Sex Differences in Parietal Lobe Activation Among Orienteering Athletes During a Mental Rotation Task: Evidence from fNIRS

Sex Differences in Parietal Lobe Activation Among Orienteering Athletes During a Mental Rotation Task: Evidence from fNIRS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Sex Differences in Parietal Lobe Activation Among Orienteering Athletes During a Mental Rotation Task: Evidence from fNIRS Yuqing Liu, Ying Qin, Mingyuan Zhao, Baoshan Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9156270/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective Using fNIRS with a mental rotation task, this study examined sex differences in spatial cognition among orienteering athletes to inform training and selection. Methods 42 athletes (21 males, 22.67 ± 1.46 years; 21 females, 19.81 ± 1.72 years) performed a letter mental rotation task. fNIRS recorded HbO, HbR, and HbT changes across 23 parietal channels. Accuracy, reaction time, and brain activation were analyzed using t-tests and correlations (FDR-corrected). Results Males showed higher accuracy (80.86%±16.50%) than females (63.70%±16.84%, p = 0.002, d = 1.03), with no reaction time difference. Males exhibited typical neurovascular coupling with significant HbR deactivation in SPL (Channel 17). Females showed high activation but greater variability and poorer coupling. BA5 HbO correlated negatively with accuracy (r=-0.436, p = 0.004), supporting neural efficiency, an effect driven by females (r=-0.630, p = 0.002) but absent in males (r = 0.004). S1 showed atypical coupling in females. No significant reaction time correlations emerged, though males showed positive trends in sensorimotor areas. Conclusions Male behavioral advantages relate to stable parietal activation. Neural efficiency manifests sex-specifically—males show low activation with low variability, females high activation with high variability—providing neural evidence for sex-differentiated orienteering training. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology orienteering mental rotation functional near-infrared spectroscopy sex differences neural efficiency hypothesis parietal lobe Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Spatial cognition ability is one of the core cognitive functions that enable humans to adapt to their environment, navigate, and solve spatial problems. Among various spatial abilities, mental rotation, as a classic paradigm for assessing spatial representation and transformation capacity, has been a prominent research focus in cognitive psychology and cognitive neuroscience since the seminal work of Shepard and Metzler 1 . Mental rotation requires individuals to mentally rotate two- or three-dimensional objects and judge their congruence with target stimuli, a process that engages complex cognitive components including spatial working memory, visual imagery, and motor simulation. The parietal cortex, particularly the posterior parietal regions, is widely recognized as a core brain area for spatial information processing. Neuroimaging studies have consistently demonstrated that mental rotation tasks significantly activate the superior parietal lobule (SPL), inferior parietal lobule (IPL), and adjacent somatosensory association cortex (Brodmann area 5, BA5) 2 . These brain regions work in concert, respectively responsible for spatial attention allocation, spatial representation manipulation, and sensorimotor integration, collectively supporting the complex cognitive process of mental rotation.Among these, the adjacent retrosplenial cortex also plays a crucial role in spatial navigation and scene recognition, and its synergistic activity with parietal regions constitutes the core network of spatial cognition 3 . However, despite considerable understanding of the neural mechanisms underlying mental rotation, the significant individual differences observed in this ability—particularly the neural basis of sex differences—remain to be fully elucidated. Extensive behavioral studies have shown that males typically exhibit dual advantages in both speed and accuracy on mental rotation tasks. Extensive behavioral studies have shown that males typically exhibit dual advantages in both speed and accuracy on mental rotation tasks 4 - 5 . Meta-analyses indicate that the effect size (Cohen's d) of sex differences in mental rotation is approximately 0.5-0.7, falling within the medium to large range 6 . Nevertheless, the origins of these differences remain controversial: biological factors such as sex hormone levels and brain structural differences, environmental factors including spatial experience and stereotypes, and cognitive strategy differences may all contribute. In recent years, advances in cognitive neuroscience have provided new perspectives for understanding these differences. The neural efficiency hypothesis posits that high-ability individuals exhibit more focused and economical brain activation patterns when performing identical cognitive tasks 9 . However, whether this hypothesis holds for sex differences in mental rotation, and how it might manifest, still requires empirical investigation. Functional near-infrared spectroscopy (fNIRS), as an emerging neuroimaging technique, is particularly suitable for investigating cognitive tasks that require the head to remain relatively still but may involve subtle movements. Unlike functional magnetic resonance imaging, which primarily focuses on blood oxygen level-dependent signals, fNIRS can simultaneously measure three indicators—oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin (HbT)—providing more comprehensive information on neurovascular coupling. Neurovascular coupling reflects the dynamic relationship between neural activity and cerebrovascular responses, and its quality may influence cognitive efficiency 8 . However, existing research has predominantly focused on HbO changes, with less systematic investigation of sex differences in HbR and HbT patterns, which may lead to an incomplete understanding of the underlying neural mechanisms. Indeed, multi-indicator joint analysis contributes to a more comprehensive characterization of the functional properties of core brain networks, just as integrating multimodal evidence to reveal core networks has become an important approach in memory and imagination research 9 . Mental rotation ability is closely related to various spatial tasks, among which orienteering serves as a typical representative. Orienteering requires participants to quickly read maps, determine directions, and plan optimal routes in natural environments, essentially constituting a dynamic, ecologically valid spatial rotation task. Research has shown that elite orienteers perform better on mental rotation tasks and exhibit different parietal activation patterns compared to the general population 10 . Therefore, understanding the neural mechanisms of mental rotation and their individual differences not only holds theoretical significance but also provides a scientific basis for training practical spatial abilities such as orienteering. The present study employs fNIRS technology to simultaneously measure HbO, HbR, and HbT, systematically investigating parietal activation patterns and their sex differences during mental rotation tasks. Specifically, this study aims to address the following questions: (1) Whether behavioral performance differences exist between sexes in mental rotation tasks. (2) How these differences manifest at the neural level, particularly regarding activation intensity, stability, and coupling quality. (3) The respective roles of different parietal regions of interest in sex differences. (4) Whether the correlation patterns between brain activation and behavioral performance exhibit sex specificity. (5) The implications of the findings for training practical spatial abilities such as orienteering. By addressing these questions, this study expects to deepen the understanding of the neural mechanisms underlying sex differences in mental rotation, reveal sex-specific manifestations of the neural efficiency hypothesis, and provide theoretical foundations for neuroevidence-based spatial ability training. The results may not only help explain the long-observed sex differences in mental rotation but also provide important empirical support for designing sex-differentiated cognitive training programs, particularly for practical tasks requiring high-level spatial abilities such as orienteering. 1 Participants and Methods 1.1 Participants The purpose of this study was to explore the sex differences in the spatial cognitive ability of orienteering athletes, using a single factor ( sex ) group design.Sample size was calculated using G*Power 3.1 software. Based on previous meta-analyses reporting sex differences in spatial cognition, the expected effect size was set at d = 0.8 (large effect size), with an α level of 0.05 and statistical power (1-β) of 0.8. The calculation indicated that, for an independent samples t-test, a minimum of 21 participants per group was required. Accordingly, 42 orienteering athletes were ultimately recruited for this study, including 21 males (22.67 ± 1.46 years) and 21 females (19.81 ± 1.72 years). All participants were in good health, with no history of neurological or psychiatric disorders, normal or corrected-to-normal vision, and were right-handed. They refrained from staying up late, consuming alcohol, or taking psychoactive substances within 24 hours prior to the experiment. This study was approved by the Ethics Committee of Harbin Sport University (Approval No.: 2026014). All experimental procedures were performed in accordance with the Declaration of Helsinki and the relevant guidelines and regulations of the institution. Written informed consent was obtained from all participants prior to their participation in the study. 1.2 Experimental Materials This experiment employed a classic mental rotation paradigm. Uppercase letters "R" and "F" in normal or mirror-image orientations were selected as standard stimuli. These letters possess asymmetric structures, making them suitable for mental rotation judgments and effectively avoiding response biases that might arise from letter symmetry. The letters were presented at five orientations, including 0°, 60°, 90°, 120°, and 180° for normal images, and 0°, 60°, 90°, 120°, and 180° for mirror images (Figure 1). In each trial, a rotated letter was presented at the center of the screen. Participants were instructed to judge whether the letter was normal or mirror-imaged as quickly and accurately as possible, pressing the "F" key for normal images and the "J" key for mirror images. The trial structure consisted of a "fixation-stimulus-blank screen" sequence. A fixation cross "+" was presented for 500 ms, followed by the rotated letter stimulus, which was displayed for a maximum duration of 1200 ms or terminated upon the participant's keypress response. A blank screen was then presented for 200 ms after the stimulus offset to allow for separation of the hemodynamic response (Figure 2). 