Evidence map›Paper›PMID 34335372›Full record

ArticleFrontiers in psychology2021

Sparsely Wiring Connectivity in the Upper Beta Band Characterizes the Brains of Top Swimming Athletes.

Xinzhen Pei, Xiaoying Qi, Yuzhou Jiang, Xunzhang Shen, An-Li Wang, Yang Cao, Chenglin Zhou, Yuguo Yu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 58% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
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

8 authors at 4 institutions in 2 countries.

Xinzhen PeiHuman Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education (MOE) Frontiers Center for Brain Science, School of Life Science and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
Xiaoying QiHuman Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education (MOE) Frontiers Center for Brain Science, School of Life Science and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
Yuzhou JiangHuman Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education (MOE) Frontiers Center for Brain Science, School of Life Science and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
Xunzhang ShenShanghai Research Institute of Sports Science, Shanghai, China.
An-Li WangDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Yang CaoHuman Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education (MOE) Frontiers Center for Brain Science, School of Life Science and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
Chenglin ZhouSchool of Psychology, Shanghai University of Sport, Shanghai, China.
Yuguo YuHuman Phenome Institute, State Key Laboratory of Medical Neurobiology and Ministry of Education (MOE) Frontiers Center for Brain Science, School of Life Science and Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
Fudan University · CNIcahn School of Medicine at Mount Sinai · USShanghai Research Institute of Sports Science · CNShanghai University of Sport · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human brains are extremely energy costly in neural connections and activities. However, it is unknown what is the difference in the brain connectivity between top athletes with long-term professional trainings and age-matched controls. Here we ask whether long-term training can lower brain-wiring cost while have better performance. Since elite swimming requires athletes to move their arms and legs at different tempos in time with high coordination skills, we selected an eye-hand-foot complex reaction (CR) task to examine the relations between the task performance and the brain connections and activities, as well as to explore the energy cost-efficiency of top athletes. Twenty-one master-level professional swimmers and 23 age-matched non-professional swimmers as controls were recruited to perform the CR task with concurrent 8-channel EEG recordings. Reaction time and accuracy of the CR task were recorded. Topological network analysis of various frequency bands was performed using the phase lag index (PLI) technique to avoid volume conduction effects. The wiring number of connections and mean frequency were calculated to reflect the wiring and activity cost, respectively. Results showed that professional athletes demonstrated better eye-hand-foot coordination than controls when performing the CR task, indexing by faster reaction time and higher accuracy. Comparing to controls, athletes' brain demonstrated significantly less connections and weaker correlations in upper beta frequency band between the frontal and parietal regions, while demonstrated stronger connectivity in the low theta frequency band between sites of F3 and Cz/C4. Additionally, athletes showed highly stable and low eye-blinking rates across different reaction performance, while controls had high blinking frequency with high variance. Elite athletes' brain may be characterized with energy efficient sparsely wiring connections in support of superior motor performance and better cognitive performance in the eye-hand-foot complex reaction task.

Indexed as

activity costelite swimmersenergy efficiencyphase lag indexwiring cost

Identifiers

PMID34335372
PMCPMC8322235
OpenAlexW3186545112

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.