Differences in Brain Activation Patterns Between Ice Hockey Experts and Novices During Sports Decision-Making: An fNIRS Study

Differences in Brain Activation Patterns Between Ice Hockey Experts and Novices During Sports Decision-Making: An fNIRS Study | 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 Differences in Brain Activation Patterns Between Ice Hockey Experts and Novices During Sports Decision-Making: An fNIRS Study Jie Ma, Jinzhen Zhao, Ying Qin, Xinyi Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9304532/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract This study explores the neural mechanisms underpinning tactical decision-making in ice hockey, focusing on the "neural efficiency" hypothesis through functional near-infrared spectroscopy (fNIRS). We compared 20 elite experts with 20 novices during a temporal occlusion task. Behavioral results confirmed a substantial expertise advantage: experts achieved significantly higher accuracy (77.0% ± 6.5%) than novices (51.0% ± 15.4%; t (38) = 6.97, p < 0.001) and exhibited faster reaction times (812.0 ± 130.1 ms vs. 1073.6 ± 160.9 ms; t (38) = -5.63, p 0.05), a targeted ROI analysis of the DLPFC (averaging Ch7, Ch22, Ch26, and Ch44) revealed a pronounced expertise-driven deactivation ( t (38) = -3.028, p = 0.0045). Furthermore, this regional HbO suppression was negatively correlated with decision accuracy ( r = -0.398, p = 0.013), suggesting that elite performance relies on the functional inhibition of deliberate executive control. These findings provide robust physiological evidence that expert-level intuition is characterized by streamlined neural processing rather than extensive cognitive mobilization. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Ice hockey Sports decision-making Functional near-infrared spectroscopy (fNIRS) Dorsolateral prefrontal cortex (DLPFC) Neural efficiency Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Ice hockey is characterized by rapid offensive-defensive transitions and severe time constraints, demanding high-intensity perceptual-cognitive processing in dynamic environments [ 1 , 2 ] . In such extreme settings, athletes must rapidly integrate complex visual cues—including puck trajectory, opponent kinematics, and teammate positioning—to execute critical tactical decisions, such as passing, shooting, or defending, within milliseconds [ 3 , 4 ] . Decision-making is among the most widely studied perceptual-cognitive skills in sports, encompassing higher-order processes like anticipation, visual search, and working memory [ 3 , 5 ] . Previous behavioral studies consistently demonstrate that expert athletes possess significant advantages in these domains. For instance, expert soccer goalkeepers are more effective at extracting predictive cues from opponents' body kinematics [ 6 ] and elite ice hockey defensemen employ more efficient visual search strategies to ground accurate tactical decisions [ 7 ] . Meta-analyses further corroborate that experts significantly outperform novices in both perceptual anticipation and decision accuracy [ 8 ] . While the behavioral advantages of elite athletes are well-documented, the underlying neural mechanisms driving these superior perceptual-cognitive skills remain a subject of debate. Two competing hypotheses prevail: one posits that expert brains recruit more neural resources (high activation) to manage complex task demands, while the neural efficiency hypothesis suggests that long-term specialized training optimizes neural networks. This optimization allows for highly automated processing, accomplishing tasks with reduced energy expenditure in relevant brain regions [ 9 , 10 ] . Functional near-infrared spectroscopy (fNIRS) has emerged as a robust, ecologically valid neuroimaging tool to monitor cortical hemodynamics during cognitive tasks [ 11 ] . Utilizing fNIRS, researchers have identified distinct neural efficiency characteristics in experts across various sports. For example, in volleyball, soccer, and orienteering, experts frequently exhibit lower or more localized activation in key prefrontal regions, such as the dorsolateral prefrontal cortex (DLPFC), during decision-making tasks [ 12 , 13 , 14 ] . However, neurocognitive research examining fast-paced, high-contact winter sports like ice hockey remains scarce. The extreme temporal and spatial pressures of ice hockey impose stringent demands on the DLPFC, a core hub of the brain's "central executive system" responsible for attentional control, working memory, and explicit monitoring [ 15 ] . Relying solely on behavioral metrics limits our understanding of how cognitive resources are allocated during millisecond-level confrontations, hindering the development of objective neurophysiological indicators for talent identification and training monitoring. Therefore, the present study aims to investigate the prefrontal neural mechanisms underlying ice hockey decision-making. Specifically, we seek to determine whether the speed and accuracy demonstrated by experts are achieved through the hyper-mobilization of the DLPFC, or through low-energy processing via functional deactivation. Furthermore, we explore whether the extent of this deactivation correlates with decision-making performance, thereby providing critical neurophysiological evidence to test the neural efficiency hypothesis in a high-complexity sporting context. 2. Results 2.1. Behavioral Performance in Tactical Decision-Making Independent-samples t -tests conducted on the sport-specific decision-making task revealed that the expert group exhibited a profound "fast and accurate" cognitive processing advantage. Specifically, the expert group achieved a decision accuracy of 77.0 ± 6.5%, which was significantly higher than the 51.0 ± 15.4% observed in the novice group ( t (38) = 6.97, p < 0.001, Cohen's d = 2.204). Regarding reaction time (RT), experts demonstrated more efficient information extraction capabilities; their mean response latency (812.0 ± 130.1 ms) was approximately 260 ms shorter than that of the novices (1073.6 ± 160.9 ms; t (38) = -5.625, p < 0.001, Cohen's d = -1.787). These behavioral metrics provide robust evidence that long-term specialized training endows elite athletes with superior perceptual-cognitive schemata and more refined anticipatory cues (Fig. 1 , Table 1 ). Table 1 Behavioral Performance Differences Between Expert and Novice Groups Measure Expert( n = 20) Novice( n = 20) t p Cohen's d Accuracy (%) 77.0 ± 6.5 51.0 ± 15.4 6.97 < 0.001 2.204 Reaction Time (ms) 812.0 ± 130.1 1073.6 ± 160.9 -5.625 < 0.001 -1.787 Note : Data are presented as Mean ± SD. Note A:Decision accuracy (%). B:Reaction time (ms). Error bars represent standard deviations (SD). The expert group demonstrated significantly higher accuracy and shorter reaction times. *** p < 0.001. 2.2. Differences in Cortical Activation Between Experts and Novices Task-evoked changes in oxygenated hemoglobin (HbO) concentration were extracted using a generalized linear model (GLM). Independent-samples t -tests were then applied to examine group differences in bilateral dorsolateral prefrontal cortex (DLPFC) activation. The analyses identified significant activation disparities across four specific channels: Ch7 (right DLPFC), Ch22 (left DLPFC), Ch26 (right DLPFC), and Ch44 (right DLPFC) (Fig. 2 , Table 2 ). Table 2 T-test results of HbO concentration changes in DLPFC ROI Channel (ROI) Expert Novice t p p adj (FDR) Ch7(R-DLPFC) -0.081 ± 0.091 -0.019 ± 0.066 -2.440 0.020 0.468 Ch22(L-DLPFC) -0.104 ± 0.120 -0.022 ± 0.079 -2.516 0.017 0.468 Ch26(R-DLPFC) -0.072 ± 0.095 -0.017 ± 0.074 -2.049 0.048 0.548 Ch44(R-DLPFC) -0.080 ± 0.106 -0.017 ± 0.085 -2.067 0.046 0.548 ROI (Ch7, 22, 26, 44) -0.0847 ± 0.0747 -0.0194 ± 0.0611 -3.028 0.0045 - Note :DLPFC = dorsolateral prefrontal cortex; HbO = oxygenated hemoglobin; ROI = Region of Interest; L = left; R = right. Data are presented as Mean ± SD. p adj represents the p-values adjusted for multiple comparisons using the Benjamini-Hochberg False Discovery Rate (FDR) method. Note The ROI was defined as the average of Ch7, Ch22, Ch26, and Ch44. Experts exhibited significant HbO deactivation compared to novices ( t (38) = -3.028, p = 0.0045). Bars represent Mean ± SD with individual data points overlaid. ** denotes p < 0.01. Independent t-tests were initially conducted on each of the 47 channels. To control for multiple comparisons, p-values were adjusted using the Benjamini-Hochberg False Discovery Rate (FDR) method. While several channels (Ch7, Ch22, Ch26, and Ch44) showed a trend toward deactivation in the expert group (all original p 0.05).However, to test the regional functional response and increase statistical power, a pre-defined ROI analysis was performed by averaging these four