Evidence map›Paper›PMID 35690063›Full record

ArticleNeuron2022

Dynamic causal communication channels between neocortical areas.

Mitra Javadzadeh, Sonja B Hofer

Open access · hybridAbstract read
In one paragraph

Article in Neuron, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
4.1field-weighted citation impact, top 5% of its field
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

26 citing papers in PubMed, 36 citations in OpenAlex.

  1. Article
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  6. No Central Executive? Decision Formation Through Multi-Area Population Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Review
  7. Article
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  11. A neural manifold view of the brain.Nature neuroscience · 2025
    Review
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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

2 authors at 1 institution in 1 country.

Mitra JavadzadehSainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK. Electronic address: mitra.no.17@ucl.ac.uk.
Sonja B HoferSainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK. Electronic address: s.hofer@ucl.ac.uk.
Sainsbury Laboratory · GB

Funding

Wellcome Trust 090843/F/09/Z
6 · The paper itself

Abstract

Processing of sensory information depends on the interactions between hierarchically connected neocortical regions, but it remains unclear how the activity in one area causally influences the activity dynamics in another and how rapidly such interactions change with time. Here, we show that the communication between the primary visual cortex (V1) and high-order visual area LM is context-dependent and surprisingly dynamic over time. By momentarily silencing one area while recording activity in the other, we find that both areas reliably affected changing subpopulations of target neurons within one hundred milliseconds while mice observed a visual stimulus. The influence of LM feedback on V1 responses became even more dynamic when the visual stimuli predicted a reward, causing fast changes in the geometry of V1 population activity and affecting stimulus coding in a context-dependent manner. Therefore, the functional interactions between cortical areas are not static but unfold through rapidly shifting communication subspaces whose dynamics depend on context when processing sensory information.

Indexed as

NeocortexVisual CortexAnimalsMiceNeuronsPhotic StimulationVisual Perceptioncausal manipulationcommunication subspaceinter-areal communicationneocortical interactionspopulation activity dynamicsvisual cortexvisual processing

Identifiers

PMID35690063
PMCPMC9616801
OpenAlexW4281700201

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.