The Influence of Long-Term Football Training on Neural Representation of Action Verb Semantics

The Influence of Long-Term Football Training on Neural Representation of Action Verb Semantics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Influence of Long-Term Football Training on Neural Representation of Action Verb Semantics Jian Wang, Hong Mou, Likai Liu, Chenglin Zhou, Yingying Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4062491/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The debate on whether sensorimotor experience can modulate the neural representation of action verbs is ongoing. This study investigated whether extensive football training alters the neural patterns representing action verbs in sensorimotor and related regions, focusing on effector-specific changes. Specifically, we assessed if training experiences of specific effectors influence semantic neural representation patterns in corresponding sensorimotor areas. Employing functional magnetic resonance imaging (fMRI), subjects (both football experts and novices) engaged in an implicit reading task, silently reading action verbs and identifying the involved body part. We used multivariate pattern analyses (MVPA) to classify effector-related information and assess decoding accuracy in the right paracentral gyrus (PCG) and left postcentral gyrus (PoCG) associated with action execution, and the left inferior parietal lobule (IPL) and left precentral gyrus (PrG) linked to action observation. Our findings revealed that both experts and novices could decode effector information from action verbs across all regions of interest. Notably, distinct activation patterns between experts and novices were observed in execution regions (PCG and PoCG), but not in observation regions (IPL and PrG), highlighting a specialized neural adaptation in PCG and PoCG. Furthermore, a significant correlation between decoding accuracy and training duration was found among football experts. Univariate analysis showed that experts exhibited higher activation intensity when processing foot-related verbs. In summary, our results suggest that long-term football training effector-specifically modulates the neural representation of action verbs in sensorimotor and related areas, predominantly driven by motor rather than sensory experience. Cognitive Neuroscience semantics fMRI MVPA sensorimotor experience football Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Understanding how motor experience influences semantic neural representations remains a crucial question in cognitive neuroscience. This study delves into how long-term football training influences neural patterns representing action verbs, especially in sensorimotor and associated areas, with a focus on effector-specific alterations. The embodied cognition theory, which suggests semantic processing is inherently linked to the sensory-motor system (Pulvermüller, 2005, 2013; Gallese & Cuccio, 2018), is supported by neuroimaging studies that show activation in motor brain regions when comprehending action words, such as the primary sensorimotor (M1 and S1) and dorsal premotor cortices (PMd) (Hauk et al., 2004; Tettamanti et al., 2005; Aziz-Zadeh et al., 2006; Willems et al., 2010). Further evidence from Beilock et al. (2008) and Lyons et al. (2010) suggests that sports-related motor experience enhances understanding of action-related language. Additionally, Xiong et al. (2023) observed that a short-term microgravity simulation reduced the neural representation of action verbs, indicating effector-specific modulation. Research has established the significant role of sensorimotor experience in semantic neural representation (Barsalou et al., 2003; Pickering & Garrod, 2013; Ralph et al., 2017, Bi, 2021). For instance, a neuroimaging study showed that language depicting actions activates motor brain areas, correlating the kinematic details of a verb with neural patterns tied to language comprehension (Van Dam et al., 2010). Hauk et al. (2004) found somatotopic activation in the premotor and frontal regions during the passive reading of action-related words. Recent studies using multivariate pattern analysis (MVPA) and predictive pattern decomposition analysis have identified common neural patterns between actions and verbs (Horoufchin et al., 2018). Crossmodal MVPA studies have demonstrated decoding abilities in areas like the lateral occipitotemporal cortex, inferior parietal lobule, and ventral premotor cortex (Karakose-Akbiyik et al., 2023; Liu et al., 2023; Wurm & Caramazza, 2019), which are crucial to the action observation network (Hardwick et al., 2018; Molenberghs et al., 2012; Caspers et al., 2010). A single neuron recording study also revealed overlapping neural substrates between words and their related sensorimotor representations (Aflalo et al., 2020). For instance, Argiris et al. (2020) provided evidence that even with lesions in the sensorimotor cortex, patients' abilities to process action verbs were not impaired. An repetitive transcranial magnetic stimulation (rTMS) study showed that M1-rTMS did not affect meaning construction, which disputes the involvement of the primary motor cortex in the construction of meaning from action language (Solana et al., 2023). Therefore, it is necessary to further explore the role of sensorimotor experience and sensorimotor cortices on the neural representation of action verb processing. This study used MVPA to explore the influence of extensive football training on the neural semantic representation of action verbs. We analyzed functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signal patterns in professional football athletes and novices. An implicit word-processing task minimized motor processing interference, with subjects responding to verbs related to lips. MVPA was applied to areas linked to action execution and observation, selecting the right paracentral gyrus (PCG) and left postcentral gyrus (PoCG) as regions of interest (ROIs) for foot and hand action execution, respectively, and the left inferior parietal lobule (IPL) and left precentral gyrus (PrG) for action observation. This ROI-based MVPA was conducted to test whether prolonged football training alters the neural representation of semantics in areas related to lower limb action execution and observation. Subsequently, we conducted a searchlight MVPA for both groups to identify brain regions capable of decoding the effector information of action verbs across the entire brain. Additionally, a univariate analysis was performed to compare the activation intensity between experts and novices when processing words related to foot and hand actions. We specifically examined if the neural representation of action verbs in both primary sensorimotor and related regions could be modulated by extensive football training, albeit in an effector-specific manner. Materials and Methods Participants We recruited a total of thirty-two active professional football athletes, including eighteen females, from the Shanghai Greenland Shenhua Football Club, and thirty-two undergraduate students, with twenty females, from Shanghai University of Sport, to participate in this study. The novice group had no prior experience in football training, and did not have the habit of watching football matches. In terms of age (experts: M±SD=21.00±1.52, range:19-24, novices: M±SD=20.23±1.92, range:18-24, t=1.678, p=0.099) and education level, the expert (athletes) and novice (students) groups were well-matched. All participants were right-handed, had normal or corrected-to-normal vision, and reported no history of neurological disorders. Due to excessive movement during data collection, four experts and two novices were excluded from the final analysis, resulting in a sample of twenty-eight professional football athletes and thirty football novices for this study. All experimental procedures were approved by the Ethics Committee of Shanghai University of Sport, China. Informed consent was obtained in written form from all participants prior to their involvement in the study. Stimuli The stimulus set comprised fourteen prominent foot action verbs, each incorporating the Chinese character component “足” (e.g., “踢”, /ti1/, meaning 'to kick'; “跑”, /pao1/, meaning 'to run'), and fourteen hand action verbs, each containing the character component “扌” (e.g., “抓”, /zhua1/, meaning 'to scratch'; “握”, /wo1/, meaning 'to grip'). We ensured that the word frequency between foot and hand action verbs was statistically comparable, as confirmed by a Mann-Whitney U test (Z = -1.378, p = 0.168). fMRI Task Stimuli in this study were presented using an event-related design. In each trial, a word stimulus was displayed for 800 ms, followed by a 500 ms fixation period. Subsequently, a blank screen with a random duration (average = 4700 ms) appeared. The experiment included three categories of action verbs: foot, hand, and lip actions. Participants were instructed to silently read each word and press a button for verbs related to lip actions (Go trials). For verbs related to foot and hand actions (NoGo trials), they were instructed not to respond. Each condition comprised 28 trials, but only the NoGo trials were included in further analyses. This task design was intended to prompt participants to comprehend the action verbs while minimizing engagement in additional cognitive processes. To ensure adherence to the instructions, participants completed a practice run outside the scanner before the actual experiment. Functional Imaging Imaging data were acquired using a Siemens Prisma 3T scanner, outfitted with a 64-channel phase-array head coil, at the Imaging Center for MRI Research located in Shanghai University of Sport. During the scanning, participants were positioned supine with their heads stabilized using foam pads to reduce head movement. The task-based functional imaging data comprised 252 continuous whole-brain functional volumes. These were captured employing a gradient echo-planar imaging (EPI) sequence with the following parameters: repetition time (TR) = 2000 ms; echo time (TE) = 30 ms; 58 slices; voxel size = 2 × 2 × 2.4 mm³; flip angle = 90°. Additionally, a high-resolution T1-weighted anatomical MRI was obtained for each participant, characterized by the following specifications: TR = 2530 ms; TE = 2.98 ms; 192 slices; voxel size = 1 × 1 × 1 mm³; flip angle = 7°. Data analysis fMRI data preprocessing For preprocessing, we employed the Statistical Parametric Mapping software (SPM 12; www.fil.ion.ucl.ac.uk/spm/). Initially, the functional images underwent slice