Immense data processing within brain networks in professional gamers

Immense data processing within brain networks in professional gamers | 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 Article Immense data processing within brain networks in professional gamers Gangta Choi, Young-Don Son, Doug Hyun Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8467566/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Internet video games represent complex systems that process large data volumes, requiring cognitive skills. We hypothesized that professional gamers must effectively process a vast amount of information to compete, likely involving altered brain functional connectivity (FC), cortical volumes, and white matter connectivity (FA) within dorsal attention and thalamocortical networks (DAN and TCN, respectively). The study recruited 23 professional gamers and 20 healthy control participants. All participants underwent magnetic resonance imaging (MRI) scanning and cognitive function tests. Professional gamers demonstrated enhanced FC, larger cortical volumes, and improved FA within DAN compared to controls. They showed increased FC from the right anterior insula to the right anterior cingulate cortex and decreased FC within the TCN. Cortical thickness and FA values in attention-related regions were also higher. Working memory backward was negatively correlated with the FC from the left thalamus to the left pre-cingulate cortex in all participants. Hence, the brains of professional gamers adapt to respond to an immense influx of external stimuli by enhancing neurotransmission in the concentration-related white matter regions, along with enhanced connectivity and volume of DAN and restriction of TCN connectivity. Biological sciences/Biological techniques Biological sciences/Neuroscience brain functional connectivity white matter connectivity dorsal attention network thalamocortical networks professional gamers Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Internet games as a complex and highly engaging activity Internet video games have evolved into intricate systems characterized by the continuous exchange and processing of large data volumes while acting as complex and engaging activities that demand significant cognitive and neural resources, as evidenced by their impact on cognitive performance and neural processing [ 1 ]. The fast-paced nature of gaming environments activates cognitive functions such as attention, working memory, and spatial reasoning, thereby showcasing brain plasticity. Neural circuits related to visual processing and motor coordination are activated during gaming, enhancing problem-solving and hand-eye coordination [ 1 , 2 ]. Despite fostering brain plasticity, prolonged gaming raises concerns about its effects on attention span and impulse control, especially in adolescents [ 3 ]. Brain imaging studies revealed cognitive changes in professional gamers and individuals with gaming disorders, highlighting both the benefits and risks of extensive gaming [ 4 ]. Professional gamers exhibit extraordinary capabilities in processing external stimuli Professional gamers, similarly to elite athletes, exhibit exceptional capabilities in processing external stimuli, which are developed through extensive practice and competition. These capabilities stem from advanced cognitive and neural adaptations that enable rapid interpretation and response to dynamic environments with precision [ 1 , 3 , 4 , 5 ]. Enhanced attention control, visual-spatial acuity, and decision-making are critical traits distinguishing professional gamers in high-pressure scenarios [ 5 ]. Research showed that professional gamers possess superior functional connectivity (FC) in brain regions linked to sensory processing, motor coordination, and executive function, resulting in faster reactions and improved visual attention compared to non-gamers [ 1 , 3 , 4 ]. These adaptations not only improve gaming performance but also enhance multitasking and rapid information processing skills in non-gaming tasks [ 2 , 6 ]. Professional gamers also demonstrate remarkable attention control and mental discipline, essential for maintaining situational awareness and simultaneously managing multiple variables. Their ability for deep focus and adaptive thinking is linked to enhanced neural plasticity and cognitive flexibility, which are cultivated through consistent practice and engagement in complex gaming environments [ 1 , 2 ]. These cognitive traits highlight the unique skill set of professional gamers, offering insights into neural mechanisms underlying their high performance. Consequently, understanding these abilities could provide potential applications in fields requiring similar skills, such as crisis management and strategic planning, showcasing the broader cognitive benefits of structured gaming [ 7 , 8 ]. Data processing within brain networks Many individuals face the challenge of sensory overload, which is characterized by an abundance of sensory stimuli. Processing large data volumes effectively requires robust neural networks, particularly the dorsal attention network and thalamocortical connectivity. Studies showed that such increased connectivity enables more efficient allocation of attentional resources, which is crucial for tasks requiring sustained attention and quick decision-making. This is particularly relevant in scenarios involving the rapid processing of large data volumes, such as professional gaming or complex decision-making environments [ 6 , 9 ]. For instance, research indicated that extensive video gaming, demanding significant data processing, is associated with functional changes in brain networks, including the ventral attention network [ 4 ]. Furthermore, the relationship between external data processing and ventral attention network connectivity highlights the adaptability of the brain to cognitive demands. This adaptability is evident in various contexts, such as media multitasking [ 10 ] and attention capture [ 11 ], in which enhanced connectivity in the ventral attention network is observed. Additionally, the ventral attention network is essential for efficiently reallocating attentional resources to quickly respond to novel and important stimuli [ 12 ]. These findings suggest potential pathways for targeted training aimed at improving attentional capacities and cognitive performance in demanding environments [ 13 ]. The thalamus, a key brain relay station, is crucial for processing sensory information and enhancing sensitivity to external stimuli. The increase in thalamic activity often coincides with a relative suppression of the cingulate gyrus, which is involved in emotion regulation and internal thought processes. The thalamus plays a significant role in brain-wide information processing, influencing perceptual acuity and responsiveness and facilitating quick reactions to environmental changes [ 14 , 15 ]. During the high activity of the thalamus, the activity of the cingulate gyrus may be reduced; hence, the brain temporarily prioritizes external stimulus processing over introspective and evaluative functions [ 14 , 15 ]. Hypothesis We hypothesized that professional gamers must effectively process a vast amount of information to compete, likely involving enhanced brain connectivity, increased cortical volumes, and white matter connectivity within the dorsal attention network, compared to controls. Furthermore, the brain connectivity within the thalamocortical network was decreased for effective data processing of the attention network as compared to healthy controls. Methods Participants and clinical assessments Twenty-three professional gamers and twenty healthy controls were recruited at the IT and Human Clinic and Research Center, Chung Ang University Medical Center. All participants were screened using the Structured Clinical Interview for DSM-5. Additionally, they completed psychological scales, including the Beck Depression Inventory (BDI-II) [ 16 ], the Dupaul Attention Deficit Hyperactivity Disorder Rating Scale Korean (Dupaul ADHD) [ 17 ], and the Young Internet Addiction Scale (YIAS) [ 18 ]. Professional gamers of two professional game teams were the members of the League of Legends Champions Korea. The lifestyle and training regimens of South Korean professional gamers have become a topic of interest, particularly