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Regional brain gray matter volume in world-class artistic gymnasts


The relationship between long-term intensive training and brain plasticity in gymnasts has recently been reported. However, the relationship between abilities in different gymnastic events and brain structural changes has not been explored. This study aimed to evaluate the correlation between world-class gymnasts (WCGs)’ specific abilities in different gymnastics events and their gray matter (GM) volume. Ten right-handed Japanese male WCGs and 10 right-handed gender- and age-matched controls with no history of gymnastic training participated in this study. Whole brain three-dimensional T1-weighted images (magnetization-prepared rapid gradient-echo sequence) with 0.90 mm3 voxels were obtained using a 3 T-MRI scanner from each subject. Volume-based morphometry (VolBM) was used to compare GM volume differences between WCGs and controls. We then explored the correlation between specific gymnastic abilities using different gymnastic apparatuses, and GM volume. Significantly higher GM volumes (false discovery rate-corrected p < 0.05) in the inferior parietal lobule, middle temporal gyrus, precentral gyrus, rostral middle frontal gyrus, and superior frontal gyrus were demonstrated in WCGs, compared with controls using VolBM. Moreover, significant positive correlations were observed between brain regions and the difficulty scores for each gymnastic event, for example, rings and inferior parietal lobule and parallel bars and rostral middle frontal gyrus. These results may reflect the neural basis of an outstanding gymnastic ability resulting from brain plasticity in areas associated with spatial perception, vision, working memory, and motor control.


Artistic gymnastics is a major competition in the Summer Olympic Games that has attracted increasing interest worldwide (International Gymnastics Federation: FIG, Japan is one such country with an increasing interest in artistic gymnastics. Artistic gymnastics is usually divided into men's and women's gymnastics. Men perform on 6 events: floor exercise, pommel horse, rings, vault, parallel bars, and horizontal bar. Gymnastics performances in competitions are evaluated by a panel of judges using two different scores, the D-score (difficulty of the routine) and the E-score (execution of the routine). The D-score is mainly assessed according to the difficulty of the elements performed within a routine under the conditions required for each event, whereas the E-score is defined by evaluating technical errors in the routine. The total score is calculated by adding the D-score and the E-score (FIG,

Gymnasts belonging to gymnastics clubs train to increase their skills using conventional and newly developed techniques. The amount of research on artistic gymnastics has increased in recent years, mainly focused on technical aspects or coaching/teaching methods. Until now, little is known about the anatomical and physiological characteristics of gymnasts. However, it is well known that many brain areas are activated during exercise and that neuronal plasticity, defined as the ability of neurons to change in structure and function in response to environment and experience, is induced by long-term training [1,2,3,4]. Through this process, athletes can acquire exceptional abilities in perception, stimulus discrimination, decision-making, motor preparation, and execution [1]. Indeed, evidence suggests that athletes' brains are characterized by specific functional and anatomical adaptations that facilitate information processing relevant to their specific sport [5,6,7,8,9,10].

Gymnasts at the top level begin training from childhood. Such long-term intensive training with specific movements may cause specific adaptations in brain structure and function. In fact, it has been demonstrated that Chinese gymnasts exhibit higher gray matter (GM) volume in somatosensory and visuospatial areas compared with non-gymnasts using voxel-based morphometry (VBM) analysis [11]. GM includes neurons, which consist of cell bodies, dendrites, axons, and synapses, as well as glial cells like oligodendrocytes, astrocytes, and microglia. Any microstructural changes to these cells may affect GM volume and function [2, 11]. However, the relationship between abilities in different gymnastic events and structural brain changes is unknown. Better understanding the neural basis of elite gymnastics performance may inform future training strategies and, moreover, it may provide a new method of identifying talent and assessing the development process [12].