channels within the DLPFC. This analysis revealed a highly significant expertise effect ( t (38) = -3.028, p = 0.0045), with experts exhibiting marked HbO deactivation ( M = -0.085, SD = 0.075) compared to the near-baseline levels in novices ( M = -0.019, SD = 0.061). To visualize these spatial activation disparities, t -statistic topographic maps were generated (Fig. 3 ). Note A༚The expert group ( n = 20) demonstrates pronounced task-induced deactivation (indicated by cool colors) within the bilateral dorsolateral prefrontal cortex (DLPFC; specifically highlighted at channels Ch7, Ch22, Ch26, and Ch44). This localized inhibition reflects the 'neural economy' characteristics of elite players. B༚The novice group ( n = 20) exhibits extensive positive activation (indicated by warm colors, red/yellow) across the prefrontal regions, suggesting a massive mobilization of cognitive resources to process the same dynamic visual cues. The color bar represents the unified scale of activation intensity applied to both groups, ensuring direct comparability. As illustrated in the topographic maps, experts (Fig. 5 A) displayed pronounced and localized cortical deactivation within the prefrontal cortex, particularly the bilateral DLPFC (indicated by cool colors). Conversely, novices (Fig. 5 B) recruited diffuse and scattered cortical networks (indicated by warm colors), reflecting inefficient neural mobilization. This distinct pattern indicates that experts employ a more economical and restrained neural strategy under equivalent task demands, directly supporting the neural efficiency hypothesis. 2.3. Brain-Behavior Correlation Analysis To elucidate the relationship between prefrontal neural hemodynamics and decision-making performance, a region of interest (ROI) approach was adopted. Previous methodological research indicates that ROI-level fNIRS analysis provides superior reliability compared to single-channel analysis by mitigating noise, motion artifacts, and individual anatomical variances [ 16 , 17 ] . Consequently, HbO values from the four significant channels were averaged to create a composite DLPFC activation index. Pearson correlation coefficients were then computed between this index and behavioral metrics (Fig. 4 , Table 3 ). The correlation analysis revealed two key findings: (1) A significant negative correlation between DLPFC activation and decision accuracy ( r = -0.398, n = 40, p = 0.013), indicating that stronger cortical deactivation is associated with higher task accuracy. (2) A marginally significant positive correlation between DLPFC activation and reaction time ( r = 0.280, n = 40, p = 0.089), suggesting a trend where diminished prefrontal activation facilitates faster responses. Table 3 Correlation Between DLPFC Activation and Behavioral Performance Variable pair r R 2 p DLPFC activation × Accuracy − .398 .158 .013 DLPFC activation × Reaction time .280 .078 .089 Note :DLPFC = dorsolateral prefrontal cortex; r = Pearson correlation coefficient. Together, these correlational findings close the logical loop of the current study, demonstrating that reduced expenditure of prefrontal cognitive resources is intrinsically linked to superior, automated athletic performance. Note A: A significant negative correlation between composite DLPFC activation (HbO concentration) and decision reaction time ( r = 0.280, p = 0.089). B: A marginally significant positive correlation between DLPFC activation and accuracy ( r = -0.398, p = 0.013). Solid lines represent the linear regression fit, and shaded areas indicate 95% confidence intervals. 3. Discussion 3.1. Behavioral Advantages in Ice Hockey Decision-Making The present study first corroborated the significant perceptual-cognitive advantages of elite ice hockey players at the behavioral level. The expert group significantly outperformed the novice group, demonstrating both superior accuracy and expedited reaction times during the sport-specific decision-making task. These behavioral disparities can be attributed to the highly structured domain-specific knowledge base that expert athletes develop through years of specialized training [ 6 ] . When confronted with complex, dynamic game scenarios, experts leverage superior perceptual-cognitive skills to rapidly extract kinematic cues and filter out irrelevant visual noise, thereby achieving automaticity in decision-making [ 3 ] . In contrast, novices lack these refined cognitive schemata, compelling them to expend greater cognitive resources to sequentially process visual information, which inevitably results in delayed and less accurate responses. 3.2. Neural Efficiency and Cortical Activation Patterns The core neuroimaging finding of this study is the pronounced task-induced deactivation within the dorsolateral prefrontal cortex (DLPFC) among expert players during high-complexity tactical decision-making. This localized deactivation starkly contrasts with the diffuse, positive activation observed in novices, providing robust empirical support for the Neural Efficiency Hypothesis. A systematic review of neuroimaging in sports indicates that expert brains typically exhibit reduced and more focused cortical activation during specialized tasks, reflecting the neural pruning and network optimization driven by extensive practice [ 9 ] . According to the long-term working memory framework, deliberate practice allows experts to construct highly structured "retrieval structures" [ 18 ] . This optimization enables them to bypass the DLPFC—a region heavily involved in general cognitive control, explicit monitoring, and logical reasoning—and directly recruit subcortical pathways or sensorimotor networks for automated processing. Thus, the prefrontal "deactivation" observed in experts does not signify brain inactivity; rather, it represents a highly efficient neural "shortcut" that maximizes decision output while minimizing metabolic costs [ 15 ] . Crucially, this prefrontal deactivation likely reflects an "active suppression" mechanism. Holper et al. (2014) demonstrated that the brain actively attenuates prefrontal activity during highly automated tasks to prevent top-down conscious control from disrupting fluid movement sequences [ 19 ] . This aligns perfectly with the Theory of Reinvestment [ 20 ] , which posits that excessive explicit monitoring (i.e., "overthinking") disrupts the execution of automated motor skills, often resulting in "analysis paralysis" under pressure. Furthermore, our findings provide direct fNIRS evidence for Dietrich's (2004) Transient Hypofrontality Hypothesis [ 21 ] , suggesting that experts temporarily downregulate higher-order prefrontal control to unleash the full potential of the sensorimotor system, ensuring intuitive and seamless tactical execution in extreme time-pressured environments. 3.3. Brain-Behavior Correlations and Practical Implications The correlational analyses further elucidate the intrinsic brain-behavior relationship: DLPFC activation levels exhibited a significant negative correlation with decision accuracy ( r = -0.398). This indicates that during millisecond-level tactical evaluations, diminished prefrontal engagement is functionally associated with superior decision-making performance. This mirrors findings in soccer anticipation research, where elite predictive capabilities are frequently coupled with attenuated activation in cognitive control regions [ 22 ] . These neurobiological insights carry critical implications for cognitive training in ice hockey. Traditional tactical coaching often relies heavily on explicit rule instruction, which may inadvertently force athletes to over-rely on the DLPFC for logical analysis during high-stress matches. Our findings suggest that training paradigms should instead prioritize the facilitation of automated neural processing. Video-based simulation training rooted in implicit learning can effectively hone athletes' anticipatory skills without imposing extraneous cognitive loads [ 23 ] . Coaches should foster "heuristic decision-making" environments; through high-repetition situational simulations, athletes can reduce their dependence on explicit monitoring and cultivate intuitive decision-making frameworks, maintaining an efficient and "cool" cortical state during elite competition [ 24 ] . 3.4. Limitations and Future Directions While this study rigorously validated the neural efficiency mechanisms of ice hockey experts using a robust block design, several limitations warrant acknowledgment. First, the static seated experimental paradigm, though necessary to ensure high fNIRS signal-to-noise ratios, lacks the proprioceptive and vestibular inputs inherent to on-ice execution, somewhat limiting its ecological validity. Second, our ROI selection was confined to the prefrontal cortex. Future investigations should adopt whole head fNIRS arrays to concurrently monitor the motor and parietal cortices, thereby constructing a more comprehensive dynamic brain network model of athletic decision-making. 