1.3 fNIRS Equipment fNIRS data were acquired using the BS-7000 near-infrared spectroscopy system (Wuhan Zilian Hongkang Technology Co., Ltd., Wuhan, China). This system employs dual-wavelength laser diodes (690 nm and 830 nm) with a sampling frequency of 10 Hz. A total of 8 emitters and 8 detectors were configured, forming 23 channels, each consisting of an emitter-detector pair with a spacing of 3 cm. The optodes were arranged according to the international 10–20 system. Spatial coordinates of reference points (Nz, Cz, AL, RL) and all optodes were recorded using a 3D digitizer (NirMap, Wuhan Zilian Hongkang Technology Co., Ltd., Wuhan, China). Spatial registration was performed using the NIRS-SPM method 11 , which accurately projects channels onto the cortical surface and matches them to corresponding Brodmann areas. The channels were subsequently divided into five regions of interest (ROIs), as detailed in Figure 3 and Table 1. Table 1. Correspondence between channels and Brodmann areas Brodmann areas Channels S1 CH1、CH2、CH3 M1 CH4、CH5、CH6、CH7 BA5 CH8、CH9、CH10、CH11、CH12 SPL CH13、CH14、CH15、CH16、CH17、CH18 IPL CH19、CH20、CH21、CH22、CH23 1.4 Data Statistics and Analysis fNIRS data analysis was performed using NirMaster software, an efficient tool specifically developed by the equipment company for fNIRS data analysis, featuring robust capabilities for data preprocessing, extraction of individual-level characteristic indicators, and group-level statistical mapping. In fNIRS research, the coefficient of variation (CV) is commonly used to assess signal quality. Prior to data preprocessing, we calculated the CV for all channels. Channels with a CV exceeding 15% were classified as low-quality channels. Participants were excluded if the proportion of low-quality channels exceeded 25%. Ultimately, a total of two participants were excluded. The data preprocessing procedure began with the conversion of raw light intensity signals to optical density. Subsequently, spline interpolation was employed to correct motion artifacts and mitigate the impact of transient interference. A bandpass filter of 0.01–0.1 Hz was then applied to remove physiological noise and drift, followed by detrending. Thereafter, based on the modified Beer–Lambert law, the concentration changes of oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb), and total hemoglobin (total-Hb) were calculated. Due to its higher signal-to-noise ratio and stronger correlation with cerebral blood flow changes compared to deoxy-Hb, oxy-Hb was selected as the primary indicator for subsequent analyses. Statistical analysis was conducted using SPSS 26.0 software. First, the Shapiro–Wilk test for normality was performed on behavioral indicators (reaction time and accuracy) and the mean hemoglobin concentration changes from each fNIRS channel of interest. If the data followed a normal distribution (P > 0.05), an independent samples t-test was used to compare differences between male and female athlete groups; if the data did not follow a normal distribution, the Mann–Whitney U test was employed. For situations involving multiple comparisons (e.g., comparisons across multiple fNIRS channels), the false discovery rate (FDR) method was used to correct P-values. The significance level was set at α = 0.05 (two-tailed). 2 Results and Analysis 2.1 Behavioral Results Descriptive statistics showed that males (n = 21) exhibited higher accuracy on the mental rotation task (M = 80.86%, SD = 16.50%) compared to females (n = 21; M = 63.70%, SD = 16.84%). An independent samples t-test revealed that this sex difference was statistically significant, t(40) = 3.335, p=.002. Males outperformed females by an average of 17.16 percentage points, 95% CI [6.76%, 27.55%]. The effect size was large, with Cohen's d = 1.03. Regarding reaction time, males (M = 667 ms, SD = 93 ms) responded slightly faster than females (M = 718 ms, SD = 141 ms), with a mean difference of -52 ms. However, this difference did not reach statistical significance, t(40)=-1.402, p=.169, 95% CI [-126 ms, 23 ms]. The effect size, Cohen's d=-0.43, fell within the medium range but was not statistically significant. This pattern of behavioral performance is consistent with previous findings in orienteering populations; Zhao also observed sex differences in mental rotation tasks among orienteers and suggested that such differences may be related to domain-specific spatial experience 12 . Table 2 Behavioral results Variable Male(n = 21) Female(n = 21) t (40) P Cohen’d Accuracy (%) 80.86 ± 16.50 63.70 ± 16.84 3.335 0.002 1.03 Reaction Time(ms) 667 ± 93 718 ± 141 -1.402 0.169 -0.43 2.2 fNIRS Results The fNIRS data analysis revealed that the female group exhibited high activation values accompanied by extremely high inter-individual variability, for instance, Channel 9: M = 52.63, SD = 197.66, whereas the male group showed moderate activation values with lower variability. After FDR correction, only Channel 17 in the male group displayed significant deactivation in HbR (t(20)=-3.735, p < 0.05). The male group demonstrated typical neurovascular coupling patterns (HbR decrease accompanied by HbO and HbT increases) across multiple channels, while the female group predominantly exhibited atypical coupling. Notably, Channel 23 showed completely opposite hemodynamic responses between sexes: females displayed a significant reduction in blood volume (HbT=-41.41), whereas males showed an increase (HbT = 6.41). Thus, males demonstrated more normative and stable neural activation patterns during the task, with clearer statistical significance and more typical neurovascular coupling. Although females showed higher activation values in certain channels, the extremely high individual variability and unconventional coupling patterns may reflect different neural processing strategies (Fig. 4 ). This sex difference aligns with recent findings; Wang observed that different motor imagery strategies during mental rotation tasks led to distinctly different brain activation patterns, suggesting that strategy selection may be an important source of individual differences in neural activation 13 . Furthermore, Liu using fNIRS to examine the interaction between orienteering experience and cognitive performance, found that experienced orienteers exhibited more efficient parietal activation patterns 14 , consistent with the more normative neural activation characteristics observed in the male group of this study. A recent study by Ou 15 , employing simultaneous eye-tracking and whole-brain fNIRS to investigate the effects of rotating terrain symbols on spatial representation in orienteers, also identified sex-specific activation patterns in parietal regions, providing external validation for the findings of the present study. 2.3 Correlation Analysis 2.3.1 Correlation Analysis Between Behavioral Reaction Time and fNIRS Data Pearson correlation analysis was conducted between reaction time on the mental rotation task and HbO concentration changes in each region of interest for the male and female groups separately. The results indicated that no correlations reached statistical significance in any brain region (all p > 0.05). In the full-sample analysis (n = 42), the correlation coefficients between HbO in each ROI and reaction time were all near zero: M1 area (r = 0.051, p = 0.748), BA5 (r = -0.055, p = 0.730), S1 (r = 0.007, p = 0.965), IPL (r = 0.081, p = 0.609), and SPL (r = 0.059, p = 0.708), showing no systematic associations. This generally weak correlation pattern may be related to inherent inter-individual anatomical and physiological variability in fNIRS signals. A study by Heinzel using simultaneous fNIRS-fMRI recordings found that task-related fNIRS signals may be influenced by regional and individual anatomical variations and systemic physiological errors 16 , which could potentially confound or bias trait-activation correlations, suggesting that future fNIRS studies on individual differences need to carefully consider these factors. Group analysis revealed potential trends in differences. In the female group, correlations between HbO in each brain region and reaction time were all non-significant, showing near-zero weak associations overall. In the male group, although still not reaching significance across all regions, certain areas displayed notable trends: S1 (r = 0.335, p = 0.138) and M1 (r = 0.285?) showed moderate positive correlation trends, indicating that male participants with longer reaction times exhibited slightly higher activation in these sensorimotor regions; whereas BA5 (r = -0.013, p = 0.955), IPL (r = 0.047, p = 0.840), and SPL (r = 0.072, p = 0.756) showed extremely weak correlations. This positive correlation trend observed in sensorimotor areas (S1, M1) may reflect individual differences in cognitive effort or processing efficiency. For the orienteering athlete population in this study, long-term sports training may have shaped the functional organization of M1. A study by Wang on basketball players found that microstructural plasticity in M1 subregions was associated with cognitive-motor integration performance, with reaction time differences related to microstructural indices in M1 17 , offering a potential explanation for the observed trends in the male group of the present study. 2.3.2 Correlation Analysis Between Behavioral Accuracy and fNIRS Data To investigate the relationship between brain activation and behavioral performance, Pearson correlation analyses were conducted between accuracy and the concentration changes of HbO and HbR in each region of interest (M1, BA5, S1, IPL, SPL). The results revealed that HbO activation in the BA5 region was significantly negatively correlated with accuracy (r = -0.436, p = 0.004), supporting the neural efficiency hypothesis. HbR in the BA5 region was also significantly negatively correlated with accuracy (r = -0.347, p = 0.024). BA5, as a key area within the parietal cortex responsible for sensorimotor integration, has been implicated in deficits of spatial representation and somatosensory information integration when damaged, as evidenced by the typical clinical manifestations in patients with Gerstmann syndrome 18 . The negative correlation between BA5 activation and accuracy in the present study further suggests that efficient processing in this region is important for successful completion of mental rotation tasks. Group analysis revealed significant sex differences: females exhibited a strong negative correlation between HbO in BA5 and accuracy (r = -0.630, p = 0.002), and a trend towards a negative correlation in the S1 region that approached significance (r = -0.401, p = 0.072). In contrast, males showed no significant correlations in any brain region. Regarding HbR analysis, females displayed consistent negative correlation trends across multiple brain regions, whereas males exhibited weak positive or near-zero correlation trends in sensorimotor areas, showing opposite patterns between the sexes. This sex-specific brain-behavior relationship pattern may be related to differences between males and females in the processing of spatial sequence information. A recent study by Mihovilovic found that the acquisition of long-range spatial sequences involves dynamic activation of parietal regions, and that individual differences in sequence processing efficiency are closely associated with parietal functional organization 19 . Notably, the S1 region exhibited an atypical coupling pattern in both the overall sample and the female group, with HbO and HbR changing in the same direction, suggesting that this brain region may possess a special hemodynamic regulatory mechanism during mental rotation tasks. 