timing correction and head motion correction. These images were then spatially normalized to the Montreal Neurological Institute (MNI) space using the EPI template, with voxels resampled to a size of 2 × 2 × 2 mm³. To facilitate spatial smoothing, a Gaussian kernel with a full width at half maximum (FWHM) of 2 mm was applied for Multivariate Pattern Analysis (MVPA), and a broader 8 mm FWHM kernel was used for univariate analysis. Finally, linear trends were removed from the data in both MVPA and univariate analyses to ensure the accuracy and quality of our results. Multivoxel pattern analyses In our study, we computed a general linear model (GLM) for each participant to obtain parameter estimate (β) images, which were associated with every four trials for each condition. The GLM included experimental regressors modeled as a boxcar function, convolved with a canonical hemodynamic response function. Movement-related variances were accounted for by incorporating realignment parameters into the model. We also applied a high-pass filter with a cutoff period of 128 seconds to each time series to remove low-frequency drifts, yielding 7 beta maps per condition. Our initial MVPA aimed to discriminate between foot and hand action verbs based on ROIs implicated in leg and arm action execution and observation. These ROIs were identified in a meta-analysis study by Hardwick et al., 2018, and did not include areas co-activated in both hand and foot movement execution or observation tasks. After excluding these regions, we selected the brain area with the largest cluster in each case. The ROIs were the right paracentral gyrus (PCG; MNI coordinates (2,-28,70)) for leg action execution, the left postcentral gyrus (PoCG; MNI coordinates (-38,-22,54)) for arm execution, the left inferior parietal lobule (IPL, MNI coordinates (-52,-32,28)) for leg action observation, and the left precentral gyrus (PrG, MNI coordinates (-50,6,34)) for arm action observation. A linear support vector machine (SVM) with L1 regularization (C=1) was employed using Nilearn (Abraham et al., 2014). For each condition, β images were extracted every four trials, and classification accuracy was calculated using a leave-one-session-out cross-validation procedure. SVMs were trained and tested separately for each group and ROI. Thus, we got a classification accuracy for each group and each ROI. These values were examined using repeated-measures analyses of variance (ANOVAs), with group (football expert, football novice) as between-subject factor and verb types (foot, hand) as within-subject factor. To clarify the effect size of the factor, we conducted a Bayesian repeated measures ANOVA using JASP software (RRID: SCR_015823; https://jasp-stats.org/ (JASP Team 2022)). The impact of factors and their interaction was determined through comparison across the matched models, following the approach recommended by van den Bergh et al. (2020). We then calculated the Pearson correlation between classification accuracy and training years for experts within each ROI. The results were subjected to FWE (Family-Wise Error, α = 0.05) multiple comparison correction. For the searchlight MVPA, we used a spherical searchlight with an 8mm radius, moving it across the brain, with each voxel serving as the center. In each sphere, a linear SVM was trained and tested as described earlier, assigning the classification accuracy score to the central voxel. Individual subjects' classification accuracy maps were spatially smoothed with a 6 mm FWHM and then analyzed at a second level, following the approach of Xu et al., (2017). The resulting T-maps displayed the statistical significance of voxel-wise accuracies against a chance-level accuracy of 50% for each group. Correction for multiple comparisons was conducted using the FWE (α = 0.001) method. Univariate analyses In our univariate analysis, we extracted β images for each participant and condition. The General Linear Model (GLM) construction included experimental regressors, which were modeled as a boxcar function and convolved with a canonical hemodynamic response function. To address movement-related variances, realignment parameters were integrated into the GLM. Additionally, a high-pass filter with a cutoff period of 128 seconds was applied to each time series, aiming to eliminate low-frequency drifts. For each subject and condition, we generated contrast images. These individual contrast images were subsequently used to create group-level contrast images in the second-level analysis. To investigate group differences in BOLD activation, we conducted a two-sample t-test for each condition. Age and sex were included as covariates in the model to control for their potential confounding effects on our results. The calculated T statistic images were thresholded at the whole-brain level using false discovery rate corrected (α = 0.05). MVPA Analyses Based on Univariate-Derived ROIs To investigate whether brain regions exhibiting group differences in BOLD activation also displayed variations in representation patterns for action verbs, we conducted an additional MVPA analysis. This analysis focused on discriminating between foot and hand action verbs based on the regions identified in the univariate analyses as showing significant differences. We utilized a linear Support Vector Machine (SVM), trained and tested as previously described. The specific ROIs targeted in this analysis were the IFG-T (Inferior Frontal Gyrus-Triangular part, with MNI coordinates (46, 28, 6)), IFG-O (Inferior Frontal Gyrus-Opercular part, with MNI coordinates (48, 6, 22)), and IPL (Inferior Parietal Lobule, with MNI coordinates (48, -46, 46)). These areas were chosen based on their noted differences in the earlier univariate analyses and were examined for their distinct patterns in processing different types of action verbs. Results Action verb classification To evaluate the effect of long-term football training on the neural representation of action verb semantics in sensorimotor and associated cortices, we initially employed ROI-based MVPA to decode the effector information of action verbs for both football experts and novices in action execution and observation regions. For the ROI associated with action execution, the results from the repeated measures ANOVA showed no significant main effects for movement execution by effector (F = 3.923, p = 0.053) or group (F = 0.137, p = 0.713). However, a significant interaction was observed between action execution by effector and group (F = 4.836, p = 0.032). The Bayesian analysis provided slight evidence for the effect of movement execution area (BF_incl = 1.375), whereas the evidence favored the null hypothesis for group (BF_incl = 0.249). Moderate evidence was found for the interaction between Factor 1 and Factor 2 (BF_incl = 2.419). Subsequent multiple t-tests revealed higher classification accuracy in the left postcentral gyrus (PoCG) compared to the right paracentral gyrus (PCG) among novices (paired T-test, t = -2.816, p = 0.009), but this pattern was not evident among experts (t = 0.165, p = 0.870). Additionally, when comparing experts and novices, no significant differences were found in classification accuracy based on either PCG (independent samples T-test, t = 1.129, p = 0.264) or PoCG (t = -1.682, p = 0.098).For ROI of action observation, the results from the repeated measures ANOVA revealed that the two main effects, action observation by effector (F = 0.036, p = 0.850) and group (F = 0.086, p = 0.771) were not significant. The interaction between action observation by effector and group (F = 0.027, p = 0.871) was not significant. Furthermore, one-sample T-tests indicated that the classification accuracies based on all ROIs for both experts and novices were significantly above chance level. For experts, the classification accuracies were: t(PCG) = 12.625, p < 0.001; t(PoG) = 10.735, p < 0.001; t(IPL) = 7.947, p < 0.001; and t(PrG) = 12.853, p < 0.001. For novices, the results were: t(PCG) = 11.397, p < 0.001; t(PoG) = 17.897, p < 0.001; t(IPL) = 11.241, p < 0.001; and t(PrG) = 11.033, p < 0.001. Lastly, we conducted a Pearson correlation analysis within the expert group to investigate the relationship between verb classification accuracy and training years for each ROI. The results, after applying FWE (Family-Wise Error, α = 0.05) multiple comparison correction, revealed that the classification accuracy in PCG (r = 0.632, p = 0.001) and PoCG (r = 0.558, p = 0.008) were significantly correlated with training years. However, no significant correlations were observed in IPL (r = 0.282, p = 0.584) and PrG (r = -0.269, p = 0.668). Searchlight MVPA To capture the broad semantic neural representation patterns across the entire brain, we conducted a searchlight MVPA analysis for both expert and novice groups. As depicted in Fig.2, an 8-mm radius searchlight analysis highlighted both similarities and differences in semantic neural representation patterns between the two groups. For the expert group, we identified significant centers in the right postcentral gyrus and IPL, left IFG, left medial frontal gyrus (with a peak at MNI coordinates (-10, 56, -2)), and right precentral gyrus (peak at MNI coordinates (42, -6, 48)). In contrast, for the novice group, significant centers were located in the left lingual gyrus (peak at MNI coordinates (-16, -78, -10)), left insula and inferior frontal gyrus (peak at MNI coordinates (-36, 10, -6)), left superior temporal gyrus (peak at MNI coordinates (-50, -20, 2)), and left superior frontal gyrus (peak at MNI coordinates (-10, 52, 38)). However, a two-sample t-test revealed no significant differences in searchlight accuracy between the expert and novice groups. Table1. Clusters identified in the searchlight MVPA Predominant regions in cluster Cluster size Peak T-value MNI coordinates x y z Expert Right Postcentral Gyrus 1368 13.66 44 -32 42 Medial Frontal Gyrus 1315 Inferior Parietal Lobule 1275 Precuneus 1235 Middle Frontal Gyrus 1009 Precentral Gyrus 867 Superior Temporal Gyrus 685 Superior Frontal Gyrus 636 Left Inferior Frontal Gyrus 185 8.98 -36 18 -10 Left Medial Frontal Gyrus 402 11.49 -10 56 -2 Right Precentral Gyrus 112 6.94 42 -6 48 Novice Left Lingual Gyrus 343 10.01 -16 -78 -10 Left Insula 265 8.59 -36 10 -6 Inferior Frontal Gyrus 156 Left Superior Temporal Gyrus 121 7.49 -50 -20 2 Left Superior Frontal Gyrus 133 8.30 -10 52 38 FEW p = 0.001, cluster size>100 Univariate analyses In our univariate analyses, we focused on comparing the BOLD signal differences between the expert and novice groups. The