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XSort

A Python/Qt application that visualizes and edits the results from spike sorting of neural activity recorded extracellularly in the Stephen G. Lisberger lab at Duke University.

Background

Researchers in the Lisberger and other laboratories use various multi-electrode systems (eg, the Omniplex Neural Recording Data Acquisition System) to record electrical activity in the brain during experiments. They analyze the recorded analog channel data streams to detect and "sort" the electrical activity into distinct "spike trains", each assigned to a different "neural unit".

The various spike sorter algorithms employed in the lab often produce spurious output -- "garbage" units triggered by system noise, two spike trains that have a very similar spike waveform and really "belong" to the same neural unit, and so on. With XSort you can visually assess and edit the original spike sorter's results.

Usage

To use the application, select a working directory containing the analog recording source file and the original spike sorter results file. After analyzing the files and building an internal cache of channel data streams and unit metrics (stored in a subfolder within the working directory), XSort offers a number of different data visualizations.

1. A tabular listing of the neural units, including metrics -- total # of spikes observed, mean firing rate, best signal-to-noise ratio (across all data channels), the primary channel (on which best SNR was measured), and others.
2. Channel traces for up to 16 analog data channels, highlighting the spikes of each of up to 3 units currently selected for display in the neural units table, known as the display focus list.
3. The per-channel mean spike waveforms, or spike templates (10ms, 1ms pre-spike time) for each unit in the display focus list.
4. A firing rate-vs-time histogram, inter-spike interval histogram, and autocorrelogram for each unit in the display focus list, along with the cross-correllogram of each unit vs another unit in the list.
5. A 3D histogram (rendered as a heatmap) representing the unit's autocorrelogram as a function of instantaneous firing rate.
6. A principal component analysis (PCA) scatter plot projecting a unit's spikes into a 2D space defined by the two highest-variance (most information) principal components. When the analysis is performed on multiple units, it can help detect distinct spike trains that really belong to a single unit. When performed on a single neural unit, it can help determmine whether a unit should be split into two or more distinct spike populations.

You can edit the table of neural units in a number of ways:

1. Attach a short descriptive label (typically, the putative neuron type) to any unit.
2. Delete one or more selected units.
3. Merge two selected units into one.
4. Split the currently selected unit into two distinct units.

XSort maintains a complete "edit history" in the current working directory (.xs.edits.txt), and you can "undo" any or all of the changes in reverse order, or undo them all at once to recover the original state of the neural unit table. Once happy with any changes made, you can save the neural unit list for use in other applications or your own scripts.