Cerebellar patients have intact feedback control that can be leveraged to improve reaching

  1. Amanda M Zimmet
  2. Di Cao
  3. Amy J Bastian
  4. Noah J Cowan  Is a corresponding author
  1. Johns Hopkins University, United States
  2. Kennedy Krieger Institute, United States

Abstract

It is thought that the brain does not simply react to sensory feedback, but rather uses an internal model of the body to predict the consequences of motor commands before sensory feedback arrives. Time-delayed sensory feedback can then be used to correct for the unexpected—perturbations, motor noise, or a moving target. The cerebellum has been implicated in this predictive control process. Here we show that the feedback gain in patients with cerebellar ataxia matches that of healthy subjects, but that patients exhibit substantially more phase lag. This difference is captured by a computational model incorporating a Smith predictor in healthy subjects that is missing in patients, supporting the predictive role of the cerebellum in feedback control. Lastly, we improve cerebellar patients’ movement control by altering (phase advancing) the visual feedback they receive from their own self movement in a simplified virtual reality setup.

Data availability

All data generated or analyzed during this study will be openly on the JHU Data Archive under DOI 10.7281/T1/BCARLC.The data will be available here: https://archive.data.jhu.edu/dataverse/LIMBS

The following data sets were generated

Article and author information

Author details

  1. Amanda M Zimmet

    Biomedical Engineering, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1457-3072
  2. Di Cao

    Mechanical Engineering, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1547-9929
  3. Amy J Bastian

    Kennedy Krieger Institute, Baltimore, United States
    Competing interests
    No competing interests declared.
  4. Noah J Cowan

    Mechanical Engineering, Johns Hopkins University, Baltimore, United States
    For correspondence
    ncowan@jhu.edu
    Competing interests
    Noah J Cowan, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2502-3770

Funding

National Institutes of Health (HD040289)

  • Amy J Bastian
  • Noah J Cowan

Applied Physics Laboratory Graduate Fellowship

  • Amanda M Zimmet

National Science Foundation (1825489)

  • Di Cao
  • Amy J Bastian
  • Noah J Cowan

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

Reviewing Editor

  1. Richard B Ivry, University of California, Berkeley, United States

Ethics

Human subjects: The experimental protocol was approved by the Institutional Review Board at Johns Hopkins University School of Medicine (protocol # IRB00182673) and all participants gave informed consent prior to joining this study, according to the Declaration of Helsinki.

Version history

  1. Received: November 1, 2019
  2. Accepted: October 6, 2020
  3. Accepted Manuscript published: October 7, 2020 (version 1)
  4. Version of Record published: October 21, 2020 (version 2)

Copyright

© 2020, Zimmet 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

  • 2,349
    views
  • 358
    downloads
  • 29
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Amanda M Zimmet
  2. Di Cao
  3. Amy J Bastian
  4. Noah J Cowan
(2020)
Cerebellar patients have intact feedback control that can be leveraged to improve reaching
eLife 9:e53246.
https://doi.org/10.7554/eLife.53246

Share this article

https://doi.org/10.7554/eLife.53246

Further reading

    1. Neuroscience
    John J Stout, Allison E George ... Amy L Griffin
    Research Article

    Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6–11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain-machine interface that initiated task trials based on the magnitude of prefrontal-hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain-machine interfacing.

    1. Neuroscience
    Tianhao Chu, Zilong Ji ... Si Wu
    Research Article

    Hippocampal place cells in freely moving rodents display both theta phase precession and procession, which is thought to play important roles in cognition, but the neural mechanism for producing theta phase shift remains largely unknown. Here, we show that firing rate adaptation within a continuous attractor neural network causes the neural activity bump to oscillate around the external input, resembling theta sweeps of decoded position during locomotion. These forward and backward sweeps naturally account for theta phase precession and procession of individual neurons, respectively. By tuning the adaptation strength, our model explains the difference between ‘bimodal cells’ showing interleaved phase precession and procession, and ‘unimodal cells’ in which phase precession predominates. Our model also explains the constant cycling of theta sweeps along different arms in a T-maze environment, the speed modulation of place cells’ firing frequency, and the continued phase shift after transient silencing of the hippocampus. We hope that this study will aid an understanding of the neural mechanism supporting theta phase coding in the brain.