Effects of sleep quality on the default mode network and on anxiety- depression symptoms in premenstrual syndrome

Effects of sleep quality on the default mode network and on anxiety- depression symptoms in premenstrual syndrome | 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 Effects of sleep quality on the default mode network and on anxiety- depression symptoms in premenstrual syndrome Haixia Qin, Yan Zhang, Shanshan Li, Yuanyuan Ou, Shihuan Lin, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6028422/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Neuroimaging evidence suggests existence of an association between the aberrant default mode network (DMN) and anxiety-depression severity in premenstrual syndrome (PMS); however, ignoring the effects of sleep prevents understanding the pathophysiology of PMS. Methods Seventy-seven PMS patients and sixty-six healthy controls (HCs) underwent resting-state functional MRI, and clinical assessment included the Pittsburgh Sleep Quality Index (PSQI), the Self-Rating Anxiety Scale, and the Self-Rating Depression Scale. PSQI scores classified PMS patients into normal sleep quality (PMS-NSQ) and poor sleep quality (PMS-PSQ) groups. Resting-state functional connectivity (rsFC) and regional homogeneity (ReHo) within the DMN were compared among the three groups. Correlation and mediation analyses examined potential associations relating sleep quality, changes in brain function, and clinical variables. Results Compared to HCs, both PMS groups exhibited increased rsFC between left inferior parietal lobule (IPL) and right middle occipital gyrus. Additionally, the PMS-NSQ group presented decreased FC of right ventromedial prefrontal cortex (VMPFC) and right posterior cingulate/precuneus, decreased ReHo in right VMPFC, and increased ReHo value in left IPL. Combined correlation and mediation analyses showed that the altered functional activity within the DMN and anxiety-depression symptoms were mediated by sleep quality in PMS patients, mainly involving the right VMPFC and left IPL regions of the brain. Conclusions The findings reveal the potential neuropathology of sleep problems in PMS, which sleep quality may mediate the association between functional connectivity within DMN and anxiety-depression severity. The right VMPFC and the left IPL may prospectively serve as potential intervention targets for the treatment of sleep disturbances in PMS. Trial registration: Chinese Clinical Trial Registry (ChiCTR1900020642) Premenstrual syndrome sleep default mode network resting-state functional MRI functional connectivity regional homogeneity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Premenstrual syndrome (PMS) affects approximately 20–30% of fertile women worldwide, with the most prevalent symptoms being sleep disturbances, anxiety, depression, and mood lability during the late luteal phase (LP) ( 1 – 3 ). It has been recognized as a high-risk factor for antenatal depression and perimenopausal depression. Additionally, the severe form of PMS evident—premenstrual dysphoric disorder (PMDD)—has been defined as a depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) ( 4 ). Therefore, early management of mood symptoms is essential for prevention and treatment of PMS ( 5 , 6 ). Sleep deficits can contribute to the development and continuance of mood disorders ( 7 , 8 ). Sleep-loss effects across multiple emotional domains were reported in a recent systematic review, mainly demonstrating a decrease in positive mood and an increase in anxiety symptoms ( 9 ). Unfortunately, the process is self-reinforcing, as the recurring symptoms of depression and anxiety negatively interfere with sleep quality ( 10 ). For the affected women, premenstrual increases in insomnia symptoms, poor sleep quality (PSQ), daytime sleepiness, and fatigue during the LP are all commonly reported ( 11 – 13 ). Understanding the neurobiological basis of the comorbidity between affective imbalance and PSQ in PMS may contribute to improving mental well-being for women of reproductive age. Studies have shown that altered functional activities in the default mode network (DMN) may potentially serve as the neural mechanisms underlying the impact of sleep on emotional regulation ( 14 , 15 ). DMN hyperactivity plays an important role in processing negative rumination; however, rumination is regarded as a key cognitive feature of both anxiety and depression, as well as sleep disorders ( 16 , 17 ). Research findings consistently suggest that the abnormal functional activities within the DMN, involving the cingulate cortex and the precuneus, might provide the neurobiological basis for the association between depression-anxiety and PSQ ( 18 – 20 ). Although previous studies have shown that altered functional activities within the DMN might increase mood-disorder-related vulnerabilities in PMS and PMDD patients ( 21 , 22 ), the neural mechanisms underlying the impact of sleep on emotional regulation in PMS is still unclear. In the present study, we analyzed the specific functional brain activities within the DMN in PMS participants with PSQ. We compared those findings with similar studies of the healthy controls (HCs), using particularly the functional connectivity (FC) and regional homogeneity (ReHo). Additionally, correlation and mediation analyses were performed to assess the underlying association among sleep quality, DMN functional activities, and anxiety-depression symptoms in PMS. Materials and methods Ethics statement The study was approved by the ethics committee of the People’s Hospital of Guangxi Zhuang Autonomous Region. This study was registered in the Chinese Clinical Trial Registry (ChiCTR1900020642). Written informed consent was obtained from all participants after they had been given detailed information about the study. Experimental design Participants were consecutively recruited, using local advertisements, from April 2019 through October 2024; all were preliminarily screened using the Premenstrual Symptoms Screening Tool ( 23 ). Then, the Daily Record of Severity of Problems (DRSP) questionnaire was acquired for two consecutive menstrual cycles to diagnose individuals with PMS and to isolate those who would serve as HCs ( 24 ). Specifically, all participants were enrolled by Z.L. and G.X.D., a gynecologist and a radiologist, respectively, both with a decade of experience, following the established experimental guidelines. Detailed inclusion and exclusion criteria are provided in Supplementary Section 1. All participants underwent resting-state functional MRI (rs-fMRI) scans, peripheral blood collection, and clinical assessment during the late LP (LLP) of the menstrual cycle. The LLP (1 to 5 days before menstruation) was determined based on urinary luteinizing hormone tests and the recorded time of the DRSP data for two months. Clinical assessment and blood sample processing The Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality ( 25 , 26 ). A global score of PSQI ≥ 8 defined PSQ, and PSQI < 8 defined normal sleep quality (NSQ). The Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) were used to assess the anxiety and depression severity levels ( 27 ). Higher scores on these three scales indicate more serious clinical symptoms. The peripheral blood samples were collected between 8:00 a.m. and 10:00 a.m. on the day of the scan to ensure relatively stable and low levels of endogenous cortisol and estradiol. The samples were left at room temperature for 30–60 min and then centrifuged at 3000 rpm for 10 min. The serum supernatant was placed in a storage tube and frozen to − 80°C. Progesterone (PROG), testosterone (TESTO), estradiol (E2), prolactin (PRL), luteinizing hormone (LH), and follicle-stimulating hormone (FSH) were analyzed using Enzyme-Linked Immunosorbent Assay (ELISA). Image data acquisition and processing The rs-fMRI data were obtained using a 3T Siemens MAGNETOM Vida MRI System (Siemens Healthineers, Forchheim, Germany) within a standard 64-channel head coil; this instrument was located at the local hospital. The parameters of the MRI acquisition protocol are summarized in Supplementary Section 2. The MRI data were preprocessed using the Data Processing and Analysis for Brain Imaging ( http://rfmri.org/dpabi , version 8.0) toolbox ( 28 ) on the MATLAB R2021b platform. The pre-processing steps included: ( 1 ) the removal of the first 10 volumes of rs-fMRI; ( 2 ) slice timing; ( 3 ) realignment and head motion correction; ( 4 ) the removal of nuisance covariates (white matter, cerebrospinal fluid signals, global signals, six motion parameters, and Friston-24 head motion parameters); ( 5 ) normalization to Montreal Neurological