4 Conclusions Through an integrated analysis of behavioral performance and cortical hemodynamics, this study demonstrates that elite ice hockey players possess a distinct perceptual-cognitive advantage, characterized by significantly higher accuracy and expedited reaction times during sport-specific decision-making. At the neural level, experts exhibited pronounced task-induced deactivation (i.e., significantly reduced HbO concentration) within the dorsolateral prefrontal cortex (DLPFC). This reveals that their execution of complex tactical tasks does not rely on the hyper-recruitment of cognitive resources but rather reflects a highly optimized "neural economy." Furthermore, the correlational analyses solidify this brain-behavior relationship: DLPFC activation levels were significantly negatively correlated with decision accuracy and showed a positive trend with reaction time. Consequently, during millisecond-level tactical evaluations, diminished prefrontal engagement directly translates to superior decision-making performance. Ultimately, these findings provide compelling neuroimaging evidence for the neural efficiency hypothesis. Through extensive specialized training, experts develop the capacity to functionally inhibit controlled conscious processing, enabling them to execute high-quality, automated decisions with minimal metabolic expenditure. 5. Methods 5.1. Participants An a priori power analysis using G * Power 3.1 software was conducted to determine the required sample size. Based on an estimated effect size ( f ) of 0.9, an alpha level of 0.05, and a statistical power (1 − β ) of 0.80, the minimum sample size was calculated as 42 participants [ 25 ] . Accordingly, 42 participants were recruited from Harbin Sport University. After excluding two participants due to excessive motion artifacts, a final valid sample of 40 participants was retained. The expert group consisted of 20 active players from the university's elite ice hockey team (mean age = 20.05 ± 1.00 years), holding technical classifications of Master of Sports, Level 1, or Level 2. The novice group comprised 20 graduate students in Physical Education and Training (mean age = 23.55 ± 0.83 years) with no prior professional ice hockey experience. Inclusion criteria for all participants were: (1) right-handedness; (2) normal or corrected-to-normal vision with no astigmatism or color vision deficiencies; and (3) no prior exposure to similar experimental paradigms. Written informed consent was obtained from all participants prior to the experiment. The study protocol was approved by the Institutional Review Board of Harbin Sport University (Approval No. : [2025333]) and adhered to the Declaration of Helsinki. 5.2. Experimental Design and Materials A cross-sectional, between-subjects design was employed, with "group" (expert vs. novice) as the independent variable. The dependent variables included behavioral metrics (decision accuracy and reaction time) and neuroimaging metrics (oxygenated hemoglobin [HbO] concentration changes). Experimental stimuli were developed using high-definition broadcast footage of official National Hockey League (NHL) games. A classic temporal occlusion paradigm was utilized to simulate high-pressure decision-making scenarios [ 26 ] . Video clips depicting an offensive player with puck possession progressing toward a tactical maneuver were extracted. The videos were occluded (i.e., cut to a black screen) exactly 0.5 seconds prior to the critical action point. Keyboard responses were mapped as follows: "1" for passing, "2" for shooting, and "3" for maintaining puck possession (dribbling/skating). To control for extraneous variables, all experimental sessions were conducted in a sound-attenuated, dimly lit laboratory. The video stimuli were muted to eliminate potential auditory cues. 5.3. Experimental Procedure The experimental protocol consisted of a practice phase and a formal testing phase, programmed and presented using E-Prime 3.0 software. The practice phase included four familiarization trials to ensure participants understood the task and key mappings; these data were excluded from the final analysis. Prior to formal testing, a 30-second resting-state baseline was recorded. The formal experiment employed a standardized block design, known for its efficacy in capturing hemodynamic responses during motor-cognitive tasks [ 18 , 22 , 27 ] . The session comprised four blocks, totaling 24 trials. The temporal sequence of a single trial consisted of a central fixation cross ("+") for 1000 ms, followed by a 3000-ms ice hockey video clip, and concluding with a 2000-ms blank screen response window. Participants were instructed to rapidly and accurately judge the puck-carrier's tactical intent during this window. Each block contained six consecutive trials (36 s total duration), interspersed with 30-second rest periods to allow hemodynamic signals to return to baseline. Responses exceeding the 2000-ms window were marked as omissions and excluded from the behavioral and neuroimaging analyses. (Fig. 5 ). Note The block design consisted of 4 blocks, each containing 6 trials. In each trial, participants were presented with a fixation cross (1000 ms), followed by a temporal occlusion video stimulus (3000 ms), and a response window (2000 ms) to judge the tactical intent. 5.4. Data Acquisition Cerebral hemodynamic variations were recorded using the BS-7000 portable fNIRS imaging system (Wuhan Znion Technology Co., Ltd., China). The system utilized dual-wavelength continuous-wave laser diodes at 690 nm and 830 nm to monitor changes in oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. Data were acquired at a constant sampling frequency of 20 Hz. The customized probe array was specifically designed for the prefrontal cortex, comprising 15 light sources and 15 detectors. This interleaved configuration formed 47 effective measurement channels, with a fixed source-detector separation of 3 cm to ensure sufficient cortical penetration depth. Adhering to the international 10–20 system, the optode cap was positioned with the reference source S3 anchored at the Fpz landmark. To ensure precise spatial registration between fNIRS channels and the underlying cortical anatomy, a 3D digitizer (Nir Map) recorded the coordinates of four anatomical landmarks (Nz, Cz, AL, RL) and all optodes. These coordinates were subsequently projected onto the standard Montreal Neurological Institute (MNI) space and mapped to corresponding Brodmann areas using the NIRS-SPM method [ 28 ] . Based on this spatial mapping, the channels were classified into specific regions of interest (ROIs), primarily encompassing the bilateral dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), Orbitofrontal cortex (OFC) and frontopolar area (FPA). Stimulus presentation and fNIRS data streams were synchronized via TTL event markers sent from E-Prime 3.0 (Supplementary Fig. 1 and Supplementary Table 1). 5.5. Data Processing and Statistical Analysis Behavioral performance was evaluated using mean reaction time (RT) and accuracy. fNIRS data were preprocessed using the Nir Master analysis software. Raw optical intensity signals were converted to optical density. Motion artifacts were corrected using spline interpolation [ 29 ] . A bandpass filter (0.01–0.1 Hz) was applied to attenuate physiological noise (e.g., cardiac and respiratory cycles) and low-frequency drift. Changes in HbO, deoxygenated hemoglobin (HbR), and total hemoglobin (HbT) were calculated via the modified Beer-Lambert law. Consistent with previous literature [ 30 , 31 ] , HbO was selected as the primary indicator of cortical activation due to its superior signal-to-noise ratio and sensitivity to task-related cerebral blood flow changes. Statistical analyses were conducted using SPSS version 29.0. Normality was confirmed using the Shapiro-Wilk test. Independent-samples t -tests were utilized to evaluate group differences (expert vs. novice) in behavioral metrics and ROI-level HbO concentration changes. To quantify the significance of task-evoked hemodynamic changes, one-sample t -tests were performed on the task-baseline differential values for each channel to evaluate their deviation from zero. Subsequently, the False Discovery Rate (FDR) correction was applied to all p -values across channels and frequency points to control for multiple comparisons. These adjusted p -values served as the statistical basis for identifying significant activation periods and facilitating subsequent visualization. Furthermore, Pearson correlation coefficients were computed to assess the relationship between cortical activation (HbO) and behavioral performance. The alpha level for statistical significance was set at p < 0.05. 