In this study, we compare GM volume differences between Japanese world-class gymnasts (WCGs) and healthy controls with no history of intensive exercise training. Furthermore, we evaluated the correlation of WCGs’ specific abilities in different gymnastics events and GM volume. Since the E-score is more susceptible to mental and physical conditions during competition than the D-score and is influenced by the referees’ subjectivity, the D-score was used as an index of the gymnasts’ specific physical abilities acquired through long-term training. We applied volume-based morphometry (VolBM) using FreeSurfer. Compared with VBM, which explores hundreds of thousands of voxel-wise GM concentrations, VolBM demonstrates a higher level of accuracy by evaluating volumes of particular regions of interest in the brain (e.g., atlas-based regions) [13].

Materials and methods


Ten right-handed Japanese WCGs (10 males; mean age, 19.9±1.3 years; age range, 18–22 years; mean years of training, 13.6 ± 2.2 years; years of training range, 10–19 years) and ten right-handed gender- and age-matched healthy controls (10 males; mean age, 20.6±1.7 years; age range, 16–22 years) without any history of gymnastics training or competition participated in this study. All WCGs in this study have won medals at gymnastics world championships. Their best D-score and E-score for each gymnastics event (floor exercise, horizontal bar, rings, vault, parallel bars, and pommel horse) obtained at a recent world gymnastics competition, are listed in Table 1. The D-score evaluates the content of a routine using three criteria: difficulty, composition requirements, and connection value, whereas the E-score evaluates technical errors in the routine. The D-score was used as an objective indicator of gymnastic ability in this study. There were no participants with a history of neurological or brain injuries or psychiatric disease. The study protocol was approved by the Ethic Committee of the Juntendo University and participants provided written informed consent before scanning.

Table 1 Characteristics of world-class gymnasts who participated in this study

MRI acquisition

Magnetic resonance imaging (MRI) data were obtained using a 3-T scanner (MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany) equipped with a 64-channel head coil. T1-weighted images (T1-WI) were acquired by a three-dimensional (3D) magnetization-prepared rapid gradient-echo sequence (MPRAGE). The acquisition parameters were: repetition time, 2300 ms; echo time, 2.32 ms; inversion time, 900 ms; voxel size, 0.90×0.90×0.90 mm3; and acquisition time, 5.31 min.

GM volume measurement methods


VolBM was performed using FreeSurfer version 5.3.0 (freesurfer-Linux-centos6_x86_64-stable-pub-v5.3.0;, as previously described [14]. FreeSurfer was run with the recon-all pipeline using the default analysis settings on each T1-WI. Briefly, this pipeline included motion correction and intensity normalization of T1-WI, removal of non-brain tissue, automated Talairach transformation, segmentation of the subcortical WM and deep GM volumetric structures (including the hippocampus, amygdala, caudate, putamen, and ventricles), tessellation of the GM–WM boundary, and derivation of cortical thickness. After image processing, we extracted volumes for the cortical structures of the 34 bilateral Desikan–Killiany atlas [15] regions. Volumes of structures were then measured by multiplying the size of each voxel by the number of voxels.

Statistical analysis

All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 22.0 (IBM Corporation, Armonk, NY, USA). The Shapiro–Wilk test was used to assess the normality of the data, whereas the data on demographics were analyzed using an unpaired Student’s t-test and χ2 test for continuous and categorical variables, respectively. Statistical significance was set to 0.05.

During the VolBM analysis, the Mann–Whitney U test was used to compare the mean (bilateral hemispheres) normalized GM volumes (nGM; GM volume/intracranial volume) between the WCGs and controls. Then, the Benjamini–Hochberg false discovery rate (FDR) correction was applied to correct for multiple testing (34 regions of interest) with the level of significance for two-tailed p values set to 0.05. The relationship between the D-scores for individual gymnastic events (floor exercise, horizontal bar, rings, vault, parallel bars, and pommel horse) or the average D-scores from these individual scores and areas showing significant FDR-corrected p-values in group comparisons were evaluated using the Spearman’s rank correlation test, with the level of significance set to FDR-corrected p<0.05. The relationship between the D-scores from individual gymnastic events and average D-scores was also evaluated using Pearson correlation analysis at a significance level of p<0.05.