results revealed that when processing foot action verbs, experts displayed significantly higher BOLD signals than novices in specific brain regions. These regions included the right inferior frontal gyrus triangularis (IFG-T), right inferior frontal gyrus opercularis (IFG-O), and the right inferior parietal lobule (IPL). In contrast, when observing hand action verbs, no significant differences in BOLD signals were observed between the expert and novice groups. Table2. Clusters identified in the univariate analysis Predominant regions in cluster Cluster size Peak T-value Cluster-level MNI coordinates P FWE-corr x y z Contrast: Experts > Novices Right inferior frontal gyrus triangularis 45 4.88 0.013 46 28 6 Right inferior frontal gyrus opercularis 68 5.30 0.002 48 6 22 Right inferior parietal lobule 117 4.63 <0.001 48 -46 46 MVPA Analyses Based on Univariate-Derived ROIs We employed the same ROI-based MVPA methodology, as previously mentioned, to analyze the IFG-T, IFG-O, and IPL identified in the univariate analysis. The results of the one-sample t-tests on the classification accuracy for each group revealed that the accuracies based on IFG-T were not significantly above chance for either experts (FWE corrected, α = 0.05, t = 1.218, p = 0.701) or novices (t = 2.531, p = 0.051). However, for IFG-O, the classification accuracies were significantly above chance for both experts (t = 3.382, p = 0.006) and novices (t = 2.740, p = 0.031). Similarly, for IPL, significant classification accuracies were observed for both experts (t = 4.500, p < 0.001) and novices (t = 3.280, p = 0.008). Furthermore, the results of the two-sample t-test indicated no significant differences between the expert and novice groups based on IFG-T, IFG-O, and IPL. Discussion The current study aimed to examine if long-term football training influences the neural representation patterns of action verbs in sensorimotor regions. Employing high spatial resolution fMRI alongside an MVPA generalization approach, we identified that PCG, PoCG, IPL, and PrG can encode effector information for both experts and novices. Moreover, football training specifically influenced neural representation in action execution areas. Our findings demonstrate that long-term professional football training specifically modulates neural representation patterns of verbs in an effector-specific manner, and thus provide compelling evidence that semantic processing is inherently linked to the sensory-motor system. Previous study found a short-term motor experience modulation altered the neural representation of action verb semantics (Xiong et al., 2023). Similarity, Kontra et al. (2015) found that the increased accuracy in understanding words denoting physical concepts (e.g., angular momentum) after hands-on learning, as opposed to merely observing the consequences of wheel manipulation, is mediated by greater activation in the primary motor (M1) and somatosensory (S1) cortices. Another study found that bilateral dorsal laryngeal motor cortex (dLMCs) was engaged with effector specificity by transcranial magnetic stimulation in a perceptual decision of lexical tone and voicing of consonant task (Liang et al., 2023). A neuroimaging study showed the premotor cortex activation observed during action verb processing relied on the access of more specific motor semantic content (Lin et al., 2015). Our results, obtained using the different method, MVPA, and get consistent result with these studies, We found that in both experts and novices, motor brain areas responsible for lower and upper limb actions can decode effector-specific information from action verbs, suggesting semantic processing is grounded in sensorimotor experiences within higher-order sensory/motor and association cortices. (Bi, 2021). More importantly, we discovered interactions between the action execution regions and the groups. Although no between-group differences were found in the decoding accuracy in the foot movement execution area, this still provides direct and compelling evidence that long-term professional football training modulates the neural representation of verb semantics in sensorimotor cortices and association cortices. We did not observe significant effects in the action observation regions, highlighting the influence of motor experience on the semantic processing of action verbs. Furthermore, the decoding accuracy of experts in the PCG and PoCG strongly correlated with their training years, which suggests long-term action training were associated with the neural representation of action verbs in sensorimotor cortices, and this association is at least partially effector-specific. Our finding consistent with previous findings (Beilock et al. 2008), and further discovered that the impact of sports-related sensorimotor experience can generalize to a broader range of verb semantic processing. However, we did not find the interaction and the correlation for action observation regions. It suggests that the modulation effects of long-term motor training are mostly due to the motor experience, rather than sensory experience. Our study is consistent with previous studies, which showed long-term extensive motor training altered brain regions associated with action execution (Calmels, 2020; Hänggi, 2010). Furthermore, our finding fills a gap in previous research, which traditionally could not directly provide strong evidence that perceptual motor experience can dynamically regulate the neural representation of verb semantics. Xiong et al (2023) demonstrated that perceptual motor experience can regulate semantic neural representations, but the researchers were unable to directly observe changes in neural representation patterns in the action execution regions before and after the intervention. Our study, by employing football players who have acquired more foot-related action experience in their long-term life, successfully identified the impact of foot action experience on semantic neural representation in the sensorimotor and associated cortices. The results from searchlight MVPA classification provide intriguing insights into the neural underpinnings of verb semantic processing, especially in the context of expertise. The analysis reveals differential brain activation patterns in experts compared to novices, particularly in sensorimotor regions including somatosensory cortex and motor cortex. These findings align with the growing body of research suggesting that sensorimotor regions are deeply involved in processing action-related language (e.g., Dreyer, 2018; Barros-Loscertales et al., 2012; Kiefer, Sim, Herrnberger, Grothe, & Hoenig, 2008). The pronounced cluster in the right postcentral gyrus and the precentral gyrus in experts, but not in novices indicates the long-term training enhance action-related semantic neural representation in sensorimotor cortices, which propose that understanding action-related language involves simulating the actions in the brain's motor systems. The heightened activity in these regions suggests that experts, more so than novices, may utilize their refined sensorimotor representations in understanding and processing verbs, particularly those related to actions. The ability of various areas within PoCG and PrG to decode effector information from verbs in experts underscores the role of these regions in linking language to specific motor representations. This finding is in line with studies showing that language processing, particularly for action-related words, can activate corresponding motor and sensory areas (Hauk et al., 2004; Pulvermüller, 2005; Liu et al., 2024). The novice group shows more activation in areas like the left lingual gyrus and the left insula, which are more traditionally associated with basic language processing (Voets et al., 2006; Oh et al., 2014). This difference might reflect a less specialized, more generalized language processing strategy in novices. These results have broader implications for our understanding of how language is processed in the brain. They lend support to the idea that sensorimotor integration is crucial in language comprehension. Univariate analysis indicated that experts exhibited stronger activation intensities in the IPL and IFG (both triangularis and opercularis) when watching foot-related verbs. IPL typically associated with the integration of sensory information and attentional processes (Grefkes et al., 2005; Igelström et al., 2017), which is an important node in AON, the significant activation in this region could mean that experts are more actively engaging in the multisensory integration or attentional mechanisms when processing the semantics of verbs. IFG is involved in phonological processing, speech production, language processing and cognitive control (Kulik et al., 2023; Ishkhanyan et al., 2020), which indicates long term motor training can influence semantic processing in higher-level cortical areas. We did not find differences in activation intensity between experts and novices in the sensorimotor cortices when watching both kinds of action verbs. This indicates that the impact of long-term motor training experience on semantic processing in the sensorimotor cortices is not reflected in differences in activation intensity, but rather in differences in neural representations, highlighting the importance of pattern analysis in the study of semantic processing. To test if IPL and IFG can encoding the semantic information of the action verbs, we conducted MVPA using these areas as ROIs. The results showed that there were no differences in semantic neural representations between experts and novices in these brain regions. However, in IFG-O and IPL, both experts and novices can decode the effector information of semantic, which suggests these regions are involved in action verbs semantic processing, but not sensitive to motor experience. Interestingly, both searchlight analysis and activation analysis show that the right IPL is influenced by action training experience. In the analysis of functional connectivity between the right IPL and both PCG and PoCG (for more information on the methods and results to the supplementary), we found that during verb observation, there was a moderate strength of correlation between the IPL and PCG in both experts and novices (when observing foot-related verbs), as well as between the IPL and PoCG (when observing hand-related verbs). These suggest that the information exchange between the IPL and the sensorimotor cortex may play a significant role in the processing of verb semantics. In addition to our main analyses, we