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Mental workload assessment based on functional near-infrared spectroscopy [in Chinese]. Acta Opt. Sin . 34 , 344–349. https://doi.org/10.3788/AOS201434.1130002 (2014). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx MNI.csv BrodmannAreaMRIcro.csv Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor invited by journal 08 Apr, 2026 Editor assigned by journal 04 Apr, 2026 Submission checks completed at journal 04 Apr, 2026 First submitted to journal 02 Apr, 2026 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. 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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-9304532","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":624952007,"identity":"728cc281-b886-4ef4-b588-345348c0ff7e","order_by":0,"name":"Jie Ma","email":"","orcid":"","institution":"Harbin Sport University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Ma","suffix":""},{"id":624952010,"identity":"022f3c67-8821-4711-933a-2be02648e867","order_by":1,"name":"Jinzhen Zhao","email":"","orcid":"","institution":"Harbin Sport University","correspondingAuthor":false,"prefix":"","firstName":"Jinzhen","middleName":"","lastName":"Zhao","suffix":""},{"id":624952012,"identity":"5f1c10be-8dad-49ab-85a7-c18bb16b4750","order_by":2,"name":"Ying Qin","email":"","orcid":"","institution":"Harbin Sport University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Qin","suffix":""},{"id":624952016,"identity":"1b41395e-2895-40a0-aa92-28394b20fa58","order_by":3,"name":"Xinyi Qu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYJACZgjF2MDAYGDDw8/fgF85D5qWNBnJGQeI1gIGh20MGhLwa7Fnb374uaDinuyGG8ltj3kKzvMYMBxg/PAxB48tPMeMpWecKTbecOZguzGPwW0ec+YGZsmZ2/BokUgwY+ZtS0jccLyxTRqkxbLhABszLz4t8s+/MfP+A2o5zAjSco7H4EACAS0SPEBbGuC2HCBCy5mcYmmeYwnGM88cbJOcY5DMIznjYDNev7C3H9/4macmQbbvRvoziTd/7Oz5+ZsPfviIRwsMgOKRgYkHwSZSC+MPotSOglEwCkbBSAMA69VPMxF5G+UAAAAASUVORK5CYII=","orcid":"","institution":"Harbin Sport University","correspondingAuthor":true,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Qu","suffix":""}],"badges":[],"createdAt":"2026-04-02 14:40:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9304532/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9304532/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107477330,"identity":"08b7f0d3-46da-499c-a9cd-4ba26483e051","added_by":"auto","created_at":"2026-04-22 00:00:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":918534,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBehavioral performance differences between expert and novice ice hockey players.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: A:Decision accuracy (%). B:Reaction time (ms). Error bars represent standard deviations (SD). The expert group demonstrated significantly higher accuracy and shorter reaction times. *** \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9304532/v1/7c2d1c19f0802aef4ffd219a.png"},{"id":107489933,"identity":"11703d3b-65c3-4fcd-9532-31015236195c","added_by":"auto","created_at":"2026-04-22 02:49:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1006599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExpertise-related differences in DLPFC ROI activation.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e:The ROI was defined as the average of Ch7, Ch22, Ch26, and Ch44. Experts exhibited significant HbO deactivation compared to novices (\u003cem\u003et\u003c/em\u003e(38) = -3.028, \u003cem\u003ep \u003c/em\u003e= 0.0045). Bars represent Mean ± SD with individual data points overlaid. ** denotes\u003cem\u003e p\u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9304532/v1/a42e7dea3f78ede32dea0f08.png"},{"id":107477334,"identity":"9b50d1c7-9e97-4a77-8f92-f97bdbd49223","added_by":"auto","created_at":"2026-04-22 00:00:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":210571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTopographic maps of prefrontal cortical activation during the tactical decision-making task.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e：A：The expert group (\u003cem\u003en\u003c/em\u003e= 20) demonstrates pronounced task-induced deactivation (indicated by cool colors) within the bilateral dorsolateral prefrontal cortex (DLPFC; specifically highlighted at channels Ch7, Ch22, Ch26, and Ch44). This localized inhibition reflects the 'neural economy' characteristics of elite players. B：The novice group (\u003cem\u003en\u003c/em\u003e= 20) exhibits extensive positive activation (indicated by warm colors, red/yellow) across the prefrontal regions, suggesting a massive mobilization of cognitive resources to process the same dynamic visual cues. The color bar represents the unified scale of activation intensity applied to both groups, ensuring direct comparability.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9304532/v1/26293719cddb1ece7bfb6537.png"},{"id":107704530,"identity":"ccc5e9f7-665e-443a-b5af-0fc645724dc9","added_by":"auto","created_at":"2026-04-24 08:46:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":797908,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eScatter plots illustrating the correlation between prefrontal cortical activation and behavioral performance.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: A: A significant negative correlation between composite DLPFC activation (HbO concentration) and decision reaction time (\u003cem\u003er\u003c/em\u003e = 0.280, \u003cem\u003ep\u003c/em\u003e = 0.089). B: A marginally significant positive correlation between DLPFC activation and accuracy (\u003cem\u003er\u003c/em\u003e = -0.398, \u003cem\u003ep\u003c/em\u003e= 0.013). Solid lines represent the linear regression fit, and shaded areas indicate 95% confidence intervals.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9304532/v1/55d3d7398ed1d76f5e7f4a23.png"},{"id":107477337,"identity":"2d17f917-a8bc-4ada-ba77-a0a747fdaf49","added_by":"auto","created_at":"2026-04-22 00:00:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":265022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSchematic illustration of the experimental procedure.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: The block design consisted of 4 blocks, each containing 6 trials. In each trial, participants were presented with a fixation cross (1000 ms), followed by a temporal occlusion video stimulus (3000 ms), and a response window (2000 ms) to judge the tactical intent.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9304532/v1/4f546efe905b11285fbed129.png"},{"id":107708496,"identity":"62a48fad-cb0d-4cb6-963b-36ac0f94eaeb","added_by":"auto","created_at":"2026-04-24 09:27:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3562831,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9304532/v1/c4e10c7c-7988-46bb-841e-613e45fee681.pdf"},{"id":107477333,"identity":"d6d97649-a781-4288-b2f4-a843a686759d","added_by":"auto","created_at":"2026-04-22 00:00:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1770267,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9304532/v1/3314c21c54298779dca6e395.docx"},{"id":107489969,"identity":"f070d9cf-c4fb-4a6e-86c9-809bde6a8fd1","added_by":"auto","created_at":"2026-04-22 02:49:26","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1634,"visible":true,"origin":"","legend":"","description":"","filename":"MNI.csv","url":"https://assets-eu.researchsquare.com/files/rs-9304532/v1/9d433d68b0477d5b2bcd9ba5.csv"},{"id":107489020,"identity":"734e330c-b7e3-46c7-a7cb-dad4b6080ba7","added_by":"auto","created_at":"2026-04-22 02:46:29","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11114,"visible":true,"origin":"","legend":"","description":"","filename":"BrodmannAreaMRIcro.csv","url":"https://assets-eu.researchsquare.com/files/rs-9304532/v1/7d11b1991d9dca5287e24308.