Effects of open-skill training on brain gray matter volume: activation likelihood estimation analysis based on voxel-based morphometry

Effects of open-skill training on brain gray matter volume: activation likelihood estimation analysis based on voxel-based morphometry | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of open-skill training on brain gray matter volume: activation likelihood estimation analysis based on voxel-based morphometry Jilong Shi, Haojie Huang, Xinghe Weng, Dezun Chen, Ruiyi Dong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7615487/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Brain plasticity refers to the brain's ability to reorganize structurally in response to experience and training. Motor skill practice, particularly in open-skill sports, has been consistently linked to structural differences in gray matter volume (GMV). However, prior research is largely limited to individual sports, lacking a comprehensive quantitative synthesis across multiple disciplines. This meta-analysis employed activation likelihood estimation to identify consistent GMV characteristics in open-skill athletes. Fifteen voxel-based morphometry studies comprising 653 participants, 76 foci, and 20 contrasts comparing athletes with non-athlete controls were included. Athletes consistently exhibited relatively larger GMV in the right superior frontal gyrus orbital part, right postcentral gyrus, right middle temporal gyrus, left inferior temporal gyrus, left precuneus, left inferior parietal lobule, and left precentral gyrus (uncorrected p < 0.001). Notably, the right superior frontal gyrus orbital part and right postcentral gyrus remained significant after familywise error correction (p < 0.05). These findings delineate a robust neuroanatomical signature associated with open-skill athletic expertise, characterized by structural distinctions within an integrated network supporting cognitive, perceptual, and sensorimotor processing. By synthesizing evidence across diverse open-skill disciplines, this meta-analysis advances our understanding of how complex, real-world motor experiences relate to brain structure, offering potential insights for cognitive-motor interventions beyond athletic contexts. neuroplasticity open-skill sports gray matter volume activation likelihood estimation voxel-based morphometry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction In recent years, advancements in neuroimaging techniques have considerably enhanced our understanding of how motor skill training reshapes the structure and function of the human brain. These insights align closely with the concept of neuroplasticity, which posits that the brain dynamically reorganizes its neural architecture in response to intensive practice and experience [ 1 – 2 ]. Numerous studies have shown that structured motor training is associated with measurable neural adaptations [ 3 – 4 ]. For instance, a longitudinal study by Draganski et al. (2004) demonstrated that even a brief period of juggling practice was sufficient to elicit localized increases in gray matter volume [ 3 ]. Over longer training durations, expert athletes display distinctive neuroanatomical profiles compared to novices [ 5 ]. These distinctions are evident as specific patterns of altered gray matter volume (GMV), reflecting experience-dependent neuroplasticity. Furthermore, accumulating evidence indicates that prolonged engagement in specialized motor skills correlates with structural brain differences between experts and non-experts [ 6 – 8 ]. Collectively, these findings underscore that intensive motor practice is closely linked with region-specific brain adaptations. Motor skills themselves can be broadly classified based on environmental context, falling along a spectrum from “open” to “closed” skills [ 9 – 11 ]. Open-skill sports are executed in dynamic, unpredictable environments (e.g. basketball, soccer, tennis or boxing) that demand continuous adaptation to external stimuli. Athletes in open-skill disciplines must react rapidly to changing situations, making split-second decisions and adjusting their movements in response to random, externally driven cues [ 5 ]. In contrast, closed-skill activities (e.g. swimming, track running, cycling or archery) take place in stable, self-paced environments with relatively fixed and repetitive motions [ 12 ]. Because open skills involve more variable scenarios and choices, they impose greater cognitive and perceptual demands than closed skills [ 13 – 14 ]. Consistent with this, open-skill athletes often outperform closed-skill athletes on tests of executive function, such as inhibitory control and cognitive flexibility [ 11 , 15 – 16 ]. In essence, open-skill sports combine complex motor coordination with high-level cognitive engagement, making them a compelling model for experience-dependent brain plasticity. The enriched sensorimotor and decision-making challenges of open-skill training are hypothesized to drive unique neural adaptations that support the required integration of perception, action, and strategy. Accumulating structural neuroimaging evidence suggests that open-skill sports training is associated with significant differences in brain structure. Most of these studies have employed voxel-based morphometry (VBM) – a high-resolution magnetic resonance imaging (MRI) technique for quantitatively comparing GMV across the whole brain. VBM enables researchers to assess GMV differences on a voxel-by-voxel basis after images are normalized to a standard brain template [ 17 ]. Using this unbiased approach, numerous cross-sectional studies have reported GMV differences between open-skill athletes and non-athletic controls. In many cases, athletes exhibit greater GMV in specific cortical regions; however, some studies have observed smaller volumes in certain areas, indicating that differences between athletes and non-athletic controls are not uniformly characterized by larger volumes in the athlete group. For example, open-skill experts often show greater GMV in prefrontal regions associated with higher cognitive functions than non-athletic controls [ 18 – 20 ]; similarly, parietal lobe structures involved in visuo-spatial processing and sensorimotor integration tend to have larger volumes in athletes than in controls [ 21 – 24 ]. Differences have also been noted in the temporal lobes: for instance, Di et al. (2012) observed a larger right superior temporal gyrus volume in badminton players relative to controls [ 25 ], whereas soccer players showed smaller anterior temporal lobe GMV compared to controls [ 26 ]. Visual processing regions in the occipital cortex also show structural differences: one study reported that elite karate practitioners had greater occipital GMV than non-athletic controls [ 27 ]. In the cerebellum, which subserves motor coordination and learning, several studies have observed greater volume in open-skill athletes compared to controls[ 27 – 28 ]. There is even evidence that subcortical structures (e.g., the basal ganglia and thalamus) also differ between athletes and non-athletic controls [ 19 ], though such findings are less consistent across studies. Converging with these cross-sectional observations, longitudinal research confirms that motor training can actively induce structural growth in the adult brain. For example, a longitudinal study of novices undergoing intensive sports practice (badminton training) found significant GMV increases in the temporal and occipital lobes after the training period [ 29 ], highlighting that targeted motor skill practice can enlarge gray matter in brain regions supporting visual–motor and auditory–motor functions. Taken together, these studies underscore that open-skill motor experience is associated with a wide range of neuroanatomical differences, encompassing fronto-parietal executive networks, occipito-temporal perceptual areas, cerebellar motor regions, and more. Although existing literature consistently suggests that open-skill training is associated with structural brain adaptations, each individual study provides only a limited perspective on this complex phenomenon. Findings across studies exhibit considerable inconsistency, and several methodological limitations hinder a comprehensive understanding of training-related neuroplasticity [ 30 ]. Many VBM studies adopt a sport-specific approach, typically focusing on athletes from a single discipline (e.g., basketball or table tennis) and employing relatively small sample sizes. This narrow focus and limited statistical power may lead to variability in reported outcomes, potentially reflecting sport-specific demands or sampling variability rather than generalizable structural features [ 30 ]. Consequently, observed GMV differences have been heterogeneous: some studies report structural distinctions in frontal regions, others in parietal or cerebellar areas, and a few have even noted smaller rather than larger volumes in certain brain regions. Moreover, athlete samples vary significantly in age, training intensity, and skill level across investigations, complicating direct comparisons and limiting the generalizability of conclusions. Finally, the existing literature demonstrates a demographic bias, as most studies predominantly include young adult male experts, leaving female athletes and other age groups underrepresented. To address these limitations, the present study adopts a meta-analytic approach to systematically integrate structural neuroimaging findings from athletes engaged in various open-skill sports. Specifically, we employ activation likelihood estimation (ALE)—a quantitative, coordinate-based meta-analysis method—to aggregate voxel-wise GMV data across a wide range of open-skill disciplines[ 31 ]. ALE identifies brain regions consistently reported across independent studies as exhibiting statistically convergent structural differences[ 31 ]. By pooling evidence from multiple sports (e.g., team ball sports, racket sports, and combat sports), our approach aims to reveal consistent GMV characteristics among open-skill athletes that transcend any single discipline [ 32 ]. In other words, instead of examining athletes from a single sport in isolation, we synthesize existing findings to pinpoint the neuroanatomical features commonly observed among individuals from diverse open-skill backgrounds. This meta-analytic integration enhances statistical power and reduces sport-specific idiosyncrasies, thus enabling identification of core patterns of brain structural variation [ 30 – 34 ]. Specifically, we aim to determine brain regions that reliably differentiate open-skill athletes from non-athletes. By highlighting these consistent GMV features, our study seeks to provide novel insights into the neural substrates underlying complex motor-skill performance. Ultimately, our objective is to clarify the common structural brain characteristics associated with open-skill training, advancing theoretical understanding of sport-related neural mechanisms and bridging existing gaps across heterogeneous findings in the literature. Table 1 List of studies included in the ALE meta-analyses Study name Subjects Athlete training (years) Contrast Foci Statistical threshold Total M/F Athletes/controls Age (athletes/controls) a) The gray matter volume of open skill athletes was larger than that of the control group Di et al., 2012 [25] 39 19/19 20/19 22.5 (4.57)/20.7 (4.25) 8.85 (3.25) badminton athletes > control group 7 p control group 1 p control group 5 p control group 6 p control group 3 p control group 4 p 10, uncorrected 29 / 13/16 20.2 (1.0)/19.3 (1.2) 7.8 (2.4) badminton athletes > control group 5 p 10, uncorrected Tan et al., 2017 [24] 42 42/0 21/21 21.3 (1.3)/21.9 (0.8) 11.4 (2.3) basketball athletes > control group 5 p 10 elite karate athlete > control group 9 p control group 2 p control group 8 p control group 5 p control group 3 p control group 2 p soccer athletes 2 p 500, uncorrected Zeng, 2014 [19] 22 / 10/12 / / control group > badminton athletes 3 p basketball athletes 4 p little-ball athletes (badminton and table tennis) 2 p < 0.005, AlphaSim corrected c) The gray matter volume showed no significant difference between open skill athletes and the control group. Chavan et al., 2017 [37] 37 37/0 19/18 27.3(0.6)/25.1(0.7) 17.2 (1.8) fencers/control group 0 p 10 fencers/control group 0 / Abbreviations. M = male, F = female, / = not applicable , FWE = family-wise error, GRF = generalized relevance false. 2. Materials and methods 2.1 Study selection We systematically searched PubMed, Web of Science, CNKI, WanFang, and VIP databases using the search terms: (VBM OR MRI OR voxel-based morphometry OR magnetic resonance imaging) AND (athlete OR player OR expert OR motor expertise) AND (gray matter OR grey matter OR brain). The inclusion criteria were defined to ensure the relevance and consistency of the studies selected for meta-analysis, as follows: a) Publication period: Studies published between January 2001 and June 2025 were included to capture two decades of research development; b) Participants: Studies must have directly compared open skill athletes with non-athlete control groups. Given the known influence of development and aging on brain structure, only studies involving young adults aged 18 to 35 years were included to reduce age-related variability; c) Methodology: Only studies using VBM for whole-brain structural analysis of GMV were considered; d) Data focus: Studies needed to compare GMV specifically. Those examining only gray matter density were excluded to maintain a consistent volumetric focus; e) Coordinate reporting: Studies were required to report spatial coordinates in either Montreal Neurological Institute (MNI) or Talairach space. Studies using region of interest (ROI) analysis without coordinate reporting, or those lacking coordinate data even after author contact, were excluded [30-31]. This meta-analysis was conducted in accordance with the latest guidelines for neuroimaging meta-analyses [30,33], and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [39]. The selection process and reasons for exclusion are illustrated in Figure 1, and characteristics of the included studies are summarized in Table 1. 2.2 Risk of Bias Assessment The risk of bias in the included studies was independently assessed by the same two researchers responsible for data extraction, using the revised Cochrane risk of bias tool for randomized controlled trials (ROB-2) [40]. The ROB-2 tool evaluates five domains of potential bias: a) bias arising from the randomization process; b) bias due to missing outcome data; c) bias in measurement of the outcome; d) bias in selection of the reported result; and e) other potential sources of bias. Overall, most studies demonstrated a low to moderate risk of bias. The majority clearly reported outcome measurements, minimizing bias in that domain. However, several studies lacked detailed descriptions of participant recruitment or allocation methods, leading to unclear risks in those areas. Despite these limitations, all included studies were considered to have sufficient methodological rigor for inclusion in the ALE meta-analysis. A visual summary of the risk of bias assessments is presented in Figure 2. 