Journal Logo


Regional Inflation of the Thalamus and Globus Pallidus in Diving Players


Author Information
Medicine & Science in Sports & Exercise: June 2013 - Volume 45 - Issue 6 - p 1077-1082
doi: 10.1249/MSS.0b013e31827f4370
  • Free


Why do experts exhibit their superior performances in specific domain? What’s the difference between the experts and the general population? Such questions have attracted increasing attention ever since the seminal study on chess players’ superior memory in the previous century by Chase and Simon (1973). Generally, expertise could be acquired by extensive training or practice for decades. In view of this, long-term experience is undoubtedly necessary for a specific expertise. What the experience brings about is not only the behavioral change but also the neural modification underlying the automatic performance as well. Among these neural assumptions of expertise, there is a structural approach emphasizing on the concept that brain structure can be modified by experience.

Many human studies have exploited anatomical imaging to reveal group differences that reflect skill, knowledge, or expertise related to experience. The first evidence to demonstrate it was the larger posterior hippocampal volume in expert taxi drivers (26). Recently, Foster et al. (13) found gray matter concentration and cortical thickness in areas of right auditory cortex covaried with behavioral ability specifically on pitch-based tests. Moreover, meditation experience (17,25,43), instrumental practice (15,38), or career practice (27) could also induce neuroplasticity at a structural level. In fact, many animal studies have shown that motor experience can up-regulate levels of brain-derived neurotrophic factor and other growth factors, stimulate neurogenesis, which may in turn lead to molecular and cellular changes in the brain structure (4,5,29).

Among the participants in the studies exploring the neuroplasticity associated with practice or training experience, the expert population obviously provides a rich source of empirical evidence on the true potential of human achievement (39,40) because this highly trained population exhibits a number of differences, including a reduction of the variability of repeated skilled movement (7), reductions in muscle activation (24), and better information-processing performance relevant to their skills or knowledge (41). Sports experts are usually engaged in motor skill learning for a long period and can display their extraordinary skill in stressful situations. The brain structures of sports experts in playing basketball, dancing, playing golf, or practicing judo have been shown to be different from that of the general people (22,23,30,31). However, the results obtained by these studies lack consistency. The neuroanatomical changes after extensive training are not fully understood.

As described in our previous study, the diving players, among all professional athletes, are more engaged in performing complex and precise skills. The object of diving is to produce the intended body parabolic trajectory with multiple twists and somersaults in accordance with the law of conservation of angular momentum. Therefore, every diving practice needs the involvement of many body organs (hands, arms, legs, feet, and so on) and learning of certain diving skills (takeoff, control of rotation, somersault, and entry work). Hence, the diving players may be an excellent model for investigating whether the cerebral neuroplastic changes, especially neural structure plasticity, occurred in sports experts compared with the novice group after long-term professional motor training.

In this study, we analyzed a sample that included 12 professional diving players and 12 demographically matched controls. On the overlapping data set, our group has performed voxel-based morphometry and cortical thickness analyses (45,46), which separately showed significantly increased regional gray matter density and cortical thickness in the diving experts in comparison with nonathletes. However, the shape differences in subcortical structures between these two groups have never been explored. Moreover, animal and human studies have also confirmed that some subcortical structures are associated with motor functioning (10,16,28,37). Recently, a model-based segmentation and registration tool was developed to localize the shape differences in the subcortical structures and has been adopted to investigate the shape differences of subcortical structures in different diseased populations (8,11,12,35). Therefore, this method was adopted to explore the possible shape differences of the subcortical structures between the diving athletes and the novice group. Given that the thalamus and the globus pallidus were frequently associated with motor functioning and motor learning (18,20,36,45), we expected that the diving player would show significant regional inflation in the thalamus and the globus pallidus.


Ethics statement.

The written informed consent from their parents was obtained, and the study “functional and anatomical plasticity of sports experts” was approved by the institutional review board of the Beijing MRI Center for Brain Research and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. The ethics committee specifically approved all of the procedures of this study. Before the scans were taken, all subjects delivered the volunteer screening forms to the Beijing MRI Center for Brain Research to exclude any subjects who had a history of hearing or vision problems, physical injury, seizures, metal implants, head trauma with loss of consciousness, or pregnancy.


All subjects of this study participated in our previous studies (44–46). The 12 professional diving players are described in detail in Table 1. In brief, the 12 diving players are professional subjects with top-level diving skills (6 women and 6 men). The control group was matched for age, educational level, and sex (6 women and 6 men) and was composed of healthy subjects who were not involved in any extensive physical training or professional experience. All of the subjects were right-handed and were medically and neurologically stable. No subjects had any lifetime histories of substance dependence.