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differences in Brain Activation Patterns Between Ice Hockey Experts and Novices During Sports Decision-Making: An fNIRS Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIce hockey is characterized by rapid offensive-defensive transitions and severe time constraints, demanding high-intensity perceptual-cognitive processing in dynamic environments \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In such extreme settings, athletes must rapidly integrate complex visual cues\u0026mdash;including puck trajectory, opponent kinematics, and teammate positioning\u0026mdash;to execute critical tactical decisions, such as passing, shooting, or defending, within milliseconds \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDecision-making is among the most widely studied perceptual-cognitive skills in sports, encompassing higher-order processes like anticipation, visual search, and working memory \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Previous behavioral studies consistently demonstrate that expert athletes possess significant advantages in these domains. For instance, expert soccer goalkeepers are more effective at extracting predictive cues from opponents' body kinematics \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e and elite ice hockey defensemen employ more efficient visual search strategies to ground accurate tactical decisions \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Meta-analyses further corroborate that experts significantly outperform novices in both perceptual anticipation and decision accuracy \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile the behavioral advantages of elite athletes are well-documented, the underlying neural mechanisms driving these superior perceptual-cognitive skills remain a subject of debate. Two competing hypotheses prevail: one posits that expert brains recruit more neural resources (high activation) to manage complex task demands, while the \u003cem\u003eneural efficiency hypothesis\u003c/em\u003e suggests that long-term specialized training optimizes neural networks. This optimization allows for highly automated processing, accomplishing tasks with reduced energy expenditure in relevant brain regions \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFunctional near-infrared spectroscopy (fNIRS) has emerged as a robust, ecologically valid neuroimaging tool to monitor cortical hemodynamics during cognitive tasks \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Utilizing fNIRS, researchers have identified distinct neural efficiency characteristics in experts across various sports. For example, in volleyball, soccer, and orienteering, experts frequently exhibit lower or more localized activation in key prefrontal regions, such as the dorsolateral prefrontal cortex (DLPFC), during decision-making tasks \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, neurocognitive research examining fast-paced, high-contact winter sports like ice hockey remains scarce. The extreme temporal and spatial pressures of ice hockey impose stringent demands on the DLPFC, a core hub of the brain's \"central executive system\" responsible for attentional control, working memory, and explicit monitoring \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Relying solely on behavioral metrics limits our understanding of how cognitive resources are allocated during millisecond-level confrontations, hindering the development of objective neurophysiological indicators for talent identification and training monitoring.\u003c/p\u003e \u003cp\u003eTherefore, the present study aims to investigate the prefrontal neural mechanisms underlying ice hockey decision-making. Specifically, we seek to determine whether the speed and accuracy demonstrated by experts are achieved through the hyper-mobilization of the DLPFC, or through low-energy processing via functional deactivation. Furthermore, we explore whether the extent of this deactivation correlates with decision-making performance, thereby providing critical neurophysiological evidence to test the neural efficiency hypothesis in a high-complexity sporting context.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Behavioral Performance in Tactical Decision-Making\u003c/h2\u003e \u003cp\u003eIndependent-samples \u003cem\u003et\u003c/em\u003e-tests conducted on the sport-specific decision-making task revealed that the expert group exhibited a profound \"fast and accurate\" cognitive processing advantage. Specifically, the expert group achieved a decision accuracy of 77.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5%, which was significantly higher than the 51.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4% observed in the novice group (\u003cem\u003et\u003c/em\u003e (38)\u0026thinsp;=\u0026thinsp;6.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eCohen's d\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.204). Regarding reaction time (RT), experts demonstrated more efficient information extraction capabilities; their mean response latency (812.0\u0026thinsp;\u0026plusmn;\u0026thinsp;130.1 ms) was approximately 260 ms shorter than that of the novices (1073.6\u0026thinsp;\u0026plusmn;\u0026thinsp;160.9 ms; \u003cem\u003et\u003c/em\u003e (38) = -5.625, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eCohen's d\u003c/em\u003e = -1.787). These behavioral metrics provide robust evidence that long-term specialized training endows elite athletes with superior perceptual-cognitive schemata and more refined anticipatory cues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 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\u003e\u003cem\u003eBehavioral Performance Differences Between Expert and Novice Groups\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpert(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNovice(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCohen's d\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e77.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e51.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReaction Time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e812.0\u0026thinsp;\u0026plusmn;\u0026thinsp;130.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1073.6\u0026thinsp;\u0026plusmn;\u0026thinsp;160.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: Data are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eA:Decision accuracy (%). B:Reaction time (ms). Error bars represent standard deviations (SD). The expert group demonstrated significantly higher accuracy and shorter reaction times. *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Differences in Cortical Activation Between Experts and Novices\u003c/h2\u003e \u003cp\u003eTask-evoked changes in oxygenated hemoglobin (HbO) concentration were extracted using a generalized linear model (GLM). Independent-samples \u003cem\u003et\u003c/em\u003e-tests were then applied to examine group differences in bilateral dorsolateral prefrontal cortex (DLPFC) activation. The analyses identified significant activation disparities across four specific channels: Ch7 (right DLPFC), Ch22 (left DLPFC), Ch26 (right DLPFC), and Ch44 (right DLPFC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eT-test results of HbO concentration changes in DLPFC ROI\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChannel (ROI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpert\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNovice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e(FDR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh7(R-DLPFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.081\u0026thinsp;\u0026plusmn;\u0026thinsp;0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-0.019\u0026thinsp;\u0026plusmn;\u0026thinsp;0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh22(L-DLPFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.104\u0026thinsp;\u0026plusmn;\u0026thinsp;0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-0.022\u0026thinsp;\u0026plusmn;\u0026thinsp;0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh26(R-DLPFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.072\u0026thinsp;\u0026plusmn;\u0026thinsp;0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh44(R-DLPFC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.080\u0026thinsp;\u0026plusmn;\u0026thinsp;0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROI (Ch7, 22, 26, 44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.0847\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-0.0194\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e:DLPFC\u0026thinsp;=\u0026thinsp;dorsolateral prefrontal cortex; HbO\u0026thinsp;=\u0026thinsp;oxygenated hemoglobin; ROI\u0026thinsp;=\u0026thinsp;Region of Interest; L\u0026thinsp;=\u0026thinsp;left; R\u0026thinsp;=\u0026thinsp;right. Data are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e represents the p-values adjusted for multiple comparisons using the Benjamini-Hochberg False Discovery Rate (FDR) method.