in the context of their extensive practice hours. Korean professional gamers generally practice about 10–14 hours daily, which not only involves playing games but also includes strategy meetings, analysis, and team meetings [ 19 ]. Age- and education-matched participants who used the Internet and played Internet games for < 2 hours/day and 19; (3) other axis I psychiatric disorders, including substance abuse; and (4) head injury or trauma history. All participants underwent the computerized comprehensive attention test [ 20 ]. Of six subsets, only two subsets, including the divided attention and working memory parts, which can impose a high cognitive load, were assessed. The Chung Ang University Hospital Institutional Review Board approved the study protocol (IRB# 1990-007-386). All participants provided written informed consent. All methods were performed in accordance with the relevant guidelines and regulations. Brain imaging data processing All MRI evaluations were performed using the 1.5 Tesla Espree MRI scanner (SIEMENS, Erlangen, Germany). Three different brain imaging types were acquired: 3D T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) as structural MRI (parameters: TR = 1500 ms; TE = 3.00 ms; inversion time = 1100 ms; FOV = 256 × 256 mm; flip angle = 15°; 128 slices; 1.0 × 1.0 × 1.33 mm voxel size), echo-planar 2D blood-oxygen-level dependent (EP2D-BOLD) sequence as resting-state functional MRI (fMRI; parameters: TR = 3000 ms; TE = 30.00 ms; 150 time points; FOV = 220 × 185 mm; 64 slices; 3.44 × 5 × 3.44 mm voxel size), and echo-planar 2D diffusion tensor imaging (EP2D-DTI) sequence as diffusion MRI (parameters: TR = 6500 ms; TE = 105 ms; 32 diffusion-weighted directions; FOV = 224 × 224 mm; 41 slices; 1.75 × 1.75 × 3.5 mm voxel size). The pre-processing and analysis of brain images were conducted using MATLAB 2020b (MathWorks, Natick, MA, USA; https://www.mathworks.com/products/matlab.html ) and NiBabel ( https://nipy.org/nibabel/ ), a Python package for neuroimaging data processing. The structural MRI processing for measuring gray and white matter volume, as well as cortical thickness, was conducted using computational anatomy toolbox (CAT12; http://www.neuro.uni-jena.de/cat/ ), which is an add-on toolbox working with Statistical Parameter Mapping (SPM12; http://www.fil.ion.ucl.ac.uk/spm/ ). All raw images were first aligned to the same coordinate space, followed by skull stripping. Then, the images were non-linearly registered to the standard MNI space using the DARTEL algorithm and underwent normalization to correct for volumetric distortions. Subsequently, a gyrification surface mesh was reconstructed from the standardized images. To suppress signal noise, a 6-mm full-width at half maximum (FWHM) Gaussian kernel was applied to the segmented white matter, gray matter, and surface mesh. Finally, cortical thickness was extracted from the surface mesh, while the volumes were computed from the segmented gray and white matter. Furthermore, regional values were derived for each image using an appropriate atlas [ 21 , 22 ]. FC was measured using the CONN-fMRI FC toolbox (ver.22.a; www.nitrc.org/projects/conn ). All functional images were initially realigned within subjects to correct for head motion, followed by slice-timing correction and co-registration with the corresponding structural image. Then, the structural images were segmented into gray matter, white matter, and cerebrospinal fluid (CSF) compartments, followed by normalization into the standard MNI space via the DARTEL algorithm. Afterward, the functional images underwent spatial smoothing using a 6-mm full-width at half maximum (FWHM). Following spatial pre-processing, temporal denoising was performed by applying band-pass filtering (0.008–0.09 Hz) and additional motion regression to reduce physiological noise. Subsequently, Pearson correlation coefficients between the extracted BOLD time series of each region of interest (ROI) and the corresponding ROI-wise signals were calculated as a measure of FC. The resulting coefficients were transformed into Z-scores using Fisher’s Z-transformation to ensure the normality of the correlation distribution. Diffusion MRI analysis was conducted using FSL ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki ) and MRtrix3 ( https://www.mrtrix.org ). The pre-processing steps were performed using FSL, beginning with motion correction via linear realignment. Denoising and intensity bias correction were applied to further reduce noise and imaging artifacts. Then, eddy current distortion and subject motion correction were performed using the eddy tool in FSL. This step accounts for eddy-current-induced distortions and movement artifacts, improving the accuracy of diffusion modeling. Afterward, the images were processed to compute fractional anisotropy (FA) maps, representing the directionality of diffusion. Subsequently, fiber orientation distributions (FOD) were estimated using constrained spherical deconvolution (CSD) in MRtrix3. Then, these FODs were then used to generate track-density imaging (TDI) maps and reconstruct white matter fiber tracts for further analysis and visualization. Finally, ROI-wise FA values and tractography for each network were extracted using the XTRACT atlas ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/XTRACT ), which provides predefined tract-based ROIs for major white matter pathways. Statistics Demographic characteristics, clinical scales, and comprehensive attention test between professional gamers and healthy comparison participants were assessed using an independent t-test. The correlations between clinical scales, FC, cortical thickness, and FA values were assessed using Pearson correlations. A p < 0.05 indicated statistical significance. All statistical analyses were conducted using the Statistical Package for the Social Science (SPSS) version 24 (IBM SPSS statistics). Results Comparison of demographic characteristics between professional gamers and controls There were no statistically significant differences in terms of age, education year, alcohol consumption, and smoking habits between professional gamers and controls. There were no significant differences in BDI scores and KARS scores between the two groups. However, significant differences were observed among the groups in terms of the YIAS scores (Table 1 ). Table 1 Comparison of demographic, clinical, and attention characteristics between professional gamers and controls Professional gamers (23) Healthy controls (20) Statistics Gender All man All man - Age 19.2 ± 1.8 18.8 ± 5.3 t = 0.35, p = 0.73 Handedness All right-handedness All right-handedness - Education years 11.6 ± 2.1 10.7 ± 3.4 t = 1.23, p = 0.23 Professional gamer years 2.4 ± 2.5 - - Alcohol Heavy use 1 (4.3%) 2 (10.0%) χ 2 = 1.46, p = 0.48 Occasional use 8 (34.8%) 4 (20.0%) No use 14 (60.9%) 14 (70.0%) Smoking Heavy smoker 2 (8.7%) 1 (5.0%) χ 2 = 0.25, p = 0.88 Occasional smoker 4 (17.4%) 4 (20.0%) No use 17 (73.9%) 15 (75.0%) Clinical scales BDI-II 6.3 ± 4.3 7.6 ± 3.4 t = 0.95, p = 0.35 Dupaul ADHD 7.4 ± 3.2 6.1 ± 2.6 t = 1.39, p = 0.17 YIAS 65.3 ± 20.3 39.5 ± 15.4 t=-4.21, p < 0.01 Comprehensive attention test Divided omission 5.2 ± 3.7 6.3 ± 5.6 t=-0.75, p = 0.46 Divided commission 2.6 ± 1.3 5.7 ± 4.1 t=-3.45, p < 0.01 WM-forward 9.7 ± 2.3 7.5 ± 1.8 t = 3.46, p < 0.01 WM-backward 9.3 ± 2.1 7.3 ± 1.6 t = 3.30, p < 0.01 BDI-II, Beck Depression Inventory; Dupaul ADHD, Dupaul attention deficit hyperactivity disorder rating scale Korean; YIAS, Young Internet Addiction Scale; WM, working memory Comparison of functional connectivity within brain networks Within the dorsal attention network, the FC from the right anterior insular to the right anterior cingulate cortex (B = 0.24, p = 0.0012) and the left anterior insular (B = 0.24, p = 0.0035) in professional gamers was increased compared to controls (Fig. 1 ). Within the thalamocortical network, The FC from the right thalamus to the right (B = − 0.26, p = 0.0001) and left pre-cingulate cortices (B = − 0.22, p = 0.0005) in professional gamers decreased compared to controls. The FC from the left thalamus to the right (B = − 0.21, p = 0.0008) and left pre-cingulate cortices (B = − 0.23, p = 0.0003) in professional gamers was decreased compared to controls (Fig. 2 ). However, the FC from the left thalamus to the fight frontal pole