Demographic characteristics of the samples.

MR scanning.

High-resolution anatomical images of the whole brain were acquired on a 3-T Trio system (Siemens, Erlangen, Germany) with a 12-channel head matrix coil using a magnetization-prepared rapid-acquisition gradient echo sequence. The following parameters were used for the volumetric acquisition: repetition time = 2530 ms, echo time = 3.37 ms, flip angle = 7°, slice thickness = 1.33 mm, field of vision = 256 mm, 512 × 512-pixel matrix. The voxel size was 0.5× 0.5 × 1.33 mm. The scan time for the T1-weighted sequence was 486 s, and the scan was conducted at the end of an fMRI session. During the scanning, each subject reclined in a supine position on the bed of the scanner and was asked to lie still during the imaging time. A foam head holder and padding were placed around the subject’s head. In addition, headphones were provided to block background noise.


Before proceeding with a further analysis of the three-dimensional brain images, we confirmed that none of the images were contaminated by major head motion. Each scan was processed using the software package FSL-FIRST (Analysis Group, FMRIB, Oxford, UK), which is a probabilistic adaptation of the active appearance model (34). To automatically segment structures, shape and appearance models were constructed from a training set of manually segmented images. First, surface meshes were obtained by parameterizing the manually generated labels with a deformable model so that the cross-subject vertex correspondence was preserved. Second, sampling of the normalized intensities along the surface normal was performed at each vertex. Third, the vertex location and intensity variation were modeled as a multivariate Gaussian distribution. Finally, this model was fit to new images, maximizing the posterior probability of the shape given the observed intensities.

Statistical analyses.

When the model was fit to a new subject, a surface mesh consisting of vertices and triangles was obtained for each subject using a deformable mesh model. Because the vertex number for each subcortical structure was fixed and because the vertices correspond with one another across subjects, group comparisons were able to be made between corresponding vertices. In this way, we were able to detect localized shape abnormalities by examining group differences in the spatial location of each vertex. Although the vertices were in correspondence, the surface mesh was in its native space. Before investigating group differences, an alignment to the mean surface in standard space was made to remove pose differences by minimizing the sum-of-squares difference between a subject’s surface and the mean surface. After that, a group analysis was performed by calculating vertex-wise F-statistics to investigate the localized shape differences. Vertex-wise correlation analysis was also performed between the vertex location changes and the years of training experience in the diving players. False discovery rate (FDR) theory was used to correct for multiple comparisons.


Comparisons of the vertex locations between the diving players and the control groups showed that diving players exhibited significant regional inflation in the bilateral thalamus (left, P = 0.0086; right, P = 0.0188, corrected) (Fig. 1) and the left globus pallidus (left, P = 0.02816, corrected) (Fig. 2). Two patches of inflation were seen in the left thalamus. One was located at the anterior end in the dorsal region and one at the posterior end in the ventral regions. Three patches of inflation were seen in the right thalamus. One was located at the anterior end in the dorsal and ventral regions and the other two were located at the posterior end in the dorsal and ventral regions. Before the FDR correction, regional inflation also exists in the right globus pallidus, indicating that this area has a tendency to inflation. In addition, no significant correlation between the vertex location changes and the years of training experience was observed in diving players.

Significant regional inflation in the bilateral thalamus in diving players. The bar indicates theF value after correction for multiple comparisons using FDR theory with P < 0.05. Inward or outward arrows on the surface indicated the direction of the changes (atrophy or inflation).
Significant regional inflation in the left globus pallidus in diving players. The bar indicates theF value after correction for multiple comparisons using FDR theory with P < 0.05. Inward or outward arrows on the surface indicated the direction of the changes (atrophy or inflation).


Using a vertex-based shape analysis method, this study aimed to investigate the shape differences of whole subcortical regions between a sport expert group and a novice group that were matched for sex and age. In the sport expert group, we found a significant regional inflation in the bilateral thalamus (vs the novice group), which is consistent with our previous study results obtained by the voxel-based morphometry analysis and might account for their excellent motor performance (44,45). We also observed significant regional inflation in the left globus pallidus in sport expert group compared with the novice group. In addition, no significant correlation between the vertex location changes and the years of training experience was observed in the diving players.