No significant difference was observed with regard to age (p=0.35) and sex (p=1.00) between the WCG and control groups. In the WCGs, the best D-scores for each gymnastic event and the average of the D-scores from a recent world gymnastics competition are shown in Table 1. Significant correlations between the average D-score and the D-scores from the rings event (p=0.0016, R2=0.73) or the parallel bars event (p=0.0029, R2=0.69) were found in the WCGs.

VolBM analysis showed statistically significant increases in mean nGM volumes (FDR-corrected p<0.05) in the inferior parietal lobule (IPL; p=0.036), middle temporal gyrus (MTG; p=0.044), precentral gyrus (PrG; p=0.044), rostral middle frontal gyrus (RMFG; p=0.044), and superior frontal gyrus (SFG; p=0.047) of WCGs compared with controls (Fig. 1, Table 2). Furthermore, the nGM volume of IPL was positively correlated with the D-score from the rings event (p=0.015, R2=0.76) and the average D-scores (p=0.041, R2=0.73), and that of the RMFG was also positively correlated with the D-score from the parallel bars event (p=0.006; R2=0.54) and average D-scores (p=0.036, R2=0.35) (Fig. 2).

Fig. 1

Comparison between normalized gray matter volumes of world-class gymnasts and healthy controls obtained using volume-based morphometry. The WCGs exhibit significantly (false discovery rate [FDR]-corrected p < 0.05) higher nGM volumes compared with the controls. nGM normalized gray matter, HC healthy controls, WCG world-class gymnasts

Table 2 Normalized gray matter (nGM) volume of controls and world-class gymnast (WCGs) obtained using volume-based morphometry analysis
Fig. 2

Correlation between normalized gray matter volumes obtained using volume-based morphometry in world-class gymnasts and the average or absolute D-scores for each gymnastics event. Statistically significant positive correlation between nGM volume in the inferior parietal and rostral middle frontal regions and the D-score from the rings event and average D-scores and D-score of parallel bars event and average D-scores, respectively. nGM normalized gray matter, WCGs world-class gymnasts


Using VolBM analysis, this study revealed that the nGM volumes of the IPL, MTG, PrG, RMFG, and SFG were higher in WCGs than in controls. Accumulating evidence has shown that increased GM volumes found in exercise-trained subjects are associated with increased functional plasticity and cognitive performance [2, 11, 16, 17]. Our findings support the concept that long-term motor skill training is associated with a complex bihemispheric cortical–subcortical network [2, 18]. The brain areas identified in this study are functionally related to motor control (i.e., PrG) [19, 20], interpretation of sensory information (i.e., IPL) [21,22,23], body image (i.e., IPL) [21, 22], spatial perception (i.e., IPL and MTG) [23, 24], spatial attention (i.e., IPL) [21,22,23], vision (i.e., IPL and MTG) [21,22,23,24], executive function (i.e., RMFG and IPL) [22, 25,26,27], and working memory (i.e., RMFG and SFG) [25,26,27,28]. A unique feature of gymnastics events is that each requires acrobatic performances comprising speed, strength, and flexibility; these physiological functions are all critical to supporting elaborate body movements. The increased GM volume in the IPL, MTG, PrG, RMFG, and SFG of WCGs is likely to result from many years of intensive training. These findings support a previous report, which suggested that, compared with non-gymnasts, Chinese gymnasts exhibit higher volumes of GM in somatosensory, motor control, and visuospatial areas [11]. The underlying mechanisms associated with training-dependent GM volume increases remain unknown, but they may involve microstructural changes in brain cells, including neurons, glial cells, and endothelial cells, induced by increased blood flow, oxygenation, and the production of brain neurotrophic factors and their receptors [2, 11, 16].

