Graph Theory Analysis of Topological Properties in White Matter Functional Networks of Adolescent Females with Primary Dysmenorrhea: A Resting-State fMRI Study

Graph Theory Analysis of Topological Properties in White Matter Functional Networks of Adolescent Females with Primary Dysmenorrhea: A Resting-State fMRI Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Graph Theory Analysis of Topological Properties in White Matter Functional Networks of Adolescent Females with Primary Dysmenorrhea: A Resting-State fMRI Study Wei Wei, Xin Tian, Yunsong Zheng, Feng Zhou, Lihua Fan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8857116/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 Objectives Primary dysmenorrhea (PDM) is associated with functional reorganization in gray matter networks, but whether white matter functional networks exhibit similar topological alterations remains unknown. This study provides the first graph-theoretical analysis of white matter functional connectomes in adolescent PDM using resting-state functional magnetic resonance imaging (rs-fMRI). Methods This study enrolled 32 healthy controls (HC) and 30 patients with PDM, who underwent structural and rs-fMRI scans and clinical assessments.After rigorous preprocessing to minimize gray matter contamination, individual white matter functional networks were constructed using a 128-node group-level parcellation. Global topological properties --including normalized clustering coefficient (γ), normalized characteristic path length (λ), small-worldness (σ), clustering coefficient (Cp), characteristic path length (Lp), global efficiency (Eg), and local efficiency (Eloc)—were computed across a sparsity range of 0.10–0.30. Group comparisons were performed with age, BMI, and head motion as covariates. Correlations between significant network metrics and Self-Rating Anxiety Scale (SAS) / Self-Rating Depression Scale (SDS) scores were examined. Results Both groups exhibited small-world architecture in white matter functional networks. Compared with HCs, PDM patients showed significantly decreased γ (P = 0.013), σ (P = 0.015), and Eg (P = 0.031), along with significantly increased Lp (P = 0.025). No group differences were found in Cp, λ, or Eloc (all P > 0.05). Correlation analyses revealed that γ and σ were negatively correlated with both SAS and SDS scores (r = − 0.350 to − 0.280, all P < 0.05). Eg was negatively correlated with SAS (r = − 0.307, P = 0.015), while Lp was positively correlated with SAS (r = 0.308, P = 0.015). Conclusion This study provides the first evidence that PDM involves disrupted small-world topology, reduced global integration, and delayed information transfer specifically within white matter functional networks. The significant correlations between these network alterations and affective symptoms establish white matter functional connectome topology as a clinically relevant neurobiological marker for emotional comorbidities in PDM. These findings extend the current gray matter‑centric model of PDM and identify graph-theoretical metrics of white matter networks as potential targets for neuromodulation. Primary Dysmenorrhea Graph Theory rs-fMRI White Matter Functional Network Figures Figure 1 Figure 2 Figure 3 Introduction Primary Dysmenorrhea (PDM), one of the most prevalent gynecological disorders among women of reproductive age, is characterized by recurrent cramping pain in the lower abdomen during the menstrual cycle(Itani et al., 2022 ). This condition significantly impairs patients' quality of life, work, and academic performance(Rencz et al., 2017 ). The underlying pain mechanisms involve not only abnormal peripheral prostaglandin levels but also significant functional remodeling within the central nervous system (CNS)(Affaitati, Costantini, Fiordaliso, Giamberardino, & Tana, 2024 ; Guimarães & Póvoa, 2020 ; Shen et al., 2019 ). Recent advances in neuroimaging have enabled researchers to investigate PDM's pathological mechanisms from the perspective of brain structure and functional networks, highlighting the CNS's crucial role in both the genesis and modulation of dysmenorrhea(Liu et al., 2023 ; Su et al., 2024 ; C. Wang et al., 2024 ; Y. Zhang et al., 2022 ). Previous functional MRI (fMRI) studies on PDM have primarily focused on abnormalities in gray matter functional connectivity. In contrast, white matter, which serves as the brain's information highway, has often been dismissed as noise and overlooked in these investigations. Although Diffusion Tensor Imaging (DTI) can explore white matter fiber structure, it cannot assess white matter function(Dun et al., 2017 ; He et al., 2021 ). Historically, resting-state fMRI (rs-fMRI) studies treated white matter signals as noise, largely due to its lower vascular density compared to gray matter, which results in weaker blood-oxygen-level-dependent (BOLD) signals with a lower signal-to-noise ratio (SNR)(Lierse & Horstmann, 1965 ; Preibisch & Haase, 2001 ). However, growing evidence demonstrates that BOLD signals in white matter likely reflect neural activity and can be detected using sufficiently sensitive imaging and analytical techniques(Marussich, Lu, Wen, & Liu, 2017 ; Mazerolle, Ohlhauser, Mayo, Sheriff, & Gawryluk, 2020 ). Researchers have successfully clustered rs-fMRI signals within white matter into highly organized, regular networks, and these constructed white matter networks correlate with cognitive networks in gray matter(Peer, Nitzan, Bick, Levin, & Arzy, 2017 ). Consequently, methods for analyzing white matter BOLD signals are now being applied to various brain disorders and cognitive neuroscience research, including Parkinson's disease, depression, and schizophrenia(Y. Jiang et al., 2019 ; J. Li et al., 2020 ; Meng et al., 2022 ). Graph Theory, a powerful mathematical framework for complex network analysis, offers a revolutionary perspective for understanding the brain's structural and functional organization(Yun & Kim, 2021 ). By calculating a range of graph-theoretical metrics, it quantitatively characterizes core topological properties of brain networks, such as global integration and local segregation capabilities, information transfer efficiency, fault tolerance, and the distribution of critical hub nodes. These properties are considered fundamental for the brain's efficient information processing. Graph theory analysis has been widely applied to studies of various neuropsychiatric disorders (e.g., chronic pain, depression, Alzheimer's disease), revealing network-level pathological features like disruptions in "small-world" properties and abnormal nodal centrality(De Pauw et al., 2020 ; Miraglia et al., 2022 ; Shi et al., 2020 ). However, it remains completely unknown whether the topological architecture of white matter functional networks is altered in PDM. To address this gap, the present study provides the first graph-theoretical analysis of white matter functional connectomes in patients with primary dysmenorrhea. Using resting-state fMRI and a 128-node white matter parcellation, we systematically compared global network properties between PDM patients and healthy controls, and further linked these neuroimaging phenotypes to anxiety and depression symptoms. This approach moves beyond traditional gray matter‑centric models and establishes white matter functional network topology as a previously unrecognized dimension of PDM pathophysiology. Materials and Methods Study Participants This study enrolled 35 PDM patients and 37 healthy individuals at Shaanxi University of Chinese Medicine between 2021 and 2022. The inclusion criteria for the PDM group were as follows: (1) Meeting the diagnostic standards for PDM outlined in the 2017 Consensus Guidelines for PDM published by the Canadian Society of Obstetricians and Gynaecologists (SOGC)(Burnett & Lemyre, 2017 ); (2) Aged 18–35 years, nulliparous, and right-handed; (3) Regular menstrual cycle (28 ± 7 days); (4) Signed the research informed consent form and voluntarily participated in the study; (5) Dysmenorrhea duration ≥ 6 months; (6) Visual Analogue Scale (VAS) pain score ≥ 4 points for three consecutive menstrual cycles; (7) No intake of analgesic medications within one week before the MRI scan. The inclusion criteria for the Healthy Control (HC) Group required meeting only criteria (2), (3), and (4) from the PDM group. The exclusion criteria for both groups were: (1) Secondary dysmenorrhea, including cases caused by endometriosis, adenomyosis, or pelvic inflammatory disease; (2) Suffering from other chronic pain conditions such as cervical spondylosis or lumbar disc herniation; (3) Serious diseases of the heart, liver, or kidneys; history of head trauma; or neurological disorders; (4) Contraindications to MRI scanning; (5) Oral intake of analgesic medications or contraceptives within the past 6 months. The study protocol was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Shaanxi University of Chinese Medicine (Approval number: SZFYIEC-YJ-BYBC-2022-[07]). All participants provided written informed consent prior to the study. MRI Data Acquisition All participants underwent MRI scans during days 1–3 of their menstrual cycle using a SIEMENS Skyra 3.0T superconducting MRI system equipped with a 20-channel head-neck coil. Prior to scanning, participants completed