Athletes, especially diving players, who not only take great specific motor practice but also simultaneously conduct learning of motor skills, may be an excellent group for investigating the possible neuroplastic changes of the brain. Therefore, the observed regional inflation of the thalamus and globus pallidus is of particular interest, and these structural changes may be closely related to the acquisition of diving skills. First, several studies have confirmed that the thalamus and the globus pallidus are associated with motor functioning. In fact, there are dense fiber connections among the basal ganglia, the thalamus, and the cerebral cortex, including the motor cortical area (21). The thalamus and the globus pallidus are two important nodes of the basal ganglia–thalamo–cortical circuit (1,14,21), which was involved in motor inhibition, automatic execution of motor plans, and acquisition and retention of motor skills (21,42). Another review emphasized the importance of the basal ganglia–thalamo–cortical circuit in the programming and control of movement (18). An animal experiment demonstrated neuroplastic functional changes in the thalamus and globus pallidus after exercise training (20). Moreover, Debaere et al. (9) reported increased activation in the thalamus and the globus pallidus during the acquisition of a new bimanual coordination task, which indicated that the thalamus and the globus pallidus may play an important role in coordination of body organs. Therefore, the observed regional inflation in the thalamus and globus pallidus might reflect the neuroplastic changes because of long-term extensive diving practice. Second, several studies have found that the thalamus and globus pallidus are implicated in the learning process. For instance, in a songbird model, Person et al. (34) found that the basal ganglia–thalamus pathway plays an important role for song learning. Using functional magnetic resonance imaging, researchers found that the thalamus and the basal ganglia were activated during the motor skill learning tasks (32,47). Da Cunha et al. (6) proposed that the globus pallidus is a key node of the basal ganglia circuitry, which is closely related to the processing of learning and memory. Hikosaka et al. (19) also found that the basal ganglia is involved in motor skill learning. Hence, the observed regional inflation in the thalamus and globus pallidus might associate with the motor learning process. Taken together, the observed regional inflation in the thalamus and globus pallidus might reflect the neuroplastic change induced by the motor practice and/or learning.

Numerous evidences have proven that training-induced structural adaptations distributed in brain regions that are involved in controlling a particular skill. For instance, Draganski et al. (10) report increased gray matter in area hMT/V5 (visual motion area) in healthy young volunteers after learning three-ball cascade juggling. In another study, Boyke et al. (3) found that aging brain exhibits similar gray matter changes in area hMT/V5, but with less proficiency in learning three-ball cascade juggling compared with adolescents. These findings may indicate efficient processing of visual motion in hMT/V5 is highly required for three-ball cascade juggling training. As a professional population with a certain expertise, athletes are ideal subjects for the investigation of the structural plasticity of the brain because of motor practice and motor skill learning. In diving experts, self-movements, spatial navigation, precise kinesthesis, and exact positions of body organs were needed to achieve best possible diving. Hence, the diving experts may exhibit neuroanatomical plasticity in brain regions that are closely related to acquisition and perception of diving skills, such as regions responsible for the processing of spatial information, coordination of body organs, and biological movement during diving (9,44–46). In basketball players, Park et al. reported a larger volume of vermian lobules VI–VII of the cerebellum compared with control group (30). The vermian lobules have been associated with the coordination between eyes and the hand as well as bimanual coordination, which were highly emphasized for basketball players (9). In golfers, Jäncke et al. (22) demonstrated plastic changes in dorsal premotor cortex, caudal premotor cortex, posterior intraparietal sulcus, and posterior parietal cortex, which were thought to be associated with the control of the golf swing. Moreover, a later longitudinal study by the same group showed that the structural adaptations due to golf practice can be observed as early as immediately after the onset of golf training (2). In summary, although one must draw conclusions carefully, we hypothesize that long-term intensive motor training may lead to neuroplastic change in certain domain-specific networks. Further network analyses by assessing the interregional correlations are needed to test this hypothesis.

In conclusion, we investigated the subcortical shape difference between diving players and novices using a vertex-based shape analysis method. We found significant regional inflation in the thalamus and globus pallidus in diving players. Consistent with recent studies, we hypothesize that the observed regional inflation are due to the effect of extensive training. However, we cannot rule out the fact that these structural differences might be innate. Future studies are warranted to determine the relative contribution of predisposition and training.

This work was supported by the Natural Science Foundation of China (grant nos. 81101000, 91132728, and 31200794), the Fundamental Research Funds for the Central Universities (grant no. ZYGX2011J098), the Knowledge Innovation Program of the Chinese Academy of Sciences (grant no. KSCX2-EW-J-8), and the Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences. The authors thank Shi-Yu Zhang of the University of Minnesota for editorial assistance. The authors do not have any conflict of interest.