3 Discussion 3.1 Neural Basis of Behavioral Advantage: Typical Coupling and Stable Activation in Males The present study found that males demonstrated significantly higher accuracy on the mental rotation task compared to females (80.86% vs. 63.70%), a behavioral advantage supported by multi-level neural evidence. First, regarding the quality of neurovascular coupling, males exhibited more typical and normative coupling patterns, particularly in the Channel 17 region, where HbR showed significant deactivation after FDR correction (t(20) = -3.735, p < 0.05), accompanied by a slight increase in HbO and an increase in HbT. This pattern of "HbR decrease accompanied by HbO/HbT increase" aligns with classical neurovascular coupling theory, reflecting the normal physiological response of increased local cerebral blood flow triggered by neural activity 20 . In contrast, the female group displayed atypical coupling patterns across multiple channels, such as Channels 9 and 23 exhibiting abnormal patterns where HbO and HbR changed in the same direction.Furthermore, the stability of brain activation was significantly higher in males than in females. Although the female group showed higher activation values in certain channels—for instance, HbO in Channel 9 reached 52.63—this was accompanied by substantial inter-individual variability. This high variability may reflect the adoption of different cognitive strategies or neural compensatory mechanisms within the female group. Cognitive load theory suggests that when task demands exceed an individual's cognitive resources, neural activation patterns exhibit greater variability 21 – 22 . In contrast, although males showed relatively lower activation values, their smaller coefficients of variation indicate more consistent and stable neural processing strategies. This finding aligns with the "neural efficiency" hypothesis, which posits that efficient cognitive processing is characterized not only by appropriate activation intensity but also by stable activation patterns 23 . The behavioral advantage in males appears to stem not from stronger activation intensity, but from superior neural resource allocation efficiency. The perfect coupling and significant HbR deactivation observed in Channel 17 may correspond to a key region within the parietal cortex responsible for sensorimotor integration. Efficient activation of this region enables males to more precisely coordinate spatial representation with motor simulation, thereby achieving better behavioral performance on mental rotation tasks. 3.2 Functional Differentiation of Parietal Subregions: Roles of Different Interest Areas in sex Differences Different subregions of the parietal cortex exhibited functional differentiation in the sex differences observed during mental rotation, reflecting the complexity of spatial processing. The BA5 region showed the most pronounced sex specificity: in females, BA5 activation was significantly negatively correlated with accuracy (r = -0.500, p = 0.021), supporting the neural efficiency hypothesis; whereas in males, this correlation was virtually zero (r = 0.004). BA5 is responsible for integrating somatosensory information with spatial representations, and the efficiency variation in this region among females may reflect different strategies—high-efficiency individuals can accomplish the same simulation process with fewer neural resources. The absence of such a correlation in males may suggest that their processing has become more automated or relies on compensation from other brain regions. The SPL, as a core region for spatial attention shifting, showed negative correlation trends in both sexes, though not reaching statistical significance. This suggests that the spatial transformation function of the SPL is relatively unaffected by sex and may serve as a "core processor" for mental rotation, with efficiency improvements benefiting both sexes. However, the SPL displayed sex differences in the HbR indicator, with males showing stronger negative correlations in SPL HbR, indicating more pronounced neural activation in this region among males. The S1 exhibited an interesting dissociation pattern: negatively correlated with accuracy but positively correlated with reaction time. This dual role was more evident in males, suggesting that the S1 assumes different functions under different task states—low activation during efficient performance and high activation during difficult processing. This functional flexibility may be related to brain rhythm regulation, with research indicating that phase-amplitude coupling in cortical-basal ganglia-thalamic circuits modulates sensorimotor information processing 24 . This flexible regulatory capacity in the S1 among males may contribute to their behavioral advantage. sex differences in M1 and IPL were relatively weaker, though coupling quality in M1 was superior in males. This functional differentiation indicates that sex differences in mental rotation are not simply attributable to differences in a single brain region, but involve functional reorganization across multiple nodes with distinct properties. Advances in motion correction techniques for fNIRS have enabled more precise isolation of these subtle regional brain differences 25 . 3.3 sex Specificity of Neural Efficiency: High Activation and High Variability Patterns in Females The neural efficiency hypothesis posits that high-ability individuals exhibit more focused and economical brain activation when performing cognitive tasks. The present study identified sex-specific manifestations of this hypothesis. Males supported the classical view of neural efficiency through typical low activation, low variability, and high-quality coupling. However, females displayed a distinctive "high activation, high variability" pattern, which challenges simplistic interpretations of neural efficiency. Multiple explanations may account for the high activation observed in females. First, this may reflect higher cognitive load. Mental rotation tasks may pose greater cognitive challenges for females, necessitating the recruitment of more neural resources. Cognitive load theory provides a framework for this interpretation 26 . Second, this may reflect different processing strategies. Females might rely more on detailed imagery rotation rather than abstract spatial transformation. Third, fundamental differences in neurovascular regulation might require females to generate stronger hemodynamic responses to support equivalent neural activity. More importantly, females exhibited high inter-individual variability. The substantial standard deviation in Channel 9 (SD = 197.66) indicates considerable heterogeneity within the female group: some females strongly activated certain brain regions, while others did not. This variability may stem from multiple factors: strategic diversity—different females adopting different rotation strategies. Individual differences in cognitive strategy represent an important topic in cognitive neuroscience research 22 ; experiential differences—variations in spatial experience leading to differences in neural plasticity. Orienteering, as a complex spatial task, involves optimization problems that themselves constitute an active research area 27 , and individuals with different experience levels may develop distinct neural representations; emotional and motivational factors—significant sex differences exist in emotional expression 28 , which may influence task engagement and neural response patterns. Furthermore, sex differences in anxiety disorders suggest females may be more susceptible to performance anxiety 29 . Broader context of sex differences: The neural pattern differences identified in this study echo findings from broader research on sex differences. sex differences are widely documented across various health and clinical domains, including non-suicidal self-injury 30 , post-traumatic stress disorder 31 , obesity comorbidity 32 , adult attention-deficit/hyperactivity disorder 33 , and narcissistic personality traits 34 . These studies indicate that sex differences are multidimensional and multi-layered, warranting systematic investigation of their neural underpinnings. This high variability suggests that training interventions for females may require greater individualization, taking into account their neural characteristics and strategic preferences. Future research could combine behavioral interventions with neurofeedback to explore approaches for helping females establish more stable and efficient neural activation patterns. 4 Conclusions Males exhibited more normative and stable neural activation patterns during mental rotation. Behaviorally, males demonstrated significantly higher accuracy than females (80.86% vs. 63.70%, p = 0.002, Cohen's d = 1.03), with a large effect size, confirming stable sex differences in mental rotation tasks. At the neural level, males displayed typical neurovascular coupling patterns, particularly significant deactivation of HbR in Channel 17 (located in the SPL region) after FDR correction (t(20) = -3.735, p < 0.05), accompanied by increases in HbO and HbT, consistent with classical neurovascular coupling theory. Furthermore, males showed significantly smaller coefficients of variation in activation across multiple channels compared to females, indicating more consistent and stable neural processing strategies. This pattern of "low activation, low variability, high-quality coupling" supports the neural efficiency hypothesis, suggesting that the behavioral advantage in males stems not from stronger neural activation intensity, but from superior neural resource allocation efficiency. Different parietal regions of interest played differentiated roles in sex differences, revealing the functional specificity of the neural mechanisms underlying mental rotation. The BA5 region exhibited significant neural efficiency characteristics in females: HbO activation in BA5 showed a strong negative correlation with accuracy (r = -0.630, p = 0.002), whereas this relationship virtually disappeared in males (r = 0.004). This indicates that efficiency differences in females during mental rotation are more concentrated in the sensorimotor integration环节, with high-efficiency females able to accomplish spatial representation and somatosensory information integration with fewer neural resources. The SPL, as a core region for spatial attention shifting, showed negative correlation trends with accuracy in both sexes, though the effect was more pronounced in the male HbR indicator, suggesting that the spatial transformation function of the SPL may be relatively unaffected by sex, serving as a "core processor" for mental rotation. The S1 exhibited task-state dependent sex differences: negatively correlated with accuracy but positively correlated with reaction time, a dual role particularly evident in males, reflecting the flexible regulatory capacity of S1 with low activation during efficient performance and high activation during difficult processing. sex differences in M1 and IPL were relatively weaker, though coupling quality in M1 was superior in males. This functional differentiation indicates that sex differences in mental rotation are not simply attributable to differences in a single brain region, but involve functional reorganization across multiple nodes with distinct properties, reflecting the complex organizational principles of the brain's spatial cognition network. Neural efficiency exhibited sex-specific manifestations, challenging traditional interpretations of the neural efficiency hypothesis. Males supported the classical view of neural efficiency through patterns of low activation, low variability, and high-quality coupling; whereas females displayed a distinctive pattern of "high activation, high variability, atypical coupling." The female group showed higher mean activation values in some channels (M = 52.63), but this was accompanied by substantial inter-individual variability (SD = 197.66), and multiple channels exhibited atypical coupling patterns where HbO and HbR changed in the same direction. This sex-specific pattern may reflect interactions among multiple factors: cognitive load differences—mental rotation tasks may pose greater cognitive challenges for females, necessitating the recruitment of more neural