the clinical scales. During the scan, participants were positioned supine and instructed to remain awake with their eyes closed. To exclude secondary dysmenorrhea and intracranial organic pathologies, conventional pelvic (fat-suppressed T2-weighted) and cranial (T2-FLAIR) scans were performed prior to acquiring the high-resolution structural and BOLD sequences. The parameters for the T1-weighted magnetization-prepared rapid gradient echo (T1-MPRAGE) sequence were as follows: Repetition Time (TR)/ Echo Time (TE) = 2530/3.37 ms; Flip Angle (FA) = 7°; Slice thickness = 1.0 mm; Slice gap = 0 mm; Number of Excitations (NEX) = 0.5; Field of View (FOV) = 256 mm × 256 mm; Matrix = 256 × 256. Images were acquired in the right-to-left phase encoding direction with 192 slices, resulting in a scan time of 5 minutes 58 seconds and yielding 192 images. For rs-fMRI, data were acquired using a gradient-echo echo-planar imaging (GRE-EPI) sequence with the following parameters: TR/TE = 2000/26 ms; Flip Angle (FA) = 90°; Slice thickness = 4.0 mm; Number of slices = 35; Inter-slice gap = 0.8 mm; FOV = 224 mm × 224 mm. The scan duration was 8 minutes 06 seconds, acquiring 240 time points. MRI Data Preprocessing Structural and functional MRI data were processed automatically using DPABI V8.1 (version 8.1_240101)(Yan, Wang, Zuo, & Zang, 2016 ) and Statistical Parametric Mapping 12 (SPM12), running in MATLAB R2017b. Figure 1 outlines the preprocessing workflow. Prior to core preprocessing, structural and functional images underwent automatic AC-PC alignment respectively ( https://github.com/lrq3000/auto_acpc_reorient ). T1 structural images were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using the New Segment + DARTEL pipeline, spatially normalized to the Montreal Neurological Institute (MNI) space, and followed by the generation of individual white matter probability masks. The rs-fMRI preprocessing steps were as follows: 1) Removal of the first 10 time points; 2) Slice timing correction; 3) Realignment (Motion Correction): Participants (6 healthy controls and 5 PDM subjects) with excessive motion (maximum translation > 1.5 mm or maximum rotation > 1.5°) were excluded from further analysis. To minimize contamination of WM BOLD signals by GM partial volume effects, subsequent preprocessing focused exclusively on the WM signal. Individual WM masks were generated by applying a strict 90% probability threshold to the WM probability maps, followed by resampling. These resampled masks were then used to spatially extract subject-specific WM functional images via dot product multiplication. 4) The extracted WM functional images were normalized to MNI space (3 × 3 × 3 mm³ resolution) using DARTEL-derived flow fields; 5) Nuisance regression was performed using covariates including the mean CSF signal and 24 Friston head motion parameters, while retaining the WM BOLD signal; 6) To mitigate motion-induced spurious correlations in functional connectivity, scrubbing was applied. One forward and two back neighbors volumes with framewise displacement (FD) exceeding 0.5 mm with Jenkinson's method were removed; 7) Linear detrending and bandpass filtering (0.01–0.15 Hz) were applied to the time series. Construction of WM Functional Connectivity Matrix The brain network connection matrix was constructed using the GRETNA software(Wang et al., 2015 ). The group-level brain WM mask was randomly divided into 128 distinct nodes. The average time series of each node was extracted separately. For each subject, a 128 × 128 correlation matrix was generated by calculating the Pearson's correlation coefficients between the averaged time series of every node pair. Subsequently, Fisher's z-transformation was applied to the WM functional connectivity matrices. Topological Properties of WM Functional Connectomes The global topological properties of WM functional connectomes were analyzed using the GRETNA software (version 2.0). The network type was set to binary, the matrix sign to positive, and then, based on the calculation procedure for sparsity, the maximum and minimum sparsity values were determined respectively. The minimum sparsity was set to 2log(N)/(N − 1), where N represents the number of template nodes. The minimum sparsity was then reassessed to ensure that the size of the giant connected component (GCC) exceeded 80% of the total number of nodes in the network. The maximum sparsity was defined as the sparsity value when the Sigma parameter just exceeded 1.1. A range of sparsity (0.1–0.3, interval = 0.01)(J. Li et al., 2020 ) was generated to evaluate the topological properties of WM functional connectomes for all subjects. This study focuses on the following global graph-theoretical network parameters: normalized clustering coefficient (Gamma, γ), normalized characteristic path length (Lambda, λ), small-worldness (Sigma, σ), clustering coefficient (Cp), characteristic path length (Lp), global efficiency (Eg), and local efficiency (Eloc). Here, γ = Cp / C r ₐ and λ = Lp / L r ₐ, where C r ₐ and L r ₐ represent the clustering coefficient and characteristic path length of a comparable random network, respectively. A network exhibits small-world properties if σ = γ/λ > 1, with higher σ values indicating stronger small-worldness. Cp measures the overall tendency of nodes to form clusters within the network. Lp reflects the speed of information transfer between nodes, where shorter average shortest paths correspond to higher transmission efficiency. Eloc describes the clustering connectivity among neighboring nodes, while Eg quantifies the efficiency of information flow across the entire brain network. Statistical Analysis Demographic and Clinical Characteristics Demographic and clinical characteristics were compared between the HC group and the PDM group. Differences were analyzed using the nonparametric Mann–Whitney U test (for data that did not follow a normal distribution) or two-sample t-tests (employing Welch's t-test if variances were unequal). A P-value < 0.05 was considered statistically significant. Global topological property comparisons Differences in the AUC values of global topological properties (including Cp, γ, λ, Lp, σ, Eg and Eloc) between groups were analyzed using the Mann-Whitney U test (for non-normally distributed data) or two-sample t-tests (for normally distributed data). Age, BMI, and FD were included as covariates. Relationships Between Global Topological Properties and Clinical Scale Scores Pearson or Spearman correlation analysis was performed between graph theory parameters showing significant between-group differences and clinical scales [ Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale(SDS) ]. A P-value < 0.05 was considered statistically significant. Correlations were categorized as follows: weak (0.1 ≤ |r| < 0.3), moderate (0.3 ≤ |r| < 0.5), and strong (|r| ≥ 0.5), with |r| ≥ 0.3 serving as the relevance threshold. All analyses were conducted using SPSS. Results Demographic and Clinical Characteristics The final analysis of this study included 32 healthy controls and 30 PDM patients. Table 1 presents the demographic characteristics of both groups. A statistically significant difference in age was observed between HCs and PDMs (P 0.05). Significant differences were identified in all clinical scale scores (P < 0.05). Table 1 clinical and demographic characteristics Clinical Data HC group(n = 32) PDM group(n = 30) P Age(Years) 25 (24, 26) 23 (21, 25) 0.028 a BMI 20.82 ± 1.87 19.96 ± 2.08 0.095 b Duration of illness (months) NA 7.37 ± 3.01 NA CMSS(Total Symptom Duration) 5.00 (3.00, 8.25) 20.00 (15.25, 24.50) < 0.001 a CMSS( Mean Symptom Severity) 5.00 (2.75, 9.00) 14.00 (12.00, 18.75) < 0.001 a SAS 27.50 ± 5.42 35.47 ± 7.81 < 0.001 b SDS 28.50 (25.00, 33.50) 32.50 (29.00, 45.00) 0.004 a head motion(FD) 0.06 (0.04, 0.07) 0.06 (0.04, 0.09) 0.495 a Note: PDM = Primary dysmenorrhea; HC = Healthy Control. a Mann-Whitney U test, b independent samples t-test. BMI = Body Mass Index, SAS = Self-Rating Anxiety Scale, SDS = Self-Rating Depression Scale, FD = Framewise Displacement, NA = not available. Group Differences in Global Topological Properties Both groups exhibited small-world properties in their white matter functional networks across the sparsity threshold range of 0.10–0.30. Compared to the healthy controls, the PDM group demonstrated significantly decreased normalized clustering coefficient, small-worldness, and global efficiency, along with a significantly increased characteristic path length (all P 0.05) (Table 2 , Fig. 2 ). Table 2 Comparison of Global Topological Properties Between groups global topological property HC group PDM group Test Statistic P Cp 0.106 ± 0.007 0.106 = ± 0.009 466 a 0.849 γ 0.294 ± 0.033 0.274 ± 0.029 2.55 b 0.013* λ 0.209 ± 0.003 0.211 ± 0.005 469 a 0.882 Lp 0.388 ± 0.020 0.405 ± 0.035 320.50 a 0.025* σ 0.280 ± 0.031 0.260 ± 0.031 2.49 b 0.015* Eg 0.105 ± 0.005 0.102 ± 0.007 633.50 a 0.031* Eloc 0.142 ± 0.004 0.139 ± 0.006 1.95 c 0.057 Note: a Mann-Whitney U test, b independent samples t-test, c Welch's t-test,*significant difference. Correlation between Global Topological Properties and Clinical Scale Scores Correlation analyses between global graph-theoretical metrics—includingγ, σ, Eg, and Lp—and SAS and SDS revealed that: γ and σ were negatively