Evidence map›Paper›PMID 32748323›Full record

ArticleBrain imaging and behavior2021

Altered dynamics of functional connectivity density associated with early and advanced stages of motor training in tennis and table tennis athletes.

Qing Gao, Yue Huang, Yu Xiang, Chengbo Yang, Mu Zhang, Jingpu Guo, Hu Wang, Jiali Yu, Qian Cui, Huafu Chen

Abstract read
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In one paragraph

Article in Brain imaging and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 33% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Qing GaoSchool of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Yue HuangSchool of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Yu XiangSchool of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Chengbo YangThe Third Department of Physical Education and Training, Chengdu Sport University, 610041, Chengdu, China.
Mu ZhangInformation Technology Center, Chengdu Sport University, 610041, Chengdu, China.
Jingpu GuoThe Third Department of Physical Education and Training, Chengdu Sport University, 610041, Chengdu, China.
Hu WangThe Third Department of Physical Education and Training, Chengdu Sport University, 610041, Chengdu, China.
Jiali YuSchool of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Qian CuiSchool of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Huafu ChenThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China. chenhf@uestc.edu.cn.
University of Electronic Science and Technology of China · CNChengdu Sport University · CN

Funding

China Scholarship Council 201506075025National Natural Science Foundation of China 61533006 and U1808204National Natural Science Foundation of China 61572112National Natural Science Foundation of China 61573085National Natural Science Foundation of China 81771919Sichuan Province Science and Technology Support Program 2018JY0562Sichuan Province Science and Technology Support Program 2019YJ0185
6 · The paper itself

Abstract

Until now, knowledge about the effects of motor training on the temporal dynamics of the brain functional organization is still limited. Here we combined dynamic functional connectivity density (dFCD) mapping and k-means clustering analyses to explore how early and advanced stages of motor training affected the brain dynamic FC architecture and dynamic states in little-ball athletes using resting-state functional magnetic resonance imaging (fMRI) data of student-athletes (SA), elite athletes (EA) and non-athlete healthy controls (NC). The ANOVA analysis demonstrated the levels of dFCD variability in the EA group had the trend to regress to the NC group levels in all statistically significant regions. Specifically, the brain regions responsible for the basic motor and sensory innervations showed more stabilized dFCD variability in EA and NC compared with SA. The results supported the idea of a stronger efficiency of functional networks and an automation process of new motor skills in EA. Furthermore, EA and NC had the increased dFCD variability in brain regions responsible for top-down visual-motor control compared with SA; while EA exhibited more flexible alterations in FCD status levels and the equilibrium probability in the long run compared with SA and NC. This suggested that regions involved in higher functions of visual-motor control exhibited more flexibility in functional regulation with other brain networks in EA. Our findings suggested the diversity and specialization of fluctuating dynamic brain adaption induced by motor training in different training stages, and highlighted the effect of motor training stages on brain functional adaption.

Indexed as

Magnetic Resonance ImagingTennisAthletesBrainBrain MappingHumansAthlete trainingClustering and state analysesDynamic functional connectivity densityNeuroplasticityResting-state functional magnetic resonance imaging

Identifiers

PMID32748323
OpenAlexW3047555621

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.