2.3 Quantitative data synthesis: Activation likelihood estimation To examine convergent patterns of GMV differences associated with open skill training, we conducted a meta-analysis using BrainMap GingerALE software (version 3.0.2) [31]. The ALE method is a voxel-based meta-analytic approach that identifies consistent spatial convergence across reported foci of structural alterations. Each reported VBM focus was modeled as a 3D Gaussian probability distribution, with the full-width at half-maximum (FWHM) determined by the number of participants in each study to account for spatial uncertainty. ALE scores were computed by comparing these distributions within and across experiments. A whole-brain ALE map was generated to represent the likelihood that specific regions exhibit GMV differences in open skill athletes relative to controls [32, 34]. All coordinates were standardized to MNI space, and Talairach coordinates were converted accordingly using GingerALE's internal tools [31]. To ensure the robustness of findings, we applied two statistical thresholds. First, a familywise error (FWE)-corrected cluster-level threshold of p < 0.05 was used, with a cluster-forming threshold of p < 0.001 at the voxel level, based on 1,000 permutations. Second, an uncorrected threshold of p < 0.001 with a minimum cluster size of 200 mm³ was applied. This dual-threshold approach enabled us to examine the consistency of results under both conservative and exploratory statistical criteria. While the primary interpretations are based on FWE-corrected outcomes, uncorrected results are also reported to highlight additional convergence patterns that may inform future hypothesis-driven research and should be interpreted with caution. Visualization of meta-analytic results was performed using Mango (http://ric.uthscsa.edu/mango) and BrainNet Viewer (http://helab.bnu.edu.cn/brainnet-viewer). 3. Results Meta-analysis: open skill athletes > control group A total of 76 foci from 15 studies, including 20 experiments (comprising 653 participants) were included in the meta-analysis. Using a cluster-level FWE correction at p < 0.05 (voxel-level threshold: p < 0.001), two significant clusters emerged. Cluster 1 was located in the right superior frontal gyrus, orbital part (BA11; x = 10, y = 40, z = –24), and Cluster 2 was found in the right postcentral gyrus (BA3; x = 48, y = –26, z = 40). In addition, five more clusters were detected when applying a more liberal, exploratory threshold (p 200 mm³). These additional results were located in the left precuneus (BA30; x = -11, y = -49, z = 12), inferior temporal gyrus (BA37; x = -44, y = -62, z = -6), inferior parietal lobule (BA40; x = -26, y = -46, z = 42), precentral gyrus (BA6; x = -50, y = 12, z = 46), and the right middle temporal gyrus (BA21; x = 50, y = -38, z = 4). Table 2 . The gray matter volume of athletes was larger than that of the control group Region (AAL) Side BA Cluster Volume (mm 3 ) MNI Coordinates ALE Z score Superior frontal gyrus orbital part* R BA11 1072 10 40 -24 0.026 5.97 Postcentral gyrus* R BA3 504 48 -26 40 0.014 4.07 Precuneus L BA30 448 -11 -49 12 0.019 4.97 Inferior temporal gyrus L BA37 384 -44 -62 -6 0.016 4.49 Inferior parietal lobule L BA40 344 -26 -46 42 0.014 3.70 Precentral gyrus L BA6 208 -50 12 46 0.012 3.75 Middle temporal gyrus R BA21 200 50 -38 4 0.011 3.62 *Denotes FWE correction at the cluster level (p < 0.05) with a cluster-forming threshold of p < 0.001. Abbreviations. AAL = automated anatomical labeling, L = left, R = right, BA = brodmann areas, MNI = montreal neurological institute, ALE = activation likelihood estimation, FWE = familywise error. 4. Discussion Our meta-analysis of VBM studies comparing open-skill athletes with non-athlete controls identified consistent structural characteristics across multiple brain regions, providing robust evidence for a neuroanatomical profile associated with open-skill expertise. Aggregating data from 15 independent studies through ALE, we found that athletes exhibited reliably larger GMV in regions including the right superior frontal gyrus orbital part, right postcentral gyrus, right middle temporal gyrus, left inferior temporal gyrus, left precuneus, left inferior parietal lobule, and left precentral gyrus (uncorrected, p < 0.001). Importantly, differences in the right superior frontal gyrus orbital part and the right postcentral gyrus remained significant after stringent correction for multiple comparisons (FWE, p < 0.05). These findings align closely with our initial hypothesis, providing clarity and integration across previously fragmented, sport-specific literature. The observed neuroanatomical pattern underscores an integrated network supporting cognitive, perceptual, and sensorimotor demands intrinsic to open-skill sports. Foremost among these is the right superior frontal gyrus orbital part, a region of the orbitofrontal cortex known to subserve higher-order executive functions such as decision-making, inhibitory control, and emotional regulation [41-42]. The orbitofrontal cortex acts as a multimodal integrative hub, receiving inputs from somatosensory, auditory, and high-level visual cortices, as well as indirect information via the thalamus [43-44]. It also communicates with motor-related structures, sending outputs to the striatum and lateral hypothalamus to influence behavior [45-46], as shown in Figure 5. The relatively larger GMV observed in this region among open-skill athletes may reflect structural profiles that help facilitate rapid information integration and executive regulation during unpredictable, fast-paced action sequences [47]. In other words, intensive training in open environments could be associated with orbitofrontal network features that help evaluate complex sensory cues and modulate behavior accordingly-capabilities that are crucial for success in sports requiring real-time strategic decisions. In addition to the orbitofrontal cortex, we found pronounced GMV differences in sensory and perceptual regions, including the right postcentral gyrus, right middle temporal gyrus, and left inferior temporal gyrus, corresponding to the primary somatosensory cortex, auditory association cortex, and high-level visual cortex, respectively. These regions showed relatively larger GMV in open-skill athletes compared to non-athlete controls, suggesting structural characteristics that may provide a stronger neural basis for sensory processing and multimodal integration. Open-skill sports demand continuous monitoring of a rapidly changing environment, requiring athletes to merge inputs from touch, sound, and vision seamlessly. The neuroanatomical differences we observed may relate to superior sensory discrimination and real-time cognitive appraisal in these athletes, supporting rapid adaptation to unpredictable conditions [12,48]. Notably, these sensory-processing functions likely operate in concert with executive regions such as the orbitofrontal cortex, which may serve as a central regulatory interface translating incoming multisensory information into context-appropriate actions—for example, recognizing an opponent’s subtle movement pattern and inhibiting an impulsive response until the optimal moment [49]. Thus, the pattern of relatively larger GMV across both sensory cortices and orbitofrontal areas supports the notion that open-skill expertise is associated with coordinated neuroanatomical profiles spanning perception and decision-making circuits. Importantly, the left inferior temporal gyrus - implicated in visual object recognition and complex pattern processing - was found to be relatively larger in open-skill athletes compared to non-athlete controls. This region is known to support the pattern recognition skills essential for high-level motor performance [50]. In fast-paced sports, athletes must swiftly interpret complex visual scenes, such as the trajectory of a ball or an opponent’s deceptive maneuver. This relatively larger GMV in the inferior temporal cortex may provide a plausible neural correlate for such expertise, aligning with behavioral evidence that expert athletes demonstrate superior pattern recognition and anticipatory vision in sport-specific contexts [29,51-52]. In essence, extensive engagement in open-skill activities may be associated with neural circuits that support efficient visual pattern analysis, enabling athletes to quickly identify critical cues and predict upcoming actions. The right middle temporal gyrus also exhibited distinctive structural characteristics in athletes. This region has been associated with processing biological motion and action understanding [53-54], including the interpretation of others’ actions and intentions [55-56]. The middle temporal gyrus, along with adjacent superior temporal regions, contributes to the internal visualization and simulation of observed movements [57-59]. A relatively larger GMV in the right middle temporal gyrus among open-skill athletes compared to non-athletes may support their capacity to infer opponents’ intentions and mentally rehearse responses, contributing to more accurate predictions of movement outcomes and strategic reactions. This interpretation aligns with the demands of open-skill sports, where understanding an opponent’s actions in real time can provide a competitive advantage. However, we note that the cluster size in the middle temporal gyrus was relatively small; thus, interpretations regarding this region should be made with caution until further confirmed by additional data. Further structural differences were observed in the left precuneus, left inferior parietal lobule, and left precentral gyrus, highlighting the involvement of parietal-motor networks in open-skill expertise. The precuneus is a multifaceted region implicated in visuo-spatial processing, motor coordination, and shifting of attention [60-62]. It also functions as a connectivity hub within parietal—frontal networks, consistent with small-world network properties, linking spatial and executive processing circuits [63]. A relatively larger GMV in the precuneus among open-skill athletes compared to non-athletes may support the integration of spatial information with motor plans and attentional control, thereby contributing to more effective spatial awareness and coordination during play. The primary motor cortex (precentral gyrus) and the inferior parietal lobule are likewise crucial for translating perception into action. The inferior parietal lobule, in particular, contributes to visuomotor transformation and the guidance of movements in space. Relatively larger GMV observed in these regions may indicate neural profiles that support precise motor execution in response to dynamic external cues. In combination, these structural characteristics in the precuneus, inferior parietal lobule, and precentral gyrus likely underpin the refined hand-eye coordination, limb control, and adaptiveness that characterize expert open-skill performance [64]. Collectively, our findings indicate that proficiency in open-skill sports is associated with a distributed network encompassing prefrontal regions (including the orbitofrontal cortex), primary sensorimotor areas, parietal association cortices (such as the inferior parietal lobule and precuneus), and higher-order temporal regions (including the middle and inferior temporal gyri). This study provides robust, cross-validated evidence for common structural characteristics among athletes across different open-skill disciplines, emphasizing the integrated nature of cognitive, sensory, and motor systems in sport-related neuroanatomical profiles. The constellation of GMV differences identified here can be viewed as a neuroanatomical signature related to open-skill training—one that includes higher-order association areas (for executive control and social-cognitive functions) together with primary sensory and motor areas. Such a pattern aligns with emerging theoretical perspectives suggesting that complex motor training is associated with widespread brain characteristics rather than isolated features confined to the motor cortex [65-66]. While the cross-sectional data in our meta-analysis preclude definitive causal inference, the consistency of these anatomical distinctions across independent samples strongly suggests a systematic relationship between open-skill practice and brain structure [24]. In line with principles of experience-dependent plasticity, years of engagement in unpredictable, cognitively demanding motor environments may be associated with a core set of brain regions that support the perceptual-motor advantages observed in expert performers [67]. Beyond illuminating the neural basis of athletic expertise, these findings carry broader implications for both theory and practice in neuroplasticity. Understanding how specific patterns of motor engagement correspond to structural brain differences can inform the design of interventions to enhance cognitive and sensorimotor function in the general population [68]. The brain regions highlighted in this meta-analysis—especially those involved in decision-making, multisensory integration, and motor coordination—are not only critical in sport but also underpin everyday skills such as driving, balance, and social interaction [41-42,49]. This evidence may thus guide the development of targeted physical activity programs or cognitive-motor training protocols aimed at promoting brain health and neuroanatomical resilience in diverse groups, including older adults, patients in rehabilitation, or individuals experiencing age-related cognitive decline [68-69]. For example, training regimens that incorporate open-skill elements (e.g., activities requiring rapid responses to unpredictability) might be leveraged to stimulate structural brain characteristics analogous to those observed in athletes, potentially yielding benefits for functional independence and cognitive resilience. The structural markers identified here could serve as reference points for future neuroimaging studies evaluating the efficacy of such interventions across different populations. To fully capitalize on these translational opportunities, longitudinal research is warranted to track the trajectory of brain characteristics associated with open-skill training and to clarify any causal relationships between specific training protocols and observed GMV patterns. Such studies would help determine how quickly these neuroanatomical differences may develop, how long they persist, and whether comparable brain profiles can be achieved in non-athlete groups for therapeutic purposes. Ultimately, our meta-analytic findings not only deepen the theoretical understanding of motor skill-related brain characteristics but also open avenues for applying this knowledge to enhance brain function and recovery in broader contexts. 