conducted seed-based functional connectivity (FC) analyses which selecting the paracentral gyrus (PCG) and postcentral gyrus (PoCG) as seed areas (for more information on the methods and results to the supplementary). The FC results showed main effect of group between PCG and right posterior middle temporal gyrus (pMTG). Right pMTG is associated with semantic processing (Krist A. Noonan et al., 2013; Papeo et al., 2019), thus during the observation of action verbs, differences in the connectivity strength between the paracentral gyrus (PCG) and posterior middle temporal gyrus (pMTG) were observed between experts and novices, providing evidence that motor experience can influence the semantic processing of verbs. Conclusion Our research conclusively demonstrates that sensorimotor experience, particularly from long-term football training, can distinctly modulate the neural representation of action-related verbs. Employing pattern analysis techniques, our study revealed effector-specific alterations in the neural patterns within primary sensorimotor and associated regions. This finding underscores the role of motor experience in shaping the semantic processing of action verbs, diverging from the influence of sensory experience. Moreover, this research emphasizes the neural adaptability resulting from extensive football training, illustrating how specialized physical training can profoundly alter the neural representation of action verbs. These changes were particularly evident in areas associated with action execution, suggesting a more nuanced understanding of the link between physical training and cognitive processes. In essence, our findings contribute to the growing body of evidence that supports the theory of embodied cognition, which posits a close relationship between sensorimotor experience and semantic processing. This research not only enhances our understanding of the cognitive impact of sports training but also opens avenues for further exploration into how different types of physical training might influence cognitive functions. Declarations Acknowledgments We would like to express our deepest gratitude to our colleagues and mentors at Shanghai University of Sport for their continuous support, insightful comments, and hard questions, all of which have significantly contributed to the improvement of this research work. This work was supported by the grants from National Natural Science Foundation of China (No.32271131). Author contributions Jian Wang: Data curation; Conceptualization; Formal analysis; Methodology; Visualization; Roles/Writing - original draft. Hong Mou: Investigation; Data curation. Likai Liu: Writing - review & editing. Chenglin Zhou: Resources; Writing - review & editing. Yingying Wang: Funding acquisition; Project administration; Writing - review & editing; Supervision. DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request (Requirements for co-authorship or inclusion in the author byline). The code to conduct MVPA analysis can be assessed at https://osf.io/3npxj/ Funding statement This work was supported by the grants from National Natural Science Foundation of China (No.32271131). conflict of interest disclosure None ORCID Jian Wang https://orcid.org/0009-0000-7303-4971 Declaration of Generative AI and AI-assisted technologies in the writing process Statement: During the preparation of this work the authors used chatgpt4 in order to improve readability and language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. References Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics , 8. https://doi.org/10.3389/fninf.2014.00014 Aflalo, T., Zhang, C. Y., Rosario, E. R., Pouratian, N., Orban, G. A., & Andersen, R. 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Annee Psychol. 2020:120(1):73–96. Voets, N. L., Adcock, J. E., Flitney, D. E., Behrens, T. E. J., Hart, Y., Stacey, R., Carpenter, K., & Matthews, P. M. (2006). Distinct right frontal lobe activation in language processing following left hemisphere injury. Brain , 129(3), 754–766. https://doi.org/10.1093/brain/awh679 Willems, R. M., Hagoort, P., & Casasanto, D. (2010). Body-specific representations of action verbs: Neural evidence from right-and left-handers. Psychological Science , 21(1), 67-74. Wurm, M. F., & Caramazza, A. (2019). Distinct roles of temporal and frontoparietal cortex in representing actions across vision and language. Nature Communications , 10(1), 289. https://doi.org/10.1038/s41467-018-08084-y Xiong, Z., Tian, Y., Wang, X., Wei, K., & Bi, Y. (2023). Gravity matters for the neural representations of action semantics. Cerebral Cortex , bhad006. https://doi.org/10.1093/cercor/bhad006 Xu, M., Baldauf, D., Chang, C. Q., Desimone, R., & Tan, L. H. (2017). Distinct distributed patterns of neural activity are associated with two languages in the bilingual brain. Science Advances , 3(7), e1603309. https://doi.org/10.1126/sciadv.1603309 Yan, C.-G., Wang, X.-D., Zuo, X.-N., & Zang, Y.-F. (2016). DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics , 14(3), 339–351. https://doi.org/10.1007/s12021-016-9299-4 Additional Declarations The authors declare no competing interests. Supplementary Files Supplementarymaterials.docx Functional connectivity results Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4062491","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277833093,"identity":"9b35193c-c101-4278-8836-80d340635bb9","order_by":0,"name":"Jian Wang","email":"","orcid":"https://orcid.org/0009-0000-7303-4971","institution":"Shanghai university of sport","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""},{"id":277833094,"identity":"c2e5105c-5c6f-48a6-8b1d-a4608f387995","order_by":1,"name":"Hong Mou","email":"","orcid":"","institution":"Shanghai university of sport","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Mou","suffix":""},{"id":277833095,"identity":"bb63f079-66a4-4e30-9861-a8484eac95fe","order_by":2,"name":"Likai Liu","email":"","orcid":"","institution":"Shanghai university of sport","correspondingAuthor":false,"prefix":"","firstName":"Likai","middleName":"","lastName":"Liu","suffix":""},{"id":277833096,"identity":"646da3e8-c219-465c-8ae8-07bc87b9a6ee","order_by":3,"name":"Chenglin Zhou","email":"","orcid":"","institution":"Shanghai university of sport","correspondingAuthor":false,"prefix":"","firstName":"Chenglin","middleName":"","lastName":"Zhou","suffix":""},{"id":277833097,"identity":"4daff3e6-b93d-4dff-b4a9-afa1b84ba324","order_by":4,"name":"Yingying Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYNACAyBmBuIPCC6RWhhnEK8FCph5iNFicPzs4dc8BXfsNhznPfza5s9hewb25m0SDDV3cGs5k5dmzWPwLHnDYb4069y2w4kNPMfKJBiOPcOpxexAjpkxj8HhZIPDPGbGuQ2HExgkcswkGBsO49Zy/g2SFguQw+TfENByI8f4MVCLHVCL8WMGtsOMDRI8+LXY33hjxjjH4HCCJNAWxt629MQ2nrRii4RjuLVI9ucYf3gDdA/f+TPGH378sbbnZz+88caHGtxagIBNChgdiQsOMLBJgLkgIgGfBmAEfvwBdKB8AwPzB/wKR8EoGAWjYKQCACyVVmVSY4MMAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai university of sport","correspondingAuthor":true,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-03-10 06:48:07","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4062491/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4062491/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52448133,"identity":"d4625a52-af62-442d-86ff-e84de5ac2e54","added_by":"auto","created_at":"2024-03-11 18:35:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68640,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental design and classification results based on ROI. A. Experimental design, subjects were instructed to read the words silently and press the button when word stimulus were related to lip action (Go trials); when word stimulus were related to foot and hand action (NoGo trials), don’t respond. B. ROI decoding accuracies for action verbs of experts(red) and novices(blue) in right paracentral gyrus(PCG, region related to action execution of leg) and left postcentral gyru(PoCG, region related to action execution of arm). Error bar indicate 95%\u003c/strong\u003e \u003cstrong\u003eConfidence Interval, and asterisks indicate effects of two-tailed t test(upper) between PCG and PoCG in novices, and FEW-corrected(α=0.05) one-tailed t tests(lower) against chance-level(50%, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001). C. ROI decoding accuracies for action verbs of experts(red) and novices(blue) in left inferior parietal lobule (IPL, region related to action observation of leg) and right precentral gyru(PoCG, region related to action observation of arm). D, E Correlation between classification accuracy and training years in PCG and PoCG for experts.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4062491/v1/ceaf200a66848f82f039214f.png"},{"id":52448135,"identity":"e96ef569-353c-4cb1-9fc6-b3eb7d23e75f","added_by":"auto","created_at":"2024-03-11 18:35:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSearchlight MVPA results for classification of action verbs. A, B. Searchlight MVPA results of experts and novices presented as a T-map indicating the statistical significance of voxel-wise classification accuracies against the chance level (50%). The maps are thresholded by FEW(α=0.001) corrected for multiple comparisons.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4062491/v1/7ba94e15703e47aaa5ad7fe9.png"},{"id":52448137,"identity":"5111b091-c78c-4ec9-8857-95584d1fb1d0","added_by":"auto","created_at":"2024-03-11 18:35:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure2. Cortical activation associated with expert minus novice when watching foot action verbs revealed by the univariate analysis (p\u0026lt;0.05, FDR-corrected).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"02.png","url":"https://assets-eu.researchsquare.com/files/rs-4062491/v1/5140e0337daad8104277303a.png"},{"id":52448136,"identity":"1d17ce49-9792-4178-83b6-8e0b1a6884ca","added_by":"auto","created_at":"2024-03-11 18:35:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure3. ROI decoding accuracies for action verbs of experts(red) and novices(blue) in right inferior frontal gyrus triangularis (IFG-T), right inferior frontal gyrus opercularis (IFG-O) and right inferior parietal lobule (IPL). Error bar indicate 95% Confidence Interval, and asterisks indicate effects of FEW-corrected(α=0.05) one-tailed t tests(lower) against chance-level(50%, *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4062491/v1/7b28b04b31f73def5fb397c5.png"},{"id":52448452,"identity":"11f3f696-a4f2-4aea-9a1a-3a91cdb1fcbf","added_by":"auto","created_at":"2024-03-11 18:43:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1101335,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4062491/v1/a7541d16-e0d0-4701-959d-3b14ace24d3c.pdf"},{"id":52448134,"identity":"6d9dc420-1da6-45cc-ae02-6ac0a526b6b8","added_by":"auto","created_at":"2024-03-11 18:35:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":242339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional connectivity results\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4062491/v1/45346fd20c14d9c14b548136.