Skip to main content

Main menu

  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Collections
    • Podcast
  • ALERTS
  • FOR AUTHORS
    • Information for Authors
    • Fees
    • Journal Clubs
    • eLetters
    • Submit
  • EDITORIAL BOARD
  • ABOUT
    • Overview
    • Advertise
    • For the Media
    • Rights and Permissions
    • Privacy Policy
    • Feedback
  • SUBSCRIBE

User menu

  • Log in
  • My Cart

Search

  • Advanced search
Journal of Neuroscience
  • Log in
  • My Cart
Journal of Neuroscience

Advanced Search

Submit a Manuscript
  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Collections
    • Podcast
  • ALERTS
  • FOR AUTHORS
    • Information for Authors
    • Fees
    • Journal Clubs
    • eLetters
    • Submit
  • EDITORIAL BOARD
  • ABOUT
    • Overview
    • Advertise
    • For the Media
    • Rights and Permissions
    • Privacy Policy
    • Feedback
  • SUBSCRIBE
PreviousNext
Journal Club

Baseline Motor Cortex Activity Contains an Internal Model Representation

Matthew I. Becker
Journal of Neuroscience 5 July 2017, 37 (27) 6389-6390; DOI: https://doi.org/10.1523/JNEUROSCI.1016-17.2017
Matthew I. Becker
Neuroscience Graduate Program, Medical Scientist Training Program, University of Colorado School of Medicine, Aurora, Colorado 80045
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info & Metrics
  • eLetters
  • PDF
Loading

As we age, our bodies change, for better or for worse. In response, our nervous system must adapt to its new physical relationship with the world. One way to handle the ever-changing interaction between our bodies and our environment is to create and update internal models that relate neural activity to movement (Shadmehr et al., 2010). In other words, these models represent how the body is expected to respond when a specific motor command is issued. By keeping track of this relationship, the nervous system can counter environmental change by altering motor commands on subsequent trials to produce the desired movement. This process is termed adaptation.

While brain structures, such as the cerebellum, have been implicated in the learning and storage of adapted motor responses (Wolpert and Miall, 1996), motor command structures, such as motor cortex, are thought to be responsible for implementation of the updated motor plan (Guo et al., 2015). Thus, conventional wisdom suggests that internal model information should be present in motor cortex during movement preparation and execution (Mandelblat-Cerf et al., 2011). In a recent paper, Stavisky et al. (2017) examined neural activity before a motor plan was selected (“baseline activity”) and asked whether representations of a recently adapted internal model existed in motor cortex.

The authors trained 2 monkeys to perform a visuomotor forelimb adaptation task while they recorded neural activity in motor cortex (primary motor and premotor cortex). The animals performed arm movements that were tracked in real time and translated into cursor movement on a screen. Animals were rewarded after successful movement to a central starting location, and again after they moved the cursor to one of eight radial target locations that appeared on the screen. This two-part instruction allowed the researchers to analyze neural activity before the radial target appeared, which they refer to as “baseline” or “pretarget” activity. To induce adaptation, the gain scaling between arm velocity and cursor velocity was modified: 0.5 gain, in which cursor velocity was relatively slow, resulted in faster arm movements to obtain reward more quickly, whereas 2.0 gain, in which cursor velocity was relatively fast, resulted in slower adapted movements to avoid target overshoot. Finally, the researchers chronically implanted multielectrode arrays into motor cortex to measure neural activity.

Stavisky et al. (2017) first sought to determine whether motor cortex activity contains a representation of the visuomotor adaptation. To do this, they measured differences in the population firing patterns measured under different adaptation gain conditions. Unsurprisingly, the differences between behavioral conditions were large during movement, consistent with the known contribution of motor cortex to kinematic control. However, when analyzing baseline activity (before the target appeared), there were significant differences in neural activity patterns between the two gain conditions. Population firing rate distances initially decreased during the center-hold epoch, and then increased before presentation of the radial target. The significant differences observed during baseline neural activity implies that motor cortex contains unique information related to the current visuomotor gain, separate from the specific motor plan about to be executed. However, further experiments and analyses were needed to confirm that these neural activity differences meaningfully represented properties of adaptation.