correlated with both SAS and SDS (r = -0.350, -0.312, -0.344, -0.280, respectively; all P < 0.05). Eg was negatively correlated with SAS (r = -0.307, P = 0.015), while Lp was positively correlated with SAS (r = 0.308, P = 0.015). ( Fig. 3 ) Discussion This study provides the first evidence that primary dysmenorrhea is associated with disrupted topological organization of white matter functional networks. Using graph theory, we demonstrated that PDM patients exhibit significant degradation of small-world architecture (↓γ, ↓σ), impaired global integration (↓Eg), and prolonged information transfer (↑Lp) specifically within white matter—findings that are distinct from previously reported gray matter abnormalities. These network-level alterations were further correlated with anxiety and depression severity, linking white matter functional connectome topology to affective symptoms in PDM. Interpretation of PDM-related Topological Properties This study reveals significant disruptions in the topological architecture of white matter functional networks in adolescent PDM patients. The observed reduction in the normalized clustering coefficient indicates impaired local connectivity efficiency, signifying compromised specialized processing within functional modules(Rubinov & Sporns, 2010 ; P. Wang et al., 2024 ). This diminished clustering likely reflects altered communication within densely interconnected sub-networks involved in pain processing, such as thalamocortical circuits and limbic pathways(Kucyi et al., 2020 ; W. Zhang et al., 2024 ). Concurrently, the decreased small-worldness suggests a fundamental shift in global network organization. Small-world topology—characterized by high clustering and short path lengths—optimizes the balance between segregated processing and integrated information transfer(Bassett & Bullmore, 2017 ; L. L. Li et al., 2024 ). Our finding of significantly reduced σ (P = 0.015) indicates a deviation toward a less efficient, potentially more random network configuration in PDM, consistent with patterns observed in other chronic pain conditions(Lenoir, Cagnie, Verhelst, & De Pauw, 2021 ; Y. P. Zhang, Hong, & Zhang, 2022 ). Complementary evidence of impaired long-range communication is provided by the increased Lp and Eg. Elevated Lp (P = 0.025) indicates prolonged information transfer between distant brain regions, while diminished Eg (P = 0.031) reflects reduced capacity for parallel information processing across the network(Latora & Marchiori, 2001 ; Zu et al., 2024 ). These alterations may stem from microstructural disruptions in major white matter tracts, particularly highly connected hub regions like the corpus callosum and cingulum bundle(Ito et al., 2025 ; Robertson, Aristi, & Hashmi, 2023 ). Potential underlying mechanisms include: Neuroinflammatory processes affecting oligodendrocyte function(L. L. Li et al., 2024 ), Altered axonal conduction velocities(P. Wang et al., 2024 ), or Dysregulated neurovascular coupling in white matter(Mazerolle et al., 2020 ; Zeng et al., 2024 ). Critically, the constellation of findings—reduced γ, σ, and Eg with increased Lp—collectively demonstrates network reorganization in PDM. The degradation of small-world properties suggests reduced network resilience and computational capacity(L. L. Li et al., 2024 ; Stam, 2014 ), potentially impairing adaptive responses to pain. This convergence toward a more randomized architecture aligns with chronic pain models where persistent nociception drives maladaptive neuroplasticity(Bosma et al., 2018 ; Mansour, Farmer, Baliki, & Apkarian, 2014 ; W. Zhang et al., 2024 ). Notably, our study is the first to demonstrate this phenomenon specifically within the white matter functional networks of PDM patients. This represents a significant departure from traditional gray matter-focused models of pain processing, highlighting the critical role of white matter dynamics in pain pathophysiology(Ito et al., 2025 ; Zhao, Ford, Flashman, McAllister, & Ji, 2016 ; Zu et al., 2024 ). Clinical Correlates and Pathophysiological Implications The robust correlations between network metrics and affective symptoms provide compelling evidence for the clinical relevance of white matter network disruption in PDM. The negative correlation between γ and SAS/SDS scores (r = -0.350/-0.312) indicates that impaired local processing efficiency exacerbates affective symptoms, potentially through disrupted modular organization of emotion-regulation circuits(Han, Zhang, Jiao, Hu, & Pan, 2024 ; Ho et al., 2016 ). Specifically, diminished clustering may reflect compromised functional segregation within limbic structures (e.g., amygdala, hippocampus) and their cortical connections, reducing capacity for contextual pain modulation(Dosenbach, Raichle, & Gordon, 2025 ; Gregory, Ritchey, & Murty, 2020 ). Similarly, the negative σ-SAS/SDS correlations (r = -0.344/-0.280) suggest that degraded global network organization undermines the integration of cognitive-affective processes necessary for pain coping(Fang, Potter, Nguyen, & Zhang, 2020 ; Pagnoni & Porro, 2014 ). The positive correlation between Lp and SAS (r = 0.308) reveals a critical relationship between delayed information transfer and anxiety severity. Slowed communication along pathways connecting interoceptive regions (e.g., insula) with prefrontal regulatory areas may impair threat appraisal and safety signaling, heightening anxiety responses(Davey, 2025 ; H. Jiang, Zhong, Huang, & Zhong, 2025 ; Klabunde et al., 2019 ). This is further supported by the negative Eg-SAS correlation (r = -0.307), indicating that reduced network-wide integration efficiency compromises top-down pain modulation—a mechanism implicated in anxiety-related hyperalgesia(Langhammer et al., 2025 ). While most rs-fMRI research emphasizes gray matter networks, our focus on white matter functional connectivity (WM-FC) reveals unique insights. WM-FC captures intrinsic BOLD fluctuations in white matter, potentially reflecting axonal activity or glial modulation. The observed topological disruptions may indicate selective vulnerability of highly connected hub tracts. Our topology-behavior mapping significantly advances PDM models in three ways: First, it establishes white matter functional networks—not merely structural pathways—as mediators of affective comorbidity. Second, it identifies specific network properties (Lp for anxiety, γ for depression) as potential treatment biomarkers. Third, it suggests PDM involves fundamental reorganization of information processing architecture, positioning it within broader chronic pain paradigms. Future studies should examine menstrual-cycle-dependent network dynamics and test whether these topological markers predict treatment response. Limitations This study has several limitations. First, the relatively small sample size reduces the statistical power for detecting subtle topological properties, which may account for our inability to identify significant correlations between certain topological attributes and clinical variables; future studies with larger cohorts are needed to validate the stability and reproducibility of the findings. Second, the cross-sectional design precludes causal inference, necessitating longitudinal tracking across the menstrual cycle. Third, the restricted age range of participants limits the generalizability of the results; subsequent research should incorporate PDM patients across broader age groups to enhance external validity. Finally, the absence of integrated functional-structural white matter analysis represents a constraint; combining functional topological properties with structural connectivity measures (e.g., DTI) in future investigations could elucidate the underlying microstructural-functional basis. Conclusion This study provides the first evidence that adolescent PDM involves measurable disruptions in the topological architecture of white matter functional networks. The observed degradation of small-world properties, diminished global integration, and delayed information transfer collectively point to a network-level pathophysiology. Importantly, the correlation of these topological metrics with anxiety and depression severity provides a mechanistic framework for understanding affective comorbidities in PDM. These findings not only advance the neurobiological model of PDM but also identify graph-theoretical metrics as potential targets for therapeutic neuromodulation. Declarations Acknowledgement : We would like to thank all participants in this study, and we also extend our gratitude to the Affiliated Hospital of Shaanxi University of Chinese Medicine for assistance with MRI data acquisition. Funding This work was supported by Special Project of Natural Science of Shaanxi Provincial Department of Education (grant no. 25JK0423). Conflicts of interest The authors declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article. Ethics approval All procedures involving human participants were approved by the Affiliated Hospital of Shaanxi University of Chinese Medicine, and written informed consent was obtained from each participant. Author Contributions Wei Wei: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization, Funding acquisition. Xin Tian: Investigation, Resources, Data curation, Writing - review & editing. Feng Zhou: Project administration, Writing - review & editing. Yunsong Zheng: Software, Validation, Writing - review & editing. Lihua Fan: Conceptualization, Methodology, Supervision, Project administration, Writing - review & editing. 