The authors disclose that there are no professional relationships with companies or manufacturers that will benefit from the results of the present study. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

Yuanchao Zhang and Gaoxia Wei contributed equally to this work.


1. Alexander GE, DeLong MR, Strick PL. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Ann Rev Neurosci. 1986; 9 (1): 357–81.
2. Bezzola L, Merillat S, Gaser C, Jancke L. Training-induced neural plasticity in golf novices. J Neurosci. 2011; 31 (35): 12444–8.
3. Boyke J, Driemeyer J, Gaser C, Büchel C, May A. Training-induced brain structure changes in the elderly. J Neurosci. 2008; 28 (28): 7031.
4. Cotman CW, Berchtold NC. Exercise: a behavioral intervention to enhance brain health and plasticity. TRENDS in Neurosciences. 2002; 25 (6): 295–301.
5. Cotman CW, Berchtold NC. Physical activity and the maintenance of cognition: learning from animal models. Alzheimer’s and Dementia. 2007; 3 (2): S30–7.
6. Da Cunha C, Wietzikoski EC, Dombrowski P, et al. Learning processing in the basal ganglia: A mosaic of broken mirrors. Behavioural brain research. 2009; 199 (1): 157–70.
7. Davids K, Bennett S, Newell K. Movement System Variability. Champaign: Human Kinetics; 2006. pp. 73–83.
8. De Jong L, van der Hiele K, Veer I, et al. Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: an MRI study. Brain. 2008; 131 (12): 3277.
9. Debaere F, Wenderoth N, Sunaert S, Van Hecke P, Swinnen SP. Changes in brain activation during the acquisition of a new bimanual coodination task. Neuropsychologia. 2004; 42 (7): 855–67.
10. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A. Neuroplasticity: changes in grey matter induced by training. Nature. 2004; 427 (6972): 311–2.
11. Du H, Zhang Y, Xie B, et al. Regional atrophy of the basal ganglia and thalamus in idiopathic generalized epilepsy. J Magn Reson Imaging. 2011; 33 (4): 817–21.
12. Fernández-Espejo D, Junque C, Bernabeu M, Roig-Rovira T, Vendrell P, Mercader JM Reductions of thalamic volume and regional shape changes in the vegetative and the minimally conscious states. J Neurotrauma. 2010; 27 (7): 1187–93.
13. Foster NE, Zatorre RJ. Cortical structure predicts success in performing musical transformation judgments. Neuroimage. 2010; 53 (1): 26–36.
14. Gallay MN, Jeanmonod D, Liu J, Morel A. Human pallidothalamic and cerebellothalamic tracts: anatomical basis for functional stereotactic neurosurgery. Brain Struct Funct. 2008; 212 (6): 443–63.
15. Gaser C, Schlaug G. Brain structures differ between musicians and non-musicians. J Neurosci. 2003; 23 (27): 9240–5.
16. Grossberg S. Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action. Philos Trans R Soc B Biol Sci. 2009; 364 (1521): 1223.
17. Hölzel BK, Ott U, Gard T, et al. Investigation of mindfulness meditation practitioners with voxel-based morphometry. Soc Cogn Affect Neurosci. 2008; 3 (1): 55.
18. Herrero MT, Barcia C, Navarro J. Functional anatomy of thalamus and basal ganglia. Childs Nerv Syst. 2002; 18 (8): 386–404.
19. Hikosaka O, Nakamura K, Sakai K, Nakahara H. Central mechanisms of motor skill learning. Curr Opin Neurobiol. 2002; 12 (2): 217–22.
20. Holschneider D, Yang J, Guo Y, Maarek JMI. Reorganization of functional brain maps after exercise training: importance of cerebellar-thalamic-cortical pathway. Brain Res. 2007; 1184: 96–107.
21. Hoover JE, Strick PL. Multiple output channels in the basal ganglia. Science. 1993; 259 (5096): 819–21.
22. Jäncke L, Koeneke S, Hoppe A, Rominger C, Hänggi J. The architecture of the golfer’s brain. PLoS One. 2009; 4 (3): e4785.
23. Jacini WFS, Cannonieri GC, Fernandes PT, Bonilha L, Cendes F, Li LM. Can exercise shape your brain? Cortical differences associated with judo practice. J Sci Med Sport. 2009; 12 (6): 688–90.
24. Lay B, Sparrow W, Hughes K, O’Dwyer N. Practice effects on coordination and control, metabolic energy expenditure, and muscle activation. Hum Mov Sci. 2002; 21 (5–6): 807–30.
