Comparative analysis of spike-sorters in large-scale brainstem recordings
Recent technological advancements in high-density multi-channel electrodes have made it possible to record large numbers of neurons from previously inaccessible regions. While the performance of automated spike-sorters has been assessed in recordings from cortex, dentate gyrus, and thalamus, the most effective and efficient approach for spike-sorting can depend on the target region due to differing morphological and physiological characteristics. We therefore assessed the performance of five commonly used sorting packages, Kilosort3, MountainSort5, Tridesclous, SpyKING CIRCUS, and IronClust, in recordings from the rostral ventromedial medulla, a region that has been characterized using single-electrode recordings but that is essentially unexplored at the high-density network level. As demonstrated in other brain regions, each sorter produced unique results. Manual curation preferentially eliminated units detected by only one sorter. Kilosort3 and IronClust required the least curation while maintaining the largest number of units, whereas SpyKING CIRCUS and MountainSort5 required substantial curation. Tridesclous consistently identified the smallest number of units. Nonetheless, all sorters successfully identified classically defined RVM physiological cell types. These findings suggest that while the level of manual curation needed may vary across sorters, each can extract meaningful data from this deep brainstem site.

Significance StatementHigh-density multichannel recording probes that can access deep brainstem structures have only recently become commercially available, but the performance of open-source spike-sorting packages applied to recordings from these regions has not yet been evaluated. The present findings demonstrate that Kilosort3, MountainSort5, Tridesclous, SpyKING CIRCUS, and IronClust can all be reasonably used to identify units in a deep brainstem structure, the rostral ventromedial medulla (RVM). However, manual curation of the output was essential for all sorters. Importantly, all sorters identified the known, physiologically defined RVM cell classes, confirming their utility for deep brainstem recordings. Our findings provide suggestions for processing parameters to use for brainstem recordings and highlight considerations when using high-density silicon probes in the brainstem.