, without the patient experiencing symptoms of the disease. Such closed-loop treatment may ensure that therapeutic treatment is only applied when required or only applied to a degree that is necessary. This has potential benefits for electroceutical devices in terms of reduced power consumption and/or improved battery life and minimisation of any off-target effects or safety issues.

In other examples, the apparatus of <FIG> may be useful for detection of neural activity such as bladder volume afferent signalling, for example, for closed loop control of bladder prostheses.

Methods and apparatus according to embodiments of the present disclosure may use non-transitory computer-readable memory medium comprising instructions to cause processing apparatus to perform the specified steps.

In general processing apparatus used in the present disclosure may comprise one or more processors and/or data storage devices. The one or more processors may each comprise one or more processing modules and the one or more storage devices may each comprise one or more storage elements. The modules and storage elements may be at one site, e.g. in a single hand-held device, or distributed across multiple sites and interconnected by a communications network such as the internet.

Claim 1:
A method of detecting neural activity in a nerve, the method comprising:
receiving a first electrical signal (<NUM>) from a first pair of electrodes, the first pair of electrodes (<NUM>) comprising two first electrodes (<NUM>, <NUM>) located proximate each other along the nerve; and
receiving a second electrical signal (<NUM>) from a second pair of electrodes (<NUM>), the second pair of electrodes (<NUM>) comprising two second electrodes (<NUM>, <NUM>) located proximate each other along the nerve, wherein the second pair of electrodes (<NUM>) is spaced from the first pair of electrodes (<NUM>) along the nerve;
characterised in that the method further comprises:
applying a correlation analysis between the first and second electrical signals (<NUM>, <NUM>), including for at least one non-zero lag time, to obtain correlation data; and
detecting, from the correlation data, at least one neural signal indicative of neural activity in the nerve, the neural signal corresponding to increased correlation between the first and second signals at the at least one non-zero lag time.