Evidence map›Paper›PMID 30814311›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2019

Medial Prefrontal Cortex Population Activity Is Plastic Irrespective of Learning.

Abhinav Singh, Adrien Peyrache, Mark D Humphries

Abstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Activity Subspaces in Medial Prefrontal Cortex Distinguish States of the World.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022
    Article
  5. Self-healing codes: How stable neural populations can track continually reconfiguring neural representations.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  6. Article
  7. Review
  8. Review
  9. Coordinated Prefrontal State Transition Leads Extinction of Reward-Seeking Behaviors.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021
    Article
  10. Article
  11. Review
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

3 authors.

Abhinav SinghFaculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, United Kingdom.
Adrien PeyracheMontreal Neurological Institute, McGill University, Montreal, Quebec H3A 1A1, Canada, and.ORCID 0000-0001-9708-309X
Mark D HumphriesFaculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, United Kingdom, mark.humphries@nottingham.ac.uk.ORCID 0000-0002-1906-2581

Funding

Medical Research Council MR/J008648/1Medical Research Council MR/J008648/2Medical Research Council MR/P005659/1Medical Research Council MR/P005659/2
6 · The paper itself

Abstract

The prefrontal cortex (PFC) is thought to learn the relationships between actions and their outcomes. But little is known about what changes to population activity in PFC are specific to learning these relationships. Here we characterize the plasticity of population activity in the medial PFC (mPFC) of male rats learning rules on a Y-maze. First, we show that the population always changes its patterns of joint activity between the periods of sleep either side of a training session on the maze, regardless of successful rule learning during training. Next, by comparing the structure of population activity in sleep and training, we show that this population plasticity differs between learning and nonlearning sessions. In learning sessions, the changes in population activity in post-training sleep incorporate the changes to the population activity during training on the maze. In nonlearning sessions, the changes in sleep and training are unrelated. Finally, we show evidence that the nonlearning and learning forms of population plasticity are driven by different neuron-level changes, with the nonlearning form entirely accounted for by independent changes to the excitability of individual neurons, and the learning form also including changes to firing rate couplings between neurons. Collectively, our results suggest two different forms of population plasticity in mPFC during the learning of action-outcome relationships: one a persistent change in population activity structure decoupled from overt rule-learning, and the other a directional change driven by feedback during behavior.

Indexed as

Neuronal PlasticityAnimalsLearningMaleMaze LearningModels, NeurologicalNeuronsPrefrontal CortexRats, Long-EvansRewardSleepneural ensemblespopulation activitystatistical inference

Identifiers

PMID30814311
PMCPMC6495133

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.