Institute (MNI) space (3 × 3 × 3 mm³ voxels) using the Diffeomorphic Anatomical Registration through Exponentiated Lie Algebra (DARTEL) function; ( 6 ) bandpass filtering from 0.01 to 0.08 Hz. Brain functional network construction After the data processing, the Pearson’s correlation coefficients were calculated between the mean time courses of each region of interest (ROI) for each individual. The 33 ROIs of the DMN were selected based on the Dosenbach 2010 atlas ( 29 ), which lists 160 ROIs sorted into six brain networks. An N×N matrix was then constructed (N = 33). Finally, the correlation coefficients were normalized to z-scores with Fisher’s r-to-z transformation. ReHo computation We further calculated the individual’s mean ReHo values of each brain region that had abnormalities in FCs within the DMN. First, the individual’s ReHo values in each ROI were obtained by applying the Kendall's Coefficient of Concordance to calculate the similarity of the time series between a single voxel and its 26 nearest voxels ( 30 ). Then, the individual’s mean ReHo mapping in each ROI was obtained by normalizing each ReHo value and smoothing using a Gaussian kernel with a full-width at half-maximum of 6 mm. Statistical analysis All statistical analyses were performed on the SPSS 19.0 platform and R software. Mediation analysis was performed using the PROCESS macro ( http://www.processmacro.org/ ) developed by Hayes (Hayes, 2009), a versatile modeling tool freely available for the SPSS. Demographic and clinical data analysis Differences in age, length of menstruation, age at menarche, length of the menstrual cycle, and the scores of the PSQI, SAS, SDS, and DRSP were examined using analysis of variance (ANOVA) with Tukey correction for normally distributed data or the Kruskal–Wallis test with Dunn correction for non-normally distributed data among groups. Statistical significance was set at p < 0.05. FC comparison ANOVA was used to examine the differences in the FC matrix among the three groups, with the statistical significance set at p < 0.05 after false discovery rate (FDR) correction. Post hoc analysis was used to determine the significant differences in these changed FCs between the groups. ReHo comparison The mean ReHo values in ROIs were compared among the three groups by ANOVA with Tukey correction, with statistical significance set at p < 0.05. Correlation analysis Pearson analyses were used to examine the relationships between FCs and clinical data, including PSQI, SAS, and SDS in PMS patients, with statistical significance set at p < 0.05 after FDR correction. Mediation analysis Four mediation models were constructed. In models 1 and 2, the FC was the independent variable X, the PSQI score was the mediator M, and the SAS and SDS scores were the dependent variables Y. In models 3 and 4, the PSQI score was the independent variable X, the FC was the mediator M, and the SAS and SDS scores were the dependent variables Y. The three paths in the models—presented in Tables 4 and 5 with their p and β values (least-squares regression coefficients)—are ( 1 ) a × b, the indirect effect of X on Y through M; ( 2 ) c′, the direct effect of X on Y not mediated by M; and ( 3 ) c, the total effect of X on Y, which is then the sum of the first two: c = (a × b) + c′. The significant indirect effect threshold was set at p < 0.05. Results Demographics and clinical characteristics The process shown in Fig. 1 was used to enroll 143 participants; of these, 77 were individuals with PMS (median age, 24.00 years [interquartile range (IQR) 22.00–26.00]) and 66 were HCs (median age, 24.00 years [IQR 22.00–25.00]). The 77 PMS patients were classified using PSQI scores, with 52 (68%) being assigned to the PMS-PSQ group (PSQI ≥ 8), and 25 (32%) to the PMS-NSQ group (PSQI < 8). There were no significant differences in age ( p = 0.851), length of menstruation ( p = 0.663), menophania ( p = 0.538), or length of menstrual cycle ( p = 0.625) among the groups. Post hoc analysis found both the PMS-PSQ and PMS-NSQ groups showed significantly higher scores for DRSP, SAS, and SDS compared to HCs ( p < 0.05). Additionally, the PMS-PSQ group showed higher SAS ( p < 0.001) and SDS ( p = 0.002) scores relative to the PMS-NSQ group. There were 7 HCs and 3 PMS participants whose missing blood data were supplemented by linear interpolation. There was no evidence of a difference among the groups in sex hormone levels, including TESTO ( p = 0.738), PROG ( p = 0.958), E2 ( p = 0.532), PRL ( p = 0.132), LH ( p = 0.452), and FSH ( p = 0.842). All demographic and clinical characteristics are shown in Table 1 and Table 2 . Table 1 Demographic and clinical characteristics of PMS patients and HCs. HC PMS-NSQ PMS-PSQ F-test post-test age 24.00(22.00–25.00) 23.00(22.00-24.75) 23.50 (22.00-25.75) 0.851 0.487/0.301/0.334 menophania 13.00(12.00–14.00) 13.00(12.00-13.75) 13.00(12.00–14.00) 0.663 0.274/0.199/0.478 cycle 30.00(28.00–30.00) 30.00(28.25–31.50) 30.00(29.00–31.00) 0.558 0.169/0.211/0/374 menstrual 6.00(5.00–7.00) 7.00(6.00–7.00) 6.00(5.00–7.00) 0.625 0.235/0.360/0.167 SAS 35.40 ± 6.25 44.05 ± 7.78 51.20 ± 8.90 < 0.001 < 0.001/<0.001/<0.001 SDS 38.53 ± 7.48 49.05 ± 12.57 56.75 ± 9.67 < 0.001 < 0.001/<0.001/0.002 PSQI 5.00(3.50-6.00) 5.00(3.25-7.00) 10.00(8.00–12.00) < 0.001 0.357/<0.001/<0.001 DRSP (LLP) 24.50(22.80-28.48) 55.20(51.40–59.70) 57.40(52.43–61.85) < 0.001 < 0.001/<0.001/0.122 Abbreviations: PSQI, Pittsburgh Sleep Quality Index; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale; DRSP, Daily Record of Severity of Problems; LLP, late luteal phase. Continuous data are presented as means ± standard deviation (SD); discrete data are presented as median (inter quartile range [IQR]). Table 2 The sex hormone levels among groups. HCs PMS-NSQ PMS-PSQ F-test TESTO 27.88(22.10-37.58) 29.50(20.45–36.25) 27.95(27.08–33.73) 0.738 PROG 6.79(1.39–11.85) 7.07(1.52–11.20) 5.32(0.77–11.48) 0.958 E2 149.30 (69.78-193.75) 137.00(72.10-229.50) 116.75(55.78–205.50) 0.532 PRL 24.50(17.15–39.25) 30.20(17.60-47.45) 25.28(19.58–34.70) 0.132 LH 5.38(3.33–8.70) 6.78(5.19–11.17) 5.44(4.27–8.69) 0.452 FSH 3.09(2.12–4.72) 2.90(2.14–4.66) 3.66(2.64–4.76) 0.842 Abbreviations: TESTO, testosterone; PROG, progesterone; E2, estradiol; PRL, prolactin; LH, luteinizing hormone; FSH, follicle-stimulating hormone. Continuous data are presented as means ± standard deviation (SD); discrete data are presented as median (inter quartile range [IQR]). Between-groups differences of FC within DMN ANOVA analysis of the matrixes obtained from measures of the FCs of the DMN ROIs, as described in Section 2.5, identified significantly different FCs among the three groups. The differences in resting-state functional connectivity (rsFC) were observed in the left inferior parietal lobule (IPL) (MNI: -53, -50, 39), in the right middle occipital gyrus (MOG) (MNI: 45, -72, 29), in the right ventromedial prefrontal cortex (VMPFC) (MNI: 8, 42, -5), and in the right posterior cingulate/precuneus (PCu) (MNI: 10, -55, 17). Then, post hoc analysis showed increased FC, relative to HCs, of the left IPL and right MOG in both PMS-NSQ and PMS-PSQ groups ( p < 0.001), as well as decreased FC, relative to PMS-PSQ and HCs, of the right VMPFC and right PCu in the PMS_NSQ group ( p < 0.001). These results are shown in Table 3 and Fig. 2 . Table 3 The difference of rsFC and ReHo values among PMS-NSQ, PMS-PSQ and HCs groups. HC (n = 66) PMS-NSQ (n = 25) PMS-PSQ (n = 52) F-test post-test FCs Right VMPFC-right PCu 0.35 ± 0.24 0.15 ± 0.16 0.36 ± 0.20 8.99 < 0.001/0.954/<0.001 Left IPL-right AG 0.14 ± 0.24 0.35 ± 0.28 0.30 ± 0.21 10.36 < 0.001/<0.001/0.602 ReHo Right VMPFC 0.36 ± 0.46 0.10 ± 0.44 0.42 ± 0.66 3.02 0.109/0.821/0.045 Left IPL 0.91 ± 0.50 1.21 ± 0.50 0.98 ± 0.55 3.06 0.039/0.749/0.164 Right PCu 1.02 ± 0.62 1.06 ± 0.65 1.05 ± 0.63 0.03 0.974/0.978/0.998 Right PCu 0.83 ± 0.75 0.94 ± 0.87 0.830.86 0.20 0.833/0.999/0.827 Abbreviations: IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal cortex; ReHo, regional homogeneity. Continuous data are presented as means ± standard deviation (SD); discrete data are presented as median (inter quartile range [IQR]). Table 4 The mediation analysis results on Model 1. a b c' c-c' X (FC of right VMPFC – right PCu)→Y (SAS) mediated by M (PSQI) β 6.089 1.482 -1.510 9.021 p < 0.001 < 0.001 0.750 0.001 X (FC of right IPL – right MOG)→Y (SAS) mediated by M (PSQI) β -1.541 1.463 2.306 -2.255 p 0.343 < 0.001 0.555 0.341 X (ReHo values of right VMPFC)→Y (SAS) mediated by M (PSQI) β 0.998 1.412 0.962 1.500 p 0.108 < 0.001 0.527 0.021 X (ReHo values of left IPL)→Y (SAS) mediated by M (PSQI) β -1.328 1.558 3.128 -2.070 p 0.055 < 0.001 0.065 0.025 X (ReHo