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Comparative analysis of spike-sorters in large-scale brainstem recordings
Caitlynn C De Preter1,2, Elizabeth M Leimer3, Alex Sonneborn1,4
1Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, 97239, USA.
Biorxiv : the Preprint Server for Biology
|November 28, 2024
Summary
This study evaluated five spike-sorting software packages for high-density neural recordings in the rostral ventromedial medulla. Kilosort3 and IronClust required minimal curation, efficiently identifying neural units in this deep brainstem region.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Electrophysiology
Background:
- High-density multi-channel electrodes enable recording from numerous neurons in previously inaccessible brain regions.
- Spike-sorting performance varies across brain regions due to distinct neural characteristics, necessitating region-specific evaluations.
Purpose of the Study:
- To assess the performance of five common spike-sorting packages (Kilosort3, MountainSort5, Tridesclous, SpyKING CIRCUS, IronClust) in the rostral ventromedial medulla (RVM).
- To determine the most effective and efficient spike-sorting approach for high-density network-level recordings in the RVM.
Main Methods:
- Recordings were made from the RVM using high-density electrodes.
- Five spike-sorting algorithms were applied to the recorded neural data.
- Manual curation was performed to refine unit identification, prioritizing units detected by multiple sorters.
Main Results:
- Each spike-sorting package produced distinct results.
- Kilosort3 and IronClust required the least manual curation and identified the most units.
- SpyKING CIRCUS and MountainSort5 necessitated significant curation, while Tridesclous identified the fewest units.
- All tested sorters successfully identified known RVM physiological cell types.
Conclusions:
- The choice of spike-sorting software impacts the required level of manual curation for RVM recordings.
- Kilosort3 and IronClust offer efficient and effective spike-sorting for high-density recordings in the RVM.
- Each tested sorter can extract meaningful neural data from this deep brainstem region, despite varying curation needs.
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