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe Influence of Long-Term Football Training on Neural Representation of Action Verb Semantics\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding how motor experience influences semantic neural representations remains a crucial question in cognitive neuroscience. This study delves into how long-term football training influences neural patterns representing action verbs, especially in sensorimotor and associated areas, with a focus on effector-specific alterations. The embodied cognition theory, which suggests semantic processing is inherently linked to the sensory-motor system (Pulvermüller, 2005, 2013; Gallese \u0026amp; Cuccio, 2018), is supported by neuroimaging studies that show activation in motor brain regions when comprehending action words, such as the primary sensorimotor (M1 and S1) and dorsal premotor cortices (PMd) (Hauk et al., 2004; Tettamanti et al., 2005; Aziz-Zadeh et al., 2006; Willems et al., 2010). Further evidence from Beilock et al. (2008) and Lyons et al. (2010) suggests that sports-related motor experience enhances understanding of action-related language. Additionally, Xiong et al. (2023) observed that a short-term microgravity simulation reduced the neural representation of action verbs, indicating effector-specific modulation.\u003c/p\u003e\n\u003cp\u003eResearch has established the significant role of sensorimotor experience in semantic neural representation (Barsalou et al., 2003; Pickering \u0026amp; Garrod, 2013; Ralph et al., 2017, Bi, 2021). For instance, a neuroimaging study showed that language depicting actions activates motor brain areas, correlating the kinematic details of a verb with neural patterns tied to language comprehension (Van Dam et al., 2010). Hauk et al. (2004) found somatotopic activation in the premotor and frontal regions during the passive reading of action-related words. Recent studies using multivariate pattern analysis (MVPA) and predictive pattern decomposition analysis have identified common neural patterns between actions and verbs (Horoufchin et al., 2018). Crossmodal MVPA studies have demonstrated decoding abilities in areas like the lateral occipitotemporal cortex, inferior parietal lobule, and ventral premotor cortex (Karakose-Akbiyik et al., 2023; Liu et al., 2023; Wurm \u0026amp; Caramazza, 2019), which are crucial to the action observation network (Hardwick et al., 2018; Molenberghs et al., 2012; Caspers et al., 2010). A single neuron recording study also revealed overlapping neural substrates between words and their related sensorimotor representations (Aflalo et al., 2020). For instance, Argiris et al. (2020) provided evidence that even with lesions in the sensorimotor cortex, patients' abilities to process action verbs were not impaired. An repetitive transcranial magnetic stimulation (rTMS) study showed that M1-rTMS did not affect meaning construction, which disputes the involvement of the primary motor cortex in the construction of meaning from action language (Solana et al., 2023). Therefore, it is necessary to further explore the role of sensorimotor experience and sensorimotor cortices on the neural representation of action verb processing.\u003c/p\u003e\n\u003cp\u003eThis study used MVPA to explore the influence of extensive football training on the neural semantic representation of action verbs. We analyzed functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signal patterns in professional football athletes and novices. An implicit word-processing task minimized motor processing interference, with subjects responding to verbs related to lips. MVPA was applied to areas linked to action execution and observation, selecting the right paracentral gyrus (PCG) and left postcentral gyrus (PoCG) as regions of interest (ROIs) for foot and hand action execution, respectively, and the left inferior parietal lobule (IPL) and left precentral gyrus (PrG) for action observation. This ROI-based MVPA was conducted to test whether prolonged football training alters the neural representation of semantics in areas related to lower limb action execution and observation. Subsequently, we conducted a searchlight MVPA for both groups to identify brain regions capable of decoding the effector information of action verbs across the entire brain. Additionally, a univariate analysis was performed to compare the activation intensity between experts and novices when processing words related to foot and hand actions. \u0026nbsp;We specifically examined if the neural representation of action verbs in both primary sensorimotor and related regions could be modulated by extensive football training, albeit in an effector-specific manner.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe recruited a total of thirty-two active professional football athletes, including eighteen females, from the Shanghai Greenland Shenhua Football Club, and thirty-two undergraduate students, with twenty females, from Shanghai University of Sport, to participate in this study. The novice group had no prior experience in football training, and did not have the habit of watching football matches. In terms of age (experts: M±SD=21.00±1.52, range:19-24, novices: M±SD=20.23±1.92, range:18-24, t=1.678, p=0.099) and education level, the expert (athletes) and novice (students) groups were well-matched. All participants were right-handed, had normal or corrected-to-normal vision, and reported no history of neurological disorders. Due to excessive movement during data collection, four experts and two novices were excluded from the final analysis, resulting in a sample of twenty-eight professional football athletes and thirty football novices for this study. All experimental procedures were approved by the Ethics Committee of Shanghai University of Sport, China. Informed consent was obtained in written form from all participants prior to their involvement in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStimuli\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe stimulus set comprised fourteen prominent foot action verbs, each incorporating the Chinese character component “足” (e.g., “踢”, /ti1/, meaning 'to kick'; “跑”, /pao1/, meaning 'to run'), and fourteen hand action verbs, each containing the character component “扌” (e.g., “抓”, /zhua1/, meaning 'to scratch'; “握”, /wo1/, meaning 'to grip'). We ensured that the word frequency between foot and hand action verbs was statistically comparable, as confirmed by a Mann-Whitney U test (Z = -1.378, p = 0.168).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003efMRI Task\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStimuli in this study were presented using an event-related design. In each trial, a word stimulus was displayed for 800 ms, followed by a 500 ms fixation period. Subsequently, a blank screen with a random duration (average = 4700 ms) appeared. The experiment included three categories of action verbs: foot, hand, and lip actions. Participants were instructed to silently read each word and press a button for verbs related to lip actions (Go trials). For verbs related to foot and hand actions (NoGo trials), they were instructed not to respond. Each condition comprised 28 trials, but only the NoGo trials were included in further analyses. This task design was intended to prompt participants to comprehend the action verbs while minimizing engagement in additional cognitive processes. To ensure adherence to the instructions, participants completed a practice run outside the scanner before the actual experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Imaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImaging data were acquired using a Siemens Prisma 3T scanner, outfitted with a 64-channel phase-array head coil, at the Imaging Center for MRI Research located in Shanghai University of Sport. During the scanning, participants were positioned supine with their heads stabilized using foam pads to reduce head movement. The task-based functional imaging data comprised 252 continuous whole-brain functional volumes. These were captured employing a gradient echo-planar imaging (EPI) sequence with the following parameters: repetition time (TR) = 2000 ms; echo time (TE) = 30 ms; 58 slices; voxel size = 2 × 2 × 2.4 mm³; flip angle = 90°. Additionally, a high-resolution T1-weighted anatomical MRI was obtained for each participant, characterized by the following specifications: TR = 2530 ms; TE = 2.98 ms; 192 slices; voxel size = 1 × 1 × 1 mm³; flip angle = 7°.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003efMRI data preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor preprocessing, we employed the Statistical Parametric Mapping software (SPM 12; www.fil.ion.ucl.ac.uk/spm/). Initially, the functional images underwent slice timing correction and head motion correction. These images were then spatially normalized to the Montreal Neurological Institute (MNI) space using the EPI template, with voxels resampled to a size of 2 × 2 × 2 mm³. To facilitate spatial smoothing, a Gaussian kernel with a full width at half maximum (FWHM) of 2 mm was applied for Multivariate Pattern Analysis (MVPA), and a broader 8 mm FWHM kernel was used for univariate analysis. Finally, linear trends were removed from the data in both MVPA and univariate analyses to ensure the accuracy and quality of our results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivoxel pattern analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, we computed a general linear model (GLM) for each participant to obtain parameter estimate (β) images, which were associated with every four trials for each condition. The GLM included experimental regressors modeled as a boxcar function, convolved with a canonical hemodynamic response function. Movement-related variances were accounted for by incorporating realignment parameters into the model. We also applied a high-pass filter with a cutoff period of 128 seconds to each time series to remove low-frequency drifts, yielding 7 beta maps per condition.