The authors followed up on these results by asking how the neural correlates of adaptation changed trial-by-trial. Instead of presenting a single, consistent level of adaptation (e.g., 0.5 gain) during a block, they randomly varied the gain on each trial, analyzing the data on the subsequent trials for effects on movement or neural activity. Random gain blocks resulted in intermediate effects: after 0.5 gain trials, the limb moved faster than normal, but not as fast as in the fully adapted constant gain 0.5 block. Thus, the authors created a range of “adaptation levels,” with random gain trials resulting in intermediate levels of adaptation compared with constant gain trials. Stavisky et al. (2017) reasoned that, if the differences in motor cortex activity identified above represent visuomotor gain, then intermediate adaptation levels (observed in random gain trials) should correlate with firing rate patterns that are “intermediate” to the two constant gain conditions. To test this idea directly, they calculated a “cursor gain axis” in neural state space, defined by the vector connecting the neural population activity centroids of the constant gain 0.5 and constant gain 2.0 conditions. They then projected single-trial neural activity onto the cursor gain axis and found an ordered, collinear arrangement of the average projection magnitude for each condition. In other words, movement in neural state space along the cursor gain axis was correlated with the current state of visuomotor gain; if the animal recently experienced 0.5 gain, it was more likely to move fast on the next trial, and also more likely to display neural activity similar to a constant gain 0.5 trial. The ordered arrangement of motor cortex activity patterns during different levels of visuomotor adaptation strongly implies that baseline activity contains some representation of an internal model of visuomotor gain.

Finally, the authors sought to determine whether the identified neural activity patterns were important for movement, and whether their analysis method could be applied to improve brain-machine interfaces (BMI). First, they tested whether baseline activity during a specific trial could predict the arm's upcoming velocity. They found that projection of baseline neural activity onto the cursor gain axis explained ∼25% of the variance in reach velocity, supporting the hypothesis that the cursor gain axis is a meaningful measure of the current level of visuomotor adaptation. Next, they furthered this result by testing the relevance of their discovery to BMI. By collecting neural data and projecting onto a defined cursor gain axis, BMI devices could detect an incorrect estimate of movement gain and correct it in real time. The authors conducted an offline proof-of-concept experiment in which they modified cursor gain based on baseline neural activity. This procedure reduced positional overshoot of the target that occurred on random gain 2.0 trials, when the monkey underestimated the speed of the cursor. In the future, this algorithm could be used for individuals with BMI to detect an underestimation of artificial limb gain and correct it in real time.

These results raise two questions: where is the internal model learned, and where is it stored? Because electrical recording experiments are correlational by nature, this study cannot offer any mechanistic insight into the generation, maintenance, or alteration of the internal model itself. One possibility mentioned by the authors is that the cerebellum learns the internal model and sends it to motor cortex (via thalamus) during baseline activity to “ready” the system for generation of the specific motor plan (Stavisky et al., 2017). An alternative possibility is that the cerebellum continuously updates motor cortex with an internal model of body movement; that way, the information relating motor commands to body position is always available for movement generation. Future experiments using rodent models could directly test via circuit manipulations whether cerebellar input to motor cortex is necessary for motor adaptation. Furthermore, electrophysiological recordings in motor cortex could reveal how manipulation of cerebellar input to motor cortex affects the generation of adaptation-relevant neural activity patterns (e.g., projection onto the cursor gain axis in this study).

The results and insightful interpretations garnered by this work are tempered by several inherent limitations in revealing the nature of the internal model under study. Internal models, defined by the authors as “the information… that movement-related areas use to generate motor commands appropriate to the current physical relationship between the nervous system and the effector,” can take on unique functional roles (Stavisky et al., 2017). For example, forward models calculate a prediction of future movement from a motor command, whereas inverse models calculate the necessary motor commands to create a desired kinematic effect (Wolpert et al., 1998). Because the results from Stavisky et al. (2017) are consistent with both forward and inverse models, future work could attempt to determine the functional relationship between internal model activity and ongoing movement, which is expected to depend directly on the internal model implementation.