in professional gamers was increased compared to control (B = 0.17, p = 0.0008) (Fig. 2 ). Comparison of cortical thickness within brain networks Within the ventral attention network, the cortical thickness within the right anterior insular (AVI, t = 3.02, p = 0.004) and anterior cingulate (a24, t = 3.25, p = 0.002; d32, t = 3.35, p = 0.002) in professional gamers was increased compared to controls. Comparison of the FA values of DTI within brain networks The FA values within both arcuate fasciculi in professional gamers were increased (right B = 0.007, p = 0.01; left B = 0.006, p = 0.03) compared to controls. The FA values within both inferior longitudinal fasciculi in professional gamers were increased (right B = 0.007, p = 0.01; left B = 0.006, p = 0.03) compared to controls. The FA values within both superior longitudinal fasciculi were increased (right B = 0.008, p = 0.03; left B = 0.006, p = 0.04) compared to controls. The FA values within the left anterior thalamic radiation increased (B = 0.006, p = 0.01) compared to controls. (Fig. 3 .) The correlations of clinical scales, functional connectivity, cortical thickness, and FA values In all participants, there were negative correlations between divided commission errors and the FC from the right anterior insular to the right anterior cingulate cortex (r = − 0.436, p = 0.003). In all participants, working memory backward was negatively correlated with the FC from the left thalamus to the left pre-cingulate cortex (r = − 0.451, p = 0.003). (Fig. 4 .) There was no correlation between other attention scale subscales, the FC, cortical thickness, and FA values of DTI. Discussion The FC within the ventral attention network (from the insular to the anterior cingulate) in professional gamers was increased, while the FC within the thalamocortical network (from the thalamus to the pre-cingulate cortex) in professional gamers was decreased compared to controls. Furthermore, the cortical thickness within the dorsal attention network (the insular and the anterior cingulate), as well as the FA values within the arcuate fasciculus, inferior longitudinal fasciculus, anterior longitudinal fasciculus, and thalamic radiation, increased compared to controls. Altered FC and cortical thickness within the dorsal attention and thalamocortical networks in professional gamers In the current study, the FC within the ventral attention network, as well as the cortical thickness of the ventral attention network, in professional gamers was increased compared to controls. Several studies of professional gamers’ brain reported those results in professional gamers [ 23 , 24 ]. Song et al. [ 24 ] reported brain activity within the superior and middle frontal gyri in professional gamers. Han et al. [ 23 ] reported that professional gamers showed increased gray matter volume within the cingulate gyrus compared to controls. These increased brain activities would be associated with the cognitive stimulations of internet games, including visual processing and motor coordination for problem-solving and hand-eye coordination [ 2 ]. The ventral attention network, known for its role in detecting and responding to unexpected or salient stimuli, is perfectly suited to handle the dynamic stimuli professional gamers encounter during gameplay. Such stimuli require swift attention shifts and responses, aligning closely with the core functionalities of the ventral attention network [ 4 , 25 ]. Additionally, extensive practice and exposure to gaming environments cause neurological adaptations strengthening the FC of the ventral attention network [ 4 ]. For adapting dynamic environments, professional gamers could possess increased FC and cortical thickness within the ventral attention network, which are linked to sensory processing, motor coordination, and executive function, resulting in faster reaction and improved visual attention compared to non-gamers [ 3 , 4 , 26 ]. In the current study, the FC within the ventral attention network was negatively correlated with divided commission errors. Hyun et al. [ 26 ] also declared that increased cortical thickness within prefrontal and parietal cortices was associated with higher performance on the Wisconsin Card Sorting Test. Those results could be associated with the core role of the ventral attention network for efficiently reallocating attentional resources to quickly respond to novel and important stimuli [ 12 ]. Interestingly, the FC within the thalamocortical network in professional gamers showed various connectivity changes, compared to controls. In professional gamers, the working memory backward (a complex cognitive task) was negatively correlated with the FC from the left thalamus to the left pre-cingulate cortex. In current results, the FC from the left thalamus to the right frontal pole in professional gamers was increased compared to control. Increased connectivity between the thalamus and the frontal pole is associated with enhanced cognitive processing abilities in the brain [ 27 ]. Thalamic is known to engage in reciprocal connections with the prefrontal cortex, which includes the frontal pole, playing a vital role in cognitive control and decision-making tasks [ 27 , 28 ]. Increasing the connectivity of the thalamo-cortical region to process large amounts of data seems like a reasonable outcome. However, an interesting finding in current result is that thalamo-cingulate connectivity decreases. In response to huge amounts of cognitive stimuli, the thalamus and cingulate would reflect a trade-off, meaning that the thalamus was highly activated during the reduced activity within the cingulate gyrus [ 29 , 30 , 31 , 32 ]. The heightened activity of the thalamus during intense cognitive tasks reflects its critical role in facilitating communication within broader neural networks, which are vital for maintaining consciousness and cognitive function [ 29 , 31 ]. In contrast, the cingulate gyrus, particularly the anterior cingulate cortex, is integral to emotional regulation and cognitive control [ 30 ]. Research indicated that the cingulate gyrus can adapt its activity based on current cognitive demands. For instance, during overwhelming stimuli, its activity may reduce — a shift potentially indicating that resources are being redirected to more immediate processing needs via other neural structures, such as the thalamus and prefrontal cortex [ 32 ]. Increased FA values of DTI within the white matter Increased FA values within the white matter in the current study may be associated with effective control of the large volume of visual process stimuli, sustained attention, strategic planning, and problem-solving abilities [ 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. The arcuate fasciculus is a major pathway connecting the frontal and temporal lobes. Furthermore, through the interaction of these regions, it supports complex cognitive functions, such as working memory [ 38 ]. The inferior longitudinal fasciculus connects the occipital and temporal lobes and is integral to the visual processing and integration of cognitive functions [ 34 , 36 ]. The inferior longitudinal fasciculus is crucial for the integration of visual information with other cognitive processes [ 36 ]. Therefore, improved microstructural integrity of the inferior longitudinal fasciculus, which is indicated by increased FA values, can translate to faster and more efficient processing of visual inputs, consequently supporting enhanced cognitive focus and concentration [ 34 ]. The superior longitudinal fasciculus connects the frontal, parietal, and occipital lobes, integrating regions critical to attention, executive function, and working memory — all of which are essential for executive set shifting and attentional control, which are essential for concentration [ 37 ]. Increased FA values within the superior longitudinal fasciculus would be associated with the enhanced capacity to concentrate, particularly during tasks requiring sustained focus and rapid task-switching [ 39 ]. Increased FA values in the anterior thalamic radiation can contribute to enhanced cognitive capacities, including processing speed and executive functioning [ 33 ]. These improvements might manifest as better strategic planning and problem-solving abilities [ 35 ]. Limitations There were several limitations in the current study. First, the