\u003c/p\u003e\n\u003cp\u003eOur initial MVPA aimed to discriminate between foot and hand action verbs based on ROIs implicated in leg and arm action execution and observation. These ROIs were identified in a meta-analysis study by Hardwick et al., 2018, and did not include areas co-activated in both hand and foot movement execution or observation tasks. After excluding these regions, we selected the brain area with the largest cluster in each case. The ROIs were the right paracentral gyrus (PCG; MNI coordinates (2,-28,70)) for leg action execution, the left postcentral gyrus (PoCG; MNI coordinates (-38,-22,54)) for arm execution, the left inferior parietal lobule (IPL, MNI coordinates (-52,-32,28)) for leg action observation, and the left precentral gyrus (PrG, MNI coordinates (-50,6,34)) for arm action observation. A linear support vector machine (SVM) with L1 regularization (C=1) was employed using Nilearn (Abraham et al., 2014). For each condition, β images were extracted every four trials, and classification accuracy was calculated using a leave-one-session-out cross-validation procedure. SVMs were trained and tested separately for each group and ROI. Thus, we got a classification accuracy for each group and each ROI. These values were examined using repeated-measures analyses of variance (ANOVAs), with group (football expert, football novice) as between-subject factor and verb types (foot, hand) as within-subject factor. To clarify the effect size of the factor, we conducted a Bayesian repeated measures ANOVA using JASP software (RRID: SCR_015823; https://jasp-stats.org/ (JASP Team 2022)). The impact of factors and their interaction was determined through comparison across the matched models, following the approach recommended by van den Bergh et al. (2020). We then calculated the Pearson correlation between classification accuracy and training years for experts within each ROI. The results were subjected to FWE (Family-Wise Error, α = 0.05) multiple comparison correction.\u003c/p\u003e\n\u003cp\u003eFor the searchlight MVPA, we used a spherical searchlight with an 8mm radius, moving it across the brain, with each voxel serving as the center. In each sphere, a linear SVM was trained and tested as described earlier, assigning the classification accuracy score to the central voxel. Individual subjects' classification accuracy maps were spatially smoothed with a 6 mm FWHM and then analyzed at a second level, following the approach of Xu et al., (2017). The resulting T-maps displayed the statistical significance of voxel-wise accuracies against a chance-level accuracy of 50% for each group. Correction for multiple comparisons was conducted using the FWE (α = 0.001) method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our univariate analysis, we extracted β images for each participant and condition. The General Linear Model (GLM) construction included experimental regressors, which were modeled as a boxcar function and convolved with a canonical hemodynamic response function. To address movement-related variances, realignment parameters were integrated into the GLM. Additionally, a high-pass filter with a cutoff period of 128 seconds was applied to each time series, aiming to eliminate low-frequency drifts.\u003c/p\u003e\n\u003cp\u003eFor each subject and condition, we generated contrast images. These individual contrast images were subsequently used to create group-level contrast images in the second-level analysis. To investigate group differences in BOLD activation, we conducted a two-sample t-test for each condition. Age and sex were included as covariates in the model to control for their potential confounding effects on our results. The calculated T statistic images were thresholded at the whole-brain level using false discovery rate corrected (α\u0026nbsp;= 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMVPA Analyses Based on Univariate-Derived ROIs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate whether brain regions exhibiting group differences in BOLD activation also displayed variations in representation patterns for action verbs, we conducted an additional MVPA analysis. This analysis focused on discriminating between foot and hand action verbs based on the regions identified in the univariate analyses as showing significant differences. We utilized a linear Support Vector Machine (SVM), trained and tested as previously described. The specific ROIs targeted in this analysis were the IFG-T (Inferior Frontal Gyrus-Triangular part, with MNI coordinates (46, 28, 6)), IFG-O (Inferior Frontal Gyrus-Opercular part, with MNI coordinates (48, 6, 22)), and IPL (Inferior Parietal Lobule, with MNI coordinates (48, -46, 46)). These areas were chosen based on their noted differences in the earlier univariate analyses and were examined for their distinct patterns in processing different types of action verbs.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAction verb classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the effect of long-term football training on the neural representation of action verb semantics in sensorimotor and associated cortices, we initially employed ROI-based MVPA to decode the effector information of action verbs for both football experts and novices in action execution and observation regions.\u003c/p\u003e\n\u003cp\u003eFor the ROI associated with action execution, the results from the repeated measures ANOVA showed no significant main effects for movement execution by effector (F = 3.923, p = 0.053) or group (F = 0.137, p = 0.713). However, a significant interaction was observed between action execution by effector and group (F = 4.836, p = 0.032). The Bayesian analysis provided slight evidence for the effect of movement execution area (BF_incl = 1.375), whereas the evidence favored the null hypothesis for group (BF_incl = 0.249). Moderate evidence was found for the interaction between Factor 1 and Factor 2 (BF_incl = 2.419). Subsequent multiple t-tests revealed higher classification accuracy in the left postcentral gyrus (PoCG) compared to the right paracentral gyrus (PCG) among novices (paired T-test, t = -2.816, p = 0.009), but this pattern was not evident among experts (t = 0.165, p = 0.870). Additionally, when comparing experts and novices, no significant differences were found in classification accuracy based on either PCG (independent samples T-test, t = 1.129, p = 0.264) or PoCG (t = -1.682, p = 0.098).For ROI of action observation, the results from the repeated measures ANOVA revealed that the two main effects, action observation by effector (F = 0.036, p = 0.850) and group (F = 0.086, p = 0.771) \u0026nbsp;were not significant. The interaction between action observation by effector and group (F = 0.027, p = 0.871) was not significant.\u003c/p\u003e\n\u003cp\u003eFurthermore, one-sample T-tests indicated that the classification accuracies based on all ROIs for both experts and novices were significantly above chance level. For experts, the classification accuracies were: t(PCG) = 12.625, p \u0026lt; 0.001; t(PoG) = 10.735, p \u0026lt; 0.001; t(IPL) = 7.947, p \u0026lt; 0.001; and t(PrG) = 12.853, p \u0026lt; 0.001. For novices, the results were: t(PCG) = 11.397, p \u0026lt; 0.001; t(PoG) = 17.897, p \u0026lt; 0.001; t(IPL) = 11.241, p \u0026lt; 0.001; and t(PrG) = 11.033, p \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eLastly, we conducted a Pearson correlation analysis within the expert group to investigate the relationship between verb classification accuracy and training years for each ROI. The results, after applying FWE (Family-Wise Error, \u0026alpha; = 0.05) multiple comparison correction, revealed that the classification accuracy in PCG (r = 0.632, p = 0.001) and PoCG (r = 0.558, p = 0.008) were significantly correlated with training years. However, no significant correlations were observed in IPL (r = 0.282, p = 0.584) and PrG (r = -0.269, p = 0.668).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearchlight MVPA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo capture the broad semantic neural representation patterns across the entire brain, we conducted a searchlight MVPA analysis for both expert and novice groups. As depicted in Fig.2, an 8-mm radius searchlight analysis highlighted both similarities and differences in semantic neural representation patterns between the two groups. For the expert group, we identified significant centers in the right postcentral gyrus and IPL, left IFG, left medial frontal gyrus (with a peak at MNI coordinates (-10, 56, -2)), and right precentral gyrus (peak at MNI coordinates (42, -6, 48)).\u003c/p\u003e\n\u003cp\u003eIn contrast, for the novice group, significant centers were located in the left lingual gyrus (peak at MNI coordinates (-16, -78, -10)), left insula and inferior frontal gyrus (peak at MNI coordinates (-36, 10, -6)), left superior temporal gyrus (peak at MNI coordinates (-50, -20, 2)), and left superior frontal gyrus (peak at MNI coordinates (-10, 52, 38)). However, a two-sample t-test revealed no significant differences in searchlight accuracy between the expert and novice groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable1. Clusters identified in the searchlight MVPA\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.80874316939891%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredominant regions in cluster\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.021857923497267%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.304189435336976%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eT-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.86520947176685%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMNI coordinates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ez\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpert\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eRight Postcentral Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e1368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e13.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eMedial Frontal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e1315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eInferior Parietal Lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e1275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003ePrecuneus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e1235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle Frontal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e1009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003ePrecentral Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eSuperior Temporal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eSuperior Frontal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Inferior Frontal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Medial Frontal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eRight Precentral Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNovice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Lingual Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e10.