Supervised Spike Sorting Pipeline for Microneurography

This repository provides a Snakemake-based pipeline for supervised spike sorting of microneurography recordings from human C-nociceptors. The pipeline is designed to address challenges such as single-electrode ambiguity, waveform variability, and high fiber similarity, which traditionally hinder the analysis of peripheral nociceptive activity.

Our method incorporates: - Spike detection via thresholding - Multiple feature set extraction methods described here - Supervised classification using: - One-Class SVM - SVM - XGBoost - Validation using experimental derived ground truth data from electrical stimulation

Spike Tracking via the marking method

During the experiment the marking method is applied, a special electrical stimulation protocl to create spike tracks (vertical alignment of fiber responses). They can be extracted and analyzed post hoc.

In our workflow, we use two the following tracking algorithm for microneurography data to identify and track spikes evoked by background stimuli: - Dapsys www.dapsys.net based on Turnquist et al., 2016

The extracted spike times and track labels are essential inputs for running our supervised spike sorting pipeline. The setup instructions are provided below.

Setup Instructions

1. Clone the repository

bash git clone https://github.com/Digital-C-Fiber/SpikeSortingForSpikingPatterns.git cd SpikeSortingForSpikingPatterns

2. Create the Conda environment

This pipeline uses conda and snakemake with Python 3.11. We provide an environment file.

bash conda env create -f environment.yml conda activate Snakemake311

If you haven't already installed snakemake: bash conda install -c conda-forge snakemake

Snakemake Directory Overview

Snakefile # Main Snakemake workflow config.yaml # Configuration for test dataset paths and parameters environment.yml # Conda environment file scripts/ # Core processing script create_nix.py detection_via_thresholding.py feature_extraction.py helper.py pre_processing.py pre_processing_classification.py read_in_data.py templates_and_snr.py model/ # Classification models one_class_svm.py svm_model.py xgboost_model.py datasets_test/ testset_1.dps # Example Dapsys file nix/ # Output folder for NIX Statistics/ # R scripts for statistical analysis

Configuration (config.yaml)

Each dataset is defined under the datasets section. Example:

```yaml datasets: ATest: path: "datasetstest/testset1.dps" # Path to raw Dapsys file pathnix: "datasetstest/nix/testset1.nix" # Output path for NIX format name: "ATest" # Identifier for the dataset pathdapsys: "NI Puls Stimulator/Continuous Recording" # Internal path in Dapsys hierarchy time1: 200 # Start time of analysis window time2: 922 # End time of analysis window trackofinterest: "Track3" # Track label for supervised training threshold: 3.00 # Spike detection threshold groundtruthflag: False # Use ground truth flag for evaluation (True/False), in test file, no grount truth available

```

Running the Pipeline

To execute the full spike sorting workflow, follow the steps below:

1. Configure Your Dataset

Open and edit the config.yaml file to specify:

• Dataset paths (path, path_nix)
• Processing window (time1, time2)
• Detection threshold (threshold)
• Track label for supervised classification (track_of_interest)
• Whether to use ground truth evaluation (ground_truth_flag)

2. Run the Pipeline

Execute the pipeline using Snakemake with the desired number of CPU cores:

```bash snakemake --cores 8

```

Contact

If you have any questions, issues, or suggestions, feel free to reach out:

Alina Troglio Email: alina.troglio@rwth-aachen.de

How to Cite

If you use this pipeline in your work, please cite our preprint: Coming soon