current results could not obtain a sufficient sample size to be applied to all professional gamers or individuals who frequently play games. However, the analysis targeting professional gamers with > 2 years of experience registered in the Korean professional gaming league is considered to have purity and specificity in subject selections. Second, the current brain analysis results were based on resting-state analysis and could not accurately reflect brain changes during actual gameplay. Therefore, future research should focus on brain changes during actual gameplay or while performing tasks that require the processing of large data amounts. Conclusions The current study analyzed brain changes in response to the large amounts of data processing using various brain assessment modules. The brains of professional gamers adapt to respond to an immense influx of external stimuli by enhancing rapid visual information processing, high concentration, strategic planning, and effective problem-solving abilities. To achieve this, neurotransmission in the white matter regions related to concentration increases, along with enhanced connectivity and volume of the attention network. Additionally, in the thalamocortical network, connectivity with the cingulate is restricted, facilitating the efficient processing of cognitive information rather than emotional information. Declarations Funding The authors received no financial support for the research, authorship, and/or publication of this article. Author Contribution G.C. performed the data analysis and inspection. Y.S. researched and developed the analysis methodology. D.H. wrote the manuscript and provided overall supervision of the study. All authors have read and agreed to the published version of the manuscript. Data Availability The data analysed during the current study are not publicly available due to the Personal Information Protection Act and Bioethics and Safety Act of South Korea, which strictly prohibit the external sharing of medical data to protect patient privacy. However, data are available from the corresponding author upon reasonable request and subject to ethical approval and data use agreements. References Dale, G., Joessel, A., Bavelier, D. & Green, C. S. A new look at the cognitive neuroscience of video game play. Ann. N. Y. Acad. Sci. 1464 , 192–203 (2020). Nahum, M. & Bavelier, D. Video games as rich environments to foster brain plasticity. Handb. Clin. Neurol. 168 , 117–136 (2020). Alho, K., Moisala, M. & Salmela-Aro, K. Effects of media multitasking and video gaming on cognitive functions and their neural bases in adolescents and young adults. European Psychologist (2022). Choi, E. et al. 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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-8467566","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":578556547,"identity":"953a790e-4d5f-478d-a151-9e7723c41acb","order_by":0,"name":"Gangta Choi","email":"","orcid":"","institution":"Chung-Ang University","correspondingAuthor":false,"prefix":"","firstName":"Gangta","middleName":"","lastName":"Choi","suffix":""},{"id":578556549,"identity":"7ff1d2a0-cba2-43d7-b73e-7daa3a595411","order_by":1,"name":"Young-Don Son","email":"","orcid":"","institution":"Gachon University","correspondingAuthor":false,"prefix":"","firstName":"Young-Don","middleName":"","lastName":"Son","suffix":""},{"id":578556551,"identity":"2edff4b5-3971-4ebc-a17a-c73c7ab42d95","order_by":2,"name":"Doug Hyun Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYBACA4YEZuY/BjZAJmPjAaK1MPBUpIG0NJCi5cxhMIc4LebsOcYGkm3n7da2HwbaUmMTTVCLZc8b4wTDttvJ284kArUcS8ttIOiwGznGBxKBWswOALUwNhwmUsvBtnPJZucfkqAlseHMATuzG0TbcuZZsTFDRXKC2Q2gLQlE+eV48mZpBgM7e7Pz6Q8ffKixIawFBhLBKhOIVQ4C9qQoHgWjYBSMghEGAH8PSaN/VdEQAAAAAElFTkSuQmCC","orcid":"","institution":"Chung-Ang University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Doug","middleName":"Hyun","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2025-12-28 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09:56:51","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92288,"visible":true,"origin":"","legend":"","description":"","filename":"8c750efd942247bd81c77d4378994a2f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8467566/v1/6e57083bf8507d2b93cbf72c.xml"},{"id":101170829,"identity":"0be5f6b4-e648-42d0-b70a-5a2905514904","added_by":"auto","created_at":"2026-01-27 00:05:14","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104001,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8467566/v1/2ec6c4115573e78c50037c48.html"},{"id":101170820,"identity":"9d38564c-63b0-431a-8acd-acea30658b33","added_by":"auto","created_at":"2026-01-27 00:05:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17360,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional connectivity within Dorsal attention network\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8467566/v1/ff7c931611843d57c8a37518.png"},{"id":101170821,"identity":"ef4f720e-f922-4f7e-aacf-365844e7d530","added_by":"auto","created_at":"2026-01-27 00:05:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15732,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional connectivity within thalamocortical network\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8467566/v1/7ecc4473b529e8879a7180d0.png"},{"id":101170824,"identity":"2ff7cd47-f2e5-4162-8274-23c2e4f0c297","added_by":"auto","created_at":"2026-01-27 00:05:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":324921,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the Fractional Anisotropy values of Diffusion Tensor Imaging within brain networks\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8467566/v1/a41bc41f2d723ffaa31d615b.png"},{"id":101170831,"identity":"a1d48cf9-e944-47c5-9a65-b48f3cb82dcc","added_by":"auto","created_at":"2026-01-27 00:05:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":343920,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlations between attention test and functional connectivity\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8467566/v1/1ab88ba8b33c0e85e26f1f53.png"},{"id":101208534,"identity":"b2b7c1f7-9ffc-4e6f-a0c5-b70c464f6cf2","added_by":"auto","created_at":"2026-01-27 10:10:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1340715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8467566/v1/428bfa02-7e06-427b-9c71-cf10cdb810dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Immense data processing within brain networks in professional gamers","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eInternet games as a complex and highly engaging activity\u003c/h2\u003e \u003cp\u003eInternet video games have evolved into intricate systems characterized by the continuous exchange and processing of large data volumes while acting as complex and engaging activities that demand significant cognitive and neural resources, as evidenced by their impact on cognitive performance and neural processing [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The fast-paced nature of gaming environments activates cognitive functions such as attention, working memory, and spatial reasoning, thereby showcasing brain plasticity. Neural circuits related to visual processing and motor coordination are activated during gaming, enhancing problem-solving and hand-eye coordination [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite fostering brain plasticity, prolonged gaming raises concerns about its effects on attention span and impulse control, especially in adolescents [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Brain imaging studies revealed cognitive changes in professional gamers and individuals with gaming disorders, highlighting both the benefits and risks of extensive gaming [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProfessional gamers exhibit extraordinary capabilities in processing external stimuli\u003c/h2\u003e \u003cp\u003eProfessional gamers, similarly to elite athletes, exhibit exceptional capabilities in processing external stimuli, which are developed through extensive practice and competition. These capabilities stem from advanced cognitive and neural adaptations that enable rapid interpretation and response to dynamic environments with precision [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Enhanced attention control, visual-spatial acuity, and decision-making