values of right PCu)→Y (SAS) mediated by M (PSQI) β -0.277 1.467 2.186 -0.406 p 0.647 < 0.001 0.128 0.644 X (ReHo values of right MOG)→Y (SAS) mediated by M (PSQI) β -0.262 1.395 -2.833 -0.3658 p 0.554 < 0.001 0.006 0.565 Abbreviations: IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal cortex; ReHo, regional homogeneity. Table 5 The mediation analysis results on Model 2. a b c' c-c' X (FC of right VMPFC – right PCu)→Y (SDS) mediated by M (PSQI) β 6.088 1.807 -7.186 11.001 p < 0.001 < 0.001 0.227 0.001 X (FC of right IPL – right MOG)→Y (SDS) mediated by M (PSQI) β -1.541 1.667 4.433 -2.569 p 0.343 < 0.001 0.368 0.323 X (ReHo values of right VMPFC)→Y (SDS) mediated by M (PSQI) β 0.998 1.598 1.006 1.595 p 0.108 < 0.001 0.600 0.026 X (ReHo values of left IPL)→Y (SDS) mediated by M (PSQI) β -1.328 1.736 2.876 -2.307 p 0.055 < 0.001 0.181 0.100 X (ReHo values of right PCu)→Y (SDS) mediated by M (PSQI) β -0.277 1.670 3.412 -0.461 p 0.647 < 0.001 0.058 0.645 X (ReHo values of right MOG)→Y (SDS) mediated by M (PSQI) β -0.262 1.547 -4.801 -0.406 p 0.554 < 0.001 < 0.001 0.076 Abbreviations: IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal cortex; ReHo, regional homogeneity; PSQI, Pittsburgh Sleep Quality Index; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale. p>Analysis of four ROIs identified from the rsFC results revealed significant differences in ReHo values in the left IPL and the right VMPFC across the three groups. Post hoc comparisons showed that the PMS_NSQ group exhibited decreased ReHo values in the left IPL relative to the PMS-PSQ group ( p = 0.045), and increased ReHo values in the right VMPFC relative to the HCs ( p = 0.039). These findings are presented in Table 3 and Fig. 3 . Correlation analysis between sleep quality, significant FCs and anxiety-depression symptoms in PMS patients In the PMS groups, there was a significant positive correlation between scores of PSQI and SAS ( p < 0.001), PSQI and SDS ( p < 0.001). The decreased FCs between the right VMPFC and the right PCu ( p = 0.005, r = 0.377), as well as the increased ReHo values in the right IPL ( p = 0.040, r = 0.287), are significantly correlated with the PSQI score. The ReHo values in the right MOG are significantly negatively correlated with the SDS score ( p = 0.014, r = 0.220). Details are shown in Fig. 4 , Supplementary Fig. 1, and Supplementary Table 1. Mediation analysis between sleep quality, significant FCs, and anxiety-depression symptoms in PMS patients The mediation analysis models and the investigated variables (see Section 2.7.5) are described in Fig. 5 , Supplementary Fig. 2, and Supplementary Tables 2 and 3. In model 1, the PSQI score significantly mediated the relationship between functional activities within the DMN and the SAS score, which is significantly linked to ( 1 ) the FC of right VMPFC and right PCu (indirect effect = 9.021, p < 0.001), ( 2 ) the ReHo values in the right VMPFC (indirect effect = 1.500, p < 0.021) and ( 3 ) the ReHo values in the left IPL (indirect effect = -2.070, p < 0.025). In model 2, the PSQI score also significantly mediated the relationship between functional activities within DMN and the SDS score, which is significantly linked to ( 1 ) the FC of the right VMPFC with the right PCu (indirect effect = 11.001, p < 0.001), and ( 2 ) the ReHo value in VMPFC (indirect effect = 1.595, p < 0.026). In model 3 and model 4, no significant mediation was found for these FCs for the relationship between PSQI and SAS or SDS. Discussion Using rs-fMRI, we explored rsFC and ReHo alterations within the DMN specific to different sleep qualities and their clinical relevance in individuals with PMS. These findings indicated that the PMS-NSQ and PMS-PSQ groups exhibited increased rsFC between left IPL and right MOG compared to HCs, and the PMS-NSQ group showed decreased rsFC between right VMPFC and right PCu compared to both the HC and PMS-PSQ groups. Additionally, the PMS-PSQ group showed increased ReHo in the right VMPFC compared to the PMS-NSQ group, and decreased ReHo in the left IPL compared to the HC group. Subsequently, the correlation and mediation analysis further revealed the underlying relationships between altered DMN function and anxiety-depression severity, potentially mediated by PSQI scores. A hyperactivated and hyperconnected DMN is widely reported in various psychiatric disorders; however, these disorders exhibit both shared and distinct patterns of DMN activity ( 31 , 32 ). Previous studies have shown increased FC in parietal-occipital brain regions within the DMN underlies the pathophysiology of a variety of mood disorders, which manifested the same symptoms as PMS ( 7 , 33 ). The IPL and MOG are respectively located in the parietal and occipital regions of the brain, and their connection is involved in emotion regulation, response inhibition, and self-referential thinking ( 34 ). Consistent with these findings, we found increased FC between the left IPL and right MOG in both the PMS-PSQ and PMS-NSQ groups compared to HCs. We speculated that the deficits in the connection between the left IPL and right MOG within DMN may reveal a wide range of physiological and pathological mechanisms of PMS. Additionally, the PMS-NSQ group showed decreased FC in the right VMPFC and the right PCu compared to both HCs and the PMS-PSQ group, and the decreased FC was associated with better sleep quality. To the best of our knowledge, maintenance of sleep and emotion depends on balanced functional activities within the DMN ( 35 , 36 ). A growing body of evidence suggests that deactivation of brain regions comprising the DMN occurs when patients without sleep disturbances are at rest ( 37 ), which may lead to decreased rsFC within the DMN. The PCu–VMPFC link is associated with sleep quality ( 38 , 39 ). Therefore, we further speculated that the decreased FC in the PMS-NSQ group may serve as a compensatory mechanism in PMS to improve sleep quality. This improvement in sleep quality may directly or indirectly affect DMN functions and clinical symptoms in PMS. The ReHo also reflects the local efficiency in information processing for impaired brain regions ( 40 , 41 ). We further found increased ReHo values in the left IPL and decreased ReHo in the right VMPFC in the PMS-NSQ group, and that the changed ReHo in the left IPL was negatively correlated with the PSQI score. The left IPL was also defined as a core brain region in the Dorsal Attention Network, which is responsible for external attention and cognition, including language processing, attention ( 42 , 43 ), and emotion body perception ( 44 ). In contrast, the right VMPFC mainly participates in emotion regulation and autonomic responses ( 45 ), managing internal attention and self-referential thinking. Consistent with previous studies ( 46 ), our findings further revealed that increased external attention allocation and reduced internal emotional burden are potentially alleviating factors involved in reducing negative rumination in PMS patients with NSQ. Previous studies have revealed that the underlying association between sleep, emotion, and the brain is best described through mediation analyses ( 18 , 47 , 48 ). In this study, the mediation analyses further suggested that sleep quality mediates the effects of the rsFC of the right VMPFC and right PCu and also the effects of the ReHo values in the left IPL and right VMPFC on depression and anxiety symptoms in PMS patients. That is, altered functional activities lead to better sleep quality, which in turn may help relieve anxiety and depression symptoms in PMS patients. Studies have shown that the left IPL may potentially serve as neural substrate in the association between anxiety-depression symptoms and PSQ in healthy adults ( 49 ), which is consistent with our findings. We also found that the right VMPFC also may prospectively serve as a potential intervention target for the treatment of sleep disturbances in PMS. Our study has limitations that should be considered. First, objective sleep measures, such as polysomnographic assessments ( 50 ), were not conducted. Second, the data were collected during the LLP, which is widely recognized as the onset period for negative symptoms. However, in future studies, these data should be supplemented with data from the follicular phase to support our findings. Conclusion The present study demonstrated shared and different brain functions within the DMN in PMS with PSQ and NSQ. Understanding of the increased rsFC between the left IPL and right MOG may contribute to the interpretation of the wide pathophysiology of PMS. Importantly, interventions or treatments that decrease FC between the right VMPFC and right PCu, and