Pain, Sleep, and Affective Symptoms in Primary Dysmenorrhea: A Symptom Network and Insular Connectivity Study

Abstract

Background

Primary dysmenorrhea (PDM) is a prevalent gynecological pain condition frequently accompanied by sleep disturbance and affective symptoms. Although neuroimaging studies have reported altered brain function in PDM, how menstrual pain relates to its multidimensional symptom profile remains incompletely understood.

Methods

A total of 113 women with PDM and 105 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging during menstruation. Clinical symptoms, including the Visual Analog Scale (VAS), Pittsburgh Sleep Quality Index (PSQI), Pain Catastrophizing Scale (PCS), Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS), were assessed using standardized questionnaires. Symptom network analysis was performed to identify core clinical symptoms in PDM. Subsequently, seed-based resting-state functional connectivity (rs-FC) analyses of insular subregions were conducted. Associations between altered insular connectivity and core clinical symptoms were further examined in patients with PDM.

Results

Symptom network analysis identified sleep disturbance and affective symptom dimensions as central or bridging features within the clinical symptom network of PDM. Compared with HCs, patients with PDM exhibited increased rs-FC between the ventral anterior insula (vAI) and regions involved in salience and cognitive–emotional processing. Exploratory Spearman correlation analyses revealed nominal positive associations between right vAI–anterior cingulate cortex (ACC) connectivity and sleep disturbance (ρ = 0.21, uncorrected P = 0.026) and anxiety (ρ = 0.26, uncorrected P = 0.005). However, neither association survived FDR correction for multiple comparisons (adjusted P = 0.194 and 0.075, respectively).

Conclusion

These findings suggest that altered anterior insula functional connectivity may represent a neural correlate of sleep disturbance and affective symptoms in PDM. Integrating symptom network analysis with insula-centered functional connectivity analysis offers complementary perspectives on menstrual pain and its co-occurring symptoms.

Introduction

Primary dysmenorrhea (PDM) is a highly prevalent gynecological pain disorder in young women, characterized by recurrent cramping lower abdominal pain during menstruation in the absence of identifiable pelvic pathology.¹ It affects approximately 17–90% of women of reproductive age, with 2–29% experiencing pain severe enough to substantially interferes with daily functioning.^2,3 In addition to menstrual pain itself, PDM is often accompanied by poor sleep quality, increased anxiety, and depressive symptoms,^4,5 highlighting its multidimensional clinical burden. Accumulating neuroimaging studies have increasingly demonstrated that PDM patients exhibit altered brain function and structure, particularly within regions implicated in pain processing, emotional regulation, and cognitive control.^6–10 However, how these central alterations relate to the co-occurrence of pain and associated symptoms remains poorly understood.

The insula is a key cortical region involved in nociception, interoception, and the integration of sensory and affective signals.¹¹ Altered insular structure and function have been consistently reported across a range of chronic pain conditions, including chronic prostatitis/chronic pelvic pain syndrome,¹² fibromyalgia,¹³ migraine,¹⁴ and temporomandibular disorder.¹⁵ Importantly, the insula is functionally heterogeneous rather than a unitary structure. Based on cytoarchitectonic and functional connectivity evidence, it is commonly subdivided into ventral anterior(vAI), dorsal anterior(dAI), and posterior (PI) subregions, each participating in distinct large-scale brain networks.¹⁶ This functional heterogeneity suggests that subregion-specific analyses may provide more precise insights into pain-related neural alterations than treating the insula as a single unit.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely applied to investigate intrinsic brain network alterations in chronic pain and to identify potential neurobiological markers.^17–19 However, conventional neuroimaging analyses primarily characterize group-level brain alterations and provide limited information about how co-occurring clinical symptoms are organized and interrelated. Symptom network analysis offers a complementary approach by conceptualizing individual symptoms as interconnected components of a network and by identifying symptoms that occupy central or bridging positions across symptom domains. Integrating this symptom-level framework with neuroimaging may therefore provide complementary information about the relationship between multidimensional symptom organization and altered brain functional connectivity.

Accordingly, this study aims to explore the multidimensional symptom structure of patients with PDM and to determine whether core symptom features are associated with alterations in insula-centered functional connectivity. By integrating symptom network analysis with subregion-specific resting-state functional connectivity, we sought to relate symptom-level organization to neural network alterations, addressing the limited integration of these two levels in previous PDM research. We hypothesized that functional connectivity between specific insular subregions and the cingulate cortex would be altered in patients with PDM and that these connectivity alterations would be significantly associated with core clinical symptoms.