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Insula\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e8.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Inferior Frontal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Superior Temporal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.89051094890511%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Superior Frontal Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.335766423357665%\" valign=\"top\"\u003e\n \u003cp\u003e8.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFEW p = 0.001, cluster size\u0026gt;100\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our univariate analyses, we focused on comparing the BOLD signal differences between the expert and novice groups. The results revealed that when processing foot action verbs, experts displayed significantly higher BOLD signals than novices in specific brain regions. These regions included the right inferior frontal gyrus triangularis (IFG-T), right inferior frontal gyrus opercularis (IFG-O), and the right inferior parietal lobule (IPL). In contrast, when observing hand action verbs, no significant differences in BOLD signals were observed between the expert and novice groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable2. Clusters identified in the univariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.14466546112116%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredominant regions in cluster\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849909584086799%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849909584086799%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eT-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster-level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.15732368896926%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMNI coordinates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.54976303317535%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003eFWE-corr\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.95734597156398%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.009478672985782%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.48341232227488%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ez\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eContrast: Experts \u0026gt; Novices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.14466546112116%\" valign=\"top\"\u003e\n \u003cp\u003eRight inferior frontal gyrus triangularis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849909584086799%\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849909584086799%\" valign=\"top\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.233273056057866%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.871609403254973%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.052441229656419%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.14466546112116%\" valign=\"top\"\u003e\n \u003cp\u003eRight inferior frontal gyrus opercularis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849909584086799%\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849909584086799%\" valign=\"top\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.233273056057866%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.871609403254973%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.052441229656419%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.14466546112116%\" valign=\"top\"\u003e\n \u003cp\u003eRight inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849909584086799%\" valign=\"top\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849909584086799%\" valign=\"top\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.998191681735985%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.233273056057866%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.871609403254973%\" valign=\"top\"\u003e\n \u003cp\u003e-46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.052441229656419%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMVPA Analyses Based on Univariate-Derived ROIs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed the same ROI-based MVPA methodology, as previously mentioned, to analyze the IFG-T, IFG-O, and IPL identified in the univariate analysis. The results of the one-sample t-tests on the classification accuracy for each group revealed that the accuracies based on IFG-T were not significantly above chance for either experts (FWE corrected, \u0026alpha; = 0.05, t = 1.218, p = 0.701) or novices (t = 2.531, p = 0.051). However, for IFG-O, the classification accuracies were significantly above chance for both experts (t = 3.382, p = 0.006) and novices (t = 2.740, p = 0.031). Similarly, for IPL, significant classification accuracies were observed for both experts (t = 4.500, p \u0026lt; 0.001) and novices (t = 3.280, p = 0.008). Furthermore, the results of the two-sample t-test indicated no significant differences between the expert and novice groups based on IFG-T, IFG-O, and IPL.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study aimed to examine if long-term football training influences the neural representation patterns of action verbs in sensorimotor regions. Employing high spatial resolution fMRI alongside an MVPA generalization approach, we identified that PCG, PoCG, IPL, and PrG can encode effector information for both experts and novices. Moreover, football training specifically influenced neural representation in action execution areas. Our findings demonstrate that long-term professional football training specifically modulates neural representation patterns of verbs in an effector-specific manner, and thus provide compelling evidence that semantic processing is inherently linked to the sensory-motor system.\u003c/p\u003e\n\u003cp\u003ePrevious study found a short-term motor experience modulation altered the neural representation of action verb semantics (Xiong et al., 2023). Similarity, Kontra et al. (2015) found that the increased accuracy in understanding words denoting physical concepts (e.g., angular momentum) after hands-on learning, as opposed to merely observing the consequences of wheel manipulation, is mediated by greater activation in the primary motor (M1) and somatosensory (S1) cortices. Another study found that bilateral dorsal laryngeal motor cortex (dLMCs) was engaged with effector specificity by transcranial magnetic stimulation in a perceptual decision of lexical tone and voicing of consonant task (Liang et al., 2023). A neuroimaging study showed the premotor cortex activation observed during action verb processing relied on the access of more specific motor semantic content (Lin et al., 2015). Our results, obtained using the different method, MVPA, and get consistent result with these studies, We found that in both experts and novices, motor brain areas responsible for lower and upper limb actions can decode effector-specific information from action verbs, suggesting semantic processing is grounded in sensorimotor experiences within higher-order sensory/motor and association cortices. (Bi, 2021).\u003c/p\u003e\n\u003cp\u003eMore importantly, we discovered interactions between the action execution regions and the groups. Although no between-group differences were found in the decoding accuracy in the foot movement execution area, this still provides direct and compelling evidence that long-term professional football training modulates the neural representation of verb semantics in sensorimotor cortices and association cortices. We did not observe significant effects in the action observation regions, highlighting the influence of motor experience on the semantic processing of action verbs. Furthermore, the decoding accuracy of experts in the PCG and PoCG strongly correlated with their training years, which suggests long-term action training were associated with the neural representation of action verbs in sensorimotor cortices, and this association is at least partially effector-specific. Our finding consistent with previous findings (Beilock et al. 2008), and further discovered that the impact of sports-related sensorimotor experience can generalize to a broader range of verb semantic processing.\u0026nbsp;However, we did not find the interaction and the correlation for action observation regions. It suggests that the modulation effects of long-term motor training are mostly due to the motor experience, rather than sensory experience. Our study is consistent with previous studies, which showed long-term extensive motor training altered brain regions associated with action execution (Calmels, 2020; Hänggi, 2010). Furthermore, our finding fills a gap in previous research, which traditionally could not directly provide strong evidence that perceptual motor experience can dynamically regulate the neural representation of verb semantics. Xiong et al (2023) demonstrated that perceptual motor experience can regulate semantic neural representations, but the researchers were unable to directly observe changes in neural representation patterns in the action execution regions before and after the intervention. Our study, by employing football players who have acquired more foot-related action experience in their long-term life, successfully identified the impact of foot action experience on semantic neural representation in the sensorimotor and associated cortices.