change ReHo values in the right VMPFC and left IPL, may alleviate the severity of depression and anxiety symptoms by improving sleep quality in PMS patients. Our findings revealed the neuropathology of sleep problems in PMS and suggested possible targeted treatments (left IPL and right VMPFC) that could improve depression and anxiety symptoms through sleep regulation. Abbreviations PMS, premenstrual syndrome; HCs, healthy controls; PSQI, Pittsburgh Sleep Quality Index; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale; DRSP, Daily Record of Severity of Problems; LLP, late luteal phase; IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal cortex; ReHo, regional homogeneity. Declarations Ethics approval and consent to participate: This study was registered with the Chinese Clinical Trial Registry (ChiCTR1900020642). Consent for publication: All participants were informed about the experimental procedures and provided written informed consent prior to their participation. Availability of data and materials: The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests: The authors declare that they have no competing interests Funding: This work was supported by the Guangxi key research and development program (No. AB22080053), National Natural Science Foundation of China (No. 82060315), and Natural Science Foundation of Guangxi (No. 2021GXNSFBA220007). Authors’ contributions: HXQ designed the study, conducted the analysis, interpreted the results, and drafted the paper. GXD and DMD designed the study, interpreted the data, and critically revised the paper. YZ, SSL, YYO, SHL, YQL, QPZ, KXZ, RJS, YJW, ZL, ZYL, YC, RW, and ZZC designed the study, recruited patients, and collected and managed data. GXD and DMD provided input on the study design, linked and interpreted the data, and revised the paper. HZ revised the paper. All authors read and approved the final manuscript. 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Abbreviations: PMS, premenstrual syndrome; HCs, healthy controls; DRSP, Daily Record of Severity of Problems; PSST, Premenstrual Symptoms screening tool; PSQI, Pittsburgh Sleep Quality Index.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6028422/v1/43d2fb031893a6bc92f6b0b5.png"},{"id":76574595,"identity":"1393edae-926a-424a-a82c-b9945bd88db8","added_by":"auto","created_at":"2025-02-18 14:07:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":484435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in rsFC within the DMN among the three groups. \u003c/strong\u003eA) and B) showing brain regions of rsFC differences among the three groups by ANOVA. C) showing rsFC between groups revealed by post hoc analysis. Abbreviations: IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal cortex. * denotes \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, ** denotes \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and *** denotes \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6028422/v1/c6b40d6e3906e9e3322fc421.png"},{"id":76577075,"identity":"907b1af2-4529-486c-84ca-8616ae6eb1f7","added_by":"auto","created_at":"2025-02-18 14:23:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe box plot for ReHo difference between groups. \u003c/strong\u003eAbbreviations: IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal vortex; ReHo, regional homogeneity. * denotes \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** denotes \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and *** denotes \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6028422/v1/59c542bf68693df5090216c2.png"},{"id":76576215,"identity":"ac853a05-e2a0-484d-ad4a-3b2c3d5f5d15","added_by":"auto","created_at":"2025-02-18 14:15:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe significant associations between the score of PSQI, SAS and SDS, and changed brain features.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6028422/v1/d1ea6fa590246ef8a5b88935.png"},{"id":76576197,"identity":"0fd85955-a227-4af9-b79f-a0abb168da33","added_by":"auto","created_at":"2025-02-18 14:15:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":573942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual diagram of mediation analysis in the PMS group based on Model 1 and Model 2.\u003c/strong\u003e A) and B) showing the results based on the rsFC and ReHo values, respectively. Abbreviations: IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal vortex. * denotes p \u0026lt; 0.05, ** denotes p \u0026lt; 0.01, and *** denotes p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6028422/v1/44be6e7d412b4ebbd702e294.png"},{"id":76674855,"identity":"93b17e10-f349-4795-8e52-77f386b0832c","added_by":"auto","created_at":"2025-02-19 14:17:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1621629,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6028422/v1/443e1999-05fb-4d3d-be0a-b3bd76398be2.pdf"},{"id":76574598,"identity":"545c8026-04dd-48c2-a126-6b1cecdb0a84","added_by":"auto","created_at":"2025-02-18 14:07:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":368010,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6028422/v1/11e76caebbfebf0a8846c5a7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of sleep quality on the default mode network and on anxiety- depression symptoms in premenstrual syndrome","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePremenstrual syndrome (PMS) affects approximately 20\u0026ndash;30% of fertile women worldwide, with the most prevalent symptoms being sleep disturbances, anxiety, depression, and mood lability during the late luteal phase (LP) (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It has been recognized as a high-risk factor for antenatal depression and perimenopausal depression. Additionally, the severe form of PMS evident\u0026mdash;premenstrual dysphoric disorder (PMDD)\u0026mdash;has been defined as a depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore, early management of mood symptoms is essential for prevention and treatment of PMS (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSleep deficits can contribute to the development and continuance of mood disorders (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Sleep-loss effects across multiple emotional domains were reported in a recent systematic review, mainly demonstrating a decrease in positive mood and an increase in anxiety symptoms (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Unfortunately, the process is self-reinforcing, as the recurring symptoms of depression and anxiety negatively interfere with sleep quality (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). For the affected women, premenstrual increases in insomnia symptoms, poor sleep quality (PSQ), daytime sleepiness, and fatigue during the LP are all commonly reported (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Understanding the neurobiological basis of the comorbidity between affective imbalance and PSQ in PMS may contribute to improving mental well-being for women of reproductive age.\u003c/p\u003e \u003cp\u003eStudies have shown that altered functional activities in the default mode network (DMN) may potentially serve as the neural mechanisms underlying the impact of sleep on emotional regulation (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). DMN hyperactivity plays an important role in processing negative rumination; however, rumination is regarded as a key cognitive feature of both anxiety and depression, as well as sleep disorders (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Research findings consistently suggest that the abnormal functional activities within the DMN, involving the cingulate cortex and the precuneus, might provide the neurobiological basis for the association between depression-anxiety and PSQ (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Although previous studies have shown that altered functional activities within the DMN might increase mood-disorder-related vulnerabilities in PMS and PMDD patients (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), the neural mechanisms underlying the impact of sleep on emotional regulation in PMS is still unclear.\u003c/p\u003e \u003cp\u003eIn the present study, we analyzed the specific functional brain activities within the DMN in PMS participants with PSQ. We compared those findings with similar studies of the healthy controls (HCs), using particularly the functional connectivity (FC) and regional homogeneity (ReHo). Additionally, correlation and mediation analyses were performed to assess the underlying association among sleep quality, DMN functional activities, and anxiety-depression symptoms in PMS.