Evidence map›Paper›PMID 41148982›Full record

ArticleEntropy (Basel, Switzerland)2025

Corticomuscular Coupling Analysis in Archery Based on Transfer Entropy.

Yunrui Zhang, Yue Leng, Xiaozhi Li, Wenjing Zhang, Hairong Yu

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yunrui ZhangSchool of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
Yue LengSchool of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
Xiaozhi LiDepartment of Physical Education, Southeast University, Nanjing 210096, China.ORCID 0009-0000-5904-0857
Wenjing ZhangDepartment of Physical Education, Southeast University, Nanjing 210096, China.
Hairong YuSchool of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.ORCID 0000-0003-3018-064X

Funding

theJiangsu Province Education Science Planning Youth Project C/2023/01/121the Startup Research Fund of Southeast University RF1028623007the Zhishan Youth Scholar Support Program of Southeast University 2242023R40044
6 · The paper itself

Abstract

Studying the information transfer between the brain and muscles during archery can help us to understand the underlying mechanisms of corticomuscular coupling during motor learning. In this study, we recruited 26 novice archers as participants and calculated the transfer entropy (TE) between their EEG and EMG signals during the archery process. This was performed to assess the characteristics of corticomuscular coupling during archery and the impact of a period of archery training on this coupling. The results indicate that information transfer from EEG to EMG in the α and β frequency bands predominates during archery, which may be related to the roles of α and β frequency bands in inhibitory control and the sustained contraction of muscle stability. Additionally, the optimization of brain resource allocation resulting from a period of archery training is primarily reflected in the prefrontal cortex and motor cortex, where the information transfer from EEG to EMG decreases while activation related to inhibitory control increases. The intensity of corticomuscular coupling weakens with an increase in the number of arrows shot, but archery training reduces the impact of fatigue-induced changes on corticomuscular coupling.

Indexed as

archerycorticalmuscular couplingtransfer entropy

Identifiers

PMID41148982
PMCPMC12564621

What OpenQuestion holds

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Registered trials

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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.