are critical traits distinguishing professional gamers in high-pressure scenarios [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Research showed that professional gamers possess superior functional connectivity (FC) in brain regions linked to sensory processing, motor coordination, and executive function, resulting in faster reactions and improved visual attention compared to non-gamers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese adaptations not only improve gaming performance but also enhance multitasking and rapid information processing skills in non-gaming tasks [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Professional gamers also demonstrate remarkable attention control and mental discipline, essential for maintaining situational awareness and simultaneously managing multiple variables. Their ability for deep focus and adaptive thinking is linked to enhanced neural plasticity and cognitive flexibility, which are cultivated through consistent practice and engagement in complex gaming environments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These cognitive traits highlight the unique skill set of professional gamers, offering insights into neural mechanisms underlying their high performance. Consequently, understanding these abilities could provide potential applications in fields requiring similar skills, such as crisis management and strategic planning, showcasing the broader cognitive benefits of structured gaming [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData processing within brain networks\u003c/h3\u003e\n\u003cp\u003eMany individuals face the challenge of sensory overload, which is characterized by an abundance of sensory stimuli. Processing large data volumes effectively requires robust neural networks, particularly the dorsal attention network and thalamocortical connectivity.\u003c/p\u003e \u003cp\u003eStudies showed that such increased connectivity enables more efficient allocation of attentional resources, which is crucial for tasks requiring sustained attention and quick decision-making. This is particularly relevant in scenarios involving the rapid processing of large data volumes, such as professional gaming or complex decision-making environments [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For instance, research indicated that extensive video gaming, demanding significant data processing, is associated with functional changes in brain networks, including the ventral attention network [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, the relationship between external data processing and ventral attention network connectivity highlights the adaptability of the brain to cognitive demands. This adaptability is evident in various contexts, such as media multitasking [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and attention capture [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], in which enhanced connectivity in the ventral attention network is observed. Additionally, the ventral attention network is essential for efficiently reallocating attentional resources to quickly respond to novel and important stimuli [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings suggest potential pathways for targeted training aimed at improving attentional capacities and cognitive performance in demanding environments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe thalamus, a key brain relay station, is crucial for processing sensory information and enhancing sensitivity to external stimuli. The increase in thalamic activity often coincides with a relative suppression of the cingulate gyrus, which is involved in emotion regulation and internal thought processes. The thalamus plays a significant role in brain-wide information processing, influencing perceptual acuity and responsiveness and facilitating quick reactions to environmental changes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. During the high activity of the thalamus, the activity of the cingulate gyrus may be reduced; hence, the brain temporarily prioritizes external stimulus processing over introspective and evaluative functions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eHypothesis\u003c/h3\u003e\n\u003cp\u003eWe hypothesized that professional gamers must effectively process a vast amount of information to compete, likely involving enhanced brain connectivity, increased cortical volumes, and white matter connectivity within the dorsal attention network, compared to controls. Furthermore, the brain connectivity within the thalamocortical network was decreased for effective data processing of the attention network as compared to healthy controls.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and clinical assessments\u003c/h2\u003e \u003cp\u003eTwenty-three professional gamers and twenty healthy controls were recruited at the IT and Human Clinic and Research Center, Chung Ang University Medical Center. All participants were screened using the Structured Clinical Interview for DSM-5. Additionally, they completed psychological scales, including the Beck Depression Inventory (BDI-II) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the Dupaul Attention Deficit Hyperactivity Disorder Rating Scale Korean (Dupaul ADHD) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and the Young Internet Addiction Scale (YIAS) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProfessional gamers of two professional game teams were the members of the League of Legends Champions Korea. The lifestyle and training regimens of South Korean professional gamers have become a topic of interest, particularly in the context of their extensive practice hours. Korean professional gamers generally practice about 10\u0026ndash;14 hours daily, which not only involves playing games but also includes strategy meetings, analysis, and team meetings [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge- and education-matched participants who used the Internet and played Internet games for \u0026lt;\u0026thinsp;2 hours/day and \u0026lt;\u0026thinsp;3 days/week were recruited as healthy controls. In both groups, exclusion criteria comprised (1) impaired behavior or distress due to internet gaming disorder; (2) BDI-II scores\u0026thinsp;\u0026gt;\u0026thinsp;19; (3) other axis I psychiatric disorders, including substance abuse; and (4) head injury or trauma history.\u003c/p\u003e \u003cp\u003eAll participants underwent the computerized comprehensive attention test [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Of six subsets, only two subsets, including the divided attention and working memory parts, which can impose a high cognitive load, were assessed.\u003c/p\u003e \u003cp\u003e The Chung Ang University Hospital Institutional Review Board approved the study protocol (IRB# 1990-007-386). All participants provided written informed consent. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBrain imaging data processing\u003c/h2\u003e \u003cp\u003eAll MRI evaluations were performed using the 1.5 Tesla Espree MRI scanner (SIEMENS, Erlangen, Germany). Three different brain imaging types were acquired: 3D T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) as structural MRI (parameters: TR\u0026thinsp;=\u0026thinsp;1500 ms; TE\u0026thinsp;=\u0026thinsp;3.00 ms; inversion time\u0026thinsp;=\u0026thinsp;1100 ms; FOV\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm; flip angle\u0026thinsp;=\u0026thinsp;15\u0026deg;; 128 slices; 1.0 \u0026times; 1.0 \u0026times; 1.33 mm voxel size), echo-planar 2D blood-oxygen-level dependent (EP2D-BOLD) sequence as resting-state functional MRI (fMRI; parameters: TR\u0026thinsp;=\u0026thinsp;3000 ms; TE\u0026thinsp;=\u0026thinsp;30.00 ms; 150 time points; FOV\u0026thinsp;=\u0026thinsp;220 \u0026times; 185 mm; 64 slices; 3.44 \u0026times; 5 \u0026times; 3.44 mm voxel size), and echo-planar 2D diffusion tensor imaging (EP2D-DTI) sequence as diffusion MRI (parameters: TR\u0026thinsp;=\u0026thinsp;6500 ms; TE\u0026thinsp;=\u0026thinsp;105 ms; 32 diffusion-weighted directions; FOV\u0026thinsp;=\u0026thinsp;224 \u0026times; 224 mm; 41 slices; 1.75 \u0026times; 1.75 \u0026times; 3.5 mm voxel size). The pre-processing and analysis of brain images were conducted using MATLAB 2020b (MathWorks, Natick, MA, USA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mathworks.com/products/matlab.html\u003c/span\u003e\u003cspan address=\"https://www.mathworks.com/products/matlab.