Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex
Abstract
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 featurebased approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.
Data availability
Data generated or analysed during this study are included in the linked Dryad repository (doi:10.5061/dryad.z612jm6cf). Source data for all figures are also in this zip file.
-
WaveMAP analysis of extracellular waveforms from monkey premotor cortex during decision-makinghttps://creativecommons.org/publicdomain/zero/1.0/.
Article and author information
Author details
Funding
National Institute of Neurological Disorders and Stroke (R00NS092972)
- Chandramouli Chandrasekaran
National Institute on Deafness and Other Communication Disorders (DC017844)
- Krishna V Shenoy
National Institute of Neurological Disorders and Stroke (NS095548)
- Krishna V Shenoy
National Institute of Neurological Disorders and Stroke (NS098968)
- Krishna V Shenoy
Defense Advanced Research Projects Agency (N66001-10-C-2010)
- Krishna V Shenoy
Defense Advanced Research Projects Agency (W911NF-14-2-0013)
- Krishna V Shenoy
Simons Foundation (325380)
- Krishna V Shenoy
Simons Foundation (543045)
- Krishna V Shenoy
National Institute of Neurological Disorders and Stroke (122969)
- Chandramouli Chandrasekaran
Office of Naval Research (N000141812158)
- Krishna V Shenoy
Larry and Pamela Garlick
- Krishna V Shenoy
National Institute of Neurological Disorders and Stroke (K99NS092972)
- Chandramouli Chandrasekaran
Wu Tsai Neurosciences Institute, Stanford University
- Krishna V Shenoy
Hong Seh and Vivian H Lim Endowed Professorship
- Krishna V Shenoy
Howard Hughes Medical Institute
- Krishna V Shenoy
National Institute of Mental Health (R00MH101234)
- Maria Medalla
National Institute of Mental Health (R01MH116008)
- Maria Medalla
Whitehall Foundation (2019-12-77)
- Chandramouli Chandrasekaran
Brain and Behavior Research Foundation (27923)
- Chandramouli Chandrasekaran
NIH Office of the Director (DP1HD075623)
- Krishna V Shenoy
National Institute on Deafness and Other Communication Disorders (DC014034)
- Krishna V Shenoy
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the procedures were approved were approved by the Stanford Administrative Panel on Laboratory Animal Care (APLAC, Protocol Number 8856, entitled "Cortical Processing of Arm Movements"). Surgical procedures were performed under anesthesia, and every effort was made to minimize suffering. Appropriate analgesia, pain relief, and antibiotics were administered to the animals when needed after surgical approval.
Copyright
© 2021, Lee et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 6,616
- views
-
- 870
- downloads
-
- 54
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
Multiplexed error-robust fluorescence in situ hybridization (MERFISH) allows genome-scale imaging of RNAs in individual cells in intact tissues. To date, MERFISH has been applied to image thin-tissue samples of ~10 µm thickness. Here, we present a thick-tissue three-dimensional (3D) MERFISH imaging method, which uses confocal microscopy for optical sectioning, deep learning for increasing imaging speed and quality, as well as sample preparation and imaging protocol optimized for thick samples. We demonstrated 3D MERFISH on mouse brain tissue sections of up to 200 µm thickness with high detection efficiency and accuracy. We anticipate that 3D thick-tissue MERFISH imaging will broaden the scope of questions that can be addressed by spatial genomics.
-
- Neuroscience
Learning alters cortical representations and improves perception. Apical tuft dendrites in cortical layer 1, which are unique in their connectivity and biophysical properties, may be a key site of learning-induced plasticity. We used both two-photon and SCAPE microscopy to longitudinally track tuft-wide calcium spikes in apical dendrites of layer 5 pyramidal neurons in barrel cortex as mice learned a tactile behavior. Mice were trained to discriminate two orthogonal directions of whisker stimulation. Reinforcement learning, but not repeated stimulus exposure, enhanced tuft selectivity for both directions equally, even though only one was associated with reward. Selective tufts emerged from initially unresponsive or low-selectivity populations. Animal movement and choice did not account for changes in stimulus selectivity. Enhanced selectivity persisted even after rewards were removed and animals ceased performing the task. We conclude that learning produces long-lasting realignment of apical dendrite tuft responses to behaviorally relevant dimensions of a task.