5. Limitations Despite the strengths of this analysis, several limitations must be acknowledged. First, our synthesis was constrained by the available literature. We aimed to include longitudinal studies to strengthen inferences about training-related structural changes; however, the scarcity of longitudinal research on open-skill training meant that our pooled evidence is derived primarily from cross-sectional comparisons. Consequently, the conclusions drawn should be viewed as provisional. The limited dataset and cross-sectional nature of included studies make it difficult to distinguish pre-existing differences from training-related characteristics, underscoring the need for a broader range of studies (including well-controlled longitudinal designs) to more definitively characterize how different types of athletic training are associated with brain structure over time. Second, the diversity of sports represented in the analysis introduces variability. Although all are classified as open-skill, each sport places unique demands on cognitive and motor systems, which could lead to sport-specific neuroanatomical patterns. By focusing on the commonalities among these sports, our analysis may underemphasize nuances that differentiate individual disciplines. It is also important to note that many “open-skill” sports incorporate certain closed-skill elements (for example, penalty kicks or free throws involve self-paced, predictable actions), even though the sport as a whole predominantly requires adaptation to changing external conditions. These factors contribute noise and potential confounds that future studies should address by examining more homogeneous groups or by directly comparing different open-skill sports. Lastly, there may be publication and selection biases in the literature we synthesized, as studies with significant findings are more likely to be published. We tried to mitigate this by applying strict inclusion criteria and objective ALE methods, but some bias might remain. Notwithstanding these limitations, our study yielded a set of reliable findings that advance understanding of how open-skill expertise relates to structural brain characteristics. By identifying consistent GMV patterns across multiple regions, we provide evidence for broad principles of sport-related neuroanatomical variation that transcend individual sports. These results lay important groundwork for future research. In particular, they highlight specific neural targets for further investigation and validate the utility of meta-analytic approaches in motor neuroscience. Going forward, expanding this line of inquiry with larger sample sizes, more diverse participant cohorts, and longitudinal data will be crucial for refining our knowledge of how athletic training relates to neuroanatomical profiles. 6. Conclusion In summary, this investigation substantiates our initial aim that diverse open-skill training regimens are systematically associated with distinctive patterns of GMV variation in specific brain regions. The consistent GMV characteristics identified in the superior frontal gyrus orbital part and postcentral gyrus—even after stringent correction for multiple comparisons—suggest the presence of robust structural features that are typical of individuals engaged in cognitively demanding and unpredictable motor environments. In addition, the patterns observed in other regions highlight a broader distributed network that may collectively support the complex perceptual, cognitive, and motor demands inherent to open-skill expertise. By quantitatively synthesizing evidence across multiple sports disciplines, this meta-analysis helps delineate a core neuroanatomical profile that bridges gaps among sport-specific findings. Together, these results offer new insights into the shared neural substrates shaped by extensive open-skill practice and provide an integrated perspective on how real-world motor engagement relates to brain structure. Overall, this work not only deepens our understanding of experience-dependent brain variation but also lays a valuable foundation for applying these insights to training, education, and broader cognitive-motor health contexts. Abbreviations ALE activation likelihood estimation FWE familywise error GMV gray matter volume MNI montreal neurological institute MRI magnetic resonance imaging VBM voxel-based morphometry Declarations Author Contributions Jilong Shi conceived and designed the study, with Anmin Li providing guidance throughout the research process. Dezun Chen and Ruiyi Dong conducted data collection and analysis. Jilong Shi drafted the manuscript, Haojie Huang and Xinghe Weng offered valuable feedback. All authors contributed to the interpretation of the findings, critically revised the manuscript, and approved the final version. Funding This research was supported by the Fujian Provincial Social Science Foundation (FJ2025C135), the Fundamental Research Funds for the Central Universities (ZK1037), and the National Natural Science Foundation of China (31971023). Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics Approval and Consent to Participate Not applicable. Consent for Publication Not applicable. Competing interests Jilong Shi, Haojie Huang, Xinghe Weng, Dezun Chen, Ruiyi Dong, and Anmin Li declare that they have no competing interests. Author details 1 Department of Physical Education, Xiamen University, Xiamen, 361005, China 2 Center for Exercise and Brain Science, Shanghai University of Sport, Shanghai 200438, China 3 School of Psychology, Shanghai University of Sport, Shanghai 200438, China References Kolb B, Whishaw IQ. Brain plasticity and behavior. Annu Rev Psychol. 1998;49:43–64. Van Praag H. Exercise and the brain: something to chew on. Trends Neurosci. 2009;32:283–290. Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A. Changes in grey matter induced by training. Nature. 2004;427:311–312. Wenger E, Brozzoli C, Lindenberger U, Lövdén M. Expansion and renormalization of human brain structure during skill acquisition. Trends Cogn Sci. 2017;21:930–939. Ren Z, Hu L, Zhang Y, Xu M, Li L, Xia F, Huang R. A review of brain plasticity of motor skill experts: Evidence from magnetic resonance imaging. China Sport Sci Technol. 2019;55:3–18. Sagi Y, Tavor I, Hofstetter S, Tzur-Moryosef S, Blumenfeld-Katzir T, Assaf Y. Learning in the fast lane: new insights into neuroplasticity. Neuron. 2012;73:1195–1203. Zatorre RJ, Fields RD, Johansen-Berg H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci. 2012;15:528–536. Olivo G, Lövdén M, Manzouri A, Terlau L, Jenner B, Jafari A, et al. Estimated Gray Matter Volume Rapidly Changes after a Short Motor Task. Cereb Cortex. 2022;32:1–12. Magill RA. Motor Learning and Control: Concepts and Applications. 7th ed. New York: McGraw-Hill; 2004. Haibach-Beach P, Reid G, Collier D. Motor Learning and Development. 2nd ed. Champaign (IL): Human Kinetics; 2018. Friehs MA, Whelan E, Güldenpenning I, Krause D, Weigelt M. Stimulating performance: A scoping review on transcranial electrical stimulation effects on Olympic sports. Psychol Sport Exerc. 2021;55:102130. Voss MW, Kramer AF, Basak C, Prakash RS, Roberts B. Are expert athletes ‘expert’ in the cognitive laboratory? A meta-analytic review of cognition and sport expertise. Appl Cogn Psychol. 2010;24:812–826. Mann DT, Williams AM, Ward P, Janelle CM. Perceptual-cognitive expertise in sport: A meta-analysis. J Sport Exerc Psychol. 2007;29:457–478. Wang CH, Chang CC, Liang YM, Shih CM, Chiu WS, Tseng P, et al. Open vs. closed skill sports and the modulation of inhibitory control. PLoS One. 2013;8:e55773. Taddei F, Bultrini A, Spinelli D, Di Russo F. Neural correlates of attentional and executive processing in middle-age fencers. Med Sci Sports Exerc. 2012;44:1057–1066. Shi J, Wang J, Lang J, Zhang Z, Bi Y, Liu R, et al. Effect of different motor skills training on motor control network in the frontal lobe and basal ganglia. Biol Sport. 2020;37:405–412. Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage. 2000;11:805–821. Schlaffke L, Lissek S, Lenz M, Brüne M, Juckel G, Hinrichs T, et al. Sports and brain morphology–a voxel-based morphometry study with endurance athletes and martial artists. Neuroscience. 2014;259:35–42. Zeng Y. Generality and specialty in brain structure of athletes in two kinds of open ball games. Shanghai Univ Sport. 2014. Master’s thesis. Hänggi J, Langer N, Lutz K, Birrer K, Mérillat S, Jäncke L. Structural brain correlates associated with professional handball playing. PLoS One. 2015;10:e0124222. Wu Y, Zhang J, Zeng YW, Shen C. Structural brain plasticity change in athletes associated with different sports. China Sport Sci. 2015;35:52–57. Su X. Research on construction of cerebrum structure network and motor system plasticity based on Bayesian theory and graph theory. Univ Electron Sci Technol China. 2018. Master’s thesis. Qiu X. Investigation of brain functional and structural features in elite basketball players during multiple object tracking. Shanghai Univ Sport. 2019. Doctoral dissertation. Tan XY, Pi YL, Wang J, et al. Morphological and functional differences between athletes and novices in cortical neuronal networks. Front Hum Neurosci. 2017;10:660. Di X, Zhu S, Jin H, Wang P, Ye Z, Zhou K, et al. Altered resting brain function and structure in professional badminton players. Brain Connect. 2012;2:225–233. Adams J, Adler CM, Jarvis K, DelBello MP, Strakowski SM. Evidence of anterior temporal atrophy in college‑level soccer players. Clin J Sport Med. 2007;17:304–306. Duru AD, Balcioglu TH. Functional and structural plasticity of brain in elite karate athletes. J Healthc Eng. 2018;2018:8310975. Gao Q, Yu Y, Su X, Tao Z, Zhang M, Wang Y, et al. Adaptation of brain functional stream architecture in athletes with fast demands of sensorimotor integration. Hum Brain Mapp. 2019;40:420–431. Bai X, Shao M, Liu T, Yin J, Jin H. Altered structural plasticity in early adulthood after badminton training. Acta Psychol Sin. 2020;52:173. Tahmasian M, Sepehry AA, Samea F, Khodadadifar T, Soltaninejad Z, Javaheripour N, et al. Practical recommendations to conduct a neuroimaging meta‑analysis for neuropsychiatric disorders. Hum Brain Mapp. 2019;40:5142–5154. Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. Coordinate‑based activation likelihood estimation meta‑analysis of neuroimaging data: A random‑effects approach based on empirical estimates of spatial uncertainty. Hum Brain Mapp. 2009;30:2907–2926. Laird AR, Eickhoff SB, Fox PM, Uecker AM, Ray KL, Saenz JJ, et al. The BrainMap strategy for standardization, sharing, and meta‑analysis of neuroimaging data. BMC Res Notes. 2011;4:1–9. Müller VI, Cieslik EC, Laird AR, Fox PT, Radua J, Mataix‑Cols D, et al. Ten simple rules for neuroimaging meta‑analysis. Neurosci Biobehav Rev. 2018;84:151–161. Turkeltaub PE, Eickhoff SB, Laird AR, Fox M, Wiener M, Fox PT. Minimizing within‑experiment and within‑group effects in activation likelihood estimation meta‑analyses. Hum Brain Mapp. 2012;33:1–14. Kim JH, Park JW, Tae WS, Rhyu IJ. Cerebral cortex changes in basketball players. J Korean Med Sci. 2022;37:e86. Li J, Cao Y, Huang M, Qin Z, Lang J. Progressive increase of brain gray matter volume in individuals with regular soccer training. Sci Rep. 2024;14:7023. Chavan C, Mouthon M, Simonet M, Hoogewoud HM, Draganski B, van der Zwaag W, Spierer L. Sustained enhancements in inhibitory control depend primarily on the reinforcement of fronto‑basal anatomical connectivity. Brain Struct Funct. 2017;222:635–643. Cordani C, Preziosa P, Gatti R, Castellani C, Filippi M, Rocca MA. Mapping brain structure and function in professional fencers: A model to study training effects on central nervous system plasticity. Hum Brain Mapp. 2022;43:3375–3385. Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta‑analyses: the PRISMA statement. Ann Intern Med. 2009;151:264–269. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898. Konishi S, Nakajima K, Uchida I, Kikyo H, Kameyama M, Miyashita Y. Common inhibitory mechanism in human inferior prefrontal cortex revealed by event‑related functional MRI. Brain. 1999;122(Pt 5):981–991. Kringelbach ML. The orbitofrontal cortex: linking reward to hedonic experience. Nat Rev Neurosci. 2015;6(9). Rolls ET. The functions of the orbitofrontal cortex. Brain Cogn. 2004;55:11–29. Öngür D, Price JL. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex. 2000;10:206–219. Eblen F, Graybiel AM. Highly restricted origin of prefrontal cortical inputs to striosomes in the macaque monkey. J Neurosci. 1995;15:5999–6013. Rempel‑Clower NL, Barbas H. Topographic organization of connections between the hypothalamus and prefrontal cortex in the rhesus monkey. J Comp Neurol. 1998;398:393–419. Kringelbach ML, Rolls ET. The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology. Prog Neurobiol. 2004;72:341–372. Zhu H, Chen A, Guo W, Zhu F, Wang B. Which type of exercise is more beneficial for cognitive function? A meta‑analysis of the effects of open‑skill exercise versus closed‑skill exercise among children, adults, and elderly populations. Appl Sci. 2020;10:2737. Shi J, Wang J, Lang J, Zhang Z, Bi Y, Liu R, et al. Effect of different motor skills training on motor control network in the frontal lobe and basal ganglia. Biol Sport. 2020;37:405–412. Bilalić M, Langner R, Erb M, Grodd W. Mechanisms and neural basis of object and pattern recognition: a study with chess experts. J Exp Psychol Gen. 2010;139:728. Smeeton NJ, Ward P, Williams AM. Do pattern recognition skills transfer across sports? A preliminary analysis. J Sports Sci. 2004;22:205–213. Hohmann T, Troje NF, Olmos A, Munzert J. The influence of motor expertise and motor experience on action and actor recognition. J Cogn Psychol. 2011;23:403–415. Grèzes J, Decety J. Functional anatomy of execution, mental simulation, observation, and verb generation of actions: a meta‑analysis. Hum Brain Mapp. 2001;12:1–13. Peuskens H, Vanrie J, Verfaillie K, Orban GA. Specificity of regions processing biological motion. Eur J Neurosci. 2005;21:2864–2875. Pelphrey KA, Morris JP, McCarthy G. Grasping the intentions of others: the perceived intentionality of an action influences activity in the superior temporal sulcus during social perception. J Cogn Neurosci. 