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and NiBabel (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nipy.org/nibabel/\u003c/span\u003e\u003cspan address=\"https://nipy.org/nibabel/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a Python package for neuroimaging data processing.\u003c/p\u003e \u003cp\u003eThe structural MRI processing for measuring gray and white matter volume, as well as cortical thickness, was conducted using computational anatomy toolbox (CAT12; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.neuro.uni-jena.de/cat/\u003c/span\u003e\u003cspan address=\"http://www.neuro.uni-jena.de/cat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is an add-on toolbox working with Statistical Parameter Mapping (SPM12; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ucl.ac.uk/spm/\u003c/span\u003e\u003cspan address=\"http://www.fil.ion.ucl.ac.uk/spm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All raw images were first aligned to the same coordinate space, followed by skull stripping. Then, the images were non-linearly registered to the standard MNI space using the DARTEL algorithm and underwent normalization to correct for volumetric distortions. Subsequently, a gyrification surface mesh was reconstructed from the standardized images. To suppress signal noise, a 6-mm full-width at half maximum (FWHM) Gaussian kernel was applied to the segmented white matter, gray matter, and surface mesh. Finally, cortical thickness was extracted from the surface mesh, while the volumes were computed from the segmented gray and white matter. Furthermore, regional values were derived for each image using an appropriate atlas [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFC was measured using the CONN-fMRI FC toolbox (ver.22.a; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.nitrc.org/projects/conn\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.nitrc.org/projects/conn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All functional images were initially realigned within subjects to correct for head motion, followed by slice-timing correction and co-registration with the corresponding structural image. Then, the structural images were segmented into gray matter, white matter, and cerebrospinal fluid (CSF) compartments, followed by normalization into the standard MNI space via the DARTEL algorithm. Afterward, the functional images underwent spatial smoothing using a 6-mm full-width at half maximum (FWHM). Following spatial pre-processing, temporal denoising was performed by applying band-pass filtering (0.008\u0026ndash;0.09 Hz) and additional motion regression to reduce physiological noise. Subsequently, Pearson correlation coefficients between the extracted BOLD time series of each region of interest (ROI) and the corresponding ROI-wise signals were calculated as a measure of FC. The resulting coefficients were transformed into Z-scores using Fisher\u0026rsquo;s Z-transformation to ensure the normality of the correlation distribution.\u003c/p\u003e \u003cp\u003eDiffusion MRI analysis was conducted using FSL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and MRtrix3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mrtrix.org\u003c/span\u003e\u003cspan address=\"https://www.mrtrix.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The pre-processing steps were performed using FSL, beginning with motion correction via linear realignment. Denoising and intensity bias correction were applied to further reduce noise and imaging artifacts. Then, eddy current distortion and subject motion correction were performed using the eddy tool in FSL. This step accounts for eddy-current-induced distortions and movement artifacts, improving the accuracy of diffusion modeling. Afterward, the images were processed to compute fractional anisotropy (FA) maps, representing the directionality of diffusion. Subsequently, fiber orientation distributions (FOD) were estimated using constrained spherical deconvolution (CSD) in MRtrix3. Then, these FODs were then used to generate track-density imaging (TDI) maps and reconstruct white matter fiber tracts for further analysis and visualization. Finally, ROI-wise FA values and tractography for each network were extracted using the XTRACT atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/XTRACT\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/XTRACT\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which provides predefined tract-based ROIs for major white matter pathways.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eDemographic characteristics, clinical scales, and comprehensive attention test between professional gamers and healthy comparison participants were assessed using an independent t-test. The correlations between clinical scales, FC, cortical thickness, and FA values were assessed using Pearson correlations. A p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance. All statistical analyses were conducted using the Statistical Package for the Social Science (SPSS) version 24 (IBM SPSS statistics).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of demographic characteristics between professional gamers and controls\u003c/h2\u003e \u003cp\u003eThere were no statistically significant differences in terms of age, education year, alcohol consumption, and smoking habits between professional gamers and controls. There were no significant differences in BDI scores and KARS scores between the two groups. However, significant differences were observed among the groups in terms of the YIAS scores (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of demographic, clinical, and attention characteristics between professional gamers and controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional gamers (23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy controls (20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll man\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll man\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;=\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll right-handedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll right-handedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;1.23, p\u0026thinsp;=\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional gamer years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.46, p\u0026thinsp;=\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (70.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eClinical scales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.95, p\u0026thinsp;=\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDupaul ADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;1.39, p\u0026thinsp;=\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYIAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-4.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eComprehensive attention test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivided omission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-0.75, p\u0026thinsp;=\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivided commission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-3.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWM-forward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;3.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWM-backward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;3.