In WCGs, we analyzed correlations between the D-scores for each gymnastics event and the GM volumes of particular brain areas identified as containing more GM in gymnasts than controls. The D-scores for the rings and parallel bars events were found to be related to the GM volumes of the IPL and RMFG, respectively. The IPL is known to be involved in various neural functions, including spatial attention, multimodal sensory integration, body image, oculomotor control, and hand–eye coordination [21,22,23]. The RMFG is critical for executive function and working memory [25,26,27]. Working memory is a series of memory operations that involve manipulating and utilizing information while temporarily retaining it in the brain. Executive function is a cognitive system that controls thoughts and actions when performing complex tasks, and working memory is included in this process.

The rings represent the only apparatus that is continually in motion. Hence, this particular event requires prodigious body balance and muscle strength to halt body movements suddenly and allow the gymnast to maintain the position. Considering our findings, a high level of IPL function may be required to follow the precise movements of the rings, adjust body balance, and hold the body in an optimal position. On the parallel bars, techniques involve hand-release from and regrasping of the bars. Therefore, a high level of visuospatial working memory may be required to identify the precise location of the bar/bars to be regrasped quickly, and this level of function may be associated closely with the level of competitive ability seen in WCGs. Structural changes in the IPL and RMFG are also likely to be the result of intensive training in WCGs.

Because the average D-score reflects the level of skill demonstrated in the individual all-around event, correlations were determined between the average D-score and GM volumes of particular brain areas identified as containing more GM in gymnasts than in controls. Interestingly, significant positive correlations were found only in the IPL and RMFG, indicating that the brain areas and functions associated with the D-scores from the rings and parallel bars were also involved in determining the average D-score. Indeed, the average D-scores were highly correlated with the D-scores for both the rings and parallel bars but not the other events. These characteristics of the WCGs who participated in this study may have caused the IPL and RMFG to be identified as brain areas that affect the average D-score. However, although each gymnastic event entails specific skills, all events involve common body movements, including handstands, somersaults, and twist techniques, all of which require complex motor skills, based on sensory perception–motor integration, to adjust balance and hold the body optimally. Therefore, our finding that the IPL and RMFG affect the average D-score may suggest that higher levels of spatial attention, multimodal sensory integration, body image, oculomotor control, hand–eye coordination, executive function, and working memory facilitate sensory perception–motor integration and elaborate body movements required to undertake all gymnastics events, thus enabling the competitor to obtain a higher score in the individual all-around event.


Since the number of Japanese WCGs who are currently active is extremely limited, the sample sizes in this study were small (10 WCGs and 10 controls). However, to confirm whether genetic factors affect the GM volumes of particular brain areas in WCGs and/or whether long-term intensive training is a main factor that contributes to the structural changes, future investigations using longitudinal studies with a large number of WCGs and/or control subjects will be required.


Overall, we found that the GM volumes of particular brain areas play an indispensable role in gymnastic ability and are closely associated with individual D-scores for specific gymnastic events in WCGs. The increased GM volumes in these areas may represent the neural basis for outstanding gymnastics performance resulting from brain plasticity. GM volumes in these brain areas of interest could become markers for the objective evaluation of gymnastic performance. In this regard, our findings demonstrate that GM volumes in the IPL and RMFG may be potential markers for evaluating the performance of a gymnast on the rings, parallel bars, or in the individual all-around event.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.





Difficulty score


False discovery rate


International Gymnastics Federation


Gray matter


Magnetization-prepared rapid gradient-echo sequence


Magnetic resonance imaging


Normalized GM


T1-weighted images


Volume-based morphometry


World-class gymnasts


White matter


Inferior parietal lobule


Middle temporal gyrus


Precentral gyrus


Rostral middle frontal gyrus


Superior frontal gyrus


Voxel-based morphometry


  1. 1.