2004;16:1706–1716. Lestou V, Pollick FE, Kourtzi Z. Neural substrates for action understanding at different description levels in the human brain. J Cogn Neurosci. 2008;20:324–341. Wright MJ, Bishop DT, Jackson RC, Abernethy B. Cortical fMRI activation to opponents’ body kinematics in sport‑related anticipation: expert‑novice differences with normal and point‑light video. Neurosci Lett. 2011;500:216–221. Abreu AM, Macaluso E, Azevedo RT, Cesari P, Urgesi C, Aglioti SM. Action anticipation beyond the action observation network: a functional magnetic resonance imaging study in expert basketball players. Eur J Neurosci. 2012;35:1646–1654. Bishop DT, Wright MJ, Jackson RC, Abernethy B. Neural bases for anticipation skill in soccer: an fMRI study. J Sport Exerc Psychol. 2013;35:98–109. Kawashima R, Roland PE, O’Sullivan BT. Functional anatomy of reaching and visuomotor learning: a positron emission tomography study. Cereb Cortex. 1995;5:111–122. Wenderoth N, Debaere F, Sunaert S, Swinnen SP. The role of anterior cingulate cortex and precuneus in the coordination of motor behaviour. Eur J Neurosci. 2005;22:235–241. Cavanna AE, Trimble MR. The precuneus: a review of its functional anatomy and behavioural correlates. Brain. 2006;129:564–583. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186–198. Marakshina J, Vartanov A, Buldakova N. Effect of eye dominance on cognitive control. In: Malykh S, Nikulchev E, eds. Psychology and Education – ICPE 2018, vol 49. Future Academy; 2018:402–408. Adkins DL, Boychuk J, Remple MS, Kleim JA. Motor training induces experience‑specific patterns of plasticity across motor cortex and spinal cord. J Appl Physiol (1985). 2006;101:1776–1782. Taubert M, Lohmann G, Margulies DS, Villringer A, Ragert P. Long‑term effects of motor training on resting‑state networks and underlying brain structure. Neuroimage. 2011;57:1492–1498. Mou H, Wang Y, Ou J, Gu N. Neural mechanisms underlying perceptual‑motor behavioral advantages in athletes: a systematic review and activation likelihood estimation meta‑analysis. Int Rev Sport Exerc Psychol. 2025. O’Callaghan G, O’Dowd A, Stapleton J, Merriman NA, Roudaia E, Newell FN. Changes in regional brain grey‑matter volume following successful completion of a sensori‑motor intervention targeted at healthy and fall‑prone older adults. Mult Scler Relat Disord. 2018;31:317–344. De Bruyn N, Essers B, Thijs L, et al. Does sensorimotor upper limb therapy post stroke alter behavior and brain connectivity differently compared to motor therapy? Protocol of a phase II randomized controlled trial. Trials. 2018;19:242. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7615487","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525865813,"identity":"06b7d280-4a4f-4610-8f70-71991cbbf77b","order_by":0,"name":"Jilong Shi","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Jilong","middleName":"","lastName":"Shi","suffix":""},{"id":525865814,"identity":"8b7c2613-3c45-4fca-b5e6-7bfca1f81859","order_by":1,"name":"Haojie Huang","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Haojie","middleName":"","lastName":"Huang","suffix":""},{"id":525865815,"identity":"9f3006c4-c801-435f-ba22-04b06f1809d5","order_by":2,"name":"Xinghe Weng","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Xinghe","middleName":"","lastName":"Weng","suffix":""},{"id":525865816,"identity":"9edfca74-946b-4235-aded-ae7947f16723","order_by":3,"name":"Dezun Chen","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Dezun","middleName":"","lastName":"Chen","suffix":""},{"id":525865817,"identity":"e8478cf6-f18c-4bd7-9358-c1cc18b8ed6f","order_by":4,"name":"Ruiyi Dong","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Ruiyi","middleName":"","lastName":"Dong","suffix":""},{"id":525865818,"identity":"d9c9a9c0-cda4-42e2-8814-26e9dead4b50","order_by":5,"name":"Anmin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYJCCAwwFEnIQJhvRWgwkjEnTwsBgwJDYQLQW/tndiYcLDCzS589If8DwoewwUKQBvxaJO2c3HJ5hIJG74UaOAeOMc4eBIgcIWHMjd8NhHpAWiRwGZt62w0B/JeDXIQ/Vki4PdBjzX2K0GEC1JDDcSDBgZiRGiyFUi+GGM28MDvacS+eRuEFAi9yN3M2feSrq5OXb0x8++FFmLcc/g4AWFHAAiHlIUD8KRsEoGAWjABcAANIpQk9qITxRAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai University of Sport","correspondingAuthor":true,"prefix":"","firstName":"Anmin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-09-15 02:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7615487/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7615487/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93029726,"identity":"878014af-316e-49db-b77f-60adc2cd04c7","added_by":"auto","created_at":"2025-10-08 09:59:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4855288,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptwithname20250915.docx","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/3e87c282c5b8055f716e849e.docx"},{"id":93030273,"identity":"b5c3449f-b27b-4118-a745-6a06ce618abe","added_by":"auto","created_at":"2025-10-08 10:07:58","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7504,"visible":true,"origin":"","legend":"","description":"","filename":"497dcd369c3d42bda2b1146c4c112060.json","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/58d2e9200d0e02dc4ffeca10.json"},{"id":93029715,"identity":"8c1321a4-dabf-4d93-86a5-9fcd661ac6b3","added_by":"auto","created_at":"2025-10-08 09:59:58","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157332,"visible":true,"origin":"","legend":"","description":"","filename":"497dcd369c3d42bda2b1146c4c1120601enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/718a9c99923d2e496ac6c0ba.xml"},{"id":93030272,"identity":"3b7dbbaf-c1ec-4de6-9d5c-64a4a4f5c3cd","added_by":"auto","created_at":"2025-10-08 10:07:58","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122989,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/6732a5c52e61f33d44060e6a.png"},{"id":93031573,"identity":"22aeb5b5-c17c-49c3-97a0-c9baa842b5e5","added_by":"auto","created_at":"2025-10-08 10:15:59","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":438788,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/8d41d850e0e216e32c1dc870.png"},{"id":93029728,"identity":"3ad4c048-dff3-47d5-8914-483cae09e918","added_by":"auto","created_at":"2025-10-08 09:59:59","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2721756,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/b10ff6bf02bf81b8f81cba2a.jpeg"},{"id":93030276,"identity":"85f75b24-7520-4421-9be7-15594c4e4fd8","added_by":"auto","created_at":"2025-10-08 10:07:59","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":213012,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/3b637734869771d390f87c44.jpeg"},{"id":93029720,"identity":"b229548c-bda7-4ca4-95c7-8e5b4731e59c","added_by":"auto","created_at":"2025-10-08 09:59:59","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1290228,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/2b1001c39a8ce764cad77ad8.jpeg"},{"id":93029719,"identity":"83d341eb-f0fd-4782-80f0-7b21623c68fd","added_by":"auto","created_at":"2025-10-08 09:59:59","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33589,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/855650f7a26e99faaa581835.png"},{"id":93030277,"identity":"e4b07824-8803-4e42-8662-5b893bec81c7","added_by":"auto","created_at":"2025-10-08 10:07:59","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108230,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/c430995ca62362a6fe6eb94e.png"},{"id":93029723,"identity":"3e2e2c17-4dfe-4a14-9019-9bc834c26b5f","added_by":"auto","created_at":"2025-10-08 09:59:59","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73635,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/424fe15dd319a9df087da8d6.png"},{"id":93030278,"identity":"67a3168d-92a9-45b3-9b98-14b09b9ce61f","added_by":"auto","created_at":"2025-10-08 10:07:59","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76986,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/2adf2bcfa317a559cb6bea3b.png"},{"id":93030275,"identity":"423d6a5f-ee23-4173-b50b-94c7dc24642d","added_by":"auto","created_at":"2025-10-08 10:07:59","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12826,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/0641aa644a4628d0cb4d8fee.png"},{"id":93029727,"identity":"5acf454c-9dfa-4fa1-a302-5103356c4562","added_by":"auto","created_at":"2025-10-08 09:59:59","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155748,"visible":true,"origin":"","legend":"","description":"","filename":"497dcd369c3d42bda2b1146c4c1120601structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/4dd5ef4c4cb7427eaef92fca.xml"},{"id":93029729,"identity":"57edead8-f902-4df1-9f60-959ba04c5a68","added_by":"auto","created_at":"2025-10-08 09:59:59","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164387,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/655a8ccb011c376afd827b58.html"},{"id":93029721,"identity":"b3a57007-d241-429c-b0bc-a13789911e4d","added_by":"auto","created_at":"2025-10-08 09:59:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109187,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram for the selection of studies\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/ef649ed41dbe57849d4bb276.jpg"},{"id":93030279,"identity":"585c4f09-6557-424d-953d-a20aef4eee4e","added_by":"auto","created_at":"2025-10-08 10:07:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164981,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of bias (quality) assessment of studies. (A) risk of bias summary; and (B) overall assessment of risk of bias.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/d808e35b6c712a3e56c1a8af.jpg"},{"id":93029713,"identity":"438d93d8-4792-4dee-a0e7-147aeefb6296","added_by":"auto","created_at":"2025-10-08 09:59:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131893,"visible":true,"origin":"","legend":"\u003cp\u003eThe GMV of open skill athletes was larger than that of the control group. FWE-corrected at the cluster level (p \u0026lt; 0.05) with a cluster-forming threshold of p \u0026lt; 0.001. L, left; R, right.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/96775e2d6b35fcf982888101.jpg"},{"id":93029711,"identity":"11562a97-e59c-4e3a-93df-e3210f78e12a","added_by":"auto","created_at":"2025-10-08 09:59:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":99526,"visible":true,"origin":"","legend":"\u003cp\u003eThe GMV of open skill athletes was larger than that of the control group. Spheres with a radius of 5 mm are visualized to present the statistical results of clusters formed. Red spheres represent results that survived FWE correction, while yellow spheres represent uncorrected results. PreCG, precentral gyrus; ORBsup: superior frontal gyrus orbital part; PoCG: postcentral gyrus; IPL: inferior parietal lobule; PCUN, precuneus; MTG: middle temporal gyrus; ITG: inferior temporal gyrus; L, left; R, right.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/96ef0a209236bfdc3ecc9141.jpg"},{"id":93029717,"identity":"03517714-778f-4c8f-9ad0-8db184349a73","added_by":"auto","created_at":"2025-10-08 09:59:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39012,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of information input and output of theorbitofrontal cortex. The orbitofrontal cortex receives input from the somatosensory, auditory and visual regions. The output from the orbitofrontal cortex to both the striatum (external) and lateral hypothalamus (internal) can then lead to behavior. TH, thalamus; SC, somatosensory cortex; AC, auditory cortex; ITC, inferior temporal visual cortex; OFC, orbitofrontal cortex; ST, striatum; LH lateral hypothalamus (figure modified from Kringelbach \u0026amp; Rolls, 2004).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/f8bcf7500c755e3fcb555e59.jpg"},{"id":94688268,"identity":"19aae4d3-64a4-453e-9370-fec363f355f7","added_by":"auto","created_at":"2025-10-29 15:53:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1458664,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7615487/v1/b40b9952-f2da-4d45-95a2-b298185493f8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of open-skill training on brain gray matter volume: activation likelihood estimation analysis based on voxel-based morphometry","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent years, advancements in neuroimaging techniques have considerably enhanced our understanding of how motor skill training reshapes the structure and function of the human brain. These insights align closely with the concept of neuroplasticity, which posits that the brain dynamically reorganizes its neural architecture in response to intensive practice and experience [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Numerous studies have shown that structured motor training is associated with measurable neural adaptations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For instance, a longitudinal study by Draganski et al. (2004) demonstrated that even a brief period of juggling practice was sufficient to elicit localized increases in gray matter volume [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Over longer training durations, expert athletes display distinctive neuroanatomical profiles compared to novices [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These distinctions are evident as specific patterns of altered gray matter volume (GMV), reflecting experience-dependent neuroplasticity. Furthermore, accumulating evidence indicates that prolonged engagement in specialized motor skills correlates with structural brain differences between experts and non-experts [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Collectively, these findings underscore that intensive motor practice is closely linked with region-specific brain adaptations.\u003c/p\u003e\u003cp\u003eMotor skills themselves can be broadly classified based on environmental context, falling along a spectrum from \u0026ldquo;open\u0026rdquo; to \u0026ldquo;closed\u0026rdquo; skills [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Open-skill sports are executed in dynamic, unpredictable environments (e.g. basketball, soccer, tennis or boxing) that demand continuous adaptation to external stimuli. Athletes in open-skill disciplines must react rapidly to changing situations, making split-second decisions and adjusting their movements in response to random, externally driven cues [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In contrast, closed-skill activities (e.g. swimming, track running, cycling or archery) take place in stable, self-paced environments with relatively fixed and repetitive motions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Because open skills involve more variable scenarios and choices, they impose greater cognitive and perceptual demands than closed skills [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consistent with this, open-skill athletes often outperform closed-skill athletes on tests of executive function, such as inhibitory control and cognitive flexibility [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In essence, open-skill sports combine complex motor coordination with high-level cognitive engagement, making them a compelling model for experience-dependent brain plasticity. The enriched sensorimotor and decision-making challenges of open-skill training are hypothesized to drive unique neural adaptations that support the required integration of perception, action, and strategy.