P-sort: an open-source software for cerebellar neurophysiology

Abstract

Analysis of electrophysiological data from Purkinje cells (P-cells) of the cerebellum presents unique challenges to spike sorting. Complex spikes have waveforms that vary significantly from one event to the next, raising the problem of misidentification. Even when complex spikes are detected correctly, the simple spikes may belong to a different P-cell, raising the danger of misattribution. To address these identification and attribution problems, we wrote an open-source, semi-automated software called P-sort, and then tested it by analyzing data from P-cells recorded in three species: marmosets, macaques, and mice. Like other sorting software, P-sort relies on nonlinear dimensionality reduction to cluster spikes. However, it also uses the statistical relationship between simple and complex spikes to merge disparate clusters and split a single cluster. In comparison with expert manual curation, occasionally P-sort identified significantly more complex spikes, as well as prevented misattribution of clusters. Three existing automatic sorters performed less well, particularly for identification of complex spikes. To improve development of analysis tools for the cerebellum, we provide labeled data for 313 recording sessions, as well as statistical characteristics of waveforms and firing patterns of P-cells in three species.

Large-scale neural recordings call for new insights to link brain and behavior

“…Such standardized comparison of different methods is helping to resolve which candidate spike sorting algorithms is most reliable. These approaches are already moving the field forward from the traditional bespoke, laboratory-specific and manually curated spike sorting results towards standardized, less subjective practices (Buccino et al, 2020) (noting that different brain areas and even species may benefit from different approaches (Sedaghat-Nejad et al, 2021)).…”

Spike sorting: new trends and challenges of the era of high-density probes

“…For example, in the cerebellum, Purkinje cells produce both simple and complex spikes, which have very different spike shapes. Complex spikes represent a well-known problem for spike sorting, so that specific cerebellum-specific spike sorters have been developed [77]. To tackle the same problem, Markanday and colleagues [51] implemented an end-to-end solution to detect complex spikes data manually labeled by experts.…”

Navigating the Statistical Minefield of Model Selection and Clustering in Neuroscience

“…Therefore, while both t-SNE and UMAP are excellent visualization tools for high-dimension datasets, UMAP is more recommended for subsequent cluster analysis ( McInnes et al, 2018 ; Diaz-Papkovich et al, 2021 ). The advantage of using nonlinear dimensionality reduction algorithms like UMAP for solving neuroscience problems is demonstrated by a novel UMAP-based spike sorting approach that can successfully sort cerebellar Purkinje cell recordings, a notoriously hard problem due the high degree of variability of simple and complex Purkinje cell spikes ( Sedaghat-Nejad et al, 2021 ).…”