Materials and Methods

Participants

A total of 113 patients with PDM and 105 Healthy controls (HCs) were recruited from a local university through advertisements. The sample partially overlaps with our previous study, including 41 patients with PDM and 39 healthy controls.²⁰ This study was approved by the Ethics Committee of Hospital of Chengdu University of TCM/TCM Hospital of Sichuan Province (Approval No. 2021KL-010) and was conducted in accordance with the Declaration of Helsinki. Before study enrollment, all participants signed a written informed consent form and underwent pelvic ultrasound examination by a gynecologist to rule out organic pelvic disease. The inclusion and exclusion criteria were defined as previously described.²⁰ The inclusion criteria for the PDM patients were as follows: (1) female, aged 18–30 years, right-handed; (2) a diagnosis of PDM based on the Society of Obstetricians and Gynecologists of Canada’s diagnostic criteria;²¹ (3) a regular menstrual cycle (28 ± 7 days); (4) a history of PDM for at least one-year; (5) no use of exogenous hormones or centrally acting medicines within the previous 6 months; and (6) an average visual analog scale (VAS) for menstrual pain was greater than 40 mm over the past three menstrual cycles. Healthy controls met the same inclusion criteria as the PDM group, except for the absence of a history of menstrual pain. The exclusion criteria for all participants included: (1) secondary dysmenorrhea caused by identifiable organic pathology by ultrasound or gynecological examination; (2) other chronic pain conditions; (3) severe systemic medical conditions; (4) current or past severe psychiatric disorders; (5) pregnancy, preparing for pregnancy, or breastfeeding; (6) use of analgesics or sedatives within one month prior to enrollment; and (7) contraindications to magnetic resonance imaging (MRI); and (8) significant structural brain abnormalities or focal lesions detected on MRI. No formal a priori sample size calculation was performed for this study.

Clinical Evaluation

All patients with PDM and HCs completed a series of clinical symptom questionnaires within one prior to MRI data acquisition. Pain intensity was assessed using the Visual Analog Scale (VAS). Sleep quality, pain-related cognition, and affective symptoms were evaluated using the Pittsburgh Sleep Quality Index (PSQI), the Pain Catastrophizing Scale (PCS), the Self-Rating Anxiety Scale (SAS), and the Self-Rating Depression Scale (SDS), respectively.

MRI Data Acquisition

Participants were instructed to abstain from caffeine and alcohol 24 hours before the MRI scan. MRI scans were performed during the menstruation phase (days 1–3 of the menstrual cycle). Imaging data were acquired using a 3.0-T GE Signa MR750 scanner (GE Healthcare, Milwaukee, WI, United States) at the University of Electronic Science and Technology of China. To minimize head movement, participants were positioned with comfortable foam cushioning and wore earplugs to reduce scanner noise. During scanning, participants were instructed to remain relaxed with their eyes closed, stay awake, and avoid deliberate cognitive activity. Resting-state functional images were acquired using a gradient-echo echo-planar imaging sequence with the following parameters: TR = 2000ms; TE = 30ms; flip angle = 90°; FOV = 240 mm × 240 mm; matrix size = 64 × 64; 35 slices; in-plane resolution = 3.75 mm × 3.75 mm; and 255 volumes. The total resting-state acquisition time was approximately 8 min 30s.

Data Preprocessing

All resting-state fMRI data were preprocessed using the Resting-State fMRI Data Analysis Toolkit plus (RESTplus V1.2), implemented in Statistical Parametrical Mapping software (SPM12, http://www.fil.ion.ucl.ac.uk/spm) running on MATLAB R2016b (The MathWorks, Inc., Natick, MA, USA). The first 10 time points were discarded to allow for magnetic field stabilization. The remaining images underwent slice timing correction and realignment for head motion correction. Functional images were subsequently spatially normalized to Montreal Neurological Institute space using an EPI template and resampled to an isotropic voxel size of 3 × 3×3 mm³. The normalized images were spatially smoothed using a Gaussian kernel with a full width at half maximum of 6 × 6 × 6 mm³. Linear trends were removed, followed by nuisance covariate regression including the six head-motion parameters, white matter signal, and cerebrospinal fluid signal. Global signal regression was not performed. Temporal band-pass filtering (0.01–0.08 Hz) was subsequently applied for resting-state functional connectivity analyses. Participants were considered for exclusion if head motion exceeded 2 mm in translation, 2° in rotation, or if mean framewise displacement exceeded 0.5 mm. No participant met these exclusion criteria. No additional volume-level motion censoring (scrubbing) was performed.