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White matter microstructure predicts measures of clinical symptoms in chronic back pain patients. Neuroimage Clin , 37 , 103309. 10.1016/j.nicl.2022.103309 Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage , 52 (3), 1059–1069. 10.1016/j.neuroimage.2009.10.003 Shen, Z., Yu, S., Wang, M., She, T., Yang, Y., Wang, Y., & Yang, J. (2019). Abnormal amygdala resting-state functional connectivity in primary dysmenorrhea. Neuroreport , 30 (5), 363–368. 10.1097/wnr.0000000000001208 Shi, Y., Li, J., Feng, Z., Xie, H., Duan, J., Chen, F., & Yang, H. (2020). Abnormal functional connectivity strength in first-episode, drug-naïve adult patients with major depressive disorder. Progress In Neuropsychopharmacology And Biological Psychiatry , 97 , 109759. 10.1016/j.pnpbp.2019.109759 Stam, C. J. (2014). Modern network science of neurological disorders. Nature Reviews Neuroscience , 15 (10), 683–695. 10.1038/nrn3801 Su, X., Li, Y., Liu, H., An, S., Yao, N., Li, C., & Huang, Z. G. (2024). Brain Network Dynamics in Women With Primary Dysmenorrhea During the Pain-Free Periovulation Phase. The Journal Of Pain : Official Journal Of The American Pain Society , 25 (10), 104618. 10.1016/j.jpain.2024.104618 Wang, C., He, J., Feng, X., Qi, X., Hong, Z., Dun, W., & Liu, J. (2024). Characteristics of pain empathic networks in healthy and primary dysmenorrhea women: an fMRI study. Brain Imaging Behav , 18 (5), 1086–1099. 10.1007/s11682-024-00901-x Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., & He, Y. (2015). GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Frontiers In Human Neuroscience , 9 , 386. 10.3389/fnhum.2015.00386 Wang, P., Bai, Y., Xiao, Y., Zheng, Y., Sun, L., Consortium, T. D., & Xue, S. (2024). Aberrant network topological structure of sensorimotor superficial white-matter system in major depressive disorder. Journal Of Zhejiang University. Science. B , 26 (1), 39–51. 10.1631/jzus.B2300880 Yan, C. G., Wang, X. D., Zuo, X. N., & Zang, Y. F. (2016). DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics , 14 (3), 339–351. 10.1007/s12021-016-9299-4 Yun, J. Y., & Kim, Y. K. (2021). Graph theory approach for the structural-functional brain connectome of depression. Progress In Neuropsychopharmacology And Biological Psychiatry , 111 , 110401. 10.1016/j.pnpbp.2021.110401 Zeng, S., Ma, L., Mao, H., Shi, Y., Xu, M., Gao, Q., & Fang, X. (2024). Dynamic functional network connectivity in patients with a mismatch between white matter hyperintensity and cognitive function. Frontiers In Aging Neuroscience , 16 , 1418173. 10.3389/fnagi.2024.1418173 Zhang, W., Zhai, X., Zhang, C., Cheng, S., Zhang, C., Bai, J., & Li, K. (2024). Regional brain structural network topology mediates the associations between white matter damage and disease severity in first-episode, Treatment-naïve pubertal children with major depressive disorder. Psychiatry Res Neuroimaging , 344 , 111862. 10.1016/j.pscychresns.2024.111862 Zhang, Y., Huang, Y., Liu, N., Wang, Z., Wu, J., Li, W., & Huo, J. (2022). Abnormal interhemispheric functional connectivity in patients with primary dysmenorrhea: a resting-state functional MRI study. Quant Imaging Med Surg , 12 (3), 1958–1967. 10.21037/qims-21-731 Zhang, Y. P., Hong, G. H., & Zhang, C. Y. (2022). Brain Network Changes in Lumbar Disc Herniation Induced Chronic Nerve Roots Compression Syndromes. Neural Plast, 2022 , 7912410. 10.1155/2022/7912410 Zhao, W., Ford, J. C., Flashman, L. A., McAllister, T. W., & Ji, S. (2016). White Matter Injury Susceptibility via Fiber Strain Evaluation Using Whole-Brain Tractography. Journal Of Neurotrauma , 33 (20), 1834–1847. 10.1089/neu.2015.4239 Zu, Z., Choi, S., Zhao, Y., Gao, Y., Li, M., Schilling, K. G., & Gore, J. C. (2024). The missing third dimension-Functional correlations of BOLD signals incorporating white matter. Science Advances , 10 (4), eadi0616. 10.1126/sciadv.adi0616 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 May, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 11 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8857116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607503242,"identity":"581afd5c-6b7b-431d-83df-e4703c93c94a","order_by":0,"name":"Wei Wei","email":"","orcid":"","institution":"Affiliated Hospital of Shaanxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wei","suffix":""},{"id":607503244,"identity":"d2c0c64a-a64b-4aa1-a732-12043230d215","order_by":1,"name":"Xin Tian","email":"","orcid":"","institution":"Affiliated Hospital of Shaanxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Tian","suffix":""},{"id":607503246,"identity":"64066b79-03bc-4df7-aff2-75b41950f4e5","order_by":2,"name":"Yunsong Zheng","email":"","orcid":"","institution":"Affiliated Hospital of Shaanxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yunsong","middleName":"","lastName":"Zheng","suffix":""},{"id":607503247,"identity":"631b2442-5003-4f0c-99cd-54364cefc34e","order_by":3,"name":"Feng Zhou","email":"","orcid":"","institution":"Affiliated Hospital of Shaanxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Zhou","suffix":""},{"id":607503248,"identity":"3151f69d-6fee-4dc8-b17b-59def891fdc1","order_by":4,"name":"Lihua Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACNoaDjQ8+VEjIsbE3HyBOCx/j4WbDGWcsjPl4jiUQp0WO+XibMG9bReI8iRwDIh3GdrCNgeeMRGIbz5mPN94w2MnpNhDSwnOw7YFEhYRxG3vvZss5DMnGZgcIaZE42G5gcEZCto3n7DZpHoYDidsIapF/2AZ0lQRjm0TOMyK1MBxskwAiRaAWNqK1NBs2nJEwZuM5Zmw5x4AIv8g3HH/4+E9FnZx8e/PDG28q7OQIakEBEjxERg2yFlJ1jIJRMApGwYgAAG4LRIRD4zNFAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Shaanxi University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Lihua","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2026-02-12 03:40:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8857116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8857116/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104887524,"identity":"dff81e95-7c5e-4dc4-8304-a9d0b101af27","added_by":"auto","created_at":"2026-03-18 10:11:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2549136,"visible":true,"origin":"","legend":"\u003cp\u003ePipeline for MRI data preprocessing, white matter (WM) functional connectivity matrix construction, and topological property calculation. The T1 structural images were segmented to generate a group-level 128-node WM mask. The rs-fMRI data then underwent a series of preprocessing steps, followed by the construction of a WM functional connectivity matrix based on the same mask. Finally, global topological properties of the WM functional network were computed across a sparsity range of 0.1 to 0.3 (interval = 0.01).\u003c/p\u003e","description":"","filename":"Figure.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8857116/v1/140629cd4a05a11faa438283.png"},{"id":104887530,"identity":"fed374d4-e124-4bac-93eb-be1b82ad58de","added_by":"auto","created_at":"2026-03-18 10:12:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":567196,"visible":true,"origin":"","legend":"\u003cp\u003eA-G Comparison of global graph theory metrics（Cp, γ, λ, Lp, σ, Eg and Eloc）between groups. No statistically significant differences between groups were observed in the global graph metrics Cp (U-test, P=0.849), λ (t-test, P=0.882), or Eloc (t-test, P=0.052). Statistically significant differences between groups were found in the \u0026nbsp;global graph metricsγ (t-test, P=0.013), Lp (U-test, P=0.025), σ(t-test, P=0.015) and Eg (U-test, P=0.031).\u003c/p\u003e","description":"","filename":"Figure.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8857116/v1/47d5b62defb9913e1fc716ee.png"},{"id":104887532,"identity":"3460eb31-fe80-4223-b936-7f4625a8b798","added_by":"auto","created_at":"2026-03-18 10:12:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":850847,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of graph theory metrics with clinical scales. (A-H) Scatter plots display the correlations between global graph theory metrics (γ, Lp, σ, Eg) and clinical scale scores, respectively, and (I) heatmap illustrates the correlation coefficients. Both aGamma and aSigma were negatively correlated with SAS and SDS. Eg was negatively correlated with SAS (r = -0.307), while Lp was positively correlated with SAS (r = 0.308). Lp and Eg showed no significant correlation with SDS (r = 0.157 and r = -0.161, respectively).\u003c/p\u003e","description":"","filename":"Figure.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8857116/v1/9c39979f5d11ab91fbdd7293.png"},{"id":105034361,"identity":"9f9b7117-0f35-4f00-9d0b-3fce1461285e","added_by":"auto","created_at":"2026-03-20 07:23:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4937458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8857116/v1/6470f0fa-2a1c-491a-9710-32042cf1cb67.