25. Lazar SW, Kerr CE, Wasserman RH, et al. Meditation experience is associated with increased cortical thickness. Neuroreport. 2005; 16 (17): 1893.
26. Maguire EA, Gadian DG, Johnsrude IS, et al. Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci U S A. 2000; 97 (8): 4398–403.
27. Maguire EA, Spiers HJ, Good CD, Hartley T, Frackowiak RSJ, Burgess N. Navigation expertise and the human hippocampus: a structural brain imaging analysis. Hippocampus. 2003; 13 (2): 250–9.
28. Mochizuki-Kawai H, Kawamura M, Hasegawa Y, et al. Deficits in long-term retention of learned motor skills in patients with cortical or subcortical degeneration. Neuropsychologia. 2004; 42 (13): 1858–63.
29. Neeper SA, Gomez-Pinilla F, Choi J, Cotman C. Exercise and brain neurotrophins. Nature. 1995; 373 (6510): 109.
30. Park IS, Lee KJ, Han JW, et al. Experience-dependent plasticity of cerebellar vermis in basketball players. Cerebellum. 2009; 8 (3): 334–9.
31. Park IS, Lee KJ, Han JW, et al. Basketball training increases striatum volume. Hum Mov Sci. 2010; 30 (1): 56–62.
32. Park JW, Kim YH, Jang SH, Chang WH, Park C, Kim ST. Dynamic changes in the cortico-subcortical network during early motor learning. NeuroRehabilitation. 2010; 26 (2): 95–103.
33. Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011; 56 (3): 907–22.
34. Person AL, Perkel DJ. Pallidal neuron activity increases during sensory relay through thalamus in a songbird circuit essential for learning. J Neurosci. 2007; 27 (32): 8687.
35. Rijpkema M, Everaerd D, van der Pol C, Franke B, Tendolkar I, Fernández G. Normal sexual dimorphism in the human basal ganglia. Hum Brain Mapp. 2012; 33 (5): 1246–52.
36. Sanes JN, Donoghue JP. Plasticity and primary motor cortex. Ann Rev Neurosci. 2000; 23 (1): 393–415.
37. Seo HG, Kim DY, Park HW, Lee SU, Park SH. Early motor balance and coordination training increased synaptophysin in subcortical regions of the ischemic rat brain. J Korean Med Sci. 2010; 25 (11): 1638.
38. Sluming V, Barrick T, Howard M, Cezayirli E, Mayes A, Roberts N. Voxel-based morphometry reveals increased gray matter density in Broca’s area in male symphony orchestra musicians. Neuroimage. 2002; 17 (3): 1613–22.
39. Starkes JL, Ericsson KA. The development of elite performance and deliberate practice: an update from the perspective of the expert-performance approach. In: Expert Performance in Sport: Recent Advances in Research on Sport Expertise. Champaign: Human Kinetics; 2003. pp. 49–81.
40. Starkes JL, Ericsson KA. How the expert performance approach differs from traditional approaches to expertise in sport: in search of a shared theoretical framework for studying expert performance. In: Expert Performance in Sports: Advances in Research on Sport Expertise. Champaign: Human Kinetics; 2003. pp. 371–402.
41. Ste-Marie DM. Expert-novice differences in gymnastic judging: an information-processing perspective. Applied Cognitive Psychology. 1999; 13 (3): 269–81.
42. Toxopeus CM, de Vries PM, de Jong BM, et al. Cerebral activation patterns related to initiation and inhibition of hand movement. Neuroreport. 2007; 18 (15): 1557.
43. Vestergaard-Poulsen P, van Beek M, Skewes J, et al. Long-term meditation is associated with increased gray matter density in the brain stem. Neuroreport. 2009; 20 (2): 170.
44. Wei G, Luo J. Sport expert’s motor imagery: Functional imaging of professional motor skills and simple motor skills. Brain Res. 2009; 1341: 52–62.
45. Wei G, Luo J, Li Y. Brain structure in diving players on MR imaging studied with voxel-based morphometry. Prog Nat Sci. 2009; 19 (10): 1397–402.
46. Wei G, Zhang Y, Jiang T, Luo J. Increased cortical thickness in sports experts: a comparison of diving players with the controls. PLoS One. 2011; 6 (2): e17112.
47. Yamada M, Kawachi T, Kawamitsu H, et al. Effect of perceptual learning on motor skills of hands: a functional magnetic resonance imaging study. Kobe J Med Sci. 2010; 56 (1): 29–37.


©2013The American College of Sports Medicine