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThe ROI was defined as the average of Ch7, Ch22, Ch26, and Ch44. Experts exhibited significant HbO deactivation compared to novices (\u003cem\u003et\u003c/em\u003e(38) = -3.028, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0045). Bars represent Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD with individual data points overlaid. ** denotes \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIndependent t-tests were initially conducted on each of the 47 channels. To control for multiple comparisons, p-values were adjusted using the Benjamini-Hochberg False Discovery Rate (FDR) method. While several channels (Ch7, Ch22, Ch26, and Ch44) showed a trend toward deactivation in the expert group (all original \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), none remained significant after FDR correction (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.05).However, to test the regional functional response and increase statistical power, a pre-defined ROI analysis was performed by averaging these four channels within the DLPFC. This analysis revealed a highly significant expertise effect (\u003cem\u003et\u003c/em\u003e(38) = -3.028, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0045), with experts exhibiting marked HbO deactivation (\u003cem\u003eM\u003c/em\u003e = -0.085, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.075) compared to the near-baseline levels in novices (\u003cem\u003eM\u003c/em\u003e = -0.019, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.061). To visualize these spatial activation disparities, \u003cem\u003et\u003c/em\u003e-statistic topographic maps were generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eA༚The expert group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20) demonstrates pronounced task-induced deactivation (indicated by cool colors) within the bilateral dorsolateral prefrontal cortex (DLPFC; specifically highlighted at channels Ch7, Ch22, Ch26, and Ch44). This localized inhibition reflects the 'neural economy' characteristics of elite players. B༚The novice group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20) exhibits extensive positive activation (indicated by warm colors, red/yellow) across the prefrontal regions, suggesting a massive mobilization of cognitive resources to process the same dynamic visual cues. The color bar represents the unified scale of activation intensity applied to both groups, ensuring direct comparability.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAs illustrated in the topographic maps, experts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) displayed pronounced and localized cortical deactivation within the prefrontal cortex, particularly the bilateral DLPFC (indicated by cool colors). Conversely, novices (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) recruited diffuse and scattered cortical networks (indicated by warm colors), reflecting inefficient neural mobilization. This distinct pattern indicates that experts employ a more economical and restrained neural strategy under equivalent task demands, directly supporting the neural efficiency hypothesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Brain-Behavior Correlation Analysis\u003c/h2\u003e \u003cp\u003eTo elucidate the relationship between prefrontal neural hemodynamics and decision-making performance, a region of interest (ROI) approach was adopted. Previous methodological research indicates that ROI-level fNIRS analysis provides superior reliability compared to single-channel analysis by mitigating noise, motion artifacts, and individual anatomical variances \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Consequently, HbO values from the four significant channels were averaged to create a composite DLPFC activation index. Pearson correlation coefficients were then computed between this index and behavioral metrics (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe correlation analysis revealed two key findings: (1) A significant negative correlation between DLPFC activation and decision accuracy (\u003cem\u003er\u003c/em\u003e = -0.398, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), indicating that stronger cortical deactivation is associated with higher task accuracy. (2) A marginally significant positive correlation between DLPFC activation and reaction time (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.280, n\u0026thinsp;=\u0026thinsp;40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.089), suggesting a trend where diminished prefrontal activation facilitates faster responses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelation Between DLPFC Activation and Behavioral Performance\u003c/em\u003e\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 \u003cp\u003eVariable pair\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLPFC activation \u0026times; Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLPFC activation \u0026times; Reaction time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e:DLPFC\u0026thinsp;=\u0026thinsp;dorsolateral prefrontal cortex; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Pearson correlation coefficient.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTogether, these correlational findings close the logical loop of the current study, demonstrating that reduced expenditure of prefrontal cognitive resources is intrinsically linked to superior, automated athletic performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eA: A significant negative correlation between composite DLPFC activation (HbO concentration) and decision reaction time (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.280, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.089). B: A marginally significant positive correlation between DLPFC activation and accuracy (\u003cem\u003er\u003c/em\u003e = -0.398, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). Solid lines represent the linear regression fit, and shaded areas indicate 95% confidence intervals.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Behavioral Advantages in Ice Hockey Decision-Making\u003c/h2\u003e \u003cp\u003eThe present study first corroborated the significant perceptual-cognitive advantages of elite ice hockey players at the behavioral level. The expert group significantly outperformed the novice group, demonstrating both superior accuracy and expedited reaction times during the sport-specific decision-making task.\u003c/p\u003e \u003cp\u003eThese behavioral disparities can be attributed to the highly structured domain-specific knowledge base that expert athletes develop through years of specialized training \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. When confronted with complex, dynamic game scenarios, experts leverage superior perceptual-cognitive skills to rapidly extract kinematic cues and filter out irrelevant visual noise, thereby achieving automaticity in decision-making \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In contrast, novices lack these refined cognitive schemata, compelling them to expend greater cognitive resources to sequentially process visual information, which inevitably results in delayed and less accurate responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Neural Efficiency and Cortical Activation Patterns\u003c/h2\u003e \u003cp\u003eThe core neuroimaging finding of this study is the pronounced task-induced deactivation within the dorsolateral prefrontal cortex (DLPFC) among expert players during high-complexity tactical decision-making. This localized deactivation starkly contrasts with the diffuse, positive activation observed in novices, providing robust empirical support for the Neural Efficiency Hypothesis. A systematic review of neuroimaging in sports indicates that expert brains typically exhibit reduced and more focused cortical activation during specialized tasks, reflecting the neural pruning and network optimization driven by extensive practice \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccording to the long-term working memory framework, deliberate practice allows experts to construct highly structured \"retrieval structures\" \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. This optimization enables them to bypass the DLPFC\u0026mdash;a region heavily involved in general cognitive control, explicit monitoring, and logical reasoning\u0026mdash;and directly recruit subcortical pathways or sensorimotor networks for automated processing. Thus, the prefrontal \"deactivation\" observed in experts does not signify brain inactivity; rather, it represents a highly efficient neural \"shortcut\" that maximizes decision output while minimizing metabolic costs \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCrucially, this prefrontal deactivation likely reflects an \"active suppression\" mechanism. Holper et al. (2014) demonstrated that the brain actively attenuates prefrontal activity during highly automated tasks to prevent top-down conscious control from disrupting fluid movement sequences \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. This aligns perfectly with the Theory of Reinvestment \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, which posits that excessive explicit