resources; strategic diversity—different rotation strategies may be adopted within the female group; fundamental differences in neurovascular regulation—females might require stronger hemodynamic responses to support equivalent neural activity; emotional and motivational factors—stereotype threat or performance anxiety may influence neural responses in some females. These findings suggest that neural efficiency is not a unidimensional concept of "low activation equals high efficiency," but rather manifests through diverse pathways across different sexes, brain regions, and task states. High efficiency in females may be reflected in the precise parameter regulation capacity of specific brain regions, rather than globally reduced activation. Correlation patterns between brain activation and behavioral performance revealed brain region specificity and sex dependence of neural efficiency. Accuracy-brain activation correlation analyses showed that the BA5 region was the only area remaining significant after FDR correction, and this negative correlation was entirely driven by females. Reaction time-brain activation correlation analyses, though not reaching statistical significance, revealed moderate positive correlation trends in the S1 and M1 regions within the male group (r = 0.335 and r = 0.267, respectively), suggesting that males with longer reaction times required stronger sensorimotor cortex activation to complete the task, potentially reflecting individual differences in cognitive effort or processing efficiency. Notably, the S1 region exhibited atypical coupling patterns with HbO and HbR changing in the same direction within the female group, suggesting that this brain region may possess a special hemodynamic regulatory mechanism during mental rotation tasks. This sex specificity in brain-behavior relationships aligns with recent research, indicating that fNIRS technology can reveal underlying neural differences not captured by behavioral data alone. Orienteering, as a typical ecologically valid spatial cognition task, relies heavily on mental rotation ability. The sex differences identified at both behavioral and neural levels in this study suggest that training practices should consider sex-specific strategies: For female athletes, training could focus on sensorimotor integration related to the BA5 region, helping establish stable and efficient neural representations through diverse spatial tasks, and considering the substantial heterogeneity within the female group, training programs should be more individualized; for male athletes, who already exhibit relatively normative and stable neural activation patterns, training could focus on maintaining and optimizing existing strategies while further enhancing neural efficiency through challenging tasks; the task-state dependent characteristics of the S1 region suggest that sensory feedback training may benefit performance in both sexes, though the underlying mechanisms may differ by sex. Declarations Competing interests The authors declare no competing interests. Funding Declaration This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Baoshan Qian and Yuqing Liu conceptualized and designed the study and drafted the manuscript; Yuqing Liu performed the experiments and collected the data; Mingyuan Zhao analyzed the data and prepared the figures and tables; Ying Qin provided technical support and equipment; Baoshan Qian supervised the study and revised the manuscript. All authors reviewed and approved the final manuscript. Acknowledgements The authors would like to thank all orienteering athletes for their active participation in this study, as well as the research team members for their assistance in data collection and analysis. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. 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Ameta-analysisongenderdifferencesinmentalrotationabilitymeasuredby the Purduespatialvisualizationtests: Visualizationofrotations(PSVT:R). EducationalPsychologyReview,25(1),69–94. (2013). Haier, R. J. & SiegelJr, B. V. MacLachlan,A.,Soderling,E.,Lottenberg,S.,&Buchsbaum,M.S.(1992).Regionalglucosemetabolicchangesafterlearningacomplexvisuospatial/motortask: apositronemissiontomographicstudy. Brainresearch,570(1–2),134–143. Tachtsidis, I. & &Scholkmann, F. Falsepositivesandfalsenegativesinfunctionalnear-infraredspectroscopy:issues,challenges,andthewayforward. Neurophotonics,3(3),031405–031405. (2016). Benoit, R. G. & Schacter, D. L. Specifying the core network supporting episodic simulation and episodic memory by activation likelihood estimation. Neuropsychologia 75 , 450–457 (2015). Brookes, J., Warburton, M. A. M. & Mon-Williams, M. (2020). &Mushtaq,F.( Studyinghumanbehaviorwithvirtualreality:TheUnityExperimentFramework Behaviorresearchmethods , 52 (2),455–463. Ye, J. C., Tak, S., Jang, K. E., Jung, J. & Jang, J. NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy. Neuroimage 44 (2), 428–447 (2009). Zhao, M., Liu, J., Liu, Y. & Kang, P. Effects of mental rotation on map representation in orienteers—behavioral and fNIRS evidence. PeerJ 11 , e16299 (2023). Wang, C. et al. The brain activation of two motor imagery strategies in a mental rotation task. Brain Sci. 15 (1), 8 (2024). Liu, J., Liu, Y. & Wu, L. Exploring the dynamics of prefrontal cortex in the interaction between orienteering experience and cognitive performance by fNIRS. Sci. Rep. 14 (1), 14918 (2024). Ou, S., Liu, T. & Liu, Y. Neural Mechanisms of the Impact of Rotated Terrain Symbols on Spatial Representation in Orienteers: Evidence from Eye-Tracking and Whole-Brain fNIRS Synchronization. Behav. Sci. 15 (10), 1314 (2025). Heinzel, S. et al. Variability of (functional) hemodynamics as measured with simultaneous fNIRS and fMRI during intertemporal choice. Neuroimage 71 , 125–134 (2013). Wang, C. C., Zhang, L. & Li, H. Microstructural plasticity in M1 subregions correlates with cognitive-motor integration performance in basketball athletes. Brain Struct. Function . 230 (2), 451–465 (2025). Shahab, Q. S., Young, I. M., Dadario, N. B., Tanglay, O., Nicholas, P. J., Lin, Y.H., … Sughrue, M. E. (2022). A connectivity model of the anatomic substrates underlying Gerstmann syndrome. Brain communications, 4(3), fcac140. Mihovilovic, M. I., Stephan, T., Straube, A., Dieterich, M. & Eggert, T. Brain activity during acquisition of long visuospatial sequences. Front. Cognition . 4 , 1493709 (2025). Tachtsidis, I. & &Scholkmann, F. Falsepositivesandfalsenegativesinfunctionalnear-infraredspectroscopy:issues,challenges,andthewayforward.Neurophotonics,3(3),031405–031405. (2016). Broadbent, D. P. et al. Cognitive load, working memory capacity and driving performance: A preliminary fNIRS and eye tracking study. Transp. Res. part. F: traffic Psychol. Behav. 92 , 121–132 (2023). Gkintoni, E., Antonopoulou, H., Sortwell, A. & Halkiopoulos, C. Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain Sci. 15 (2), 203 (2025). Neubauer, A. C. & &Fink, A. Intelligenceandneuralefficiency Neuroscience&BiobehavioralReviews , 33 (7),1004–1023. (2009). De Hemptinne, C. et al. Therapeutic deep brain stimulation reduces cortical phase-amplitude coupling in Parkinson's disease. Nat. Neurosci. 18 (5), 779–786 (2015). Fishburn, F. A., Ludlum, R. S., Vaidya, C. J. & Medvedev, A. V. Temporal derivative distribution repair (TDDR): a motion correction method for fNIRS. Neuroimage 184 , 171–179 (2019). Broadbent, D. P. et al. Cognitive load, working memory capacity and driving performance: A preliminary fNIRS and eye tracking study. Transp. Res. part. F: traffic Psychol. Behav. 92 , 121–132 (2023). Gunawan, A., Lau, H. C. & Vansteenwegen, P. Orienteering problem: A survey of recent variants, solution approaches and applications. Eur. J. Oper. Res. 255 (2), 315–332 (2016). Chaplin, T. M. Gender and emotion expression: A developmental contextual perspective. Emot. Rev. 7 (1), 14–21 (2015). Asher, M., Asnaani, A. & Aderka, I. M. Gender differences in social anxiety disorder: A review. Clin. Psychol. Rev. 56 , 1–12 (2017). Bresin, K. & Schoenleber, M. Gender differences in the prevalence of nonsuicidal self-injury: A meta-analysis. Clin. Psychol. Rev. 38 , 55–64 (2015). Christiansen, D. M. & Berke, E. T. Gender-and sex-based contributors to sex differences in PTSD. Curr. psychiatry Rep. 22 (4), 19 (2020). Cooper, A. J., Gupta, S. R., Moustafa, A. F. & Chao, A. M. Sex/gender differences in obesity prevalence, comorbidities, and treatment. Curr. Obes. Rep. 10 (4), 458–466 (2021). Faheem, M. et al. Gender-based differences in prevalence and effects of ADHD in adults: A systematic review. Asian J. psychiatry . 75 , 103205 (2022). Grijalva, E. et al. Gender differences in narcissism: a meta-analytic review. Psychol. Bull. 141 (2), 261 (2015). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 24 Apr, 2026 Editor invited by journal 26 Mar, 2026 Submission checks completed at journal 21 Mar, 2026 First submitted to journal 21 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9156270","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":634262455,"identity":"96c4d003-3a6b-46fb-bbc9-79c3cc54a6f5","order_by":0,"name":"Yuqing Liu","email":"","orcid":"","institution":"Harbin Sport University","correspondingAuthor":false,"prefix":"","firstName":"Yuqing","middleName":"","lastName":"Liu","suffix":""},{"id":634262467,"identity":"a1beeea8-7c61-4ecb-ba83-ae31c31663f5","order_by":1,"name":"Ying Qin","email":"","orcid":"","institution":"Harbin Sport 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procedure\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9156270/v1/0243acf036643df8fbd221ae.png"},{"id":108514559,"identity":"132f879e-188f-448d-9cfe-462587d7a237","added_by":"auto","created_at":"2026-05-05 13:20:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":404309,"visible":true,"origin":"","legend":"\u003cp\u003efNIRS channel configuration\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9156270/v1/45de194767db27a0e8b4bb11.png"},{"id":108514560,"identity":"8dc9d83a-64b5-4687-8944-95b8b8390799","added_by":"auto","created_at":"2026-05-05 13:20:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":416184,"visible":true,"origin":"","legend":"\u003cp\u003esex differences in fNIRS brain activation\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9156270/v1/b2d1c233f556fd711d99260f.png"},{"id":108804758,"identity":"586ad890-5094-4d62-8131-e998a00b8684","added_by":"auto","created_at":"2026-05-08 15:23:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":148381,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between behavioral reaction time and HbO data\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9156270/v1/82427704e40d002a98140d84.png"},{"id":108514561,"identity":"d773e7c0-e459-4e7b-958c-1273b31f43e4","added_by":"auto","created_at":"2026-05-05 13:20:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":148768,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between behavioral reaction time and HbR data\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9156270/v1/3714400fed9ce7d02ac67805.png"},{"id":108804173,"identity":"4288d66a-f628-4864-ab84-2ab0137a7a78","added_by":"auto","created_at":"2026-05-08 15:17:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":166863,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between behavioral accuracy and HbO data\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9156270/v1/6568cda48353c5c61b0d511a.png"},{"id":108514564,"identity":"14d6cd70-9b2b-4c70-b024-85cb7676c199","added_by":"auto","created_at":"2026-05-05 13:20:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":173686,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between behavioral accuracy and HbR data\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9156270/v1/91a58da758a9df6a6fc0e8c3.png"},{"id":108809560,"identity":"6e228e0a-c872-46ba-ac1f-ecb352a43a4e","added_by":"auto","created_at":"2026-05-08 15:53:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2251872,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9156270/v1/e110e10f-2ee8-4242-8aec-9deb414cdd34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sex Differences in Parietal Lobe Activation Among Orienteering Athletes During a Mental Rotation Task: Evidence from fNIRS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpatial cognition ability is one of the core cognitive functions that enable humans to adapt to their environment, navigate, and solve spatial problems. Among various spatial abilities, mental rotation, as a classic paradigm for assessing spatial representation and transformation capacity, has been a prominent research focus in cognitive psychology and cognitive neuroscience since the seminal work of Shepard and Metzler\u003csup\u003e1\u003c/sup\u003e. Mental rotation requires individuals to mentally rotate two- or three-dimensional objects and judge their congruence with target stimuli, a process that engages complex cognitive components including spatial working memory, visual imagery, and motor simulation.