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Graph Theory Analysis of Topological Properties in White Matter Functional Networks of Adolescent Females with Primary Dysmenorrhea: A Resting-State fMRI Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary Dysmenorrhea (PDM), one of the most prevalent gynecological disorders among women of reproductive age, is characterized by recurrent cramping pain in the lower abdomen during the menstrual cycle(Itani et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This condition significantly impairs patients' quality of life, work, and academic performance(Rencz et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The underlying pain mechanisms involve not only abnormal peripheral prostaglandin levels but also significant functional remodeling within the central nervous system (CNS)(Affaitati, Costantini, Fiordaliso, Giamberardino, \u0026amp; Tana, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Guimar\u0026atilde;es \u0026amp; P\u0026oacute;voa, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shen et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent advances in neuroimaging have enabled researchers to investigate PDM's pathological mechanisms from the perspective of brain structure and functional networks, highlighting the CNS's crucial role in both the genesis and modulation of dysmenorrhea(Liu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; C. Wang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Y. Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous functional MRI (fMRI) studies on PDM have primarily focused on abnormalities in gray matter functional connectivity. In contrast, white matter, which serves as the brain's information highway, has often been dismissed as noise and overlooked in these investigations. Although Diffusion Tensor Imaging (DTI) can explore white matter fiber structure, it cannot assess white matter function(Dun et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; He et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Historically, resting-state fMRI (rs-fMRI) studies treated white matter signals as noise, largely due to its lower vascular density compared to gray matter, which results in weaker blood-oxygen-level-dependent (BOLD) signals with a lower signal-to-noise ratio (SNR)(Lierse \u0026amp; Horstmann, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Preibisch \u0026amp; Haase, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). However, growing evidence demonstrates that BOLD signals in white matter likely reflect neural activity and can be detected using sufficiently sensitive imaging and analytical techniques(Marussich, Lu, Wen, \u0026amp; Liu, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mazerolle, Ohlhauser, Mayo, Sheriff, \u0026amp; Gawryluk, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Researchers have successfully clustered rs-fMRI signals within white matter into highly organized, regular networks, and these constructed white matter networks correlate with cognitive networks in gray matter(Peer, Nitzan, Bick, Levin, \u0026amp; Arzy, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consequently, methods for analyzing white matter BOLD signals are now being applied to various brain disorders and cognitive neuroscience research, including Parkinson's disease, depression, and schizophrenia(Y. Jiang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; J. Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Meng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGraph Theory, a powerful mathematical framework for complex network analysis, offers a revolutionary perspective for understanding the brain's structural and functional organization(Yun \u0026amp; Kim, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By calculating a range of graph-theoretical metrics, it quantitatively characterizes core topological properties of brain networks, such as global integration and local segregation capabilities, information transfer efficiency, fault tolerance, and the distribution of critical hub nodes. These properties are considered fundamental for the brain's efficient information processing. Graph theory analysis has been widely applied to studies of various neuropsychiatric disorders (e.g., chronic pain, depression, Alzheimer's disease), revealing network-level pathological features like disruptions in \"small-world\" properties and abnormal nodal centrality(De Pauw et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Miraglia et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, it remains completely unknown whether the topological architecture of white matter functional networks is altered in PDM. To address this gap, the present study provides the first graph-theoretical analysis of white matter functional connectomes in patients with primary dysmenorrhea. Using resting-state fMRI and a 128-node white matter parcellation, we systematically compared global network properties between PDM patients and healthy controls, and further linked these neuroimaging phenotypes to anxiety and depression symptoms. This approach moves beyond traditional gray matter‑centric models and establishes white matter functional network topology as a previously unrecognized dimension of PDM pathophysiology.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eThis study enrolled 35 PDM patients and 37 healthy individuals at Shaanxi University of Chinese Medicine between 2021 and 2022. The inclusion criteria for the PDM group were as follows: (1) Meeting the diagnostic standards for PDM outlined in the 2017 Consensus Guidelines for PDM published by the Canadian Society of Obstetricians and Gynaecologists (SOGC)(Burnett \u0026amp; Lemyre, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); (2) Aged 18\u0026ndash;35 years, nulliparous, and right-handed; (3) Regular menstrual cycle (28\u0026thinsp;\u0026plusmn;\u0026thinsp;7 days); (4) Signed the research informed consent form and voluntarily participated in the study; (5) Dysmenorrhea duration\u0026thinsp;\u0026ge;\u0026thinsp;6 months; (6) Visual Analogue Scale (VAS) pain score\u0026thinsp;\u0026ge;\u0026thinsp;4 points for three consecutive menstrual cycles; (7) No intake of analgesic medications within one week before the MRI scan. The inclusion criteria for the Healthy Control (HC) Group required meeting only criteria (2), (3), and (4) from the PDM group. The exclusion criteria for both groups were: (1) Secondary dysmenorrhea, including cases caused by endometriosis, adenomyosis, or pelvic inflammatory disease; (2) Suffering from other chronic pain conditions such as cervical spondylosis or lumbar disc herniation; (3) Serious diseases of the heart, liver, or kidneys; history of head trauma; or neurological disorders; (4) Contraindications to MRI scanning; (5) Oral intake of analgesic medications or contraceptives within the past 6 months. The study protocol was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Shaanxi University of Chinese Medicine (Approval number: SZFYIEC-YJ-BYBC-2022-[07]). All participants provided written informed consent prior to the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMRI Data Acquisition\u003c/h3\u003e\n\u003cp\u003eAll participants underwent MRI scans during days 1\u0026ndash;3 of their menstrual cycle using a SIEMENS Skyra 3.0T superconducting MRI system equipped with a 20-channel head-neck coil. Prior to scanning, participants completed the clinical scales. During the scan, participants were positioned supine and instructed to remain awake with their eyes closed. To exclude secondary dysmenorrhea and intracranial organic pathologies, conventional pelvic (fat-suppressed T2-weighted) and cranial (T2-FLAIR) scans were performed prior to acquiring the high-resolution structural and BOLD sequences. The parameters for the T1-weighted magnetization-prepared rapid gradient echo (T1-MPRAGE) sequence were as follows: Repetition Time (TR)/ Echo Time (TE)\u0026thinsp;=\u0026thinsp;2530/3.37 ms; Flip Angle (FA)\u0026thinsp;=\u0026thinsp;7\u0026deg;; Slice thickness\u0026thinsp;=\u0026thinsp;1.0 mm; Slice gap\u0026thinsp;=\u0026thinsp;0 mm; Number of Excitations (NEX)\u0026thinsp;=\u0026thinsp;0.5; Field of View (FOV)\u0026thinsp;=\u0026thinsp;256 mm \u0026times; 256 mm; Matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256. Images were acquired in the right-to-left phase encoding direction with 192 slices, resulting in a scan time of 5 minutes 58 seconds and yielding 192 images. For rs-fMRI, data were acquired using a gradient-echo echo-planar imaging (GRE-EPI) sequence with the following parameters: TR/TE\u0026thinsp;=\u0026thinsp;2000/26 ms; Flip Angle (FA)\u0026thinsp;=\u0026thinsp;90\u0026deg;; Slice thickness\u0026thinsp;=\u0026thinsp;4.0 mm; Number of slices\u0026thinsp;=\u0026thinsp;35; Inter-slice gap\u0026thinsp;=\u0026thinsp;0.8 mm; FOV\u0026thinsp;=\u0026thinsp;224 mm \u0026times; 224 mm. The scan duration was 8 minutes 06 seconds, acquiring 240 time points.\u003c/p\u003e\n\u003ch3\u003eMRI Data Preprocessing\u003c/h3\u003e\n\u003cp\u003eStructural and functional MRI data were processed automatically using DPABI V8.1 (version 8.1_240101)(Yan, Wang, Zuo, \u0026amp; Zang, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Statistical Parametric Mapping 12 (SPM12), running in MATLAB R2017b. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the preprocessing workflow. Prior to core preprocessing, structural and functional images underwent automatic AC-PC alignment respectively (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/lrq3000/auto_acpc_reorient\u003c/span\u003e\u003cspan address=\"https://github.com/lrq3000/auto_acpc_reorient\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). T1 structural images were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using the New Segment\u0026thinsp;+\u0026thinsp;DARTEL pipeline, spatially normalized to the Montreal Neurological Institute (MNI) space, and followed by the generation of individual white matter probability masks. The rs-fMRI preprocessing steps were as follows: 1) Removal of the first 10 time points; 2) Slice timing correction; 3) Realignment (Motion Correction): Participants (6 healthy controls and 5 PDM subjects) with excessive motion (maximum translation\u0026thinsp;\u0026gt;\u0026thinsp;1.5 mm or maximum rotation\u0026thinsp;\u0026gt;\u0026thinsp;1.5\u0026deg;) were excluded from further analysis.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eTo minimize contamination of WM BOLD signals by GM partial volume effects, subsequent preprocessing focused exclusively on the WM signal. Individual WM masks were generated by applying a strict 90% probability threshold to the WM probability maps, followed by resampling. These resampled masks were then used to spatially extract subject-specific WM functional images via dot product multiplication. 4) The extracted WM functional images were normalized to MNI space (3 \u0026times; 3 \u0026times; 3 mm\u0026sup3; resolution) using DARTEL-derived flow fields; 5) Nuisance regression was performed using covariates including the mean CSF signal and 24 Friston head motion parameters, while retaining the WM BOLD signal; 6) To mitigate motion-induced spurious correlations in functional connectivity, scrubbing was applied. One forward and two back neighbors volumes with framewise displacement (FD) exceeding 0.5 mm with Jenkinson's method were removed; 7) Linear detrending and bandpass filtering (0.01\u0026ndash;0.15 Hz) were applied to the time series.\u003c/p\u003e\n\u003ch3\u003eConstruction of WM Functional Connectivity Matrix\u003c/h3\u003e\n\u003cp\u003eThe brain network connection matrix was constructed using the GRETNA software(Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The group-level brain WM mask was randomly divided into 128 distinct nodes. The average time series of each node was extracted separately. For each subject, a 128 \u0026times; 128 correlation matrix was generated by calculating the Pearson's correlation coefficients between the averaged time series of every node pair. Subsequently, Fisher's z-transformation was applied to the WM functional connectivity matrices.\u003c/p\u003e\n\u003ch3\u003eTopological Properties of WM Functional Connectomes\u003c/h3\u003e\n\u003cp\u003eThe global topological properties of WM functional connectomes were analyzed using the GRETNA software (version 2.0). The network type was set to binary, the matrix sign to positive, and then, based on the calculation procedure for sparsity, the maximum and minimum sparsity values were determined respectively. The minimum sparsity was set to 2log(N)/(N\u0026thinsp;\u0026minus;\u0026thinsp;1), where N represents the number of template nodes. The minimum sparsity was then reassessed to ensure that the size of the giant connected component (GCC) exceeded 80% of the total number of nodes in the network. The maximum sparsity was defined as the sparsity value when the Sigma parameter just exceeded 1.1. A range of sparsity (0.1\u0026ndash;0.3, interval\u0026thinsp;=\u0026thinsp;0.01)(J. Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was generated to evaluate the topological properties of WM functional connectomes for all subjects.\u003c/p\u003e \u003cp\u003eThis study focuses on the following global graph-theoretical network parameters: normalized clustering coefficient (Gamma, γ), normalized characteristic path length (Lambda, λ), small-worldness (Sigma, σ), clustering coefficient (Cp), characteristic path length (Lp), global efficiency (Eg), and local efficiency (Eloc). Here, γ\u0026thinsp;=\u0026thinsp;Cp / C\u003csub\u003er\u003c/sub\u003eₐ and λ\u0026thinsp;=\u0026thinsp;Lp / L\u003csub\u003er\u003c/sub\u003eₐ, where C\u003csub\u003er\u003c/sub\u003eₐ and L\u003csub\u003er\u003c/sub\u003eₐ represent the clustering coefficient and characteristic path length of a comparable random network, respectively. A network exhibits small-world properties if σ\u0026thinsp;=\u0026thinsp;γ/λ\u0026thinsp;\u0026gt;\u0026thinsp;1, with higher σ values indicating stronger small-worldness. Cp measures the overall tendency of nodes to form clusters within the network. Lp reflects the speed of information transfer between nodes, where shorter average shortest paths correspond to higher transmission efficiency. Eloc describes the clustering connectivity among neighboring nodes, while Eg quantifies the efficiency of information flow across the entire brain network.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eDemographic and clinical characteristics were compared between the HC group and the PDM group. Differences were analyzed using the nonparametric Mann\u0026ndash;Whitney U test (for data that did not follow a normal distribution) or two-sample t-tests (employing Welch's t-test if variances were unequal). A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eGlobal topological property comparisons\u003c/h3\u003e\n\u003cp\u003eDifferences in the AUC values of global topological properties (including Cp, γ, λ, Lp, σ, Eg and Eloc) between groups were analyzed using the Mann-Whitney U test (for non-normally distributed data) or two-sample t-tests (for normally distributed data). Age, BMI, and FD were included as covariates.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRelationships Between Global Topological Properties and Clinical Scale Scores\u003c/h2\u003e \u003cp\u003ePearson or Spearman correlation analysis was performed between graph theory parameters showing significant between-group differences and clinical scales [ Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale(SDS) ]. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Correlations were categorized as follows: weak (0.1 \u0026le; |r| \u0026lt; 0.3), moderate (0.3 \u0026le; |r| \u0026lt; 0.5), and strong (|r| \u0026ge; 0.5), with |r| \u0026ge; 0.3 serving as the relevance threshold. All analyses were conducted using SPSS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eThe final analysis of this study included 32 healthy controls and 30 PDM patients. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic characteristics of both groups. A statistically significant difference in age was observed between HCs and PDMs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant differences were found in BMI or head motion (FD) between the groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Significant differences were identified in all clinical scale scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eclinical and demographic characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC group(n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDM group(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (24, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (21, 25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of illness (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMSS(Total Symptom Duration)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.00 (3.00, 8.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.00 (15.25, 24.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMSS( Mean Symptom Severity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.00 (2.75, 9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.00 (12.00, 18.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.50 (25.00, 33.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.50 (29.00, 45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehead motion(FD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.04, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: PDM\u0026thinsp;=\u0026thinsp;Primary dysmenorrhea; HC\u0026thinsp;=\u0026thinsp;Healthy Control. \u003csup\u003ea\u003c/sup\u003e Mann-Whitney U test, \u003csup\u003eb\u003c/sup\u003eindependent samples t-test. BMI\u0026thinsp;=\u0026thinsp;Body Mass Index, SAS\u0026thinsp;=\u0026thinsp;Self-Rating Anxiety Scale, SDS\u0026thinsp;=\u0026thinsp;Self-Rating Depression Scale, FD\u0026thinsp;=\u0026thinsp;Framewise Displacement, NA\u0026thinsp;=\u0026thinsp;not available.