Computational NeuroscienceAutomated detection of latency tracks in microneurography recordings using track correlation

- We developed a method for automatically tracking changes in action potential latency in microneurographic recordings.
- Our track correlation filter enhances the contrast between latency tracks and background noise to detect spikes that would be missed using other techniques.
- Our track correlation filter eliminates signals not temporally correlated with the stimulus, e.g. noise spikes and spontaneous neural firing.
- Our track correlation filter detects signals independent of spike amplitude and spike shape characteristics.

BackgroundThe marking technique in microneurography uses stimulus-induced changes in neural conduction velocity to characterize human C-fibers. Changes in conduction velocity are manifested as variations in the temporal latency between periodic electrical stimuli and the resulting APs. When successive recorded sweeps are displayed vertically in a “waterfall” format, APs correlated with the stimulus form visible vertical tracks. Automated detection of these latency tracks is made difficult by sometimes poor signal-to-noise ratio in recordings, spontaneous neural firings uncorrelated with the stimuli, and multi-unit recordings with crossing or closely parallel tracks.New methodWe developed an automated track-detection technique based on a local linearization of the latency tracks of stimulus-correlated APs. This technique enhances latency tracks, eliminates transient noise spikes and spontaneous neural activity not correlated with the stimulus, and automatically detects latency tracks across successive sweeps in a recording.ResultsWe evaluated our method on microneurography recordings showing varying signal quality, spontaneous firing, and multiple tracks that run closely parallel and cross. The method showed excellent detection of latency tracks in all of our recordings.Comparison with existing method(s)We compare our method to the commonly used track detection method of Hammarberg as implemented in the Drever program.ConclusionsOur method is a robust means of automatically detecting latency tracks in typical microneurography recordings.