Supervised Spike Sorting Feasibility of Noisy Single-Electrode Extracellular Recordings: Systematic Study of Human C-Nociceptors recorded via Microneurography
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Sorting spikes from noisy single-channel in-vivo extracellular recordings is challenging, particularly due to the lack of ground truth data. Microneurography, an electrophysiological technique for studying peripheral sensory systems, employs experimental protocols that time-lock a subset of spikes. Stable propagation speed of nerve signals enables reliable sorting of these spikes. Leveraging this property, we established ground truth labels for data collected in two European laboratories and designed an open-source pipeline to process data across diverse hardware and software systems. Using the labels derived from the time-locked spikes, we employed a supervised approach instead of the unsupervised methods typically used in spike sorting.
We evaluated multiple low-dimensional representations of spikes and found that raw signal features consistently outperformed more complex approaches, which are effective in brain recordings. However, the choice of the optimal features remained dataset-specific, influenced by the similarity of average spike shapes and the number of fibers contributing to the signal.
Based on our findings, we recommend tailoring lightweight algorithms to individual recordings and assessing the “sortability feasibility” based on achieved accuracy and the research question before proceeding with sorting of non-time-locked spikes. Our approach provides the foundation for further development of spike sorting algorithms in noisy extracellular recordings of neural activity.
Author Summary
Using electrophysiological methods like microneurography, scientists can record nerve activity in humans to understand how peripheral nerves transmit sensations such as pain and itch. These recordings capture electrical signals, known as spikes, which represent nerve impulses. However, since several nerve fibers are often recorded simultaneously, the differentiation of the individual spikes, known as spike sorting, is critical for accurate analysis.
Existing methods for spike sorting in single electrode in-vivo recordings are often insufficient due to low signal-to-noise ratios and the absence of ground truth data needed for validation. In microneurography, low-frequency electrical stimulation (marking method) is used routinely to label part of the recorded spikes. We applied the marking method to create a ground truth data set for developing and validating a supervised approach for spike sorting.
Our transparent and lightweight algorithm showed promising results. Their high variability between the recordings, with a strong reversed link between the morphological similarities of the different fibers’ spikes and the sorting accuracy indicated a possibility to assess the “sortability” of individual recordings by applying use-case specific thresholds. This work provides a foundation for improving spike sorting in noisy peripheral nerve recordings, helping researchers study better how the nervous system processes sensations like pain and itch.