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBDI-II, Beck Depression Inventory; Dupaul ADHD, Dupaul attention deficit hyperactivity disorder rating scale Korean; YIAS, Young Internet Addiction Scale; WM, working memory\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of functional connectivity within brain networks\u003c/h2\u003e \u003cp\u003eWithin the dorsal attention network, the FC from the right anterior insular to the right anterior cingulate cortex (B\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;=\u0026thinsp;0.0012) and the left anterior insular (B\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;=\u0026thinsp;0.0035) in professional gamers was increased compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the thalamocortical network, The FC from the right thalamus to the right (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.0001) and left pre-cingulate cortices (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.22, p\u0026thinsp;=\u0026thinsp;0.0005) in professional gamers decreased compared to controls. The FC from the left thalamus to the right (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.21, p\u0026thinsp;=\u0026thinsp;0.0008) and left pre-cingulate cortices (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.23, p\u0026thinsp;=\u0026thinsp;0.0003) in professional gamers was decreased compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the FC from the left thalamus to the fight frontal pole in professional gamers was increased compared to control (B\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;=\u0026thinsp;0.0008) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of cortical thickness within brain networks\u003c/h2\u003e \u003cp\u003eWithin the ventral attention network, the cortical thickness within the right anterior insular (AVI, t\u0026thinsp;=\u0026thinsp;3.02, p\u0026thinsp;=\u0026thinsp;0.004) and anterior cingulate (a24, t\u0026thinsp;=\u0026thinsp;3.25, p\u0026thinsp;=\u0026thinsp;0.002; d32, t\u0026thinsp;=\u0026thinsp;3.35, p\u0026thinsp;=\u0026thinsp;0.002) in professional gamers was increased compared to controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison of the FA values of DTI within brain networks\u003c/h2\u003e \u003cp\u003eThe FA values within both arcuate fasciculi in professional gamers were increased (right B\u0026thinsp;=\u0026thinsp;0.007, p\u0026thinsp;=\u0026thinsp;0.01; left B\u0026thinsp;=\u0026thinsp;0.006, p\u0026thinsp;=\u0026thinsp;0.03) compared to controls.\u003c/p\u003e \u003cp\u003eThe FA values within both inferior longitudinal fasciculi in professional gamers were increased (right B\u0026thinsp;=\u0026thinsp;0.007, p\u0026thinsp;=\u0026thinsp;0.01; left B\u0026thinsp;=\u0026thinsp;0.006, p\u0026thinsp;=\u0026thinsp;0.03) compared to controls.\u003c/p\u003e \u003cp\u003eThe FA values within both superior longitudinal fasciculi were increased (right B\u0026thinsp;=\u0026thinsp;0.008, p\u0026thinsp;=\u0026thinsp;0.03; left B\u0026thinsp;=\u0026thinsp;0.006, p\u0026thinsp;=\u0026thinsp;0.04) compared to controls.\u003c/p\u003e \u003cp\u003eThe FA values within the left anterior thalamic radiation increased (B\u0026thinsp;=\u0026thinsp;0.006, p\u0026thinsp;=\u0026thinsp;0.01) compared to controls. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe correlations of clinical scales, functional connectivity, cortical thickness, and FA values\u003c/h2\u003e \u003cp\u003eIn all participants, there were negative correlations between divided commission errors and the FC from the right anterior insular to the right anterior cingulate cortex (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.436, p\u0026thinsp;=\u0026thinsp;0.003). In all participants, working memory backward was negatively correlated with the FC from the left thalamus to the left pre-cingulate cortex (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.451, p\u0026thinsp;=\u0026thinsp;0.003). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.) There was no correlation between other attention scale subscales, the FC, cortical thickness, and FA values of DTI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe FC within the ventral attention network (from the insular to the anterior cingulate) in professional gamers was increased, while the FC within the thalamocortical network (from the thalamus to the pre-cingulate cortex) in professional gamers was decreased compared to controls. Furthermore, the cortical thickness within the dorsal attention network (the insular and the anterior cingulate), as well as the FA values within the arcuate fasciculus, inferior longitudinal fasciculus, anterior longitudinal fasciculus, and thalamic radiation, increased compared to controls.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAltered FC and cortical thickness within the dorsal attention and thalamocortical networks in professional gamers\u003c/h2\u003e \u003cp\u003eIn the current study, the FC within the ventral attention network, as well as the cortical thickness of the ventral attention network, in professional gamers was increased compared to controls. Several studies of professional gamers\u0026rsquo; brain reported those results in professional gamers [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Song et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported brain activity within the superior and middle frontal gyri in professional gamers. Han et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] reported that professional gamers showed increased gray matter volume within the cingulate gyrus compared to controls.\u003c/p\u003e \u003cp\u003eThese increased brain activities would be associated with the cognitive stimulations of internet games, including visual processing and motor coordination for problem-solving and hand-eye coordination [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The ventral attention network, known for its role in detecting and responding to unexpected or salient stimuli, is perfectly suited to handle the dynamic stimuli professional gamers encounter during gameplay. Such stimuli require swift attention shifts and responses, aligning closely with the core functionalities of the ventral attention network [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, extensive practice and exposure to gaming environments cause neurological adaptations strengthening the FC of the ventral attention network [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor adapting dynamic environments, professional gamers could possess increased FC and cortical thickness within the ventral attention network, which are linked to sensory processing, motor coordination, and executive function, resulting in faster reaction and improved visual attention compared to non-gamers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the current study, the FC within the ventral attention network was negatively correlated with divided commission errors. Hyun et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] also declared that increased cortical thickness within prefrontal and parietal cortices was associated with higher performance on the Wisconsin Card Sorting Test. Those results could be associated with the core role of the ventral attention network for efficiently reallocating attentional resources to quickly respond to novel and important stimuli [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, the FC within the thalamocortical network in professional gamers showed various connectivity changes, compared to controls. In professional gamers, the working memory backward (a complex cognitive task) was negatively correlated with the FC from the left thalamus to the left pre-cingulate cortex.\u003c/p\u003e \u003cp\u003eIn current results, the FC from the left thalamus to the right frontal pole in professional gamers was increased compared to control. Increased connectivity between the thalamus and the frontal pole is associated with enhanced cognitive processing abilities in the brain [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thalamic is known to engage in reciprocal connections with the prefrontal cortex, which includes the frontal pole, playing a vital role in cognitive control and decision-making tasks [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Increasing the connectivity of the thalamo-cortical region to process large amounts of data seems like a reasonable outcome.