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The study was approved by the ethics committee of the People\u0026rsquo;s Hospital of Guangxi Zhuang Autonomous Region. This study was registered in the Chinese Clinical Trial Registry (ChiCTR1900020642). Written informed consent was obtained from all participants after they had been given detailed information about the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental design\u003c/h3\u003e\n\u003cp\u003eParticipants were consecutively recruited, using local advertisements, from April 2019 through October 2024; all were preliminarily screened using the Premenstrual Symptoms Screening Tool (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Then, the Daily Record of Severity of Problems (DRSP) questionnaire was acquired for two consecutive menstrual cycles to diagnose individuals with PMS and to isolate those who would serve as HCs (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Specifically, all participants were enrolled by Z.L. and G.X.D., a gynecologist and a radiologist, respectively, both with a decade of experience, following the established experimental guidelines. Detailed inclusion and exclusion criteria are provided in Supplementary Section 1.\u003c/p\u003e \u003cp\u003e All participants underwent resting-state functional MRI (rs-fMRI) scans, peripheral blood collection, and clinical assessment during the late LP (LLP) of the menstrual cycle. The LLP (1 to 5 days before menstruation) was determined based on urinary luteinizing hormone tests and the recorded time of the DRSP data for two months.\u003c/p\u003e\n\u003ch3\u003eClinical assessment and blood sample processing\u003c/h3\u003e\n\u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). A global score of PSQI\u0026thinsp;\u0026ge;\u0026thinsp;8 defined PSQ, and PSQI\u0026thinsp;\u0026lt;\u0026thinsp;8 defined normal sleep quality (NSQ). The Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) were used to assess the anxiety and depression severity levels (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Higher scores on these three scales indicate more serious clinical symptoms.\u003c/p\u003e \u003cp\u003eThe peripheral blood samples were collected between 8:00 a.m. and 10:00 a.m. on the day of the scan to ensure relatively stable and low levels of endogenous cortisol and estradiol. The samples were left at room temperature for 30\u0026ndash;60 min and then centrifuged at 3000 rpm for 10 min. The serum supernatant was placed in a storage tube and frozen to \u0026minus;\u0026thinsp;80\u0026deg;C. Progesterone (PROG), testosterone (TESTO), estradiol (E2), prolactin (PRL), luteinizing hormone (LH), and follicle-stimulating hormone (FSH) were analyzed using Enzyme-Linked Immunosorbent Assay (ELISA).\u003c/p\u003e\n\u003ch3\u003eImage data acquisition and processing\u003c/h3\u003e\n\u003cp\u003eThe rs-fMRI data were obtained using a 3T Siemens MAGNETOM Vida MRI System (Siemens Healthineers, Forchheim, Germany) within a standard 64-channel head coil; this instrument was located at the local hospital. The parameters of the MRI acquisition protocol are summarized in Supplementary Section 2.\u003c/p\u003e \u003cp\u003eThe MRI data were preprocessed using the Data Processing and Analysis for Brain Imaging (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rfmri.org/dpabi\u003c/span\u003e\u003cspan address=\"http://rfmri.org/dpabi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 8.0) toolbox (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) on the MATLAB R2021b platform. The pre-processing steps included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the removal of the first 10 volumes of rs-fMRI; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) slice timing; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) realignment and head motion correction; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) the removal of nuisance covariates (white matter, cerebrospinal fluid signals, global signals, six motion parameters, and Friston-24 head motion parameters); (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) normalization to Montreal Neurological Institute (MNI) space (3 \u0026times; 3 \u0026times; 3 mm\u0026sup3; voxels) using the Diffeomorphic Anatomical Registration through Exponentiated Lie Algebra (DARTEL) function; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) bandpass filtering from 0.01 to 0.08 Hz.\u003c/p\u003e\n\u003ch3\u003eBrain functional network construction\u003c/h3\u003e\n\u003cp\u003eAfter the data processing, the Pearson\u0026rsquo;s correlation coefficients were calculated between the mean time courses of each region of interest (ROI) for each individual. The 33 ROIs of the DMN were selected based on the Dosenbach 2010 atlas (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), which lists 160 ROIs sorted into six brain networks. An N\u0026times;N matrix was then constructed (N\u0026thinsp;=\u0026thinsp;33). Finally, the correlation coefficients were normalized to z-scores with Fisher\u0026rsquo;s r-to-z transformation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReHo computation\u003c/h2\u003e \u003cp\u003eWe further calculated the individual\u0026rsquo;s mean ReHo values of each brain region that had abnormalities in FCs within the DMN. First, the individual\u0026rsquo;s ReHo values in each ROI were obtained by applying the Kendall's Coefficient of Concordance to calculate the similarity of the time series between a single voxel and its 26 nearest voxels (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Then, the individual\u0026rsquo;s mean ReHo mapping in each ROI was obtained by normalizing each ReHo value and smoothing using a Gaussian kernel with a full-width at half-maximum of 6 mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed on the SPSS 19.0 platform and R software. Mediation analysis was performed using the PROCESS macro (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.processmacro.org/\u003c/span\u003e\u003cspan address=\"http://www.processmacro.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) developed by Hayes (Hayes, 2009), a versatile modeling tool freely available for the SPSS.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDemographic and clinical data analysis\u003c/h3\u003e\n\u003cp\u003eDifferences in age, length of menstruation, age at menarche, length of the menstrual cycle, and the scores of the PSQI, SAS, SDS, and DRSP were examined using analysis of variance (ANOVA) with Tukey correction for normally distributed data or the Kruskal\u0026ndash;Wallis test with Dunn correction for non-normally distributed data among groups. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFC comparison\u003c/h2\u003e \u003cp\u003eANOVA was used to examine the differences in the FC matrix among the three groups, with the statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after false discovery rate (FDR) correction. Post hoc analysis was used to determine the significant differences in these changed FCs between the groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eReHo comparison\u003c/h2\u003e \u003cp\u003eThe mean ReHo values in ROIs were compared among the three groups by ANOVA with Tukey correction, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis\u003c/h2\u003e \u003cp\u003ePearson analyses were used to examine the relationships between FCs and clinical data, including PSQI, SAS, and SDS in PMS patients, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after FDR correction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMediation analysis\u003c/h2\u003e \u003cp\u003eFour mediation models were constructed. In models 1 and 2, the FC was the independent variable X, the PSQI score was the mediator M, and the SAS and SDS scores were the dependent variables Y. In models 3 and 4, the PSQI score was the independent variable X, the FC was the mediator M, and the SAS and SDS scores were the dependent variables Y. The three paths in the models\u0026mdash;presented in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e5\u003c/span\u003e with their p and β values (least-squares regression coefficients)\u0026mdash;are (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a \u0026times; b, the indirect effect of X on Y through M; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) c\u0026prime;, the direct effect of X on Y not mediated by M; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) c, the total effect of X on Y, which is then the sum of the first two: c = (a \u0026times; b)\u0026thinsp;+\u0026thinsp;c\u0026prime;. The significant indirect effect threshold was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDemographics and clinical characteristics\u003c/h2\u003e \u003cp\u003eThe process shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e was used to enroll 143 participants; of these, 77 were individuals with PMS (median age, 24.00 years [interquartile range (IQR) 22.00\u0026ndash;26.00]) and 66 were HCs (median age, 24.00 years [IQR 22.00\u0026ndash;25.00]). The 77 PMS patients were classified using PSQI scores, with 52 (68%) being assigned to the PMS-PSQ group (PSQI\u0026thinsp;\u0026ge;\u0026thinsp;8), and 25 (32%) to the PMS-NSQ group (PSQI\u0026thinsp;\u0026lt;\u0026thinsp;8).