\u003c/p\u003e\n\u003cp\u003eThe results from searchlight MVPA classification provide intriguing insights into the neural underpinnings of verb semantic processing, especially in the context of expertise. The analysis reveals differential brain activation patterns in experts compared to novices, particularly in sensorimotor regions including somatosensory cortex and motor cortex. These findings align with the growing body of research suggesting that sensorimotor regions are deeply involved in processing action-related language (e.g., Dreyer, 2018; Barros-Loscertales et al., 2012; Kiefer, Sim, Herrnberger, Grothe, \u0026amp; Hoenig, 2008). The pronounced cluster in the right postcentral gyrus and the precentral gyrus in experts, but not in novices indicates the long-term training enhance action-related semantic neural representation in sensorimotor cortices, which propose that understanding action-related language involves simulating the actions in the brain's motor systems. The heightened activity in these regions suggests that experts, more so than novices, may utilize their refined sensorimotor representations in understanding and processing verbs, particularly those related to actions. The ability of various areas within PoCG and PrG to decode effector information from verbs in experts underscores the role of these regions in linking language to specific motor representations. This finding is in line with studies showing that language processing, particularly for action-related words, can activate corresponding motor and sensory areas (Hauk et al., 2004; Pulvermüller, 2005; Liu et al., 2024). The novice group shows more activation in areas like the left lingual gyrus and the left insula, which are more traditionally associated with basic language processing (Voets et al., 2006; Oh et al., 2014). This difference might reflect a less specialized, more generalized language processing strategy in novices. These results have broader implications for our understanding of how language is processed in the brain. They lend support to the idea that sensorimotor integration is crucial in language comprehension.\u003c/p\u003e\n\u003cp\u003eUnivariate analysis indicated that experts exhibited stronger activation intensities in the IPL and IFG (both triangularis and opercularis) when watching foot-related verbs. IPL typically associated with the integration of sensory information and attentional processes (Grefkes \u0026nbsp;et al., 2005; Igelström et al., 2017), which is an important node in AON, the significant activation in this region could mean that experts are more actively engaging in the multisensory integration or attentional mechanisms when processing the semantics of verbs. IFG is involved in phonological processing, speech production, language processing and cognitive control (Kulik et al., 2023; Ishkhanyan et al., 2020), which indicates long term motor training can influence semantic processing in higher-level cortical areas. We did not find differences in activation intensity between experts and novices in the sensorimotor cortices when watching both kinds of action verbs. This indicates that the impact of long-term motor training experience on semantic processing in the sensorimotor cortices is not reflected in differences in activation intensity, but rather in differences in neural representations, highlighting the importance of pattern analysis in the study of semantic processing. To test if IPL and IFG can encoding the semantic information of the action verbs, we conducted MVPA using these areas as ROIs. The results showed that there were no differences in semantic neural representations between experts and novices in these brain regions. However, in IFG-O and IPL, both experts and novices can decode the effector information of semantic, which suggests these regions are involved in action verbs semantic processing, but not sensitive to motor experience. Interestingly, both searchlight analysis and activation analysis show that the right IPL is influenced by action training experience. In the analysis of functional connectivity between the right IPL and both PCG and PoCG (for more information on the methods and results to the supplementary), we found that during verb observation, there was a moderate strength of correlation between the IPL and PCG in both experts and novices (when observing foot-related verbs), as well as between the IPL and PoCG (when observing hand-related verbs). These suggest that the information exchange between the IPL and the sensorimotor cortex may play a significant role in the processing of verb semantics.\u0026nbsp;In addition to our main analyses, we conducted seed-based functional connectivity (FC) analyses which selecting the paracentral gyrus (PCG) and postcentral gyrus (PoCG) as seed areas (for more information on the methods and results to the supplementary). \u0026nbsp;The FC results showed main effect of group between PCG and right posterior middle temporal gyrus (pMTG). Right pMTG is associated with semantic processing (Krist A. Noonan et al., 2013; Papeo et al., 2019), thus during the observation of action verbs, differences in the connectivity strength between the paracentral gyrus (PCG) and posterior middle temporal gyrus (pMTG) were observed between experts and novices, providing evidence that motor experience can influence the semantic processing of verbs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research conclusively demonstrates that sensorimotor experience, particularly from long-term football training, can distinctly modulate the neural representation of action-related verbs. Employing pattern analysis techniques, our study revealed effector-specific alterations in the neural patterns within primary sensorimotor and associated regions. This finding underscores the role of motor experience in shaping the semantic processing of action verbs, diverging from the influence of sensory experience. Moreover, this research emphasizes the neural adaptability resulting from extensive football training, illustrating how specialized physical training can profoundly alter the neural representation of action verbs. These changes were particularly evident in areas associated with action execution, suggesting a more nuanced understanding of the link between physical training and cognitive processes. In essence, our findings contribute to the growing body of evidence that supports the theory of embodied cognition, which posits a close relationship between sensorimotor experience and semantic processing. This research not only enhances our understanding of the cognitive impact of sports training but also opens avenues for further exploration into how different types of physical training might influence cognitive functions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our deepest gratitude to our colleagues and mentors at Shanghai University of Sport for their continuous support, insightful comments, and hard questions, all of which have significantly contributed to the improvement of this research work. This work was supported by the grants from National Natural Science Foundation of China (No.32271131).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJian Wang: Data curation; Conceptualization; Formal analysis; Methodology; Visualization; Roles/Writing - original draft. Hong Mou: Investigation; Data curation. Likai Liu: Writing - review \u0026amp; editing. Chenglin Zhou: Resources; Writing - review \u0026amp; editing. Yingying Wang: Funding acquisition; Project administration; Writing - review \u0026amp; editing; Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request (Requirements for co-authorship or inclusion in the author byline). The code to conduct MVPA analysis can be assessed at https://osf.io/3npxj/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the grants from National Natural Science Foundation of China (No.32271131).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003econflict of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJian Wang https://orcid.org/0009-0000-7303-4971\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatement: During the preparation of this work the authors used chatgpt4 in order to improve readability and language. 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DPABI: Data Processing \u0026amp; Analysis for (Resting-State) Brain Imaging. \u003cem\u003eNeuroinformatics\u003c/em\u003e, 14(3), 339\u0026ndash;351. https://doi.org/10.1007/s12021-016-9299-4\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Shanghai University of Sport","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"semantics, fMRI, MVPA, sensorimotor experience, football","lastPublishedDoi":"10.21203/rs.3.rs-4062491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4062491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe debate on whether sensorimotor experience can modulate the neural representation of action verbs is ongoing. This study investigated whether extensive football training alters the neural patterns representing action verbs in sensorimotor and related regions, focusing on effector-specific changes. Specifically, we assessed if training experiences of specific effectors influence semantic neural representation patterns in corresponding sensorimotor areas. Employing functional magnetic resonance imaging (fMRI), subjects (both football experts and novices) engaged in an implicit reading task, silently reading action verbs and identifying the involved body part. We used multivariate pattern analyses (MVPA) to classify effector-related information and assess decoding accuracy in the right paracentral gyrus (PCG) and left postcentral gyrus (PoCG) associated with action execution, and the left inferior parietal lobule (IPL) and left precentral gyrus (PrG) linked to action observation. Our findings revealed that both experts and novices could decode effector information from action verbs across all regions of interest. Notably, distinct activation patterns between experts and novices were observed in execution regions (PCG and PoCG), but not in observation regions (IPL and PrG), highlighting a specialized neural adaptation in PCG and PoCG. Furthermore, a significant correlation between decoding accuracy and training duration was found among football experts. Univariate analysis showed that experts exhibited higher activation intensity when processing foot-related verbs. In summary, our results suggest that long-term football training effector-specifically modulates the neural representation of action verbs in sensorimotor and related areas, predominantly driven by motor rather than sensory experience.\u003c/p\u003e","manuscriptTitle":"The Influence of Long-Term Football Training on Neural Representation of Action Verb Semantics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 18:35:49","doi":"10.21203/rs.3.rs-4062491/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"659048d8-29f8-4a8c-93b4-e2d8b81b5b96","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29269875,"name":"Cognitive Neuroscience"}],"tags":[],"updatedAt":"2024-03-11T18:35:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 18:35:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4062491","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4062491","identity":"rs-4062491","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}