monitoring (i.e., \"overthinking\") disrupts the execution of automated motor skills, often resulting in \"analysis paralysis\" under pressure. Furthermore, our findings provide direct fNIRS evidence for Dietrich's (2004) Transient Hypofrontality Hypothesis \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, suggesting that experts temporarily downregulate higher-order prefrontal control to unleash the full potential of the sensorimotor system, ensuring intuitive and seamless tactical execution in extreme time-pressured environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Brain-Behavior Correlations and Practical Implications\u003c/h2\u003e \u003cp\u003eThe correlational analyses further elucidate the intrinsic brain-behavior relationship: DLPFC activation levels exhibited a significant negative correlation with decision accuracy (\u003cem\u003er\u003c/em\u003e = -0.398). This indicates that during millisecond-level tactical evaluations, diminished prefrontal engagement is functionally associated with superior decision-making performance. This mirrors findings in soccer anticipation research, where elite predictive capabilities are frequently coupled with attenuated activation in cognitive control regions \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese neurobiological insights carry critical implications for cognitive training in ice hockey. Traditional tactical coaching often relies heavily on explicit rule instruction, which may inadvertently force athletes to over-rely on the DLPFC for logical analysis during high-stress matches. Our findings suggest that training paradigms should instead prioritize the facilitation of automated neural processing. Video-based simulation training rooted in implicit learning can effectively hone athletes' anticipatory skills without imposing extraneous cognitive loads \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Coaches should foster \"heuristic decision-making\" environments; through high-repetition situational simulations, athletes can reduce their dependence on explicit monitoring and cultivate intuitive decision-making frameworks, maintaining an efficient and \"cool\" cortical state during elite competition \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Limitations and Future Directions\u003c/h2\u003e \u003cp\u003eWhile this study rigorously validated the neural efficiency mechanisms of ice hockey experts using a robust block design, several limitations warrant acknowledgment. First, the static seated experimental paradigm, though necessary to ensure high fNIRS signal-to-noise ratios, lacks the proprioceptive and vestibular inputs inherent to on-ice execution, somewhat limiting its ecological validity. Second, our ROI selection was confined to the prefrontal cortex. Future investigations should adopt whole head fNIRS arrays to concurrently monitor the motor and parietal cortices, thereby constructing a more comprehensive dynamic brain network model of athletic decision-making.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThrough an integrated analysis of behavioral performance and cortical hemodynamics, this study demonstrates that elite ice hockey players possess a distinct perceptual-cognitive advantage, characterized by significantly higher accuracy and expedited reaction times during sport-specific decision-making. At the neural level, experts exhibited pronounced task-induced deactivation (i.e., significantly reduced HbO concentration) within the dorsolateral prefrontal cortex (DLPFC). This reveals that their execution of complex tactical tasks does not rely on the hyper-recruitment of cognitive resources but rather reflects a highly optimized \"neural economy.\"\u003c/p\u003e \u003cp\u003eFurthermore, the correlational analyses solidify this brain-behavior relationship: DLPFC activation levels were significantly negatively correlated with decision accuracy and showed a positive trend with reaction time. Consequently, during millisecond-level tactical evaluations, diminished prefrontal engagement directly translates to superior decision-making performance. Ultimately, these findings provide compelling neuroimaging evidence for the neural efficiency hypothesis. Through extensive specialized training, experts develop the capacity to functionally inhibit controlled conscious processing, enabling them to execute high-quality, automated decisions with minimal metabolic expenditure.\u003c/p\u003e"},{"header":"5. Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Participants\u003c/h2\u003e \u003cp\u003eAn \u003cem\u003ea priori\u003c/em\u003e power analysis using G\u003csup\u003e*\u003c/sup\u003ePower 3.1 software was conducted to determine the required sample size. Based on an estimated effect size (\u003cem\u003ef\u003c/em\u003e) of 0.9, an alpha level of 0.05, and a statistical power (1\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e) of 0.80, the minimum sample size was calculated as 42 participants \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Accordingly, 42 participants were recruited from Harbin Sport University. After excluding two participants due to excessive motion artifacts, a final valid sample of 40 participants was retained.\u003c/p\u003e \u003cp\u003eThe expert group consisted of 20 active players from the university's elite ice hockey team (mean age\u0026thinsp;=\u0026thinsp;20.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00 years), holding technical classifications of Master of Sports, Level 1, or Level 2. The novice group comprised 20 graduate students in Physical Education and Training (mean age\u0026thinsp;=\u0026thinsp;23.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83 years) with no prior professional ice hockey experience. Inclusion criteria for all participants were: (1) right-handedness; (2) normal or corrected-to-normal vision with no astigmatism or color vision deficiencies; and (3) no prior exposure to similar experimental paradigms. Written informed consent was obtained from all participants prior to the experiment. The study protocol was approved by the Institutional Review Board of Harbin Sport University (Approval No. : [2025333]) and adhered to the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Experimental Design and Materials\u003c/h2\u003e \u003cp\u003eA cross-sectional, between-subjects design was employed, with \"group\" (expert vs. novice) as the independent variable. The dependent variables included behavioral metrics (decision accuracy and reaction time) and neuroimaging metrics (oxygenated hemoglobin [HbO] concentration changes).\u003c/p\u003e \u003cp\u003eExperimental stimuli were developed using high-definition broadcast footage of official National Hockey League (NHL) games. A classic temporal occlusion paradigm was utilized to simulate high-pressure decision-making scenarios \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Video clips depicting an offensive player with puck possession progressing toward a tactical maneuver were extracted. The videos were occluded (i.e., cut to a black screen) exactly 0.5 seconds prior to the critical action point. Keyboard responses were mapped as follows: \"1\" for passing, \"2\" for shooting, and \"3\" for maintaining puck possession (dribbling/skating). To control for extraneous variables, all experimental sessions were conducted in a sound-attenuated, dimly lit laboratory. The video stimuli were muted to eliminate potential auditory cues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Experimental Procedure\u003c/h2\u003e \u003cp\u003eThe experimental protocol consisted of a practice phase and a formal testing phase, programmed and presented using E-Prime 3.0 software. The practice phase included four familiarization trials to ensure participants understood the task and key mappings; these data were excluded from the final analysis.\u003c/p\u003e \u003cp\u003ePrior to formal testing, a 30-second resting-state baseline was recorded. The formal experiment employed a standardized block design, known for its efficacy in capturing hemodynamic responses during motor-cognitive tasks \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. The session comprised four blocks, totaling 24 trials. The temporal sequence of a single trial consisted of a central fixation cross (\"+\") for 1000 ms, followed by a 3000-ms ice hockey video clip, and concluding with a 2000-ms blank screen response window. Participants were instructed to rapidly and accurately judge the puck-carrier's tactical intent during this window. Each block contained six consecutive trials (36 s total duration), interspersed with 30-second rest periods to allow hemodynamic signals to return to baseline. Responses exceeding the 2000-ms window were marked as omissions and excluded from the behavioral and neuroimaging analyses. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThe block design consisted of 4 blocks, each containing 6 trials. In each trial, participants were presented with a fixation cross (1000 ms), followed by a temporal occlusion video stimulus (3000 ms), and a response window (2000 ms) to judge the tactical intent.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Data Acquisition\u003c/h2\u003e \u003cp\u003eCerebral hemodynamic variations were recorded using the BS-7000 portable fNIRS imaging system (Wuhan Znion Technology Co., Ltd., China). The system utilized dual-wavelength continuous-wave laser diodes at 690 nm and 830 nm to monitor changes in oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. Data were acquired at a constant sampling frequency of 20 Hz.\u003c/p\u003e \u003cp\u003eThe customized probe array was specifically designed for the prefrontal cortex, comprising 15 light sources and 15 detectors. This interleaved configuration formed 47 effective measurement channels, with a fixed source-detector separation of 3 cm to ensure sufficient cortical penetration depth. Adhering to the international 10\u0026ndash;20 system, the optode cap was positioned with the reference source S3 anchored at the Fpz landmark.\u003c/p\u003e \u003cp\u003eTo ensure precise spatial registration between fNIRS channels and the underlying cortical anatomy, a 3D digitizer (Nir Map) recorded the coordinates of four anatomical landmarks (Nz, Cz, AL, RL) and all optodes. These coordinates were subsequently projected onto the standard Montreal Neurological Institute (MNI) space and mapped to corresponding Brodmann areas using the NIRS-SPM method \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on this spatial mapping, the channels were classified into specific regions of interest (ROIs), primarily encompassing the bilateral dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), Orbitofrontal cortex (OFC) and frontopolar area (FPA). Stimulus presentation and fNIRS data streams were synchronized via TTL event markers sent from E-Prime 3.0 (Supplementary Fig.\u0026nbsp;1 and Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.5. Data Processing and Statistical Analysis\u003c/h2\u003e \u003cp\u003eBehavioral performance was evaluated using mean reaction time (RT) and accuracy. fNIRS data were preprocessed using the Nir Master analysis software. Raw optical intensity signals were converted to optical density. Motion artifacts were corrected using spline interpolation \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. A bandpass filter (0.01\u0026ndash;0.1 Hz) was applied to attenuate physiological noise (e.g., cardiac and respiratory cycles) and low-frequency drift. Changes in HbO, deoxygenated hemoglobin (HbR), and total hemoglobin (HbT) were calculated via the modified Beer-Lambert law. Consistent with previous literature \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, HbO was selected as the primary indicator of cortical activation due to its superior signal-to-noise ratio and sensitivity to task-related cerebral blood flow changes.\u003c/p\u003e \u003cp\u003eStatistical analyses were conducted using SPSS version 29.0. Normality was confirmed using the Shapiro-Wilk test. Independent-samples \u003cem\u003et\u003c/em\u003e-tests were utilized to evaluate group differences (expert vs. novice) in behavioral metrics and ROI-level HbO concentration changes. To quantify the significance of task-evoked hemodynamic changes, one-sample \u003cem\u003et\u003c/em\u003e-tests were performed on the task-baseline differential values for each channel to evaluate their deviation from zero. Subsequently, the False Discovery Rate (FDR) correction was applied to all \u003cem\u003ep\u003c/em\u003e-values across channels and frequency points to control for multiple comparisons. These adjusted \u003cem\u003ep\u003c/em\u003e-values served as the statistical basis for identifying significant activation periods and facilitating subsequent visualization. Furthermore, Pearson correlation coefficients were computed to assess the relationship between cortical activation (HbO) and behavioral performance. The alpha level for statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.M. contributed to conceptualization, methodology, formal analysis, and wrote the original draft. Y.Q. contributed to investigation, data curation, and funding acquisition. J.Z. was responsible for software and validation. X.Q. supervised the project and contributed to writing, reviewing, and editing. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Heilongjiang Province under Grant No.: LH2024C063.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoczniok, R. et al. Physiological, physical and anthropometric variables determining the playing position in ice hockey. \u003cem\u003eBiol. 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Mental workload assessment based on functional near-infrared spectroscopy [in Chinese]. \u003cem\u003eActa Opt. Sin\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e, 344\u0026ndash;349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3788/AOS201434.1130002\u003c/span\u003e\u003cspan address=\"10.3788/AOS201434.1130002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\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":false,"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":"Ice hockey, Sports decision-making, Functional near-infrared spectroscopy (fNIRS), Dorsolateral prefrontal cortex (DLPFC), Neural efficiency","lastPublishedDoi":"10.21203/rs.3.rs-9304532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9304532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the neural mechanisms underpinning tactical decision-making in ice hockey, focusing on the \"neural efficiency\" hypothesis through functional near-infrared spectroscopy (fNIRS). We compared 20 elite experts with 20 novices during a temporal occlusion task. Behavioral results confirmed a substantial expertise advantage: experts achieved significantly higher accuracy (77.0% \u0026plusmn; 6.5%) than novices (51.0% \u0026plusmn; 15.4%; \u003cem\u003et\u003c/em\u003e (38)\u0026thinsp;=\u0026thinsp;6.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and exhibited faster reaction times (812.0\u0026thinsp;\u0026plusmn;\u0026thinsp;130.1 ms vs. 1073.6\u0026thinsp;\u0026plusmn;\u0026thinsp;160.9 ms; \u003cem\u003et\u003c/em\u003e (38) = -5.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding neural activity, while individual channel differences did not survive FDR correction (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.05), a targeted ROI analysis of the DLPFC (averaging Ch7, Ch22, Ch26, and Ch44) revealed a pronounced expertise-driven deactivation (\u003cem\u003et\u003c/em\u003e (38) = -3.028, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0045). Furthermore, this regional HbO suppression was negatively correlated with decision accuracy (\u003cem\u003er\u003c/em\u003e = -0.398, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), suggesting that elite performance relies on the functional inhibition of deliberate executive control. These findings provide robust physiological evidence that expert-level intuition is characterized by streamlined neural processing rather than extensive cognitive mobilization.\u003c/p\u003e","manuscriptTitle":"Differences in Brain Activation Patterns Between Ice Hockey Experts and Novices During Sports Decision-Making: An fNIRS Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 00:00:53","doi":"10.21203/rs.3.rs-9304532/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-26T15:39:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29916559297858676980210817650558274243","date":"2026-04-17T11:53:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20567620614699049247378587253740698530","date":"2026-04-17T10:04:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T13:21:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-08T11:48:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-04T06:38:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-04T06:38:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-02T14:23:13+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":"6b19c55e-3fd3-4c15-8b7d-5470fccae20d","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66521109,"name":"Biological sciences/Neuroscience"},{"id":66521110,"name":"Biological sciences/Psychology"},{"id":66521111,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-22T00:00:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 00:00:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9304532","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9304532","identity":"rs-9304532","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}