\u003c/p\u003e\n\u003cp\u003eThe parietal cortex, particularly the posterior parietal regions, is widely recognized as a core brain area for spatial information processing. Neuroimaging studies have consistently demonstrated that mental rotation tasks significantly activate the superior parietal lobule (SPL), inferior parietal lobule (IPL), and adjacent somatosensory association cortex (Brodmann area 5, BA5)\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e. These brain regions work in concert, respectively responsible for spatial attention allocation, spatial representation manipulation, and sensorimotor integration, collectively supporting the complex cognitive process of mental rotation.Among these, the adjacent retrosplenial cortex also plays a crucial role in spatial navigation and scene recognition, and its synergistic activity with parietal regions constitutes the core network of spatial cognition\u003csup\u003e3\u003c/sup\u003e. However, despite considerable understanding of the neural mechanisms underlying mental rotation, the significant individual differences observed in this ability\u0026mdash;particularly the neural basis of sex differences\u0026mdash;remain to be fully elucidated. Extensive behavioral studies have shown that males typically exhibit dual advantages in both speed and accuracy on mental rotation tasks.\u003c/p\u003e\n\u003cp\u003eExtensive behavioral studies have shown that males typically exhibit dual advantages in both speed and accuracy on mental rotation tasks\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e. Meta-analyses indicate that the effect size (Cohen\u0026apos;s d) of sex differences in mental rotation is approximately 0.5-0.7, falling within the medium to large range\u003csup\u003e6\u003c/sup\u003e. Nevertheless, the origins of these differences remain controversial: biological factors such as\u0026nbsp;sex\u0026nbsp;hormone levels and brain structural differences, environmental factors including spatial experience and stereotypes, and cognitive strategy differences may all contribute. In recent years, advances in cognitive neuroscience have provided new perspectives for understanding these differences. The neural efficiency hypothesis posits that high-ability individuals exhibit more focused and economical brain activation patterns when performing identical cognitive tasks\u003csup\u003e9\u003c/sup\u003e. However, whether this hypothesis holds for\u0026nbsp;sex\u0026nbsp;differences in mental rotation, and how it might manifest, still requires empirical investigation.\u003c/p\u003e\n\u003cp\u003eFunctional near-infrared spectroscopy (fNIRS), as an emerging neuroimaging technique, is particularly suitable for investigating cognitive tasks that require the head to remain relatively still but may involve subtle movements. Unlike functional magnetic resonance imaging, which primarily focuses on blood oxygen level-dependent signals, fNIRS can simultaneously measure three indicators\u0026mdash;oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin (HbT)\u0026mdash;providing more comprehensive information on neurovascular coupling. Neurovascular coupling reflects the dynamic relationship between neural activity and cerebrovascular responses, and its quality may influence cognitive efficiency\u003csup\u003e8\u003c/sup\u003e. However, existing research has predominantly focused on HbO changes, with less systematic investigation of sex differences in HbR and HbT patterns, which may lead to an incomplete understanding of the underlying neural mechanisms. Indeed, multi-indicator joint analysis contributes to a more comprehensive characterization of the functional properties of core brain networks, just as integrating multimodal evidence to reveal core networks has become an important approach in memory and imagination research\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMental rotation ability is closely related to various spatial tasks, among which orienteering serves as a typical representative. Orienteering requires participants to quickly read maps, determine directions, and plan optimal routes in natural environments, essentially constituting a dynamic, ecologically valid spatial rotation task. Research has shown that elite orienteers perform better on mental rotation tasks and exhibit different parietal activation patterns compared to the general population\u003csup\u003e10\u003c/sup\u003e. Therefore, understanding the neural mechanisms of mental rotation and their individual differences not only holds theoretical significance but also provides a scientific basis for training practical spatial abilities such as orienteering. The present study employs fNIRS technology to simultaneously measure HbO, HbR, and HbT, systematically investigating parietal activation patterns and their sex differences during mental rotation tasks. Specifically, this study aims to address the following questions: (1) Whether behavioral performance differences exist between sexes in mental rotation tasks. (2) How these differences manifest at the neural level, particularly regarding activation intensity, stability, and coupling quality. (3) The respective roles of different parietal regions of interest in sex differences. (4) Whether the correlation patterns between brain activation and behavioral performance exhibit sex specificity. (5) The implications of the findings for training practical spatial abilities such as orienteering. By addressing these questions, this study expects to deepen the understanding of the neural mechanisms underlying sex differences in mental rotation, reveal sex-specific manifestations of the neural efficiency hypothesis, and provide theoretical foundations for neuroevidence-based spatial ability training. The results may not only help explain the long-observed sex differences in mental rotation but also provide important empirical support for designing sex-differentiated cognitive training programs, particularly for practical tasks requiring high-level spatial abilities such as orienteering.\u003c/p\u003e"},{"header":"1 Participants and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of this study was to explore the sex differences in the spatial cognitive ability of orienteering athletes, using a single factor ( sex ) group design.Sample size was calculated using G*Power 3.1 software. Based on previous meta-analyses reporting sex differences in spatial cognition, the expected effect size was set at d = 0.8 (large effect size), with an \u0026alpha; level of 0.05 and statistical power (1-\u0026beta;) of 0.8. The calculation indicated that, for an independent samples t-test, a minimum of 21 participants per group was required. Accordingly, 42 orienteering athletes were ultimately recruited for this study, including 21 males (22.67 \u0026plusmn; 1.46 years) and 21 females (19.81 \u0026plusmn; 1.72 years).\u003c/p\u003e\n\u003cp\u003eAll participants were in good health, with no history of neurological or psychiatric disorders, normal or corrected-to-normal vision, and were right-handed. They refrained from staying up late, consuming alcohol, or taking psychoactive substances within 24 hours prior to the experiment. This study was approved by the Ethics Committee of Harbin Sport University (Approval No.: 2026014). All experimental procedures were performed in accordance with the Declaration of Helsinki and the relevant guidelines and regulations of the institution. Written informed consent was obtained from all participants prior to their participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Experimental Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis experiment employed a classic mental rotation paradigm. Uppercase letters \u0026quot;R\u0026quot; and \u0026quot;F\u0026quot; in normal or mirror-image orientations were selected as standard stimuli. These letters possess asymmetric structures, making them suitable for mental rotation judgments and effectively avoiding response biases that might arise from letter symmetry. The letters were presented at five orientations, including 0\u0026deg;, 60\u0026deg;, 90\u0026deg;, 120\u0026deg;, and 180\u0026deg; for normal images, and 0\u0026deg;, 60\u0026deg;, 90\u0026deg;, 120\u0026deg;, and 180\u0026deg; for mirror images (Figure 1). In each trial, a rotated letter was presented at the center of the screen. Participants were instructed to judge whether the letter was normal or mirror-imaged as quickly and accurately as possible, pressing the \u0026quot;F\u0026quot; key for normal images and the \u0026quot;J\u0026quot; key for mirror images.\u003c/p\u003e\n\u003cp\u003eThe trial structure consisted of a \u0026quot;fixation-stimulus-blank screen\u0026quot; sequence. A fixation cross \u0026quot;+\u0026quot; was presented for 500 ms, followed by the rotated letter stimulus, which was displayed for a maximum duration of 1200 ms or terminated upon the participant\u0026apos;s keypress response. A blank screen was then presented for 200 ms after the stimulus offset to allow for separation of the hemodynamic response (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 fNIRS Equipment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003efNIRS data were acquired using the BS-7000 near-infrared spectroscopy system (Wuhan Zilian Hongkang Technology Co., Ltd., Wuhan, China). This system employs dual-wavelength laser diodes (690 nm and 830 nm) with a sampling frequency of 10 Hz. A total of 8 emitters and 8 detectors were configured, forming 23 channels, each consisting of an emitter-detector pair with a spacing of 3 cm. The optodes were arranged according to the international 10\u0026ndash;20 system. Spatial coordinates of reference points (Nz, Cz, AL, RL) and all optodes were recorded using a 3D digitizer (NirMap, Wuhan Zilian Hongkang Technology Co., Ltd., Wuhan, China). Spatial registration was performed using the NIRS-SPM method\u003csup\u003e11\u003c/sup\u003e, which accurately projects channels onto the cortical surface and matches them to corresponding Brodmann areas. The channels were subsequently divided into five regions of interest (ROIs), as detailed in Figure 3 and Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. Correspondence between channels and Brodmann areas\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eBrodmann areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eChannels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCH1、CH2、CH3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCH4、CH5、CH6、CH7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eBA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCH8、CH9、CH10、CH11、CH12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eSPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCH13、CH14、CH15、CH16、CH17、CH18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eIPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003eCH19、CH20、CH21、CH22、CH23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Data Statistics and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003efNIRS data analysis was performed using NirMaster software, an efficient tool specifically developed by the equipment company for fNIRS data analysis, featuring robust capabilities for data preprocessing, extraction of individual-level characteristic indicators, and group-level statistical mapping. In fNIRS research, the coefficient of variation (CV) is commonly used to assess signal quality. Prior to data preprocessing, we calculated the CV for all channels. Channels with a CV exceeding 15% were classified as low-quality channels. Participants were excluded if the proportion of low-quality channels exceeded 25%. Ultimately, a total of two participants were excluded.