Evidence map›Paper›PMID 41298437›Full record

ArticleNature communications2025

A hierarchy of time constants and reliable signal propagation in the marmoset cerebral cortex.

Guanchun Li, Songting Li, Xiao-Jing Wang

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

3 authors.

Guanchun LiCourant Institute, New York University, New York, NY, USA.ORCID http://orcid.org/0009-0001-5209-0525
Songting LiSchool of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China. songting@sjtu.edu.cn.ORCID http://orcid.org/0000-0002-0977-6943
Xiao-Jing WangCenter for Neural Science, New York University, New York, NY, USA. xjwang@nyu.edu.ORCID http://orcid.org/0000-0003-3124-8474

Funding

National Natural Science Foundation of China (National Science Foundation of China) 12250710674National Natural Science Foundation of China (National Science Foundation of China) 12271361Science and Technology Commission of Shanghai Municipality (Shanghai Municipal Science and Technology Commission) 24JS2810400Simons Foundation NC-GB-CULM-00003138
6 · The paper itself

Abstract

A hierarchy of timescales in the cerebral cortex is functionally desirable for rapid information processing in sensory areas and slow time integration in association areas. Here, through an analysis of electrocorticography (ECoG) data, we identified a timescale hierarchy in the neocortex of marmoset, a primate species commonly used in neuroscience. Constrained by the anatomical and electrophysiological data, we developed a multi-regional model of the marmoset neocortex that captures the observed timescale hierarchy phenomenon. Furthermore, we used the model to investigate how the neocortex reconciles information integration on distinct timescales in local areas with reliable signal propagation globally across regions. We found that a near-criticality state is optimal for both localized signal integration within areas and reliable signal propagation across areas in the multi-regional neocortex. Our model also mechanically explains recent experimental observations that the structural and functional connectivities are less correlated in association areas than in sensory areas.

Indexed as

CallithrixCerebral CortexNeocortexAnimalsElectrocorticographyMaleModels, NeurologicalTime Factors

Identifiers

PMID41298437
PMCPMC12749727

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.