\u003c/p\u003e\u003cp\u003eAccumulating structural neuroimaging evidence suggests that open-skill sports training is associated with significant differences in brain structure. Most of these studies have employed voxel-based morphometry (VBM) \u0026ndash; a high-resolution magnetic resonance imaging (MRI) technique for quantitatively comparing GMV across the whole brain. VBM enables researchers to assess GMV differences on a voxel-by-voxel basis after images are normalized to a standard brain template [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Using this unbiased approach, numerous cross-sectional studies have reported GMV differences between open-skill athletes and non-athletic controls. In many cases, athletes exhibit greater GMV in specific cortical regions; however, some studies have observed smaller volumes in certain areas, indicating that differences between athletes and non-athletic controls are not uniformly characterized by larger volumes in the athlete group. For example, open-skill experts often show greater GMV in prefrontal regions associated with higher cognitive functions than non-athletic controls [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; similarly, parietal lobe structures involved in visuo-spatial processing and sensorimotor integration tend to have larger volumes in athletes than in controls [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Differences have also been noted in the temporal lobes: for instance, Di et al. (2012) observed a larger right superior temporal gyrus volume in badminton players relative to controls [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], whereas soccer players showed smaller anterior temporal lobe GMV compared to controls [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Visual processing regions in the occipital cortex also show structural differences: one study reported that elite karate practitioners had greater occipital GMV than non-athletic controls [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the cerebellum, which subserves motor coordination and learning, several studies have observed greater volume in open-skill athletes compared to controls[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. There is even evidence that subcortical structures (e.g., the basal ganglia and thalamus) also differ between athletes and non-athletic controls [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], though such findings are less consistent across studies. Converging with these cross-sectional observations, longitudinal research confirms that motor training can actively induce structural growth in the adult brain. For example, a longitudinal study of novices undergoing intensive sports practice (badminton training) found significant GMV increases in the temporal and occipital lobes after the training period [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], highlighting that targeted motor skill practice can enlarge gray matter in brain regions supporting visual\u0026ndash;motor and auditory\u0026ndash;motor functions. Taken together, these studies underscore that open-skill motor experience is associated with a wide range of neuroanatomical differences, encompassing fronto-parietal executive networks, occipito-temporal perceptual areas, cerebellar motor regions, and more.\u003c/p\u003e\u003cp\u003eAlthough existing literature consistently suggests that open-skill training is associated with structural brain adaptations, each individual study provides only a limited perspective on this complex phenomenon. Findings across studies exhibit considerable inconsistency, and several methodological limitations hinder a comprehensive understanding of training-related neuroplasticity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Many VBM studies adopt a sport-specific approach, typically focusing on athletes from a single discipline (e.g., basketball or table tennis) and employing relatively small sample sizes. This narrow focus and limited statistical power may lead to variability in reported outcomes, potentially reflecting sport-specific demands or sampling variability rather than generalizable structural features [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Consequently, observed GMV differences have been heterogeneous: some studies report structural distinctions in frontal regions, others in parietal or cerebellar areas, and a few have even noted smaller rather than larger volumes in certain brain regions. Moreover, athlete samples vary significantly in age, training intensity, and skill level across investigations, complicating direct comparisons and limiting the generalizability of conclusions. Finally, the existing literature demonstrates a demographic bias, as most studies predominantly include young adult male experts, leaving female athletes and other age groups underrepresented.\u003c/p\u003e\u003cp\u003eTo address these limitations, the present study adopts a meta-analytic approach to systematically integrate structural neuroimaging findings from athletes engaged in various open-skill sports. Specifically, we employ activation likelihood estimation (ALE)\u0026mdash;a quantitative, coordinate-based meta-analysis method\u0026mdash;to aggregate voxel-wise GMV data across a wide range of open-skill disciplines[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. ALE identifies brain regions consistently reported across independent studies as exhibiting statistically convergent structural differences[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. By pooling evidence from multiple sports (e.g., team ball sports, racket sports, and combat sports), our approach aims to reveal consistent GMV characteristics among open-skill athletes that transcend any single discipline [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In other words, instead of examining athletes from a single sport in isolation, we synthesize existing findings to pinpoint the neuroanatomical features commonly observed among individuals from diverse open-skill backgrounds. This meta-analytic integration enhances statistical power and reduces sport-specific idiosyncrasies, thus enabling identification of core patterns of brain structural variation [\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Specifically, we aim to determine brain regions that reliably differentiate open-skill athletes from non-athletes. By highlighting these consistent GMV features, our study seeks to provide novel insights into the neural substrates underlying complex motor-skill performance. Ultimately, our objective is to clarify the common structural brain characteristics associated with open-skill training, advancing theoretical understanding of sport-related neural mechanisms and bridging existing gaps across heterogeneous findings in the literature.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"794\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 794px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 List of studies included in the ALE meta-analyses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 330px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubjects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAthlete training (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContrast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFoci\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical threshold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM/F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAthletes/controls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (athletes/controls)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 794px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ea) The gray matter volume of open skill athletes was larger than that of the control group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col\u003e\n \u003cli\u003eDi et al., 2012 [25]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e19/19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e20/19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e22.5 (4.57)/20.7 (4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.85 (3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ebadminton athletes \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001, nonstationary corrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003eSchlaffke et al., 2014 [18]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13/13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e22.7 (5.5)/28 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003emartial arts group (judokas, karatekas, kickboxers, ajukateka and tang-soo-doka) \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001, uncorrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 113px;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003eZeng, 2014 [19]\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ebadminton athletes \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001, uncorrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ebasketball athletes \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001, uncorrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003eH\u0026auml;nggi et al., 2015 [20]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e23.6 (2.9)/25.5 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12.5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ehandball athletes \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.05, permutation test 5000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 113px;\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003eWu et al., 2015 [21]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13/16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e19.6 (1.3)/19.3 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.4 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ebasketball athletes \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001, voxel \u0026gt; 10,\u0026nbsp;uncorrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13/16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e20.2 (1.0)/19.3 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7.8 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ebadminton athletes \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001, voxel \u0026gt; 10,\u0026nbsp;uncorrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"6\"\u003e\n \u003cli\u003eTan et al., 2017 [24]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e42/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e21/21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e21.3 (1.3)/21.9 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e11.4 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ebasketball athletes \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.05, AlphaSim corrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"7\"\u003e\n \u003cli\u003eDuru et al., 2018 [27]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e14/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13/13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e22.3 (4.6)/26.7 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eelite karate athlete \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt;0.05, FWE corrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"8\"\u003e\n \u003cli\u003eSu, 2018 [22]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e53/22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e38/37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e21.8 (0.5)/21.3 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.6 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003elittle-ball athletes (badminton and table tennis) \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.005, AlphaSim\u0026nbsp;corrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"9\"\u003e\n \u003cli\u003eGao et al., 2019 [28]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e34/24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e29/29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e19.9 (0.7)/20.2 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.4 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003elittle-ball athletes (badminton and table tennis) \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.01, AlphaSim\u0026nbsp;corrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"10\"\u003e\n \u003cli\u003eQiu, 2019\u0026nbsp;[23]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e28/30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e21.04 (1.91)/21.4 (2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ebasketball athletes \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.05, GRF corrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"11\"\u003e\n \u003cli\u003eKim et al., 2022 [35]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e39/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e19/20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ebasketball athletes \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.005, uncorrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"12\"\u003e\n \u003cli\u003eLi et al., 2024 [36]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e42/35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e36/41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e19.3(1.3)/19.5(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1~15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003esoccer athletes \u0026gt; control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.05, FWE\u0026nbsp;\u0026nbsp;corrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 794px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eb) The gray matter volume of the control group was larger than that of open skill athletes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col\u003e\n \u003cli\u003eAdams et al., 2007 [26]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e21 (2)/26 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003econtrol group \u0026gt; soccer athletes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.01,\u0026nbsp;voxel \u0026gt; 500, uncorrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 113px;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003eZeng, 2014 [19]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003econtrol group \u0026gt; badminton athletes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001, uncorrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003econtrol group \u0026gt; basketball athletes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001, uncorrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003eSu, 2018 [22]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e53/22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e38/37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e21.8 (0.5)/21.3 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.6 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003econtrol group \u0026gt; little-ball athletes (badminton and table tennis)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.005, AlphaSim\u0026nbsp;corrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 794px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ec)\u003c/strong\u003e \u003cstrong\u003eThe gray matter volume showed no significant difference between open skill athletes and the control group.