\u003c/p\u003e\n\u003cp\u003eThe data preprocessing procedure began with the conversion of raw light intensity signals to optical density. Subsequently, spline interpolation was employed to correct motion artifacts and mitigate the impact of transient interference. A bandpass filter of 0.01\u0026ndash;0.1 Hz was then applied to remove physiological noise and drift, followed by detrending. Thereafter, based on the modified Beer\u0026ndash;Lambert law, the concentration changes of oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb), and total hemoglobin (total-Hb) were calculated. Due to its higher signal-to-noise ratio and stronger correlation with cerebral blood flow changes compared to deoxy-Hb, oxy-Hb was selected as the primary indicator for subsequent analyses.\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using SPSS 26.0 software. First, the Shapiro\u0026ndash;Wilk test for normality was performed on behavioral indicators (reaction time and accuracy) and the mean hemoglobin concentration changes from each fNIRS channel of interest. If the data followed a normal distribution (P \u0026gt; 0.05), an independent samples t-test was used to compare differences between male and female athlete groups; if the data did not follow a normal distribution, the Mann\u0026ndash;Whitney U test was employed. For situations involving multiple comparisons (e.g., comparisons across multiple fNIRS channels), the false discovery rate (FDR) method was used to correct P-values. The significance level was set at \u0026alpha; = 0.05 (two-tailed).\u003c/p\u003e"},{"header":"2 Results and Analysis","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Behavioral Results\u003c/h2\u003e \u003cp\u003eDescriptive statistics showed that males (n\u0026thinsp;=\u0026thinsp;21) exhibited higher accuracy on the mental rotation task (M\u0026thinsp;=\u0026thinsp;80.86%, SD\u0026thinsp;=\u0026thinsp;16.50%) compared to females (n\u0026thinsp;=\u0026thinsp;21; M\u0026thinsp;=\u0026thinsp;63.70%, SD\u0026thinsp;=\u0026thinsp;16.84%). An independent samples t-test revealed that this sex difference was statistically significant, t(40)\u0026thinsp;=\u0026thinsp;3.335, p=.002. Males outperformed females by an average of 17.16 percentage points, 95% CI [6.76%, 27.55%]. The effect size was large, with Cohen's d\u0026thinsp;=\u0026thinsp;1.03. Regarding reaction time, males (M\u0026thinsp;=\u0026thinsp;667 ms, SD\u0026thinsp;=\u0026thinsp;93 ms) responded slightly faster than females (M\u0026thinsp;=\u0026thinsp;718 ms, SD\u0026thinsp;=\u0026thinsp;141 ms), with a mean difference of -52 ms. However, this difference did not reach statistical significance, t(40)=-1.402, p=.169, 95% CI [-126 ms, 23 ms]. The effect size, Cohen's d=-0.43, fell within the medium range but was not statistically significant. This pattern of behavioral performance is consistent with previous findings in orienteering populations; Zhao also observed sex differences in mental rotation tasks among orienteers and suggested that such differences may be related to domain-specific spatial experience\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBehavioral results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003e(40)\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCohen\u0026rsquo;d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e80.86\u0026thinsp;\u0026plusmn;\u0026thinsp;16.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e63.70\u0026thinsp;\u0026plusmn;\u0026thinsp;16.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReaction Time(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e667\u0026thinsp;\u0026plusmn;\u0026thinsp;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e718\u0026thinsp;\u0026plusmn;\u0026thinsp;141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 fNIRS Results\u003c/h2\u003e \u003cp\u003eThe fNIRS data analysis revealed that the female group exhibited high activation values accompanied by extremely high inter-individual variability, for instance, Channel 9: M\u0026thinsp;=\u0026thinsp;52.63, SD\u0026thinsp;=\u0026thinsp;197.66, whereas the male group showed moderate activation values with lower variability. After FDR correction, only Channel 17 in the male group displayed significant deactivation in HbR (t(20)=-3.735, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The male group demonstrated typical neurovascular coupling patterns (HbR decrease accompanied by HbO and HbT increases) across multiple channels, while the female group predominantly exhibited atypical coupling. Notably, Channel 23 showed completely opposite hemodynamic responses between sexes: females displayed a significant reduction in blood volume (HbT=-41.41), whereas males showed an increase (HbT\u0026thinsp;=\u0026thinsp;6.41). Thus, males demonstrated more normative and stable neural activation patterns during the task, with clearer statistical significance and more typical neurovascular coupling. Although females showed higher activation values in certain channels, the extremely high individual variability and unconventional coupling patterns may reflect different neural processing strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This sex difference aligns with recent findings; Wang observed that different motor imagery strategies during mental rotation tasks led to distinctly different brain activation patterns, suggesting that strategy selection may be an important source of individual differences in neural activation\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, Liu using fNIRS to examine the interaction between orienteering experience and cognitive performance, found that experienced orienteers exhibited more efficient parietal activation patterns\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, consistent with the more normative neural activation characteristics observed in the male group of this study. A recent study by Ou\u003csup\u003e15\u003c/sup\u003e, employing simultaneous eye-tracking and whole-brain fNIRS to investigate the effects of rotating terrain symbols on spatial representation in orienteers, also identified sex-specific activation patterns in parietal regions, providing external validation for the findings of the present study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Correlation Analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Correlation Analysis Between Behavioral Reaction Time and fNIRS Data\u003c/h2\u003e \u003cp\u003ePearson correlation analysis was conducted between reaction time on the mental rotation task and HbO concentration changes in each region of interest for the male and female groups separately. The results indicated that no correlations reached statistical significance in any brain region (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the full-sample analysis (n\u0026thinsp;=\u0026thinsp;42), the correlation coefficients between HbO in each ROI and reaction time were all near zero: M1 area (r\u0026thinsp;=\u0026thinsp;0.051, p\u0026thinsp;=\u0026thinsp;0.748), BA5 (r = -0.055, p\u0026thinsp;=\u0026thinsp;0.730), S1 (r\u0026thinsp;=\u0026thinsp;0.007, p\u0026thinsp;=\u0026thinsp;0.965), IPL (r\u0026thinsp;=\u0026thinsp;0.081, p\u0026thinsp;=\u0026thinsp;0.609), and SPL (r\u0026thinsp;=\u0026thinsp;0.059, p\u0026thinsp;=\u0026thinsp;0.708), showing no systematic associations. This generally weak correlation pattern may be related to inherent inter-individual anatomical and physiological variability in fNIRS signals. A study by Heinzel using simultaneous fNIRS-fMRI recordings found that task-related fNIRS signals may be influenced by regional and individual anatomical variations and systemic physiological errors\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, which could potentially confound or bias trait-activation correlations, suggesting that future fNIRS studies on individual differences need to carefully consider these factors.\u003c/p\u003e \u003cp\u003eGroup analysis revealed potential trends in differences. In the female group, correlations between HbO in each brain region and reaction time were all non-significant, showing near-zero weak associations overall. In the male group, although still not reaching significance across all regions, certain areas displayed notable trends: S1 (r\u0026thinsp;=\u0026thinsp;0.335, p\u0026thinsp;=\u0026thinsp;0.138) and M1 (r\u0026thinsp;=\u0026thinsp;0.285?) showed moderate positive correlation trends, indicating that male participants with longer reaction times exhibited slightly higher activation in these sensorimotor regions; whereas BA5 (r = -0.013, p\u0026thinsp;=\u0026thinsp;0.955), IPL (r\u0026thinsp;=\u0026thinsp;0.047, p\u0026thinsp;=\u0026thinsp;0.840), and SPL (r\u0026thinsp;=\u0026thinsp;0.072, p\u0026thinsp;=\u0026thinsp;0.756) showed extremely weak correlations. This positive correlation trend observed in sensorimotor areas (S1, M1) may reflect individual differences in cognitive effort or processing efficiency. For the orienteering athlete population in this study, long-term sports training may have shaped the functional organization of M1. A study by Wang on basketball players found that microstructural plasticity in M1 subregions was associated with cognitive-motor integration performance, with reaction time differences related to microstructural indices in M1\u003csup\u003e17\u003c/sup\u003e, offering a potential explanation for the observed trends in the male group of the present study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Correlation Analysis Between Behavioral Accuracy and fNIRS Data\u003c/h2\u003e \u003cp\u003eTo investigate the relationship between brain activation and behavioral performance, Pearson correlation analyses were conducted between accuracy and the concentration changes of HbO and HbR in each region of interest (M1, BA5, S1, IPL, SPL). The results revealed that HbO activation in the BA5 region was significantly negatively correlated with accuracy (r = -0.436, p\u0026thinsp;=\u0026thinsp;0.004), supporting the neural efficiency hypothesis. HbR in the BA5 region was also significantly negatively correlated with accuracy (r = -0.347, p\u0026thinsp;=\u0026thinsp;0.024). BA5, as a key area within the parietal cortex responsible for sensorimotor integration, has been implicated in deficits of spatial representation and somatosensory information integration when damaged, as evidenced by the typical clinical manifestations in patients with Gerstmann syndrome\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The negative correlation between BA5 activation and accuracy in the present study further suggests that efficient processing in this region is important for successful completion of mental rotation tasks.