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGroup Differences in Global Topological Properties\u003c/h2\u003e \u003cp\u003eBoth groups exhibited small-world properties in their white matter functional networks across the sparsity threshold range of 0.10\u0026ndash;0.30. Compared to the healthy controls, the PDM group demonstrated significantly decreased normalized clustering coefficient, small-worldness, and global efficiency, along with a significantly increased characteristic path length (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No statistically significant differences were found in the other global metrics: Cp, λ, and Eloc (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Global Topological Properties Between groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eglobal topological property\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDM group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.106\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.106\u0026thinsp;=\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e466\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eγ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.294\u0026thinsp;\u0026plusmn;\u0026thinsp;0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.274\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.55\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eλ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.209\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.211\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e469\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.388\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.405\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e320.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.280\u0026thinsp;\u0026plusmn;\u0026thinsp;0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.260\u0026thinsp;\u0026plusmn;\u0026thinsp;0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.49\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.105\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.102\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e633.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEloc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.142\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.139\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.95\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: \u003csup\u003ea\u003c/sup\u003e Mann-Whitney U test, \u003csup\u003eb\u003c/sup\u003e independent samples t-test, \u003csup\u003ec\u003c/sup\u003e Welch's t-test,*significant difference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between Global Topological Properties and Clinical Scale Scores\u003c/h2\u003e \u003cp\u003eCorrelation analyses between global graph-theoretical metrics\u0026mdash;includingγ, σ, Eg, and Lp\u0026mdash;and SAS and SDS revealed that: γ and σ were negatively correlated with both SAS and SDS (r = -0.350, -0.312, -0.344, -0.280, respectively; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Eg was negatively correlated with SAS (r = -0.307, P\u0026thinsp;=\u0026thinsp;0.015), while Lp was positively correlated with SAS (r\u0026thinsp;=\u0026thinsp;0.308, P\u0026thinsp;=\u0026thinsp;0.015). ( Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides the first evidence that primary dysmenorrhea is associated with disrupted topological organization of white matter functional networks. Using graph theory, we demonstrated that PDM patients exhibit significant degradation of small-world architecture (\u0026darr;γ, \u0026darr;σ), impaired global integration (\u0026darr;Eg), and prolonged information transfer (\u0026uarr;Lp) specifically within white matter\u0026mdash;findings that are distinct from previously reported gray matter abnormalities. These network-level alterations were further correlated with anxiety and depression severity, linking white matter functional connectome topology to affective symptoms in PDM.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of PDM-related Topological Properties\u003c/h2\u003e \u003cp\u003eThis study reveals significant disruptions in the topological architecture of white matter functional networks in adolescent PDM patients. The observed reduction in the normalized clustering coefficient indicates impaired local connectivity efficiency, signifying compromised specialized processing within functional modules(Rubinov \u0026amp; Sporns, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; P. Wang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This diminished clustering likely reflects altered communication within densely interconnected sub-networks involved in pain processing, such as thalamocortical circuits and limbic pathways(Kucyi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; W. Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConcurrently, the decreased small-worldness suggests a fundamental shift in global network organization. Small-world topology\u0026mdash;characterized by high clustering and short path lengths\u0026mdash;optimizes the balance between segregated processing and integrated information transfer(Bassett \u0026amp; Bullmore, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; L. L. Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our finding of significantly reduced σ (P\u0026thinsp;=\u0026thinsp;0.015) indicates a deviation toward a less efficient, potentially more random network configuration in PDM, consistent with patterns observed in other chronic pain conditions(Lenoir, Cagnie, Verhelst, \u0026amp; De Pauw, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Y. P. Zhang, Hong, \u0026amp; Zhang, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComplementary evidence of impaired long-range communication is provided by the increased Lp and Eg. Elevated Lp (P\u0026thinsp;=\u0026thinsp;0.025) indicates prolonged information transfer between distant brain regions, while diminished Eg (P\u0026thinsp;=\u0026thinsp;0.031) reflects reduced capacity for parallel information processing across the network(Latora \u0026amp; Marchiori, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Zu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These alterations may stem from microstructural disruptions in major white matter tracts, particularly highly connected hub regions like the corpus callosum and cingulum bundle(Ito et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Robertson, Aristi, \u0026amp; Hashmi, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Potential underlying mechanisms include: Neuroinflammatory processes affecting oligodendrocyte function(L. L. Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Altered axonal conduction velocities(P. Wang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), or Dysregulated neurovascular coupling in white matter(Mazerolle et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCritically, the constellation of findings\u0026mdash;reduced γ, σ, and Eg with increased Lp\u0026mdash;collectively demonstrates network reorganization in PDM. The degradation of small-world properties suggests reduced network resilience and computational capacity(L. L. Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Stam, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), potentially impairing adaptive responses to pain. This convergence toward a more randomized architecture aligns with chronic pain models where persistent nociception drives maladaptive neuroplasticity(Bosma et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mansour, Farmer, Baliki, \u0026amp; Apkarian, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; W. Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, our study is the first to demonstrate this phenomenon specifically within the white matter functional networks of PDM patients. This represents a significant departure from traditional gray matter-focused models of pain processing, highlighting the critical role of white matter dynamics in pain pathophysiology(Ito et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhao, Ford, Flashman, McAllister, \u0026amp; Ji, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eClinical Correlates and Pathophysiological Implications\u003c/h2\u003e \u003cp\u003eThe robust correlations between network metrics and affective symptoms provide compelling evidence for the clinical relevance of white matter network disruption in PDM. The negative correlation between γ and SAS/SDS scores (r = -0.350/-0.312) indicates that impaired local processing efficiency exacerbates affective symptoms, potentially through disrupted modular organization of emotion-regulation circuits(Han, Zhang, Jiao, Hu, \u0026amp; Pan, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ho et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Specifically, diminished clustering may reflect compromised functional segregation within limbic structures (e.g., amygdala, hippocampus) and their cortical connections, reducing capacity for contextual pain modulation(Dosenbach, Raichle, \u0026amp; Gordon, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gregory, Ritchey, \u0026amp; Murty, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, the negative σ-SAS/SDS correlations (r = -0.344/-0.280) suggest that degraded global network organization undermines the integration of cognitive-affective processes necessary for pain coping(Fang, Potter, Nguyen, \u0026amp; Zhang, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pagnoni \u0026amp; Porro, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe positive correlation between Lp and SAS (r\u0026thinsp;=\u0026thinsp;0.308) reveals a critical relationship between delayed information transfer and anxiety severity. Slowed communication along pathways connecting interoceptive regions (e.g., insula) with prefrontal regulatory areas may impair threat appraisal and safety signaling, heightening anxiety responses(Davey, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; H. Jiang, Zhong, Huang, \u0026amp; Zhong, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Klabunde