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere were no significant differences in age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.851), length of menstruation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.663), menophania (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.538), or length of menstrual cycle (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.625) among the groups. Post hoc analysis found both the PMS-PSQ and PMS-NSQ groups showed significantly higher scores for DRSP, SAS, and SDS compared to HCs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the PMS-PSQ group showed higher SAS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SDS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) scores relative to the PMS-NSQ group.\u003c/p\u003e \u003cp\u003e There were 7 HCs and 3 PMS participants whose missing blood data were supplemented by linear interpolation. There was no evidence of a difference among the groups in sex hormone levels, including TESTO (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.738), PROG (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.958), E2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.532), PRL (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.132), LH (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.452), and FSH (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.842). All demographic and clinical characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of PMS patients and HCs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMS-NSQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePMS-PSQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epost-test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.00(22.00\u0026ndash;25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.00(22.00-24.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.50 (22.00-25.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.487/0.301/0.334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emenophania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.00(12.00\u0026ndash;14.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00(12.00-13.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.00(12.00\u0026ndash;14.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.274/0.199/0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.00(28.00\u0026ndash;30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.00(28.25\u0026ndash;31.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.00(29.00\u0026ndash;31.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.169/0.211/0/374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emenstrual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.00(5.00\u0026ndash;7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00(6.00\u0026ndash;7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00(5.00\u0026ndash;7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.235/0.360/0.167\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.40\u0026thinsp;\u0026plusmn;\u0026thinsp;6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.05\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001/\u0026lt;0.001/\u0026lt;0.001\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.53\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.05\u0026thinsp;\u0026plusmn;\u0026thinsp;12.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.75\u0026thinsp;\u0026plusmn;\u0026thinsp;9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001/\u0026lt;0.001/0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00(3.50-6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00(3.25-7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.00(8.00\u0026ndash;12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.357/\u0026lt;0.001/\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRSP\u003c/p\u003e \u003cp\u003e(LLP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.50(22.80-28.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.20(51.40\u0026ndash;59.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.40(52.43\u0026ndash;61.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001/\u0026lt;0.001/0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: PSQI, Pittsburgh Sleep Quality Index; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale; DRSP, Daily Record of Severity of Problems; LLP, late luteal phase. Continuous data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD); discrete data are presented as median (inter quartile range [IQR]).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe sex hormone levels among 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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMS-NSQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePMS-PSQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTESTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.88(22.10-37.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.50(20.45\u0026ndash;36.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.95(27.08\u0026ndash;33.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.79(1.39\u0026ndash;11.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.07(1.52\u0026ndash;11.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.32(0.77\u0026ndash;11.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149.30 (69.78-193.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137.00(72.10-229.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116.75(55.78\u0026ndash;205.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.50(17.15\u0026ndash;39.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.20(17.60-47.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.28(19.58\u0026ndash;34.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.38(3.33\u0026ndash;8.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.78(5.19\u0026ndash;11.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.44(4.27\u0026ndash;8.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.09(2.12\u0026ndash;4.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.90(2.14\u0026ndash;4.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.66(2.64\u0026ndash;4.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: TESTO, testosterone; PROG, progesterone; E2, estradiol; PRL, prolactin; LH, luteinizing hormone; FSH, follicle-stimulating hormone. Continuous data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD); discrete data are presented as median (inter quartile range [IQR]).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBetween-groups differences of FC within DMN\u003c/h2\u003e \u003cp\u003eANOVA analysis of the matrixes obtained from measures of the FCs of the DMN ROIs, as described in Section 2.5, identified significantly different FCs among the three groups. The differences in resting-state functional connectivity (rsFC) were observed in the left inferior parietal lobule (IPL) (MNI: -53, -50, 39), in the right middle occipital gyrus (MOG) (MNI: 45, -72, 29), in the right ventromedial prefrontal cortex (VMPFC) (MNI: 8, 42, -5), and in the right posterior cingulate/precuneus (PCu) (MNI: 10, -55, 17). Then, post hoc analysis showed increased FC, relative to HCs, of the left IPL and right MOG in both PMS-NSQ and PMS-PSQ groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as decreased FC, relative to PMS-PSQ and HCs, of the right VMPFC and right PCu in the PMS_NSQ group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe difference of rsFC and ReHo values among PMS-NSQ, PMS-PSQ and HCs groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMS-NSQ (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePMS-PSQ (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epost-test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFCs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight VMPFC-right PCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001/0.954/\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft IPL-right AG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001/\u0026lt;0.001/0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReHo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight VMPFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.109/0.821/0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft IPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039/0.749/0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight PCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.974/0.978/0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight PCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.830.