\u003c/p\u003e \u003cp\u003eGroup analysis revealed significant sex differences: females exhibited a strong negative correlation between HbO in BA5 and accuracy (r = -0.630, p\u0026thinsp;=\u0026thinsp;0.002), and a trend towards a negative correlation in the S1 region that approached significance (r = -0.401, p\u0026thinsp;=\u0026thinsp;0.072). In contrast, males showed no significant correlations in any brain region. Regarding HbR analysis, females displayed consistent negative correlation trends across multiple brain regions, whereas males exhibited weak positive or near-zero correlation trends in sensorimotor areas, showing opposite patterns between the sexes. This sex-specific brain-behavior relationship pattern may be related to differences between males and females in the processing of spatial sequence information. A recent study by Mihovilovic found that the acquisition of long-range spatial sequences involves dynamic activation of parietal regions, and that individual differences in sequence processing efficiency are closely associated with parietal functional organization\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Notably, the S1 region exhibited an atypical coupling pattern in both the overall sample and the female group, with HbO and HbR changing in the same direction, suggesting that this brain region may possess a special hemodynamic regulatory mechanism during mental rotation tasks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Neural Basis of Behavioral Advantage: Typical Coupling and Stable Activation in Males\u003c/h2\u003e \u003cp\u003eThe present study found that males demonstrated significantly higher accuracy on the mental rotation task compared to females (80.86% vs. 63.70%), a behavioral advantage supported by multi-level neural evidence. First, regarding the quality of neurovascular coupling, males exhibited more typical and normative coupling patterns, particularly in the Channel 17 region, where HbR showed significant deactivation after FDR correction (t(20) = -3.735, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), accompanied by a slight increase in HbO and an increase in HbT. This pattern of \"HbR decrease accompanied by HbO/HbT increase\" aligns with classical neurovascular coupling theory, reflecting the normal physiological response of increased local cerebral blood flow triggered by neural activity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In contrast, the female group displayed atypical coupling patterns across multiple channels, such as Channels 9 and 23 exhibiting abnormal patterns where HbO and HbR changed in the same direction.Furthermore, the stability of brain activation was significantly higher in males than in females. Although the female group showed higher activation values in certain channels\u0026mdash;for instance, HbO in Channel 9 reached 52.63\u0026mdash;this was accompanied by substantial inter-individual variability. This high variability may reflect the adoption of different cognitive strategies or neural compensatory mechanisms within the female group. Cognitive load theory suggests that when task demands exceed an individual's cognitive resources, neural activation patterns exhibit greater variability\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In contrast, although males showed relatively lower activation values, their smaller coefficients of variation indicate more consistent and stable neural processing strategies. This finding aligns with the \"neural efficiency\" hypothesis, which posits that efficient cognitive processing is characterized not only by appropriate activation intensity but also by stable activation patterns\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The behavioral advantage in males appears to stem not from stronger activation intensity, but from superior neural resource allocation efficiency. The perfect coupling and significant HbR deactivation observed in Channel 17 may correspond to a key region within the parietal cortex responsible for sensorimotor integration. Efficient activation of this region enables males to more precisely coordinate spatial representation with motor simulation, thereby achieving better behavioral performance on mental rotation tasks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Functional Differentiation of Parietal Subregions: Roles of Different Interest Areas in sex Differences\u003c/h2\u003e \u003cp\u003eDifferent subregions of the parietal cortex exhibited functional differentiation in the sex differences observed during mental rotation, reflecting the complexity of spatial processing. The BA5 region showed the most pronounced sex specificity: in females, BA5 activation was significantly negatively correlated with accuracy (r = -0.500, p\u0026thinsp;=\u0026thinsp;0.021), supporting the neural efficiency hypothesis; whereas in males, this correlation was virtually zero (r\u0026thinsp;=\u0026thinsp;0.004). BA5 is responsible for integrating somatosensory information with spatial representations, and the efficiency variation in this region among females may reflect different strategies\u0026mdash;high-efficiency individuals can accomplish the same simulation process with fewer neural resources. The absence of such a correlation in males may suggest that their processing has become more automated or relies on compensation from other brain regions. The SPL, as a core region for spatial attention shifting, showed negative correlation trends in both sexes, though not reaching statistical significance. This suggests that the spatial transformation function of the SPL is relatively unaffected by sex and may serve as a \"core processor\" for mental rotation, with efficiency improvements benefiting both sexes. However, the SPL displayed sex differences in the HbR indicator, with males showing stronger negative correlations in SPL HbR, indicating more pronounced neural activation in this region among males. The S1 exhibited an interesting dissociation pattern: negatively correlated with accuracy but positively correlated with reaction time. This dual role was more evident in males, suggesting that the S1 assumes different functions under different task states\u0026mdash;low activation during efficient performance and high activation during difficult processing. This functional flexibility may be related to brain rhythm regulation, with research indicating that phase-amplitude coupling in cortical-basal ganglia-thalamic circuits modulates sensorimotor information processing\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This flexible regulatory capacity in the S1 among males may contribute to their behavioral advantage. sex differences in M1 and IPL were relatively weaker, though coupling quality in M1 was superior in males. This functional differentiation indicates that sex differences in mental rotation are not simply attributable to differences in a single brain region, but involve functional reorganization across multiple nodes with distinct properties. Advances in motion correction techniques for fNIRS have enabled more precise isolation of these subtle regional brain differences\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 sex Specificity of Neural Efficiency: High Activation and High Variability Patterns in Females\u003c/h2\u003e \u003cp\u003eThe neural efficiency hypothesis posits that high-ability individuals exhibit more focused and economical brain activation when performing cognitive tasks. The present study identified sex-specific manifestations of this hypothesis. Males supported the classical view of neural efficiency through typical low activation, low variability, and high-quality coupling. However, females displayed a distinctive \"high activation, high variability\" pattern, which challenges simplistic interpretations of neural efficiency. Multiple explanations may account for the high activation observed in females. First, this may reflect higher cognitive load. Mental rotation tasks may pose greater cognitive challenges for females, necessitating the recruitment of more neural resources. Cognitive load theory provides a framework for this interpretation\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Second, this may reflect different processing strategies. Females might rely more on detailed imagery rotation rather than abstract spatial transformation. Third, fundamental differences in neurovascular regulation might require females to generate stronger hemodynamic responses to support equivalent neural activity. More importantly, females exhibited high inter-individual variability. The substantial standard deviation in Channel 9 (SD\u0026thinsp;=\u0026thinsp;197.66) indicates considerable heterogeneity within the female group: some females strongly activated certain brain regions, while others did not. This variability may stem from multiple factors: strategic diversity\u0026mdash;different females adopting different rotation strategies. Individual differences in cognitive strategy represent an important topic in cognitive neuroscience research\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e; experiential differences\u0026mdash;variations in spatial experience leading to differences in neural plasticity. Orienteering, as a complex spatial task, involves optimization problems that themselves constitute an active research area\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and individuals with different experience levels may develop distinct neural representations; emotional and motivational factors\u0026mdash;significant sex differences exist in emotional expression\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, which may influence task engagement and neural response patterns. Furthermore, sex differences in anxiety disorders suggest females may be more susceptible to performance anxiety\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Broader context of sex differences: The neural pattern differences identified in this study echo findings from broader research on sex differences. sex differences are widely documented across various health and clinical domains, including non-suicidal self-injury\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, post-traumatic stress disorder\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, obesity comorbidity\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, adult attention-deficit/hyperactivity disorder\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and narcissistic personality traits\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. These studies indicate that sex differences are multidimensional and multi-layered, warranting systematic investigation of their neural underpinnings. This high variability suggests that training interventions for females may require greater individualization, taking into account their neural characteristics and strategic preferences. Future research could combine behavioral interventions with neurofeedback to explore approaches for helping females establish more stable and efficient neural activation patterns.