et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is further supported by the negative Eg-SAS correlation (r = -0.307), indicating that reduced network-wide integration efficiency compromises top-down pain modulation\u0026mdash;a mechanism implicated in anxiety-related hyperalgesia(Langhammer et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile most rs-fMRI research emphasizes gray matter networks, our focus on white matter functional connectivity (WM-FC) reveals unique insights. WM-FC captures intrinsic BOLD fluctuations in white matter, potentially reflecting axonal activity or glial modulation. The observed topological disruptions may indicate selective vulnerability of highly connected hub tracts. Our topology-behavior mapping significantly advances PDM models in three ways: First, it establishes white matter functional networks\u0026mdash;not merely structural pathways\u0026mdash;as mediators of affective comorbidity. Second, it identifies specific network properties (Lp for anxiety, γ for depression) as potential treatment biomarkers. Third, it suggests PDM involves fundamental reorganization of information processing architecture, positioning it within broader chronic pain paradigms. Future studies should examine menstrual-cycle-dependent network dynamics and test whether these topological markers predict treatment response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the relatively small sample size reduces the statistical power for detecting subtle topological properties, which may account for our inability to identify significant correlations between certain topological attributes and clinical variables; future studies with larger cohorts are needed to validate the stability and reproducibility of the findings. Second, the cross-sectional design precludes causal inference, necessitating longitudinal tracking across the menstrual cycle. Third, the restricted age range of participants limits the generalizability of the results; subsequent research should incorporate PDM patients across broader age groups to enhance external validity. Finally, the absence of integrated functional-structural white matter analysis represents a constraint; combining functional topological properties with structural connectivity measures (e.g., DTI) in future investigations could elucidate the underlying microstructural-functional basis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first evidence that adolescent PDM involves measurable disruptions in the topological architecture of white matter functional networks. The observed degradation of small-world properties, diminished global integration, and delayed information transfer collectively point to a network-level pathophysiology. Importantly, the correlation of these topological metrics with anxiety and depression severity provides a mechanistic framework for understanding affective comorbidities in PDM. These findings not only advance the neurobiological model of PDM but also identify graph-theoretical metrics as potential targets for therapeutic neuromodulation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e: We would like to thank all participants in this study, and we also extend our gratitude to the Affiliated Hospital of Shaanxi University of Chinese Medicine for assistance with MRI data acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Special Project of Natural Science of Shaanxi Provincial Department of Education (grant no. 25JK0423).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures involving human participants were approved by the Affiliated Hospital of Shaanxi University of Chinese Medicine, and written informed consent was obtained from each participant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWei Wei: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization, Funding acquisition. Xin Tian: Investigation, Resources, Data curation, Writing - review \u0026amp; editing. Feng Zhou: Project administration, Writing - review \u0026amp; editing. Yunsong Zheng: Software, Validation, Writing - review \u0026amp; editing. Lihua Fan: Conceptualization, Methodology, Supervision, Project administration, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData that support the findings of this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAffaitati, G., Costantini, R., Fiordaliso, M., Giamberardino, M. A., \u0026amp; Tana, C. (2024). 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The missing third dimension-Functional correlations of BOLD signals incorporating white matter. \u003cem\u003eScience Advances\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(4), eadi0616. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/sciadv.adi0616\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.adi0616\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Primary Dysmenorrhea, Graph Theory, rs-fMRI, White Matter, Functional Network","lastPublishedDoi":"10.21203/rs.3.rs-8857116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8857116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003ePrimary dysmenorrhea (PDM) is associated with functional reorganization in gray matter networks, but whether white matter functional networks exhibit similar topological alterations remains unknown. This study provides the first graph-theoretical analysis of white matter functional connectomes in adolescent PDM using resting-state functional magnetic resonance imaging (rs-fMRI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study enrolled 32 healthy controls (HC) and 30 patients with PDM, who underwent structural and rs-fMRI scans and clinical assessments.After rigorous preprocessing to minimize gray matter contamination, individual white matter functional networks were constructed using a 128-node group-level parcellation. Global topological properties --including normalized clustering coefficient (γ), normalized characteristic path length (λ), small-worldness (σ), clustering coefficient (Cp), characteristic path length (Lp), global efficiency (Eg), and local efficiency (Eloc)\u0026mdash;were computed across a sparsity range of 0.10\u0026ndash;0.30. Group comparisons were performed with age, BMI, and head motion as covariates. Correlations between significant network metrics and Self-Rating Anxiety Scale (SAS) / Self-Rating Depression Scale (SDS) scores were examined.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBoth groups exhibited small-world architecture in white matter functional networks. Compared with HCs, PDM patients showed significantly decreased γ (P\u0026thinsp;=\u0026thinsp;0.013), σ (P\u0026thinsp;=\u0026thinsp;0.015), and Eg (P\u0026thinsp;=\u0026thinsp;0.031), along with significantly increased Lp (P\u0026thinsp;=\u0026thinsp;0.025). No group differences were found in Cp, λ, or Eloc (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Correlation analyses revealed that γ and σ were negatively correlated with both SAS and SDS scores (r = \u0026minus;\u0026thinsp;0.350 to \u0026minus;\u0026thinsp;0.280, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Eg was negatively correlated with SAS (r = \u0026minus;\u0026thinsp;0.307, P\u0026thinsp;=\u0026thinsp;0.015), while Lp was positively correlated with SAS (r\u0026thinsp;=\u0026thinsp;0.308, P\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study provides the first evidence that PDM involves disrupted small-world topology, reduced global integration, and delayed information transfer specifically within white matter functional networks. The significant correlations between these network alterations and affective symptoms establish white matter functional connectome topology as a clinically relevant neurobiological marker for emotional comorbidities in PDM. These findings extend the current gray matter‑centric model of PDM and identify graph-theoretical metrics of white matter networks as potential targets for neuromodulation.\u003c/p\u003e","manuscriptTitle":"Graph Theory Analysis of Topological Properties in White Matter Functional Networks of Adolescent Females with Primary Dysmenorrhea: A Resting-State fMRI Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 10:08:57","doi":"10.21203/rs.3.rs-8857116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"94887378593370245123686163041180185645","date":"2026-05-09T21:23:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T11:51:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180880944882649924344937125072424872249","date":"2026-03-16T13:36:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T13:31:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T13:09:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-13T01:11:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Imaging and Behavior","date":"2026-02-12T03:24:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4546ac76-cd0a-4bb6-ad1c-9def0c8bae12","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"94887378593370245123686163041180185645","date":"2026-05-09T21:23:28+00:00","index":32,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T10:08:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 10:08:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8857116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8857116","identity":"rs-8857116","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}