Evidence map›Paper›PMID 30277624›Full record

ArticleHuman brain mapping2019

Adaptation of brain functional stream architecture in athletes with fast demands of sensorimotor integration.

Qing Gao, Yi Yu, Xiaolong Su, Zhongping Tao, Mu Zhang, Yifeng Wang, Jinsong Leng, Jorge Sepulcre, Huafu Chen

Open access · greenAbstract read
In one paragraph

Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

14 citing papers in PubMed, 33 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

9 authors at 3 institutions in 2 countries.

Qing GaoSchool of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Yi YuSchool of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Xiaolong SuSchool of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Zhongping TaoInformation Technology Center, Chengdu Sport University, Chengdu, People's Republic of China.
Mu ZhangInformation Technology Center, Chengdu Sport University, Chengdu, People's Republic of China.
Yifeng WangSchool of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Jinsong LengSchool of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Jorge SepulcreGordon Center for Medical Imaging, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Huafu ChenSchool of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.ORCID 0000-0002-4062-4753
University of Electronic Science and Technology of China · CNChengdu Sport University · CNHarvard University · US

Funding

China Scholarship Council 201506075025National Natural Science Foundation of China 11271001National Natural Science Foundation of China 31600930National Natural Science Foundation of China 6137014National Natural Science Foundation of China 61533006National Natural Science Foundation of China 61573085Sichuan Science and Technology Program 2015FZ0088Sichuan Science and Technology Program 2017JY0198Sichuan Science and Technology Program 2018TJP0016
6 · The paper itself

Abstract

Training-induced neuroplasticity has been described in athletes' population. However, it remains largely unknown how regular training and sports proficiency modifies neuronal circuits in the human brain. In this study, we used voxel-based morphometry and stepwise functional connectivity (SFC) analyses to uncover connectivity changes in the functional stream architecture in student-athletes at early stages of sensorimotor skill training. Thirty-two second-year student-athletes whose major was little-ball sports and thirty-four nonathlete controls were recruited for the study. We found that athletes showed greater gray matter volume in the right sensorimotor area, the limbic lobe, and the anterior lobe of the cerebellum. Furthermore, SFC analysis demonstrated that athletes displayed significantly smaller optimal connectivity distance from those seed regions to the dorsal attention network (DAN) and larger optimal connectivity distance to the default mode network (DMN) compared to controls. The Attention Network Test showed that the reaction time of the orienting attention subnetwork was positively correlated with SFC between the seeds and the DAN, while negatively correlated with SFC between the seeds and the DMN. Our findings suggest that neuroplastic adaptations on functional connectivity streams after motor skill training may enable novel information flow from specific areas of the cortex toward distributed networks such as the DAN and the DMN. This could potentially regulate the focus of external and internal attention synchronously in athletes, and consequently accelerate the reaction time of orienting attention in athletes.

Indexed as

AthletesCerebellumAdaptation, PhysiologicalAdultAttentionCerebral CortexConnectomeGray MatterHumansLimbic LobeMagnetic Resonance ImagingMotor SkillsNerve NetNeuronal PlasticitySensorimotor CortexYoung Adultathlete trainingfunctional magnetic resonance imagingneuroplasticityoptimal connectivity distancestepwise functional connectivity

Identifiers

PMID30277624
PMCPMC6865555
OpenAlexW2890477162

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.