    Nakata H, Yoshie M, Miura A, Kudo K (2010) Characteristics of the athletes' brain: evidence from neurophysiology and neuroimaging. Brain Res Rev 62:197–211

    Article  Google Scholar 

  2. 2.

    Dayan E, Cohen LG (2011) Neuroplasticity subserving motor skill learning. Neuron 72:443–454

    CAS  Article  Google Scholar 

  3. 3.

    Budde H, Wegner M, Soya H, Voelcker-Rehage C, McMorris T (2016) Neuroscience of exercise: neuroplasticity and its behavioral consequences. Neural Plast.

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Yarrow K, Brown P, Krakauer JW (2009) Inside the brain of an elite athlete: the neural processes that support high achievement in sports. Nat Rev Neurosci 10:585–596

    CAS  Article  Google Scholar 

  5. 5.

    Jäncke L, Koeneke S, Hoppe A, Rominger C, Hänggi J (2009) The architecture of the golfer's brain. PLoS ONE.

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Di Paola M, Caltagirone C, Petrosini L (2013) Prolonged rock climbing activity induces structural changes in cerebellum and parietal lobe. Hum Brain Mapp 34:2707–2714

    Article  Google Scholar 

  7. 7.

    Wang B, Fan Y, Lu M et al (2013) Brain anatomical networks in world class gymnasts: a DTI tractography study. Neuroimage 65:476–487

    Article  Google Scholar 

  8. 8.

    Wang J, Lu M, Fan Y, Wen X, Zhang R, Wang B, Ma Q, Song Z, He Y, Wang J, Huang R (2016) Exploring brain functional plasticity in world class gymnasts: a network analysis. Brain Struct Funct 221:3503–3519

    Article  Google Scholar 

  9. 9.

    Kim J, Lee HM, Kim WJ, Park HJ, Kim SW, Moon DH, Woo M, Tennant LK (2008) Neural correlates of pre-performance routines in expert and novice archers. Neurosci Lett 445:236–241

    CAS  Article  Google Scholar 

  10. 10.

    Di X, Zhu S, Jin H, Wang P, Ye Z, Zhou K, Zhuo Y, Rao H (2012) Altered resting brain function and structure in professional badminton players. Brain Connect 2:225–233

    Article  Google Scholar 

  11. 11.

    Huang R, Lu M, Song Z, Wang J (2015) Long-term intensive training induced brain structural changes in world class gymnasts. Brain Struct Funct 220:625–644

    Article  Google Scholar 

  12. 12.

    Chang Y (2014) Reorganization and plastic changes of the human brain associated with skill learning and expertise. Front Hum Neurosci.

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Schmitter D, Roche A, Maréchal B, Ribes D, Abdulkadir A, Bach-Cuadra M, Daducci A, Granziera C, Klöppel S, Maeder P, Meuli R, Krueger G (2014) An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease. Neuroimage Clin 7:7–17

    Article  Google Scholar 

  14. 14.

    Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97:11050–11055

    CAS  Article  Google Scholar 

  15. 15.

    Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980

    Article  Google Scholar 

  16. 16.

    Killgore WD, Olson EA, Weber M (2013) Physical exercise habits correlate with gray matter volume of the hippocampus in healthy adult humans. Sci Rep.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A (2004) Neuroplasticity: changes in grey matter induced by training. Nature 427:311–312

    CAS  Article  Google Scholar 

  18. 18.

    Floyer-Lea A, Matthews PM (2005) Distinguishable brain activation networks for short- and long-term motor skill learning. J Neurophysiol 94:512–518

    CAS  Article  Google Scholar 

  19. 19.

    Kovac S, Scott CA, Maglajlija V, Rodionov R, McEvoy AW, Diehl B (2011) Extraoperative electrical cortical stimulation: characteristics of motor responses and correlation with precentral gyrus. J Clin Neurophysiol 28:618–624

    Article  Google Scholar 

  20. 20.