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col\u003e\n \u003cli\u003eChavan et al., 2017 [37]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e37/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e19/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e27.3(0.6)/25.1(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17.2 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003efencers/control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.05, FWE corrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003eCordani et al., 2022 [38]\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e29/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e22.3/22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003efencers/control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations. M = male, F = female, / = not applicable , FWE = family-wise error, GRF = generalized relevance false.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003ch3\u003e2.1 Study selection\u003c/h3\u003e\n\u003cp\u003eWe systematically searched PubMed, Web of Science, CNKI, WanFang, and VIP databases using the search terms: (VBM OR MRI OR voxel-based morphometry OR magnetic resonance imaging) AND (athlete OR player OR expert OR motor expertise) AND (gray matter OR grey matter OR brain). The inclusion criteria were defined to ensure the relevance and consistency of the studies selected for meta-analysis, as follows: a) Publication period: Studies published between January 2001 and June 2025 were included to capture two decades of research development; b) Participants: Studies must have directly compared open skill athletes with non-athlete control groups. Given the known influence of development and aging on brain structure, only studies involving young adults aged 18 to 35 years were included to reduce age-related variability; c) Methodology: Only studies using VBM for whole-brain structural analysis of GMV were considered; d) Data focus: Studies needed to compare GMV specifically. Those examining only gray matter density were excluded to maintain a consistent volumetric focus; e) Coordinate reporting: Studies were required to report spatial coordinates in either Montreal Neurological Institute (MNI) or Talairach space. Studies using region of interest (ROI) analysis without coordinate reporting, or those lacking coordinate data even after author contact, were excluded [30-31]. This meta-analysis was conducted in accordance with the latest guidelines for neuroimaging meta-analyses [30,33], and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [39]. The selection process and reasons for exclusion are illustrated in Figure 1, and characteristics of the included studies are summarized in Table 1.\u003c/p\u003e\n\u003ch3\u003e2.2 Risk of Bias Assessment\u003c/h3\u003e\n\u003cp\u003eThe risk of bias in the included studies was independently assessed by the same two researchers responsible for data extraction, using the revised Cochrane risk of bias tool for randomized controlled trials (ROB-2) [40]. The ROB-2 tool evaluates five domains of potential bias: a) bias arising from the randomization process; b) bias due to missing outcome data; c) bias in measurement of the outcome; d) bias in selection of the reported result; and e) other potential sources of bias. Overall, most studies demonstrated a low to moderate risk of bias. The majority clearly reported outcome measurements, minimizing bias in that domain. However, several studies lacked detailed descriptions of participant recruitment or allocation methods, leading to unclear risks in those areas. Despite these limitations, all included studies were considered to have sufficient methodological rigor for inclusion in the ALE meta-analysis. A visual summary of the risk of bias assessments is presented in Figure 2.\u003c/p\u003e\n\u003ch3\u003e2.3 Quantitative data synthesis: Activation likelihood estimation\u003c/h3\u003e\n\u003cp\u003eTo examine convergent patterns of GMV differences associated with open skill training, we conducted a meta-analysis using BrainMap GingerALE software (version 3.0.2) [31]. The ALE method is a voxel-based meta-analytic approach that identifies consistent spatial convergence across reported foci of structural alterations. Each reported VBM focus was modeled as a 3D Gaussian probability distribution, with the full-width at half-maximum (FWHM) determined by the number of participants in each study to account for spatial uncertainty. ALE scores were computed by comparing these distributions within and across experiments. A whole-brain ALE map was generated to represent the likelihood that specific regions exhibit GMV differences in open skill athletes relative to controls [32, 34]. All coordinates were standardized to MNI space, and Talairach coordinates were converted accordingly using GingerALE\u0026apos;s internal tools [31]. To ensure the robustness of findings, we applied two statistical thresholds. First, a familywise error (FWE)-corrected cluster-level threshold of p \u0026lt; 0.05 was used, with a cluster-forming threshold of p \u0026lt; 0.001 at the voxel level, based on 1,000 permutations. Second, an uncorrected threshold of p \u0026lt; 0.001 with a minimum cluster size of 200 mm\u0026sup3; was applied. This dual-threshold approach enabled us to examine the consistency of results under both conservative and exploratory statistical criteria. While the primary interpretations are based on FWE-corrected outcomes, uncorrected results are also reported to highlight additional convergence patterns that may inform future hypothesis-driven research and should be interpreted with caution. Visualization of meta-analytic results was performed using Mango (http://ric.uthscsa.edu/mango) and BrainNet Viewer (http://helab.bnu.edu.cn/brainnet-viewer).\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch3\u003eMeta-analysis: open skill athletes \u0026gt; control group\u003c/h3\u003e\n\u003cp\u003eA total of 76 foci from 15 studies, including 20 experiments (comprising 653 participants) were included in the meta-analysis. Using a cluster-level FWE correction at p \u0026lt; 0.05 (voxel-level threshold: p \u0026lt; 0.001), two significant clusters emerged. Cluster 1 was located in the right superior frontal gyrus, orbital part (BA11; x = 10, y = 40, z = \u0026ndash;24), and Cluster 2 was found in the right postcentral gyrus (BA3; x = 48, y = \u0026ndash;26, z = 40). In addition, five more clusters were detected when applying a more liberal, exploratory threshold (p \u0026lt; 0.001, uncorrected; minimum cluster volume \u0026gt; 200 mm\u0026sup3;).\u0026nbsp;These additional results were located in the left precuneus (BA30; x = -11, y = -49, z = 12), inferior temporal gyrus (BA37; x = -44, y = -62, z = -6), inferior parietal lobule (BA40; x = -26, y = -46, z = 42), precentral gyrus (BA6; x = -50, y = 12, z = 46), and the right middle temporal gyrus (BA21; x = 50, y = -38, z = 4).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 633px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e. The gray matter volume of athletes was larger than that of the control group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion (AAL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster Volume (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMNI Coordinates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eSuperior frontal gyrus orbital part*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBA11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003ePostcentral gyrus*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003ePrecuneus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBA30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eInferior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBA37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eInferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBA40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003ePrecentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eMiddle temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBA21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Denotes FWE correction at the cluster level (p \u0026lt; 0.05) with a cluster-forming threshold of p \u0026lt; 0.001. Abbreviations. AAL = automated anatomical labeling, L = left, R = right, BA = brodmann areas, MNI = montreal neurological institute, ALE = activation likelihood estimation, FWE = familywise error.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur meta-analysis of VBM studies comparing open-skill athletes with non-athlete controls identified consistent structural characteristics across multiple brain regions, providing robust evidence for a neuroanatomical profile associated with open-skill expertise. Aggregating data from 15 independent studies through ALE, we found that athletes exhibited reliably larger GMV in regions including the right superior frontal gyrus orbital part, right postcentral gyrus, right middle temporal gyrus, left inferior temporal gyrus, left precuneus, left inferior parietal lobule, and left precentral gyrus (uncorrected, p \u0026lt; 0.001). Importantly, differences in the right superior frontal gyrus orbital part and the right postcentral gyrus remained significant after stringent correction for multiple comparisons (FWE, p \u0026lt; 0.05). These findings align closely with our initial hypothesis, providing clarity and integration across previously fragmented, sport-specific literature.\u003c/p\u003e\n\u003cp\u003eThe observed neuroanatomical pattern underscores an integrated network supporting cognitive, perceptual, and sensorimotor demands intrinsic to open-skill sports. Foremost among these is the right superior frontal gyrus orbital part, a region of the orbitofrontal cortex known to subserve higher-order executive functions such as decision-making, inhibitory control, and emotional regulation [41-42]. The orbitofrontal cortex acts as a multimodal integrative hub, receiving inputs from somatosensory, auditory, and high-level visual cortices, as well as indirect information via the thalamus [43-44]. It also communicates with motor-related structures, sending outputs to the striatum and lateral hypothalamus to influence behavior [45-46], as shown in Figure 5. The relatively larger GMV observed in this region among open-skill athletes may reflect structural profiles that help facilitate rapid information integration and executive regulation during unpredictable, fast-paced action sequences [47]. In other words, intensive training in open environments could be associated with orbitofrontal network features that help evaluate complex sensory cues and modulate behavior accordingly-capabilities that are crucial for success in sports requiring real-time strategic decisions.\u003c/p\u003e\n\u003cp\u003eIn addition to the orbitofrontal cortex, we found pronounced GMV differences in sensory and perceptual regions, including the right postcentral gyrus, right middle temporal gyrus, and left inferior temporal gyrus, corresponding to the primary somatosensory cortex, auditory association cortex, and high-level visual cortex, respectively. These regions showed relatively larger GMV in open-skill athletes compared to non-athlete controls, suggesting structural characteristics that may provide a stronger neural basis for sensory processing and multimodal integration. Open-skill sports demand continuous monitoring of a rapidly changing environment, requiring athletes to merge inputs from touch, sound, and vision seamlessly. The neuroanatomical differences we observed may relate to superior sensory discrimination and real-time cognitive appraisal in these athletes, supporting rapid adaptation to unpredictable conditions [12,48]. Notably, these sensory-processing functions likely operate in concert with executive regions such as the orbitofrontal cortex, which may serve as a central regulatory interface translating incoming multisensory information into context-appropriate actions\u0026mdash;for example, recognizing an opponent\u0026rsquo;s subtle movement pattern and inhibiting an impulsive response until the optimal moment [49]. Thus, the pattern of relatively larger GMV across both sensory cortices and orbitofrontal areas supports the notion that open-skill expertise is associated with coordinated neuroanatomical profiles spanning perception and decision-making circuits.\u003c/p\u003e\n\u003cp\u003eImportantly, the left inferior temporal gyrus - implicated in visual object recognition and complex pattern processing - was found to be relatively larger in open-skill athletes compared to non-athlete controls. This region is known to support the pattern recognition skills essential for high-level motor performance [50]. In fast-paced sports, athletes must swiftly interpret complex visual scenes, such as the trajectory of a ball or an opponent\u0026rsquo;s deceptive maneuver. This relatively larger GMV in the inferior temporal cortex may provide a plausible neural correlate for such expertise, aligning with behavioral evidence that expert athletes demonstrate superior pattern recognition and anticipatory vision in sport-specific contexts [29,51-52]. In essence, extensive engagement in open-skill activities may be associated with neural circuits that support efficient visual pattern analysis, enabling athletes to quickly identify critical cues and predict upcoming actions.