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eMales exhibited more normative and stable neural activation patterns during mental rotation. Behaviorally, males demonstrated significantly higher accuracy than females (80.86% vs. 63.70%, p\u0026thinsp;=\u0026thinsp;0.002, Cohen's d\u0026thinsp;=\u0026thinsp;1.03), with a large effect size, confirming stable sex differences in mental rotation tasks. At the neural level, males displayed typical neurovascular coupling patterns, particularly significant deactivation of HbR in Channel 17 (located in the SPL region) after FDR correction (t(20) = -3.735, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), accompanied by increases in HbO and HbT, consistent with classical neurovascular coupling theory. Furthermore, males showed significantly smaller coefficients of variation in activation across multiple channels compared to females, indicating more consistent and stable neural processing strategies. This pattern of \"low activation, low variability, high-quality coupling\" supports the neural efficiency hypothesis, suggesting that the behavioral advantage in males stems not from stronger neural activation intensity, but from superior neural resource allocation efficiency.\u003c/p\u003e \u003cp\u003eDifferent parietal regions of interest played differentiated roles in sex differences, revealing the functional specificity of the neural mechanisms underlying mental rotation. The BA5 region exhibited significant neural efficiency characteristics in females: HbO activation in BA5 showed a strong negative correlation with accuracy (r = -0.630, p\u0026thinsp;=\u0026thinsp;0.002), whereas this relationship virtually disappeared in males (r\u0026thinsp;=\u0026thinsp;0.004). This indicates that efficiency differences in females during mental rotation are more concentrated in the sensorimotor integration环节, with high-efficiency females able to accomplish spatial representation and somatosensory information integration with fewer neural resources. The SPL, as a core region for spatial attention shifting, showed negative correlation trends with accuracy in both sexes, though the effect was more pronounced in the male HbR indicator, suggesting that the spatial transformation function of the SPL may be relatively unaffected by sex, serving as a \"core processor\" for mental rotation. The S1 exhibited task-state dependent sex differences: negatively correlated with accuracy but positively correlated with reaction time, a dual role particularly evident in males, reflecting the flexible regulatory capacity of S1 with low activation during efficient performance and high activation during difficult processing. sex differences in M1 and IPL were relatively weaker, though coupling quality in M1 was superior in males. This functional differentiation indicates that sex differences in mental rotation are not simply attributable to differences in a single brain region, but involve functional reorganization across multiple nodes with distinct properties, reflecting the complex organizational principles of the brain's spatial cognition network.\u003c/p\u003e \u003cp\u003eNeural efficiency exhibited sex-specific manifestations, challenging traditional interpretations of the neural efficiency hypothesis. Males supported the classical view of neural efficiency through patterns of low activation, low variability, and high-quality coupling; whereas females displayed a distinctive pattern of \"high activation, high variability, atypical coupling.\" The female group showed higher mean activation values in some channels (M\u0026thinsp;=\u0026thinsp;52.63), but this was accompanied by substantial inter-individual variability (SD\u0026thinsp;=\u0026thinsp;197.66), and multiple channels exhibited atypical coupling patterns where HbO and HbR changed in the same direction. This sex-specific pattern may reflect interactions among multiple factors: cognitive load differences\u0026mdash;mental rotation tasks may pose greater cognitive challenges for females, necessitating the recruitment of more neural resources; strategic diversity\u0026mdash;different rotation strategies may be adopted within the female group; fundamental differences in neurovascular regulation\u0026mdash;females might require stronger hemodynamic responses to support equivalent neural activity; emotional and motivational factors\u0026mdash;stereotype threat or performance anxiety may influence neural responses in some females. These findings suggest that neural efficiency is not a unidimensional concept of \"low activation equals high efficiency,\" but rather manifests through diverse pathways across different sexes, brain regions, and task states. High efficiency in females may be reflected in the precise parameter regulation capacity of specific brain regions, rather than globally reduced activation.\u003c/p\u003e \u003cp\u003eCorrelation patterns between brain activation and behavioral performance revealed brain region specificity and sex dependence of neural efficiency. Accuracy-brain activation correlation analyses showed that the BA5 region was the only area remaining significant after FDR correction, and this negative correlation was entirely driven by females. Reaction time-brain activation correlation analyses, though not reaching statistical significance, revealed moderate positive correlation trends in the S1 and M1 regions within the male group (r\u0026thinsp;=\u0026thinsp;0.335 and r\u0026thinsp;=\u0026thinsp;0.267, respectively), suggesting that males with longer reaction times required stronger sensorimotor cortex activation to complete the task, potentially reflecting individual differences in cognitive effort or processing efficiency. Notably, the S1 region exhibited atypical coupling patterns with HbO and HbR changing in the same direction within the female group, suggesting that this brain region may possess a special hemodynamic regulatory mechanism during mental rotation tasks. This sex specificity in brain-behavior relationships aligns with recent research, indicating that fNIRS technology can reveal underlying neural differences not captured by behavioral data alone.\u003c/p\u003e \u003cp\u003eOrienteering, as a typical ecologically valid spatial cognition task, relies heavily on mental rotation ability. The sex differences identified at both behavioral and neural levels in this study suggest that training practices should consider sex-specific strategies: For female athletes, training could focus on sensorimotor integration related to the BA5 region, helping establish stable and efficient neural representations through diverse spatial tasks, and considering the substantial heterogeneity within the female group, training programs should be more individualized; for male athletes, who already exhibit relatively normative and stable neural activation patterns, training could focus on maintaining and optimizing existing strategies while further enhancing neural efficiency through challenging tasks; the task-state dependent characteristics of the S1 region suggest that sensory feedback training may benefit performance in both sexes, though the underlying mechanisms may differ by sex.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eBaoshan Qian and Yuqing Liu conceptualized and designed the study and drafted the manuscript; Yuqing Liu performed the experiments and collected the data; Mingyuan Zhao analyzed the data and prepared the figures and tables; Ying Qin provided technical support and equipment; Baoshan Qian supervised the study and revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank all orienteering athletes for their active participation in this study, as well as the research team members for their assistance in data collection and analysis.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Public sharing of data is subject to the relevant regulations of the institutional ethics committee.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eShepard, R. N. \u0026amp; \u0026amp;Metzler, J. Mentalrotationofthree-dimensionalobjects.\u003cem\u003eScience\u003c/em\u003e,\u003cem\u003e171\u003c/em\u003e(3972),701\u0026ndash;703. (1971).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHarris, I. 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Bull.\u003c/em\u003e \u003cstrong\u003e141\u003c/strong\u003e (2), 261 (2015).\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"orienteering, mental rotation, functional near-infrared spectroscopy, sex differences, neural efficiency hypothesis, parietal lobe","lastPublishedDoi":"10.21203/rs.3.rs-9156270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9156270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eUsing fNIRS with a mental rotation task, this study examined sex differences in spatial cognition among orienteering athletes to inform training and selection.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e42 athletes (21 males, 22.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46 years; 21 females, 19.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72 years) performed a letter mental rotation task. fNIRS recorded HbO, HbR, and HbT changes across 23 parietal channels. Accuracy, reaction time, and brain activation were analyzed using t-tests and correlations (FDR-corrected).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMales showed higher accuracy (80.86%\u0026plusmn;16.50%) than females (63.70%\u0026plusmn;16.84%, p\u0026thinsp;=\u0026thinsp;0.002, d\u0026thinsp;=\u0026thinsp;1.03), with no reaction time difference. Males exhibited typical neurovascular coupling with significant HbR deactivation in SPL (Channel 17). Females showed high activation but greater variability and poorer coupling. BA5 HbO correlated negatively with accuracy (r=-0.436, p\u0026thinsp;=\u0026thinsp;0.004), supporting neural efficiency, an effect driven by females (r=-0.630, p\u0026thinsp;=\u0026thinsp;0.002) but absent in males (r\u0026thinsp;=\u0026thinsp;0.004). S1 showed atypical coupling in females. No significant reaction time correlations emerged, though males showed positive trends in sensorimotor areas.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMale behavioral advantages relate to stable parietal activation. Neural efficiency manifests sex-specifically\u0026mdash;males show low activation with low variability, females high activation with high variability\u0026mdash;providing neural evidence for sex-differentiated orienteering training.\u003c/p\u003e","manuscriptTitle":"Sex Differences in Parietal Lobe Activation Among Orienteering Athletes During a Mental Rotation Task: Evidence from fNIRS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 13:20:07","doi":"10.21203/rs.3.rs-9156270/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"307031161601111693374842934902758886180","date":"2026-05-04T14:23:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314907175943991269306184700534755171667","date":"2026-04-24T15:28:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T15:21:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-24T15:06:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-26T08:03:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-22T02:52:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-22T02:46:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"44de7e82-46ec-4276-945c-076a9d138e95","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"307031161601111693374842934902758886180","date":"2026-05-04T14:23:16+00:00","index":90,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67499751,"name":"Biological sciences/Neuroscience"},{"id":67499752,"name":"Biological sciences/Psychology"},{"id":67499753,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-05T13:20:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 13:20:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9156270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9156270","identity":"rs-9156270","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}