Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex

  1. Eric Kenji Lee
  2. Hymavathy Balasubramanian
  3. Alexandra Tsolias
  4. Stephanie Udochukwu Anakwe
  5. Maria Medalla
  6. Krishna V Shenoy
  7. Chandramouli Chandrasekaran  Is a corresponding author
  1. BOSTON UNIVERSITY, United States
  2. Bernstein Center for Computational Neuroscience, Germany
  3. Boston University, United States
  4. Stanford University, United States

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.

The following data sets were generated

Article and author information

Author details

  1. Eric Kenji Lee

    Psychological and Brain Sciences, BOSTON UNIVERSITY, BOSTON, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7166-0909
  2. Hymavathy Balasubramanian

    Bernstein Center for Computational Neuroscience, Bernstein Center for Computational Neuroscience, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Alexandra Tsolias

    Anatomy and Neurobiology, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Stephanie Udochukwu Anakwe

    Undergraduate Program in Neuroscience, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Maria Medalla

    Anatomy & Neurobiology, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Krishna V Shenoy

    Department of Electrical Engineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Chandramouli Chandrasekaran

    Anatomy and Neurobiology, Boston University, Boston, United States
    For correspondence
    cchandr1@bu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1711-590X

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.

Reviewing Editor

  1. Emilio Salinas, Wake Forest School of Medicine, United States

Publication history

  1. Preprint posted: February 8, 2021 (view preprint)
  2. Received: February 12, 2021
  3. Accepted: August 4, 2021
  4. Accepted Manuscript published: August 6, 2021 (version 1)
  5. Accepted Manuscript updated: August 10, 2021 (version 2)
  6. Accepted Manuscript updated: August 12, 2021 (version 3)
  7. Version of Record published: September 20, 2021 (version 4)

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

  • 3,502
    Page views
  • 564
    Downloads
  • 7
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

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)

  1. Eric Kenji Lee
  2. Hymavathy Balasubramanian
  3. Alexandra Tsolias
  4. Stephanie Udochukwu Anakwe
  5. Maria Medalla
  6. Krishna V Shenoy
  7. Chandramouli Chandrasekaran
(2021)
Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex
eLife 10:e67490.
https://doi.org/10.7554/eLife.67490

Further reading

    1. Developmental Biology
    2. Neuroscience
    Emily L Heckman, Chris Q Doe
    Research Advance

    The organization of neural circuits determines nervous system function. Variability can arise during neural circuit development (e.g. neurite morphology, axon/dendrite position). To ensure robust nervous system function, mechanisms must exist to accommodate variation in neurite positioning during circuit formation. Previously we developed a model system in the Drosophila ventral nerve cord to conditionally induce positional variability of a proprioceptive sensory axon terminal, and used this model to show that when we altered the presynaptic position of the sensory neuron, its major postsynaptic interneuron partner modified its dendritic arbor to match the presynaptic contact, resulting in functional synaptic input (Sales et al., 2019). Here we investigate the cellular mechanisms by which the interneuron dendrites detect and match variation in presynaptic partner location and input strength. We manipulate the presynaptic sensory neuron by (a) ablation; (b) silencing or activation; or (c) altering its location in the neuropil. From these experiments we conclude that there are two opposing mechanisms used to establish functional connectivity in the face of presynaptic variability: presynaptic contact stimulates dendrite outgrowth locally, whereas presynaptic activity inhibits postsynaptic dendrite outgrowth globally. These mechanisms are only active during an early larval critical period for structural plasticity. Collectively, our data provide new insights into dendrite development, identifying mechanisms that allow dendrites to flexibly respond to developmental variability in presynaptic location and input strength.

    1. Epidemiology and Global Health
    2. Neuroscience
    Lorenza Dall'Aglio, Hannah H Kim ... Henning Tiemeier
    Research Article Updated

    Background:

    Associations between attention-deficit/hyperactivity disorder (ADHD) and brain morphology have been reported, although with several inconsistencies. These may partly stem from confounding bias, which could distort associations and limit generalizability. We examined how associations between brain morphology and ADHD symptoms change with adjustments for potential confounders typically overlooked in the literature (aim 1), and for the intelligence quotient (IQ) and head motion, which are generally corrected for but play ambiguous roles (aim 2).

    Methods:

    Participants were 10-year-old children from the Adolescent Brain Cognitive Development (N = 7722) and Generation R (N = 2531) Studies. Cortical area, volume, and thickness were measured with MRI and ADHD symptoms with the Child Behavior Checklist. Surface-based cross-sectional analyses were run.

    Results:

    ADHD symptoms related to widespread cortical regions when solely adjusting for demographic factors. Additional adjustments for socioeconomic and maternal behavioral confounders (aim 1) generally attenuated associations, as cluster sizes halved and effect sizes substantially reduced. Cluster sizes further changed when including IQ and head motion (aim 2), however, we argue that adjustments might have introduced bias.

    Conclusions:

    Careful confounder selection and control can help identify more robust and specific regions of associations for ADHD symptoms, across two cohorts. We provided guidance to minimizing confounding bias in psychiatric neuroimaging.

    Funding:

    Authors are supported by an NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200 to HT) for HT, LDA, SL, and the Sophia Foundation S18-20, and Erasmus University and Erasmus MC Fellowship for RLM.