\u003c/p\u003e \u003cp\u003eHowever, an interesting finding in current result is that thalamo-cingulate connectivity decreases. In response to huge amounts of cognitive stimuli, the thalamus and cingulate would reflect a trade-off, meaning that the thalamus was highly activated during the reduced activity within the cingulate gyrus [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The heightened activity of the thalamus during intense cognitive tasks reflects its critical role in facilitating communication within broader neural networks, which are vital for maintaining consciousness and cognitive function [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In contrast, the cingulate gyrus, particularly the anterior cingulate cortex, is integral to emotional regulation and cognitive control [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Research indicated that the cingulate gyrus can adapt its activity based on current cognitive demands. For instance, during overwhelming stimuli, its activity may reduce \u0026mdash; a shift potentially indicating that resources are being redirected to more immediate processing needs via other neural structures, such as the thalamus and prefrontal cortex [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIncreased FA values of DTI within the white matter\u003c/h2\u003e \u003cp\u003eIncreased FA values within the white matter in the current study may be associated with effective control of the large volume of visual process stimuli, sustained attention, strategic planning, and problem-solving abilities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The arcuate fasciculus is a major pathway connecting the frontal and temporal lobes. Furthermore, through the interaction of these regions, it supports complex cognitive functions, such as working memory [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The inferior longitudinal fasciculus connects the occipital and temporal lobes and is integral to the visual processing and integration of cognitive functions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The inferior longitudinal fasciculus is crucial for the integration of visual information with other cognitive processes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, improved microstructural integrity of the inferior longitudinal fasciculus, which is indicated by increased FA values, can translate to faster and more efficient processing of visual inputs, consequently supporting enhanced cognitive focus and concentration [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe superior longitudinal fasciculus connects the frontal, parietal, and occipital lobes, integrating regions critical to attention, executive function, and working memory \u0026mdash; all of which are essential for executive set shifting and attentional control, which are essential for concentration [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Increased FA values within the superior longitudinal fasciculus would be associated with the enhanced capacity to concentrate, particularly during tasks requiring sustained focus and rapid task-switching [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Increased FA values in the anterior thalamic radiation can contribute to enhanced cognitive capacities, including processing speed and executive functioning [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These improvements might manifest as better strategic planning and problem-solving abilities [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThere were several limitations in the current study. First, the current results could not obtain a sufficient sample size to be applied to all professional gamers or individuals who frequently play games. However, the analysis targeting professional gamers with \u0026gt;\u0026thinsp;2 years of experience registered in the Korean professional gaming league is considered to have purity and specificity in subject selections. Second, the current brain analysis results were based on resting-state analysis and could not accurately reflect brain changes during actual gameplay. Therefore, future research should focus on brain changes during actual gameplay or while performing tasks that require the processing of large data amounts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe current study analyzed brain changes in response to the large amounts of data processing using various brain assessment modules. The brains of professional gamers adapt to respond to an immense influx of external stimuli by enhancing rapid visual information processing, high concentration, strategic planning, and effective problem-solving abilities. To achieve this, neurotransmission in the white matter regions related to concentration increases, along with enhanced connectivity and volume of the attention network. Additionally, in the thalamocortical network, connectivity with the cingulate is restricted, facilitating the efficient processing of cognitive information rather than emotional information.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.C. performed the data analysis and inspection. Y.S. researched and developed the analysis methodology. D.H. wrote the manuscript and provided overall supervision of the study. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data analysed during the current study are not publicly available due to the Personal Information Protection Act and Bioethics and Safety Act of South Korea, which strictly prohibit the external sharing of medical data to protect patient privacy. However, data are available from the corresponding author upon reasonable request and subject to ethical approval and data use agreements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDale, G., Joessel, A., Bavelier, D. \u0026amp; Green, C. S. A new look at the cognitive neuroscience of video game play. \u003cem\u003eAnn. N. Y. Acad. 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Psychiatry\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 999384 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"brain functional connectivity, white matter connectivity, dorsal attention network, thalamocortical networks, professional gamers","lastPublishedDoi":"10.21203/rs.3.rs-8467566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8467566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInternet video games represent complex systems that process large data volumes, requiring cognitive skills. We hypothesized that professional gamers must effectively process a vast amount of information to compete, likely involving altered brain functional connectivity (FC), cortical volumes, and white matter connectivity (FA) within dorsal attention and thalamocortical networks (DAN and TCN, respectively). The study recruited 23 professional gamers and 20 healthy control participants. All participants underwent magnetic resonance imaging (MRI) scanning and cognitive function tests. Professional gamers demonstrated enhanced FC, larger cortical volumes, and improved FA within DAN compared to controls. They showed increased FC from the right anterior insula to the right anterior cingulate cortex and decreased FC within the TCN. Cortical thickness and FA values in attention-related regions were also higher. Working memory backward was negatively correlated with the FC from the left thalamus to the left pre-cingulate cortex in all participants. Hence, the brains of professional gamers adapt to respond to an immense influx of external stimuli by enhancing neurotransmission in the concentration-related white matter regions, along with enhanced connectivity and volume of DAN and restriction of TCN connectivity.\u003c/p\u003e","manuscriptTitle":"Immense data processing within brain networks in professional gamers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 00:05:09","doi":"10.21203/rs.3.rs-8467566/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T09:28:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T21:25:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T09:39:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7819709890059225917468038151280160365","date":"2026-01-22T06:53:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108349345733647833999817324109101940393","date":"2026-01-21T22:56:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T22:17:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T22:16:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-02T18:07:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-01T12:04:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-01T11:56:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53f207f0-b3af-4feb-97c7-0b5bc8224ae6","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61558743,"name":"Biological sciences/Biological techniques"},{"id":61558744,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-05-14T07:10:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 00:05:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8467566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8467566","identity":"rs-8467566","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}