    Newton JM, Dong Y, Hidler J, Plummer-D'Amato P, Marehbian J, Albistegui-Dubois RM, Woods RP, Dobkin BH (2008) Reliable assessment of lower limb motor representations with fMRI: use of a novel MR compatible device for real-time monitoring of ankle, knee and hip torques. Neuroimage 43:136–146

    Article  Google Scholar 

  21. 21.

    Andersen RA (1987) Inferior parietal lobule function in spatial perception and visuomotor integration. In: Mountcastle VB, Plum F, Geiger SR (eds) Handbook of physiology Section I: the nervous system, vol V. American Physiological Society, Bethesda, pp 483–518

    Google Scholar 

  22. 22.

    Torrey EF (2007) Schizophrenia and the inferior parietal lobule. Schizophr Res 97:215–225

    Article  Google Scholar 

  23. 23.

    Clower DM, West RA, Lynch JC, Strick PL (2001) The inferior parietal lobule is the target of output from the superior colliculus, hippocampus, and cerebellum. J Neurosci 21:6283–6291

    CAS  Article  Google Scholar 

  24. 24.

    Jastorff J, Clavagnier S, Gergely G, Orban GA (2011) Neural mechanisms of understanding rational actions: middle temporal gyrus activation by contextual violation. Cereb Cortex 21:318–329

    Article  Google Scholar 

  25. 25.

    Kikinis Z, Fallon JH, Niznikiewicz M, Nestor P, Davidson C, Bobrow L, Pelavin PE, Fischl B, Yendiki A, McCarley RW, Kikinis R, Kubicki M, Shenton ME (2010) Gray matter volume reduction in rostral middle frontal gyrus in patients with chronic schizophrenia. Schizophr Res 123:153–159

    CAS  Article  Google Scholar 

  26. 26.

    van Vugt B, van Kerkoerle T, Vartak D, Roelfsema PR (2020) The contribution of AMPA and NMDA receptors to persistent firing in the dorsolateral prefrontal cortex in working memory. J Neurosci 40:2458–2470

    Article  Google Scholar 

  27. 27.

    Blumenfeld RS, Ranganath C (2006) Dorsolateral prefrontal cortex promotes long-term memory formation through its role in working memory organization. J Neurosci 26:916–925

    CAS  Article  Google Scholar 

  28. 28.

    Alagapan S, Lustenberger C, Hadar E, Shin HW, Frӧhlich F (2019) Low-frequency direct cortical stimulation of left superior frontal gyrus enhances working memory performance. Neuroimage 184:697–706

    Article  Google Scholar 

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We gratefully acknowledge all the participants for agreeing to participate in the study.


This study was partially supported by Private University Research Branding Project (Ministry of Education, Culture, Sports, Science and Technology, Japan) and JSPS KAKENHI Grant Number JP18K09005 to YTan.

Author information




MF, KK, HW, HS, SA, and HN conceived and planned the experiments. MF, KK, MK, HT, YTan, and TM performed the experiments. MF, KK, CA, TM, HS, YTan, WU, YTak, AH, MH, MG, and MH analyzed the data. MF, KK, MK, CA, HT, TM, WU, YTak, AH, MG, and MH interpreted the results of the experiments. MF, KK, MK, CA, and HW prepared the figures. MF, KK, CA, and HW drafted the manuscript. MF, KK, CA, HW, HT, MH, SA, and HN revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hidefumi Waki.

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The study protocol was approved by the Ethic Committee of the Juntendo University and participants provided written informed consent before scanning.

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Informed consent was obtained from all individual participants included in the study.

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The authors declare that they have no competing interest.

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Fukuo, M., Kamagata, K., Kuramochi, M. et al. Regional brain gray matter volume in world-class artistic gymnasts. J Physiol Sci 70, 43 (2020).

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  • Magnetic resonance imaging
  • Volume-based morphometry
  • Brain plasticity
  • Difficulty score