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.833/0.999/0.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal cortex; ReHo, regional homogeneity. Continuous data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD); discrete data are presented as median (inter quartile range [IQR]).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mediation analysis results on Model 1.\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec'\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ec-c'\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (FC of right VMPFC \u0026ndash; right PCu)\u0026rarr;Y (SAS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (FC of right IPL \u0026ndash; right MOG)\u0026rarr;Y (SAS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (ReHo values of right VMPFC)\u0026rarr;Y (SAS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (ReHo values of left IPL)\u0026rarr;Y (SAS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (ReHo values of right PCu)\u0026rarr;Y (SAS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (ReHo values of right MOG)\u0026rarr;Y (SAS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.3658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal cortex; ReHo, regional homogeneity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mediation analysis results on Model 2.\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec'\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ec-c'\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (FC of right VMPFC \u0026ndash; right PCu)\u0026rarr;Y (SDS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (FC of right IPL \u0026ndash; right MOG)\u0026rarr;Y (SDS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (ReHo values of right VMPFC)\u0026rarr;Y (SDS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (ReHo values of left IPL)\u0026rarr;Y (SDS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (ReHo values of right PCu)\u0026rarr;Y (SDS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eX (ReHo values of right MOG)\u0026rarr;Y (SDS) mediated by M (PSQI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal cortex; ReHo, regional homogeneity; PSQI, Pittsburgh Sleep Quality Index; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003ep\u003eAnalysis of four ROIs identified from the rsFC results revealed significant differences in ReHo values in the left IPL and the right VMPFC across the three groups. Post hoc comparisons showed that the PMS_NSQ group exhibited decreased ReHo values in the left IPL relative to the PMS-PSQ group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), and increased ReHo values in the right VMPFC relative to the HCs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039). These findings are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis between sleep quality, significant FCs and anxiety-depression symptoms in PMS patients\u003c/h2\u003e \u003cp\u003eIn the PMS groups, there was a significant positive correlation between scores of PSQI and SAS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PSQI and SDS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe decreased FCs between the right VMPFC and the right PCu (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, r\u0026thinsp;=\u0026thinsp;0.377), as well as the increased ReHo values in the right IPL (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040, r\u0026thinsp;=\u0026thinsp;0.287), are significantly correlated with the PSQI score. The ReHo values in the right MOG are significantly negatively correlated with the SDS score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, r\u0026thinsp;=\u0026thinsp;0.220). Details are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Fig.\u0026nbsp;1, and Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMediation analysis between sleep quality, significant FCs, and anxiety-depression symptoms in PMS patients\u003c/h2\u003e \u003cp\u003eThe mediation analysis models and the investigated variables (see Section 2.7.5) are described in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplementary Fig.\u0026nbsp;2, and Supplementary Tables\u0026nbsp;2 and 3. In model 1, the PSQI score significantly mediated the relationship between functional activities within the DMN and the SAS score, which is significantly linked to (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the FC of right VMPFC and right PCu (indirect effect\u0026thinsp;=\u0026thinsp;9.021, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the ReHo values in the right VMPFC (indirect effect\u0026thinsp;=\u0026thinsp;1.500, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.021) and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the ReHo values in the left IPL (indirect effect = -2.070, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.025). In model 2, the PSQI score also significantly mediated the relationship between functional activities within DMN and the SDS score, which is significantly linked to (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the FC of the right VMPFC with the right PCu (indirect effect\u0026thinsp;=\u0026thinsp;11.001, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the ReHo value in VMPFC (indirect effect\u0026thinsp;=\u0026thinsp;1.595, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.026). In model 3 and model 4, no significant mediation was found for these FCs for the relationship between PSQI and SAS or SDS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing rs-fMRI, we explored rsFC and ReHo alterations within the DMN specific to different sleep qualities and their clinical relevance in individuals with PMS. These findings indicated that the PMS-NSQ and PMS-PSQ groups exhibited increased rsFC between left IPL and right MOG compared to HCs, and the PMS-NSQ group showed decreased rsFC between right VMPFC and right PCu compared to both the HC and PMS-PSQ groups. Additionally, the PMS-PSQ group showed increased ReHo in the right VMPFC compared to the PMS-NSQ group, and decreased ReHo in the left IPL compared to the HC group. Subsequently, the correlation and mediation analysis further revealed the underlying relationships between altered DMN function and anxiety-depression severity, potentially mediated by PSQI scores.\u003c/p\u003e \u003cp\u003eA hyperactivated and hyperconnected DMN is widely reported in various psychiatric disorders; however, these disorders exhibit both shared and distinct patterns of DMN activity (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Previous studies have shown increased FC in parietal-occipital brain regions within the DMN underlies the pathophysiology of a variety of mood disorders, which manifested the same symptoms as PMS (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The IPL and MOG are respectively located in the parietal and occipital regions of the brain, and their connection is involved in emotion regulation, response inhibition, and self-referential thinking (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Consistent with these findings, we found increased FC between the left IPL and right MOG in both the PMS-PSQ and PMS-NSQ groups compared to HCs. We speculated that the deficits in the connection between the left IPL and right MOG within DMN may reveal a wide range of physiological and pathological mechanisms of PMS.\u003c/p\u003e \u003cp\u003eAdditionally, the PMS-NSQ group showed decreased FC in the right VMPFC and the right PCu compared to both HCs and the PMS-PSQ group, and the decreased FC was associated with better sleep quality. To the best of our knowledge, maintenance of sleep and emotion depends on balanced functional activities within the DMN (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). A growing body of evidence suggests that deactivation of brain regions comprising the DMN occurs when patients without sleep disturbances are at rest (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), which may lead to decreased rsFC within the DMN. The PCu\u0026ndash;VMPFC link is associated with sleep quality (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Therefore, we further speculated that the decreased FC in the PMS-NSQ group may serve as a compensatory mechanism in PMS to improve sleep quality. This improvement in sleep quality may directly or indirectly affect DMN functions and clinical symptoms in PMS.