DOI: 10.5061/dryad.z612jm6cf
title: WaveMAP analysis of extracellular waveforms from monkey premotor cortex during decision-making

description: Cortical circuits are thought to contain a large number of cell types that
 coordinate to produce behavior. Current in vivo methods
 rely on clustering of specified features of extracellular waveforms to
 identify putative cell types, but these capture only a small amount of
 variation. Here, we develop a new method (WaveMAP) that combines
 non-linear dimensionality reduction with graph clustering to identify
 putative cell types. We apply WaveMAP to extracellular waveforms recorded
 from dorsal premotor cortex of macaque monkeys performing a
 decision-making task. Using WaveMAP, we robustly establish eight
 waveform clusters and show that these clusters recapitulate previously
 identified narrow- and broad-spiking types while revealing previously
 unknown diversity within these subtypes. The eight clusters
 exhibited distinct laminar distributions, characteristic firing rate
 patterns, and decision-related dynamics. Such insights were
 weaker when using feature-based approaches. WaveMAP therefore
 provides a more nuanced understanding of the dynamics of cell types in
 cortical circuits.
description: The electrophysiology data was collected from two rhesus macaques
 (<em>Macaca mulatta</em>) performing a red-green
 discrimination task. The trained animals performed reaches towards red or
 green targets to the left or right of a central static checkerboard
 showing different combinations of red or green squares. Either single
 tungsten electrodes or linear multi-contact 16-channel U-probes were
 inserted into dorsal premotor cortex to obtain neural data described
 below. We recorded 996 units (778 from U-probes) from
 monkey dorsal premotor cortex. Neurons were isolated through standard
 procedures for extracelluar recordings and verified, where needed,
 through offline spike sorting (Plexon Offline Sorter). A snippet of 1.6 ms
 (30 kHz sampling rate thus 48 time points) of each spike with trough at
 0.4 ms was captured and then filtered with a 4th-order 250 Hz high-pass
 Butterworth filter. These spikes were then upsampled to 480 points, fit
 with a cubic spline, normalized to be between -1 and +1, and averaged per
 unit. Waveforms with initial positive phase were excluded from analysis in
 the manuscript due to their putative association with axons.
 The behavioral data is described in Chandrasekaran,
 C. <em>et al. </em>(2017). The confocal
 imaging z-stack data was extracted from 30 micrometer coronal sections of
 rhesus macaque dorsal premotor cortex (n = 6; 2 samples each from 3
 specimens).  For each sample, immunohistochemical stains were conducted
 for the non-overlapping inhibitory cell type markers of parvalbumin (PV),
 calretinin (CR), and calbindin (CB). Z-stacks were converted to maximum
 intensity projections (.tiff files) and positively-staining cells were
 counted by hand using the CellCounter plugin for Fiji (.xml files). Depth
 measurements for each cell was obtained and converted to laminar
 distributions in Python.
description: Instructions for how to use the data files used in the analysis
 and to generate manuscript figures (in Python) are contained in the
 Readme.md contained in the Zip file WaveMAP_Paper.zip. Manuscript figures
 are also included in the zipped folder Manuscript_figures.zip
 Instructions for pre-processing raw averaged waveforms (in
 MATLAB) and associated data are contained in the README.md file in the
 zipped Preprocessing folder. Raw confocal images with
 stereological cell count annotations are uploaded in the Zip file
 Image_data.zip.