\u003c/p\u003e\n\u003cp\u003eThe right middle temporal gyrus also exhibited distinctive structural characteristics in athletes. This region has been associated with processing biological motion and action understanding [53-54], including the interpretation of others\u0026rsquo; actions and intentions [55-56]. The middle temporal gyrus, along with adjacent superior temporal regions, contributes to the internal visualization and simulation of observed movements [57-59]. A relatively larger GMV in the right middle temporal gyrus among open-skill athletes compared to non-athletes may support their capacity to infer opponents\u0026rsquo; intentions and mentally rehearse responses, contributing to more accurate predictions of movement outcomes and strategic reactions. This interpretation aligns with the demands of open-skill sports, where understanding an opponent\u0026rsquo;s actions in real time can provide a competitive advantage. However, we note that the cluster size in the middle temporal gyrus was relatively small; thus, interpretations regarding this region should be made with caution until further confirmed by additional data.\u003c/p\u003e\n\u003cp\u003eFurther structural differences were observed in the left precuneus, left inferior parietal lobule, and left precentral gyrus, highlighting the involvement of parietal-motor networks in open-skill expertise. The precuneus is a multifaceted region implicated in visuo-spatial processing, motor coordination, and shifting of attention [60-62]. It also functions as a connectivity hub within parietal\u0026mdash;frontal networks, consistent with small-world network properties, linking spatial and executive processing circuits [63]. A relatively larger GMV in the precuneus among open-skill athletes compared to non-athletes may support the integration of spatial information with motor plans and attentional control, thereby contributing to more effective spatial awareness and coordination during play. The primary motor cortex (precentral gyrus) and the inferior parietal lobule are likewise crucial for translating perception into action. The inferior parietal lobule, in particular, contributes to visuomotor transformation and the guidance of movements in space. Relatively larger GMV observed in these regions may indicate neural profiles that support precise motor execution in response to dynamic external cues. In combination, these structural characteristics in the precuneus, inferior parietal lobule, and precentral gyrus likely underpin the refined hand-eye coordination, limb control, and adaptiveness that characterize expert open-skill performance [64].\u003c/p\u003e\n\u003cp\u003eCollectively, our findings indicate that proficiency in open-skill sports is associated with a distributed network encompassing prefrontal regions (including the orbitofrontal cortex), primary sensorimotor areas, parietal association cortices (such as the inferior parietal lobule and precuneus), and higher-order temporal regions (including the middle and inferior temporal gyri). This study provides robust, cross-validated evidence for common structural characteristics among athletes across different open-skill disciplines, emphasizing the integrated nature of cognitive, sensory, and motor systems in sport-related neuroanatomical profiles. The constellation of GMV differences identified here can be viewed as a neuroanatomical signature related to open-skill training\u0026mdash;one that includes higher-order association areas (for executive control and social-cognitive functions) together with primary sensory and motor areas. Such a pattern aligns with emerging theoretical perspectives suggesting that complex motor training is associated with widespread brain characteristics rather than isolated features confined to the motor cortex [65-66]. While the cross-sectional data in our meta-analysis preclude definitive causal inference, the consistency of these anatomical distinctions across independent samples strongly suggests a systematic relationship between open-skill practice and brain structure [24]. In line with principles of experience-dependent plasticity, years of engagement in unpredictable, cognitively demanding motor environments may be associated with a core set of brain regions that support the perceptual-motor advantages observed in expert performers [67].\u003c/p\u003e\n\u003cp\u003eBeyond illuminating the neural basis of athletic expertise, these findings carry broader implications for both theory and practice in neuroplasticity. Understanding how specific patterns of motor engagement correspond to structural brain differences can inform the design of interventions to enhance cognitive and sensorimotor function in the general population [68]. The brain regions highlighted in this meta-analysis\u0026mdash;especially those involved in decision-making, multisensory integration, and motor coordination\u0026mdash;are not only critical in sport but also underpin everyday skills such as driving, balance, and social interaction [41-42,49]. This evidence may thus guide the development of targeted physical activity programs or cognitive-motor training protocols aimed at promoting brain health and neuroanatomical resilience in diverse groups, including older adults, patients in rehabilitation, or individuals experiencing age-related cognitive decline [68-69]. For example, training regimens that incorporate open-skill elements (e.g., activities requiring rapid responses to unpredictability) might be leveraged to stimulate structural brain characteristics analogous to those observed in athletes, potentially yielding benefits for functional independence and cognitive resilience. The structural markers identified here could serve as reference points for future neuroimaging studies evaluating the efficacy of such interventions across different populations. To fully capitalize on these translational opportunities, longitudinal research is warranted to track the trajectory of brain characteristics associated with open-skill training and to clarify any causal relationships between specific training protocols and observed GMV patterns. Such studies would help determine how quickly these neuroanatomical differences may develop, how long they persist, and whether comparable brain profiles can be achieved in non-athlete groups for therapeutic purposes. Ultimately, our meta-analytic findings not only deepen the theoretical understanding of motor skill-related brain characteristics but also open avenues for applying this knowledge to enhance brain function and recovery in broader contexts.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eDespite the strengths of this analysis, several limitations must be acknowledged. First, our synthesis was constrained by the available literature. We aimed to include longitudinal studies to strengthen inferences about training-related structural changes; however, the scarcity of longitudinal research on open-skill training meant that our pooled evidence is derived primarily from cross-sectional comparisons. Consequently, the conclusions drawn should be viewed as provisional. The limited dataset and cross-sectional nature of included studies make it difficult to distinguish pre-existing differences from training-related characteristics, underscoring the need for a broader range of studies (including well-controlled longitudinal designs) to more definitively characterize how different types of athletic training are associated with brain structure over time. Second, the diversity of sports represented in the analysis introduces variability. Although all are classified as open-skill, each sport places unique demands on cognitive and motor systems, which could lead to sport-specific neuroanatomical patterns. By focusing on the commonalities among these sports, our analysis may underemphasize nuances that differentiate individual disciplines. It is also important to note that many \u0026ldquo;open-skill\u0026rdquo; sports incorporate certain closed-skill elements (for example, penalty kicks or free throws involve self-paced, predictable actions), even though the sport as a whole predominantly requires adaptation to changing external conditions. These factors contribute noise and potential confounds that future studies should address by examining more homogeneous groups or by directly comparing different open-skill sports. Lastly, there may be publication and selection biases in the literature we synthesized, as studies with significant findings are more likely to be published. We tried to mitigate this by applying strict inclusion criteria and objective ALE methods, but some bias might remain. Notwithstanding these limitations, our study yielded a set of reliable findings that advance understanding of how open-skill expertise relates to structural brain characteristics. By identifying consistent GMV patterns across multiple regions, we provide evidence for broad principles of sport-related neuroanatomical variation that transcend individual sports. These results lay important groundwork for future research. In particular, they highlight specific neural targets for further investigation and validate the utility of meta-analytic approaches in motor neuroscience. Going forward, expanding this line of inquiry with larger sample sizes, more diverse participant cohorts, and longitudinal data will be crucial for refining our knowledge of how athletic training relates to neuroanatomical profiles.\u0026nbsp;\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn summary, this investigation substantiates our initial aim that diverse open-skill training regimens are systematically associated with distinctive patterns of GMV variation in specific brain regions. The consistent GMV characteristics identified in the superior frontal gyrus orbital part and postcentral gyrus\u0026mdash;even after stringent correction for multiple comparisons\u0026mdash;suggest the presence of robust structural features that are typical of individuals engaged in cognitively demanding and unpredictable motor environments. In addition, the patterns observed in other regions highlight a broader distributed network that may collectively support the complex perceptual, cognitive, and motor demands inherent to open-skill expertise. By quantitatively synthesizing evidence across multiple sports disciplines, this meta-analysis helps delineate a core neuroanatomical profile that bridges gaps among sport-specific findings. Together, these results offer new insights into the shared neural substrates shaped by extensive open-skill practice and provide an integrated perspective on how real-world motor engagement relates to brain structure. Overall, this work not only deepens our understanding of experience-dependent brain variation but also lays a valuable foundation for applying these insights to training, education, and broader cognitive-motor health contexts.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;activation likelihood estimation\u003c/p\u003e\n\u003cp\u003eFWE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;familywise error\u003c/p\u003e\n\u003cp\u003eGMV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;gray matter volume\u003c/p\u003e\n\u003cp\u003eMNI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;montreal neurological institute\u003c/p\u003e\n\u003cp\u003eMRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eVBM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; voxel-based morphometry\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJilong Shi conceived and designed the study, with Anmin Li providing guidance throughout the research process. Dezun Chen and Ruiyi Dong conducted data collection and analysis. Jilong Shi drafted the manuscript, Haojie Huang and Xinghe Weng offered valuable feedback. All authors contributed to the interpretation of the findings, critically revised the manuscript, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Fujian Provincial Social Science Foundation (FJ2025C135), the Fundamental Research Funds for the Central Universities (ZK1037), and the National Natural Science Foundation of China (31971023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJilong Shi, Haojie Huang, Xinghe Weng, Dezun Chen, Ruiyi Dong, and Anmin Li declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Department of Physical Education, Xiamen University, Xiamen, 361005, China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eCenter for Exercise and Brain Science, Shanghai University of Sport, Shanghai 200438, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eSchool of Psychology, Shanghai University of Sport, Shanghai 200438, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKolb B, Whishaw IQ. Brain plasticity and behavior. Annu Rev Psychol. 1998;49:43\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eVan Praag H. Exercise and the brain: something to chew on. Trends Neurosci. 2009;32:283\u0026ndash;290.\u003c/li\u003e\n\u003cli\u003eDraganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A. Changes in grey matter induced by training. Nature. 2004;427:311\u0026ndash;312.\u003c/li\u003e\n\u003cli\u003eWenger E, Brozzoli C, Lindenberger U, L\u0026ouml;vd\u0026eacute;n M. Expansion and renormalization of human brain structure during skill acquisition. Trends Cogn Sci. 2017;21:930\u0026ndash;939.\u003c/li\u003e\n\u003cli\u003eRen Z, Hu L, Zhang Y, Xu M, Li L, Xia F, Huang R. A review of brain plasticity of motor skill experts: Evidence from magnetic resonance imaging. China Sport Sci Technol. 2019;55:3\u0026ndash;18.\u003c/li\u003e\n\u003cli\u003eSagi Y, Tavor I, Hofstetter S, Tzur-Moryosef S, Blumenfeld-Katzir T, Assaf Y. Learning in the fast lane: new insights into neuroplasticity. Neuron. 2012;73:1195\u0026ndash;1203.\u003c/li\u003e\n\u003cli\u003eZatorre RJ, Fields RD, Johansen-Berg H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci. 2012;15:528\u0026ndash;536.\u003c/li\u003e\n\u003cli\u003eOlivo G, L\u0026ouml;vd\u0026eacute;n M, Manzouri A, Terlau L, Jenner B, Jafari A, et al. Estimated Gray Matter Volume Rapidly Changes after a Short Motor Task. Cereb Cortex. 2022;32:1\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eMagill RA. Motor Learning and Control: Concepts and Applications. 7th ed. New York: McGraw-Hill; 2004.\u003c/li\u003e\n\u003cli\u003eHaibach-Beach P, Reid G, Collier D. Motor Learning and Development. 2nd ed. Champaign (IL): Human Kinetics; 2018.\u003c/li\u003e\n\u003cli\u003eFriehs MA, Whelan E, G\u0026uuml;ldenpenning I, Krause D, Weigelt M. Stimulating performance: A scoping review on transcranial electrical stimulation effects on Olympic sports. Psychol Sport Exerc. 2021;55:102130.\u003c/li\u003e\n\u003cli\u003eVoss MW, Kramer AF, Basak C, Prakash RS, Roberts B. Are expert athletes \u0026lsquo;expert\u0026rsquo; in the cognitive laboratory? A meta-analytic review of cognition and sport expertise. Appl Cogn Psychol. 