Hybrid knowledge- and data-driven modelling for robust spike detection and sorting in human C-fiber microneurography

Scientific Reports, vol. 16

Abstract

Analyzing temporal spike patterns in C-fibers recorded via microneurography is challenging due to the use of a single recording electrode, waveform variability, and high similarity of spike shapes across neurons, limiting the interpretation of sensory coding, such as pain and itch. We present a computational pipeline combining peak detection and supervised classification for spike sorting to improve the analysis of discharges, identified through activity-dependent conduction velocity changes. In the knowledge-driven step, we extract spike templates from electrically evoked spikes obtained during low-frequency stimulation and focus on the “best” template as the fiber of interest. Spike detection is further restricted to intervals showing activity-dependent latency shifts, substantially reducing the search space compared to unsupervised clustering. In the data-driven steps, we systematically evaluate three feature sets and machine learning models: One-class SVM, SVM, and XGBoost. For the evaluation, we created a specialized stimulation protocol, providing reliable ground truth labels for all electrically evoked spikes, allowing precise spike time-locking. Compared to Spike2 software, our approach achieved higher F1-scores and reduced false positives, indicating improved spike sorting. Although XGBoost achieved the highest median F1-scores, optimal performance was dependent on individual combinations of feature sets and models for each recording. In some recordings with many nerve fibers and a low signal-to-noise ratio, reliable sorting was not feasible. This highlights the necessity to determine sortability and optimal configurations for individual recordings. To illustrate the potential of our approach to sensory spike train analysis, we present a proof-of-concept application of the pipeline to chemically induced C-fiber activity. These findings represent an important step toward reliable analysis of activity associated with pain and itch signaling.

Authors 5

1. Alina Troglio corresponding Aachen Institute of Neurophysiology

   Universitätsklinikum Aachen · Universitätsklinikum Würzburg · RWTH Aachen University

   Affiliation as printed

   Department of Anesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Würzburg, Center for Interdisciplinary Pain Medicine, Würzburg, Germany. alina.troglio@rwth-aachen.de

   Institute of Neurophysiology, Uniklinik RWTH Aachen University, Aachen, Germany. alina.troglio@rwth-aachen.de

2. Andrea Fiebig Aachen Research group Neuroscience Interdisciplinary Center for Clinical Research (IZKF) Faculty of Medicine

   RWTH Aachen University

   Affiliation as printed

   Research Group Neuroscience, Interdisciplinary Centre for Clinical Research (IZKF), Faculty of Medicine, RWTH Aachen University, Aachen, Germany

3. Anna Maxion Aachen Institute for Computational Biomedicine

   RWTH Aachen University

   Affiliation as printed

   Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany

4. Ekaterina Kutafina

   University of Cologne · University Hospital Cologne

   Affiliation as printed

   Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

5. Barbara Namer

   Universitätsklinikum Würzburg

   Affiliation as printed

   Department of Anesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Würzburg, Center for Interdisciplinary Pain Medicine, Würzburg, Germany