1. Neuroscience
Download icon

Postural control of arm and fingers through integration of movement commands

  1. Scott T Albert  Is a corresponding author
  2. Alkis M Hadjiosif
  3. Jihoon Jang
  4. Andrew J Zimnik
  5. Demetris S Soteropoulos
  6. Stuart N Baker
  7. Mark M Churchland
  8. John W Krakauer
  9. Reza Shadmehr  Is a corresponding author
  1. Johns Hopkins School of Medicine, United States
  2. Columbia University Medical Center, United States
  3. Newcastle University, United Kingdom
Research Article
  • Cited 4
  • Views 2,787
  • Annotations
Cite this article as: eLife 2020;9:e52507 doi: 10.7554/eLife.52507

Abstract

Every movement ends in a period of stillness. Current models assume that commands that hold the limb at a target location do not depend on the commands that moved the limb to that location. Here, we report a surprising relationship between movement and posture in primates: on a within-trial basis, the commands that hold the arm and finger at a target location depend on the mathematical integration of the commands that moved the limb to that location. Following damage to the corticospinal tract, both the move and hold period commands become more variable. However, the hold period commands retain their dependence on the integral of the move period commands. Thus, our data suggest that the postural controller possesses a feedforward module that uses move commands to calculate a component of hold commands. This computation may arise within an unknown subcortical system that integrates cortical commands to stabilize limb posture.

Data availability

Source data files generated or analyzed during this study are included for Figures 1-7 and have also been deposited in OSF under accession code YC64A.

The following data sets were generated

Article and author information

Author details

  1. Scott T Albert

    Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States
    For correspondence
    scottalbert1@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9140-1077
  2. Alkis M Hadjiosif

    Department of Neurology, Johns Hopkins School of Medicine, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Jihoon Jang

    Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Andrew J Zimnik

    Department of Neuroscience, Columbia University Medical Center, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Demetris S Soteropoulos

    Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Stuart N Baker

    Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Mark M Churchland

    Department of Neuroscience, Columbia University Medical Center, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9123-6526
  8. John W Krakauer

    Department of Neurology, Johns Hopkins School of Medicine, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4316-1846
  9. Reza Shadmehr

    Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, United States
    For correspondence
    shadmehr@jhu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7686-2569

Funding

National Institute of Neurological Disorders and Stroke (R01NS078311)

  • Reza Shadmehr

Sheikh Khalifa Stroke Institute

  • John W Krakauer

Medical Research Council (MR/K023012/1)

  • Demetris S Soteropoulos

National Institute of Neurological Disorders and Stroke (1DP2NS083037)

  • Mark M Churchland

National Institute of Neurological Disorders and Stroke (R01NS100066)

  • Mark M Churchland

National Institute of Neurological Disorders and Stroke (1U19NS104649)

  • Mark M Churchland

National Institute of Neurological Disorders and Stroke (F31NS095706)

  • Scott T Albert

National Institute of Neurological Disorders and Stroke (F32NS092350)

  • Mark M Churchland

National Science Foundation (1723967)

  • Reza Shadmehr

Simons Foundation (SCGB#542957)

  • Mark M Churchland

Medical Research Council (MR/P023967)

  • Stuart N Baker

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All animal procedures in the U.S. were conducted in accord with the US National Institutes of Health guidelines and were approved by the Columbia University Institutional Animal Care and Use Committee (AC-AAAQ7409). These data were originally published in Lara, Cunningham, & Churchland (2018) as well as Lara, Elsayed, Zimnik, Cunningham, & Churchland (2018). All procedures in the U.K. were carried out under appropriate UK Home Office licenses in accordance with the Animals (Scientific Procedures) Act 1986, and were approved by the Local Research Ethics Committee of Newcastle University. These data were originally published in Soteropoulos, Williams, & Baker (2012).

Human subjects: Informed consent was obtained from all participants. All human subjects work was approved by the Johns Hopkins School of Medicine Institutional Review Board, protocol number NA_00037510.

Reviewing Editor

  1. Kunlin Wei, Peking University, China

Publication history

  1. Received: October 6, 2019
  2. Accepted: February 3, 2020
  3. Accepted Manuscript published: February 11, 2020 (version 1)
  4. Version of Record published: March 9, 2020 (version 2)

Copyright

© 2020, Albert 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

  • 2,787
    Page views
  • 472
    Downloads
  • 4
    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)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    Anirudh Wodeyar et al.
    Tools and Resources Updated

    Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low-frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets, and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the Open Ephys acquisition system, making it widely available for use in experiments.

    1. Evolutionary Biology
    2. Neuroscience
    Lucia L Prieto-Godino et al.
    Research Article

    Olfactory receptor repertoires exhibit remarkable functional diversity, but how these proteins have evolved is poorly understood. Through analysis of extant and ancestrally-reconstructed drosophilid olfactory receptors from the Ionotropic receptor (Ir) family, we investigated evolution of two organic acid-sensing receptors, Ir75a and Ir75b. Despite their low amino acid identity, we identify a common 'hotspot' in their ligand-binding pocket that has a major effect on changing the specificity of both Irs, as well as at least two distinct functional transitions in Ir75a during evolution. Moreover, we show that odor specificity is refined by changes in additional, receptor-specific sites, including those outside the ligand-binding pocket. Our work reveals how a core, common determinant of ligand-tuning acts within epistatic and allosteric networks of substitutions to lead to functional evolution of olfactory receptors.