In addition, it remains unknown whether internal models, including the one identified by Stavisky et al. (2017), operate on a specific movement-related parameter. In the current study, adaptation-related changes in baseline motor cortex activity predicted upcoming reach velocity, suggesting that arm velocity was an important neural control parameter during the task and may be important for arm movements in general (Yttri and Dudman, 2016). Interestingly, population encoding of a forward model of movement velocity was recently demonstrated in the cerebellum (Herzfeld et al., 2015). An alternative to parameter control is presented by the authors in the present study, who argue for a dynamical systems perspective of motor cortex activity, in which encoding of movement parameters is deemphasized for analysis of how neural activity patterns produce temporal sequences required for movement (Shenoy et al., 2013). Finally, as the current study was limited to examining the effects of motor adaptation, it remains unknown what other aspects of the internal model may be encoded by baseline motor cortex activity.

Overall, Stavisky et al. (2017) demonstrate that baseline motor cortex activity patterns (i.e., before a specific motor plan is generated) represent some aspect of an internal model relating motor commands to movement. Moreover, adaptation of the internal model occurs on a trial-by-trial basis, demonstrating the incredible responsiveness of the nervous system to environmental change. As we continue to learn about the basic neural strategies underlying motor control, we can begin to ascribe functional relevance to the circuits under study, paving the way for technological cures of neurological disease.

Footnotes

  • Editor's Note: These short reviews of recent JNeurosci articles, written exclusively by students or postdoctoral fellows, summarize the important findings of the paper and provide additional insight and commentary. If the authors of the highlighted article have written a response to the Journal Club, the response can be found by viewing the Journal Club at www.jneurosci.org. For more information on the format, review process, and purpose of Journal Club articles, please see http://jneurosci.org/content/preparing-manuscript#journalclub.

  • I thank Dr. Abigail Person, Dr. Joel Zylberberg, and Christy Beitzel for comments on the manuscript.

  • The author declares no competing financial interests.

  • Correspondence should be addressed to Matthew I. Becker, 12800 E 19th Avenue NW, RC1N-7402K, Aurora, CO 80045. matt.becker{at}ucdenver.edu

References

  1. ↵
    1. Guo JZ,
    2. Graves AR,
    3. Guo WW,
    4. Zheng J,
    5. Lee A,
    6. Rodríguez-González J,
    7. Li N,
    8. Macklin JJ,
    9. Phillips JW,
    10. Mensh BD,
    11. Branson K,
    12. Hantman AW
    (2015) Cortex commands the performance of skilled movement. Elife 4:e10774. doi:10.7554/eLife.10774 pmid:26633811
    OpenUrlCrossRefPubMed
  2. ↵
    1. Herzfeld DJ,
    2. Kojima Y,
    3. Soetedjo R,
    4. Shadmehr R
    (2015) Encoding of action by the Purkinje cells of the cerebellum. Nature 526:439–442. doi:10.1038/nature15693 pmid:26469054
    OpenUrlCrossRefPubMed
  3. ↵
    1. Mandelblat-Cerf Y,
    2. Novick I,
    3. Paz R,
    4. Link Y,
    5. Freeman S,
    6. Vaadia E
    (2011) The neuronal basis of long-term sensorimotor learning. J Neurosci 31:300–313. doi:10.1523/JNEUROSCI.4055-10.2011 pmid:21209216
    OpenUrlAbstract/FREE Full Text
  4. ↵
    1. Shadmehr R,
    2. Smith MA,
    3. Krakauer JW
    (2010) Error correction, sensory prediction, and adaptation in motor control. Annu Rev Neurosci 33:89–108. doi:10.1146/annurev-neuro-060909-153135 pmid:20367317
    OpenUrlCrossRefPubMed
  5. ↵
    1. Shenoy KV,
    2. Sahani M,
    3. Churchland MM
    (2013) Cortical control of arm movements: a dynamical systems perspective. Annu Rev Neurosci 36:337–359. doi:10.1146/annurev-neuro-062111-150509 pmid:23725001
    OpenUrlCrossRefPubMed
  6. ↵
    1. Stavisky SD,
    2. Kao JC,
    3. Ryu SI,
    4. Shenoy KV
    (2017) Trial-by-trial motor cortical correlates of a rapidly adapting visuomotor internal model. J Neurosci 37:1721–1732. doi:10.1523/JNEUROSCI.1091-16.2016 pmid:28087767
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Wolpert DM,
    2. Miall RC
    (1996) Forward models for physiological motor control. Neural Netw 9:1265–1279. doi:10.1016/S0893-6080(96)00035-4 pmid:12662535
    OpenUrlCrossRefPubMed
  8. ↵
    1. Wolpert DM,
    2. Miall RC,
    3. Kawato M
    (1998) Internal models in the cerebellum. Trends Cogn Sci 2:338–347. doi:10.1016/S1364-6613(98)01221-2 pmid:21227230
    OpenUrlCrossRefPubMed
  9. ↵
    1. Yttri EA,
    2. Dudman JT
    (2016) Opponent and bidirectional control of movement velocity in the basal ganglia. Nature 533:1–16. doi:10.1038/nature17639 pmid:27135927
    OpenUrlCrossRefPubMed
Back to top