\u003c/p\u003e \u003cp\u003eThe ReHo also reflects the local efficiency in information processing for impaired brain regions (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). We further found increased ReHo values in the left IPL and decreased ReHo in the right VMPFC in the PMS-NSQ group, and that the changed ReHo in the left IPL was negatively correlated with the PSQI score. The left IPL was also defined as a core brain region in the Dorsal Attention Network, which is responsible for external attention and cognition, including language processing, attention (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), and emotion body perception (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). In contrast, the right VMPFC mainly participates in emotion regulation and autonomic responses (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), managing internal attention and self-referential thinking. Consistent with previous studies (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), our findings further revealed that increased external attention allocation and reduced internal emotional burden are potentially alleviating factors involved in reducing negative rumination in PMS patients with NSQ.\u003c/p\u003e \u003cp\u003ePrevious studies have revealed that the underlying association between sleep, emotion, and the brain is best described through mediation analyses (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). In this study, the mediation analyses further suggested that sleep quality mediates the effects of the rsFC of the right VMPFC and right PCu and also the effects of the ReHo values in the left IPL and right VMPFC on depression and anxiety symptoms in PMS patients. That is, altered functional activities lead to better sleep quality, which in turn may help relieve anxiety and depression symptoms in PMS patients. Studies have shown that the left IPL may potentially serve as neural substrate in the association between anxiety-depression symptoms and PSQ in healthy adults (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), which is consistent with our findings. We also found that the right VMPFC also may prospectively serve as a potential intervention target for the treatment of sleep disturbances in PMS.\u003c/p\u003e \u003cp\u003eOur study has limitations that should be considered. First, objective sleep measures, such as polysomnographic assessments (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), were not conducted. Second, the data were collected during the LLP, which is widely recognized as the onset period for negative symptoms. However, in future studies, these data should be supplemented with data from the follicular phase to support our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study demonstrated shared and different brain functions within the DMN in PMS with PSQ and NSQ. Understanding of the increased rsFC between the left IPL and right MOG may contribute to the interpretation of the wide pathophysiology of PMS. Importantly, interventions or treatments that decrease FC between the right VMPFC and right PCu, and change ReHo values in the right VMPFC and left IPL, may alleviate the severity of depression and anxiety symptoms by improving sleep quality in PMS patients. Our findings revealed the neuropathology of sleep problems in PMS and suggested possible targeted treatments (left IPL and right VMPFC) that could improve depression and anxiety symptoms through sleep regulation.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003ePMS, premenstrual syndrome; HCs, healthy controls; PSQI, Pittsburgh Sleep Quality Index; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale; DRSP, Daily Record of Severity of Problems; LLP, late luteal phase; IPL, inferior parietal lobule; PCu, posterior cingulate/precuneus; MOG, middle occipital gyrus; VMPFC, ventromedial prefrontal cortex; ReHo, regional homogeneity.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was registered with the Chinese Clinical Trial Registry (ChiCTR1900020642).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll participants were informed about the experimental procedures and provided written informed consent prior to their participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Guangxi key research and development program (No. AB22080053), National Natural Science Foundation of China (No. 82060315), and Natural Science Foundation of Guangxi (No. 2021GXNSFBA220007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eHXQ designed the study, conducted the analysis, interpreted the results, and drafted the paper. GXD and DMD designed the study, interpreted the data, and critically revised the paper. YZ, SSL, YYO, SHL, YQL, QPZ, KXZ, RJS, YJW, ZL, ZYL, YC, RW, and ZZC designed the study, recruited patients, and collected and managed data. GXD and DMD provided input on the study design, linked and interpreted the data, and revised the paper. HZ revised the paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHurt SW, Schnurr PP, Severino SK, Freeman EW, Gise LH, Rivera-Tovar A, Steege JF. Late luteal phase dysphoric disorder in 670 women evaluated for premenstrual complaints. 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Semin Neurol. 1990;10(2):111\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1055/s-2008-1041260\u003c/span\u003e\u003cspan address=\"10.1055/s-2008-1041260\" 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":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Premenstrual syndrome, sleep, default mode network, resting-state functional MRI, functional connectivity, regional homogeneity","lastPublishedDoi":"10.21203/rs.3.rs-6028422/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6028422/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeuroimaging evidence suggests existence of an association between the aberrant default mode network (DMN) and anxiety-depression severity in premenstrual syndrome (PMS); however, ignoring the effects of sleep prevents understanding the pathophysiology of PMS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeventy-seven PMS patients and sixty-six healthy controls (HCs) underwent resting-state functional MRI, and clinical assessment included the Pittsburgh Sleep Quality Index (PSQI), the Self-Rating Anxiety Scale, and the Self-Rating Depression Scale. PSQI scores classified PMS patients into normal sleep quality (PMS-NSQ) and poor sleep quality (PMS-PSQ) groups. Resting-state functional connectivity (rsFC) and regional homogeneity (ReHo) within the DMN were compared among the three groups. Correlation and mediation analyses examined potential associations relating sleep quality, changes in brain function, and clinical variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to HCs, both PMS groups exhibited increased rsFC between left inferior parietal lobule (IPL) and right middle occipital gyrus. Additionally, the PMS-NSQ group presented decreased FC of right ventromedial prefrontal cortex (VMPFC) and right posterior cingulate/precuneus, decreased ReHo in right VMPFC, and increased ReHo value in left IPL. Combined correlation and mediation analyses showed that the altered functional activity within the DMN and anxiety-depression symptoms were mediated by sleep quality in PMS patients, mainly involving the right VMPFC and left IPL regions of the brain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings reveal the potential neuropathology of sleep problems in PMS, which sleep quality may mediate the association between functional connectivity within DMN and anxiety-depression severity. The right VMPFC and the left IPL may prospectively serve as potential intervention targets for the treatment of sleep disturbances in PMS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration: \u003c/strong\u003eChinese Clinical Trial Registry (ChiCTR1900020642)\u003c/p\u003e","manuscriptTitle":"Effects of sleep quality on the default mode network and on anxiety- depression symptoms in premenstrual syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-18 14:07:14","doi":"10.21203/rs.3.rs-6028422/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eaf43fba-28f2-4bb6-b88b-747f92d9d46c","owner":[],"postedDate":"February 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T01:08:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-18 14:07:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6028422","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6028422","identity":"rs-6028422","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}