Blog post by Manuel Martinez, Research Software Engineer and Dr Jim Dunham, Clinical Lecturer, from the School of Physiology, Pharmacology and Neuroscience at the University of Bristol
We are developing new tools to analyse human pain nerve activity in real time. This will aid diagnosis in chronic pain and enable individualised, targeted treatments.
Some patients with chronic pain have abnormally increased activity in their “pain detecting” nerves (nociceptors). We do not know which patients have this problem and which do not. If we could determine which individuals suffer with these ‘sensitised’ nociceptors, we could treat them more effectively, by giving medicines to ‘quieten’ their nerves.
We record from human nociceptors using a technique called microneurography. Sadly, this technique is only used in research as it is too time consuming and unreliable to use clinically. To bring microneurography closer to the clinic we sought to:
Improve Real-time Data Visualisation
- Improve the way real-time neural data is displayed by replacing a legacy oscilloscope-like trace with a 4D ‘smart’ visualiser.
Close the Loop
- Develop and implement automated real-time robust spike detection algorithms.
- Develop and implement closed-loop dynamic thresholding algorithms to automatically control the electrical stimulus energy.
These developments have the potential to significantly increase experimental efficiency and data yields.
Real-time Data Visualisation
Microneurography allows for nerve activity to be recorded by means of a fine electrode inserted through the skin into the nerve. After insertion into the nerve, the skin supplied by that nerve is electrically stimulated to cause activity in the nociceptors (Figure 1). Recording this activity is difficult; it requires careful positioning of the electrode and is further complicated by the small amplitude of the nerve signal in comparison to noise.
Figure 2A shows the legacy oscilloscope-like visualiser commonly used in microneurography. The signal trace represents the voltage measured in the recording electrode as a function of time. The evoked neural spikes are indicated by green arrows. The large spikes (indicated by the red lighting symbol) correspond to a signal artefact caused by the electrical stimulation system.
“Pain” nerves conduct slowly and therefore have characteristically long latencies. These latencies show good correlation between successive firings. Therefore, accurate electrode placement can be verified by the presence of consecutive spikes of similar latency after the stimulus event.
Figure 2B shows our novel 4D visualiser. Here, the signal amplitude is encoded via colour, with lighter colours representing high amplitudes. This colour scaling can be adjusted in real time by the user. The vertical axis corresponds to latency after the stimulus event and the horizontal axis to a series of stimulus events. Therefore, a constant latency spike manifests itself as a line in this visualiser.
This is a significant improvement over the legacy visualiser as the subtle changes in colour and the alignment between two consecutive spikes can be readily identified by eye in real time. This greatly increases the clinician’s situational awareness and contributes to maximising experimental yield.
Closed-loop stimulation control
The electrical energies required to evoke nociceptor activity are not constant. These changes in electrical ‘threshold’ may be useful in understanding why patients’ nerves are abnormally excitable. Unfortunately, balancing signal detection against stimulation energy in the context of real time analysis of small amplitude signals is difficult and primed for failure.
To improve reliability and reproducibility, we have developed a dynamic thresholding algorithm that automatically controls stimulation energy once a unit has been manually identified (i.e. a line can be seen in the visualiser). This is conceptually simple: decrease the stimulation energy until the unit ceases to fire, then increase it until it starts firing again.
In practice, the robust detection of spikes is challenging as existing approaches are only successful in environments with high signal-to-noise ratios (SNRs). To address this, our proof-of-concept algorithm first takes a set of candidate spikes (obtained using a simple threshold crossing method – green points in Figure 2C). Then, these candidate spikes are temporally (latency) filtered so that only those around a small region of interest near the detected track remain. This detection algorithm, despite its simplicity, has shown promising performance on pre-recorded and simulated data and is now ready for testing in microneurography.
Revolutionising human microneurography
We seek to revolutionise human microneurography: bringing it into the clinic as a diagnostic tool; informing treatment decisions and demonstrating ‘on target’ efficacy of new analgesics.
The novel 4D visualiser and automated closed-loop experimental tools developed here will be validated in microneurography experiments in healthy volunteers and then made publicly available in the spirit of open-source research. Additionally, we will integrate more advanced methods of ‘spike’ detection into the algorithm to maximise sensitivity and specificity.
We anticipate our first patient trials of these novel tools within the next 12 months. Our visualiser will enable rapid identification of abnormal activity in nociceptors, paving the way towards data-driven, personalised treatments for patients living with chronic pain.
Contacts and Links
Mr Manuel Martinez Perez (Research Software Engineer, School of Physiology, Pharmacology & Neuroscience)
Dr Jim Dunham (Clinical Lecturer, School of Physiology, Pharmacology & Neuroscience)
Dr Gethin Williams (Research Computing Manager, IT Services)
Dr Anna Sales (Research Associate, School of Physiology, Pharmacology & Neuroscience)
Mr Aidan Nickerson (PhD student, School of Physiology, Pharmacology & Neuroscience)
Prof Nathan Lepora (Professor of Robotics and AI, Department of Engineering Mathematics)
Prof Tony Pickering (Professor of Neuroscience and Anaesthesia, School of Physiology, Pharmacology & Neuroscience)
Jean Golding Institute Seed Corn Funding Scheme
The Jean Golding Institute run an annual seed corn funding scheme and have supported many interdisciplinary projects. Our next round of funding will be in Autumn 2020. Find out more about our Funding opportunities