Evidence map›Paper›PMID 30540551›Full record

ArticleJournal of neurophysiology2019

Automated home cage training of mice in a hold-still center-out reach task.

Tejapratap Bollu, Samuel C Whitehead, Nikil Prasad, Jackson Walker, Nitin Shyamkumar, Raghav Subramaniam, Brian Kardon, Itai Cohen, Jesse H Goldberg

Abstract read
In one paragraph

Article in Journal of neurophysiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Distributed and Localized Dynamics Emerge in the Mouse Neocortex during Reach-to-Grasp Behavior.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022
    Article
  12. Article
  13. Impaired Refinement of Kinematic Variability in Huntington Disease Mice on an Automated Home Cage Forelimb Motor Task.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021
    Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Review
  19. Online control of reach accuracy in mice.Journal of neurophysiology · 2020
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Tejapratap BolluDepartment of Neurobiology and Behavior, Cornell University , Ithaca, New York.
Samuel C WhiteheadDepartment of Physics, Cornell University , Ithaca, New York.
Nikil PrasadDepartment of Neurobiology and Behavior, Cornell University , Ithaca, New York.
Jackson WalkerDepartment of Neurobiology and Behavior, Cornell University , Ithaca, New York.
Nitin ShyamkumarDepartment of Neurobiology and Behavior, Cornell University , Ithaca, New York.
Raghav SubramaniamDepartment of Neurobiology and Behavior, Cornell University , Ithaca, New York.
Brian KardonDepartment of Neurobiology and Behavior, Cornell University , Ithaca, New York.
Itai CohenDepartment of Physics, Cornell University , Ithaca, New York.
Jesse H GoldbergDepartment of Neurobiology and Behavior, Cornell University , Ithaca, New York.

Funding

Identifying pathways for motor variability in the mammalian brainDP2HD087952 · NICHD · CORNELL UNIVERSITY · PI GOLDBERG, JESSE HEYMANN · 2015 to 2015
$2.4M
NICHD NIH HHS DP2 HD087952
6 · The paper itself

Abstract

An obstacle to understanding neural mechanisms of movement is the complex, distributed nature of the mammalian motor system. Here we present a novel behavioral paradigm for high-throughput dissection of neural circuits underlying mouse forelimb control. Custom touch-sensing joysticks were used to quantify mouse forelimb trajectories with micron-millisecond spatiotemporal resolution. Joysticks were integrated into computer-controlled, rack-mountable home cages, enabling batches of mice to be trained in parallel. Closed loop behavioral analysis enabled online control of reward delivery for automated training. We used this system to show that mice can learn, with no human handling, a direction-specific hold-still center-out reach task in which a mouse first held its right forepaw still before reaching out to learned spatial targets. Stabilogram diffusion analysis of submillimeter-scale micromovements produced during the hold demonstrate that an active control process, akin to upright balance, was implemented to maintain forepaw stability. Trajectory decomposition methods, previously used in primates, were used to segment hundreds of thousands of forelimb trajectories into millions of constituent kinematic primitives. This system enables rapid dissection of neural circuits for controlling motion primitives from which forelimb sequences are built. NEW & NOTEWORTHY A novel joystick design resolves mouse forelimb kinematics with micron-millisecond precision. Home cage training is used to train mice in a hold-still center-out reach task. Analytical methods, previously used in primates, are used to decompose mouse forelimb trajectories into kinematic primitives.

Indexed as

LearningMovementSpatial BehaviorAnimalsAutomationBiomechanical PhenomenaForelimbMaleMiceMice, Inbred C57BLNeurophysiologyjoystickmotor primitivesmouse forelimbreach

Identifiers

PMID30540551
PMCPMC6397391

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.