2010;24:812\u0026ndash;826.\u003c/li\u003e\n\u003cli\u003eMann DT, Williams AM, Ward P, Janelle CM. Perceptual-cognitive expertise in sport: A meta-analysis. J Sport Exerc Psychol. 2007;29:457\u0026ndash;478.\u003c/li\u003e\n\u003cli\u003eWang CH, Chang CC, Liang YM, Shih CM, Chiu WS, Tseng P, et al. Open vs. closed skill sports and the modulation of inhibitory control. PLoS One. 2013;8:e55773.\u003c/li\u003e\n\u003cli\u003eTaddei F, Bultrini A, Spinelli D, Di Russo F. Neural correlates of attentional and executive processing in middle-age fencers. Med Sci Sports Exerc. 2012;44:1057\u0026ndash;1066.\u003c/li\u003e\n\u003cli\u003eShi J, Wang J, Lang J, Zhang Z, Bi Y, Liu R, et al. Effect of different motor skills training on motor control network in the frontal lobe and basal ganglia. Biol Sport. 2020;37:405\u0026ndash;412.\u003c/li\u003e\n\u003cli\u003eAshburner J, Friston KJ. Voxel-based morphometry\u0026mdash;the methods. Neuroimage. 2000;11:805\u0026ndash;821.\u003c/li\u003e\n\u003cli\u003eSchlaffke L, Lissek S, Lenz M, Br\u0026uuml;ne M, Juckel G, Hinrichs T, et al. Sports and brain morphology\u0026ndash;a voxel-based morphometry study with endurance athletes and martial artists. Neuroscience. 2014;259:35\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eZeng Y. Generality and specialty in brain structure of athletes in two kinds of open ball games. Shanghai Univ Sport. 2014. Master\u0026rsquo;s thesis.\u003c/li\u003e\n\u003cli\u003eH\u0026auml;nggi J, Langer N, Lutz K, Birrer K, M\u0026eacute;rillat S, J\u0026auml;ncke L. Structural brain correlates associated with professional handball playing. PLoS One. 2015;10:e0124222.\u003c/li\u003e\n\u003cli\u003eWu Y, Zhang J, Zeng YW, Shen C. Structural brain plasticity change in athletes associated with different sports. China Sport Sci. 2015;35:52\u0026ndash;57.\u003c/li\u003e\n\u003cli\u003eSu X. Research on construction of cerebrum structure network and motor system plasticity based on Bayesian theory and graph theory. Univ Electron Sci Technol China. 2018. Master\u0026rsquo;s thesis.\u003c/li\u003e\n\u003cli\u003eQiu X. Investigation of brain functional and structural features in elite basketball players during multiple object tracking. Shanghai Univ Sport. 2019. Doctoral dissertation.\u003c/li\u003e\n\u003cli\u003eTan XY, Pi YL, Wang J, et al. Morphological and functional differences between athletes and novices in cortical neuronal networks. Front Hum Neurosci. 2017;10:660.\u003c/li\u003e\n\u003cli\u003eDi X, Zhu S, Jin H, Wang P, Ye Z, Zhou K, et al. Altered resting brain function and structure in professional badminton players. Brain Connect. 2012;2:225\u0026ndash;233.\u003c/li\u003e\n\u003cli\u003eAdams J, Adler CM, Jarvis K, DelBello MP, Strakowski SM. Evidence of anterior temporal atrophy in college‑level soccer players. Clin J Sport Med. 2007;17:304\u0026ndash;306.\u003c/li\u003e\n\u003cli\u003eDuru AD, Balcioglu TH. Functional and structural plasticity of brain in elite karate athletes. J Healthc Eng. 2018;2018:8310975.\u003c/li\u003e\n\u003cli\u003eGao Q, Yu Y, Su X, Tao Z, Zhang M, Wang Y, et al. Adaptation of brain functional stream architecture in athletes with fast demands of sensorimotor integration. Hum Brain Mapp. 2019;40:420\u0026ndash;431.\u003c/li\u003e\n\u003cli\u003eBai X, Shao M, Liu T, Yin J, Jin H. Altered structural plasticity in early adulthood after badminton training. Acta Psychol Sin. 2020;52:173.\u003c/li\u003e\n\u003cli\u003eTahmasian M, Sepehry AA, Samea F, Khodadadifar T, Soltaninejad Z, Javaheripour N, et al. Practical recommendations to conduct a neuroimaging meta‑analysis for neuropsychiatric disorders. Hum Brain Mapp. 2019;40:5142\u0026ndash;5154.\u003c/li\u003e\n\u003cli\u003eEickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT. Coordinate‑based activation likelihood estimation meta‑analysis of neuroimaging data: A random‑effects approach based on empirical estimates of spatial uncertainty. Hum Brain Mapp. 2009;30:2907\u0026ndash;2926.\u003c/li\u003e\n\u003cli\u003eLaird AR, Eickhoff SB, Fox PM, Uecker AM, Ray KL, Saenz JJ, et al. The BrainMap strategy for standardization, sharing, and meta‑analysis of neuroimaging data. BMC Res Notes. 2011;4:1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller VI, Cieslik EC, Laird AR, Fox PT, Radua J, Mataix‑Cols D, et al. Ten simple rules for neuroimaging meta‑analysis. Neurosci Biobehav Rev. 2018;84:151\u0026ndash;161.\u003c/li\u003e\n\u003cli\u003eTurkeltaub PE, Eickhoff SB, Laird AR, Fox M, Wiener M, Fox PT. Minimizing within‑experiment and within‑group effects in activation likelihood estimation meta‑analyses. Hum Brain Mapp. 2012;33:1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eKim JH, Park JW, Tae WS, Rhyu IJ. Cerebral cortex changes in basketball players. J Korean Med Sci. 2022;37:e86.\u003c/li\u003e\n\u003cli\u003eLi J, Cao Y, Huang M, Qin Z, Lang J. Progressive increase of brain gray matter volume in individuals with regular soccer training. Sci Rep. 2024;14:7023.\u003c/li\u003e\n\u003cli\u003eChavan C, Mouthon M, Simonet M, Hoogewoud HM, Draganski B, van der Zwaag W, Spierer L. Sustained enhancements in inhibitory control depend primarily on the reinforcement of fronto‑basal anatomical connectivity. Brain Struct Funct. 2017;222:635\u0026ndash;643.\u003c/li\u003e\n\u003cli\u003eCordani C, Preziosa P, Gatti R, Castellani C, Filippi M, Rocca MA. Mapping brain structure and function in professional fencers: A model to study training effects on central nervous system plasticity. Hum Brain Mapp. 2022;43:3375\u0026ndash;3385.\u003c/li\u003e\n\u003cli\u003eMoher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta‑analyses: the PRISMA statement. Ann Intern Med. 2009;151:264\u0026ndash;269.\u003c/li\u003e\n\u003cli\u003eSterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898.\u003c/li\u003e\n\u003cli\u003eKonishi S, Nakajima K, Uchida I, Kikyo H, Kameyama M, Miyashita Y. Common inhibitory mechanism in human inferior prefrontal cortex revealed by event‑related functional MRI. Brain. 1999;122(Pt 5):981\u0026ndash;991.\u003c/li\u003e\n\u003cli\u003eKringelbach ML. The orbitofrontal cortex: linking reward to hedonic experience. Nat Rev Neurosci. 2015;6(9).\u003c/li\u003e\n\u003cli\u003eRolls ET. The functions of the orbitofrontal cortex. Brain Cogn. 2004;55:11\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;ng\u0026uuml;r D, Price JL. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex. 2000;10:206\u0026ndash;219.\u003c/li\u003e\n\u003cli\u003eEblen F, Graybiel AM. Highly restricted origin of prefrontal cortical inputs to striosomes in the macaque monkey. J Neurosci. 1995;15:5999\u0026ndash;6013.\u003c/li\u003e\n\u003cli\u003eRempel‑Clower NL, Barbas H. Topographic organization of connections between the hypothalamus and prefrontal cortex in the rhesus monkey. J Comp Neurol. 1998;398:393\u0026ndash;419.\u003c/li\u003e\n\u003cli\u003eKringelbach ML, Rolls ET. The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology. Prog Neurobiol. 2004;72:341\u0026ndash;372.\u003c/li\u003e\n\u003cli\u003eZhu H, Chen A, Guo W, Zhu F, Wang B. Which type of exercise is more beneficial for cognitive function? A meta‑analysis of the effects of open‑skill exercise versus closed‑skill exercise among children, adults, and elderly populations. Appl Sci. 2020;10:2737.\u003c/li\u003e\n\u003cli\u003eShi J, Wang J, Lang J, Zhang Z, Bi Y, Liu R, et al. Effect of different motor skills training on motor control network in the frontal lobe and basal ganglia. Biol Sport. 2020;37:405\u0026ndash;412.\u003c/li\u003e\n\u003cli\u003eBilalić M, Langner R, Erb M, Grodd W. Mechanisms and neural basis of object and pattern recognition: a study with chess experts. J Exp Psychol Gen. 2010;139:728.\u003c/li\u003e\n\u003cli\u003eSmeeton NJ, Ward P, Williams AM. Do pattern recognition skills transfer across sports? A preliminary analysis. J Sports Sci. 2004;22:205\u0026ndash;213.\u003c/li\u003e\n\u003cli\u003eHohmann T, Troje NF, Olmos A, Munzert J. The influence of motor expertise and motor experience on action and actor recognition. J Cogn Psychol. 2011;23:403\u0026ndash;415.\u003c/li\u003e\n\u003cli\u003eGr\u0026egrave;zes J, Decety J. Functional anatomy of execution, mental simulation, observation, and verb generation of actions: a meta‑analysis. Hum Brain Mapp. 2001;12:1\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003ePeuskens H, Vanrie J, Verfaillie K, Orban GA. Specificity of regions processing biological motion. Eur J Neurosci. 2005;21:2864\u0026ndash;2875.\u003c/li\u003e\n\u003cli\u003ePelphrey KA, Morris JP, McCarthy G. Grasping the intentions of others: the perceived intentionality of an action influences activity in the superior temporal sulcus during social perception. J Cogn Neurosci. 2004;16:1706\u0026ndash;1716.\u003c/li\u003e\n\u003cli\u003eLestou V, Pollick FE, Kourtzi Z. Neural substrates for action understanding at different description levels in the human brain. J Cogn Neurosci. 2008;20:324\u0026ndash;341.\u003c/li\u003e\n\u003cli\u003eWright MJ, Bishop DT, Jackson RC, Abernethy B. Cortical fMRI activation to opponents\u0026rsquo; body kinematics in sport‑related anticipation: expert‑novice differences with normal and point‑light video. Neurosci Lett. 2011;500:216\u0026ndash;221.\u003c/li\u003e\n\u003cli\u003eAbreu AM, Macaluso E, Azevedo RT, Cesari P, Urgesi C, Aglioti SM. Action anticipation beyond the action observation network: a functional magnetic resonance imaging study in expert basketball players. Eur J Neurosci. 2012;35:1646\u0026ndash;1654.\u003c/li\u003e\n\u003cli\u003eBishop DT, Wright MJ, Jackson RC, Abernethy B. Neural bases for anticipation skill in soccer: an fMRI study. J Sport Exerc Psychol. 2013;35:98\u0026ndash;109.\u003c/li\u003e\n\u003cli\u003eKawashima R, Roland PE, O\u0026rsquo;Sullivan BT. Functional anatomy of reaching and visuomotor learning: a positron emission tomography study. Cereb Cortex. 1995;5:111\u0026ndash;122.\u003c/li\u003e\n\u003cli\u003eWenderoth N, Debaere F, Sunaert S, Swinnen SP. The role of anterior cingulate cortex and precuneus in the coordination of motor behaviour. Eur J Neurosci. 2005;22:235\u0026ndash;241.\u003c/li\u003e\n\u003cli\u003eCavanna AE, Trimble MR. The precuneus: a review of its functional anatomy and behavioural correlates. Brain. 2006;129:564\u0026ndash;583.\u003c/li\u003e\n\u003cli\u003eBullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186\u0026ndash;198.\u003c/li\u003e\n\u003cli\u003eMarakshina J, Vartanov A, Buldakova N. Effect of eye dominance on cognitive control. In: Malykh S, Nikulchev E, eds. Psychology and Education \u0026ndash; ICPE 2018, vol 49. Future Academy; 2018:402\u0026ndash;408.\u003c/li\u003e\n\u003cli\u003eAdkins DL, Boychuk J, Remple MS, Kleim JA. Motor training induces experience‑specific patterns of plasticity across motor cortex and spinal cord. J Appl Physiol (1985). 2006;101:1776\u0026ndash;1782.\u003c/li\u003e\n\u003cli\u003eTaubert M, Lohmann G, Margulies DS, Villringer A, Ragert P. Long‑term effects of motor training on resting‑state networks and underlying brain structure. Neuroimage. 2011;57:1492\u0026ndash;1498.\u003c/li\u003e\n\u003cli\u003eMou H, Wang Y, Ou J, Gu N. Neural mechanisms underlying perceptual‑motor behavioral advantages in athletes: a systematic review and activation likelihood estimation meta‑analysis. Int Rev Sport Exerc Psychol. 2025.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Callaghan G, O\u0026rsquo;Dowd A, Stapleton J, Merriman NA, Roudaia E, Newell FN. Changes in regional brain grey‑matter volume following successful completion of a sensori‑motor intervention targeted at healthy and fall‑prone older adults. Mult Scler Relat Disord. 2018;31:317\u0026ndash;344.\u003c/li\u003e\n\u003cli\u003eDe Bruyn N, Essers B, Thijs L, et al. Does sensorimotor upper limb therapy post stroke alter behavior and brain connectivity differently compared to motor therapy? Protocol of a phase II randomized controlled trial. Trials. 2018;19:242.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"neuroplasticity, open-skill sports, gray matter volume, activation likelihood estimation, voxel-based morphometry","lastPublishedDoi":"10.21203/rs.3.rs-7615487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7615487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrain plasticity refers to the brain's ability to reorganize structurally in response to experience and training. Motor skill practice, particularly in open-skill sports, has been consistently linked to structural differences in gray matter volume (GMV). However, prior research is largely limited to individual sports, lacking a comprehensive quantitative synthesis across multiple disciplines. This meta-analysis employed activation likelihood estimation to identify consistent GMV characteristics in open-skill athletes. Fifteen voxel-based morphometry studies comprising 653 participants, 76 foci, and 20 contrasts comparing athletes with non-athlete controls were included. Athletes consistently exhibited relatively larger GMV in the right superior frontal gyrus orbital part, right postcentral gyrus, right middle temporal gyrus, left inferior temporal gyrus, left precuneus, left inferior parietal lobule, and left precentral gyrus (uncorrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the right superior frontal gyrus orbital part and right postcentral gyrus remained significant after familywise error correction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings delineate a robust neuroanatomical signature associated with open-skill athletic expertise, characterized by structural distinctions within an integrated network supporting cognitive, perceptual, and sensorimotor processing. By synthesizing evidence across diverse open-skill disciplines, this meta-analysis advances our understanding of how complex, real-world motor experiences relate to brain structure, offering potential insights for cognitive-motor interventions beyond athletic contexts.\u003c/p\u003e","manuscriptTitle":"Effects of open-skill training on brain gray matter volume: activation likelihood estimation analysis based on voxel-based morphometry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 09:59:53","doi":"10.21203/rs.3.rs-7615487/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98eba7c3-1684-4a25-861b-7ed1612378b7","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T15:53:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 09:59:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7615487","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7615487","identity":"rs-7615487","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}