In this issue

The Journal of Neuroscience: 37 (27)
Journal of Neuroscience
Vol. 37, Issue 27
5 Jul 2017
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
  • Advertising (PDF)
  • Ed Board (PDF)
Email

Thank you for sharing this Journal of Neuroscience article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Baseline Motor Cortex Activity Contains an Internal Model Representation
(Your Name) has forwarded a page to you from Journal of Neuroscience
(Your Name) thought you would be interested in this article in Journal of Neuroscience.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Print
View Full Page PDF
Citation Tools
Baseline Motor Cortex Activity Contains an Internal Model Representation
Matthew I. Becker
Journal of Neuroscience 5 July 2017, 37 (27) 6389-6390; DOI: 10.1523/JNEUROSCI.1016-17.2017

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Respond to this article
Request Permissions
Share
Baseline Motor Cortex Activity Contains an Internal Model Representation
Matthew I. Becker
Journal of Neuroscience 5 July 2017, 37 (27) 6389-6390; DOI: 10.1523/JNEUROSCI.1016-17.2017
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Footnotes
    • References
  • Info & Metrics
  • eLetters
  • PDF

Responses to this article

Respond to this article

Jump to comment:

No eLetters have been published for this article.

Related Articles

Cited By...

More in this TOC Section

  • Selective and Systems-Level Face Processing Impairments in ASD
  • Transplanted Astrocytes Show Functional Flexibility in the Recipient Brain
  • Social Experience during Adolescence Shapes Maturation of Parvalbumin-Positive Interneurons in the Left Orbitofrontal Cortex
Show more Journal Club
  • Home
  • Alerts
  • Visit Society for Neuroscience on Facebook
  • Follow Society for Neuroscience on Twitter
  • Follow Society for Neuroscience on LinkedIn
  • Visit Society for Neuroscience on Youtube
  • Follow our RSS feeds

Content

  • Early Release
  • Current Issue
  • Issue Archive
  • Collections

Information

  • For Authors
  • For Advertisers
  • For the Media
  • For Subscribers

About

  • About the Journal
  • Editorial Board
  • Privacy Policy
  • Contact
(JNeurosci logo)
(SfN logo)

Copyright © 2023 by the Society for Neuroscience.
JNeurosci Online ISSN: 1529-2401

The ideas and opinions expressed in JNeurosci do not necessarily reflect those of SfN or the JNeurosci Editorial Board. Publication of an advertisement or other product mention in JNeurosci should not be construed as an endorsement of the manufacturer’s claims. SfN does not assume any responsibility for any injury and/or damage to persons or property arising from or related to any use of any material contained in JNeurosci.