Seed-Based Resting-State Functional Connectivity Analysis

Seed-based resting-state functional connectivity (rs-FC) analyses were conducted using the RESTplus V1.2 toolbox. Regions of interest (ROIs) were defined as insular subregions based on a previously published functional parcellation.²² Specifically, six spherical seed ROIs (radius = 6 mm) were centered in MNI space, including the right ventral anterior insula (R-vAI; 32, 10, −6), right dorsal anterior insula (R-dAI; 35, 7, 3), right posterior insula (R-PI; 35, −11, 6), left ventral anterior insula (L-vAI; −33, 13, −7), left dorsal anterior insula (L-dAI; −38, 6, 2), and left posterior insula (L-PI; −38, - 6, 5). For each seed region, the mean time series was extracted and correlated with the time series of all other voxels across the brain using Pearson’s correlation analysis. The resulting Pearson’s correlation coefficients were subsequently transformed into z scores using Fisher’s r-to-z transformation to improve normality.

Statistical Analysis

Clinical Data

Statistical analyses of demographic and clinical data were performed using SPSS version 22.0 (IBM Corp., Chicago, IL, USA). Continuous variables are presented as mean ± standard deviation (SD). Normality of the data distribution was assessed using the Shapiro–Wilk test. Between-group differences in age, years of education, disease duration, and clinical symptom scores (VAS, PSQI, PCS, SAS, and SDS) were examined using independent-samples t tests or Mann–Whitney U-tests where appropriate. A two-tailed P value < 0.05 was considered statistically significant.

Symptom Network Construction and Centrality Analysis

Cross-sectional network analysis was conducted using R software (version 4.5.1) with the qgraph and bootnet packages. Contemporaneous symptom networks were estimated at the dimension/component level to examine associations among symptoms derived from the PSQI,²³ SAS,²⁴ SDS²⁵ and PCS.²⁶ A total of 18 dimension/component scores derived from the PSQI, SAS, SDS, and PCS were entered as network nodes, with higher scores indicating greater symptom severity. The PSQI comprised seven component scores:²⁷ subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. The SAS was represented by four symptom dimensions:²⁸ anxious mood, autonomic dysfunction, motor tension, and mixed anxiety and autonomic symptoms. The SDS was represented by four dimensions:²⁹ psycho-affective symptoms, somatic symptoms, psychomotor symptoms, and depressive psychological symptoms. The PCS comprised three dimensions:³⁰ rumination, magnification, and helplessness.

Networks were estimated using a Gaussian graphical model (GGM) with regularized partial correlations. Given the potential non-normality of symptom data, Spearman correlations were used as input, implemented with the EBICglasso approach. The extended Bayesian information criterion (EBIC) hyperparameter γ was set to 0.5. Expected Influence (EI) was calculated to quantify node centrality, with standardized z-scores used to facilitate comparison across nodes. Bridge Expected Influence (BEI) was computed based on predefined symptom communities to identify symptoms linking different domains. Network accuracy and stability were evaluated using nonparametric and case-dropping bootstrap procedures, with centrality stability assessed using the correlation stability (CS) coefficient (CS > 0.25 indicating acceptable stability).

Imaging Data

Seed-based between-group differences in rs-FC were examined using SPM12. Group comparisons between patients with PDM and HCs were performed using two-sample t tests, with age and years of education included as covariates. Statistical significance was determined using a voxel-wise false discovery rate (FDR) correction at P < 0.05, with a cluster extent threshold of 30 voxels. To assess clinical relevance, Spearman’s rank correlation analyses were performed to examine the associations between altered insular subregional FC and clinical symptom measures in patients with PDM. Based on the symptom network findings, three clinical measures (PSQI, SAS, and SDS scores) were selected for correlation analyses with five altered FC measures, resulting in 15 correlation tests. To account for multiple comparisons, the Benjamini–Hochberg FDR procedure was applied across the 15 tests. Statistical significance was defined as an FDR-adjusted P < 0.05. Uncorrected P values were also reported for descriptive and exploratory purposes.

Results

Demographics

The demographic and clinical information of the participants are summarized in Table 1. The two groups were matched for age (24.42 (2.71), 25.06 (2.84)), respectively, P = 0.076) and years of education (17.12 (1.77), 16.89 (2.04)), respectively, P = 0.663). In contrast, compared with HCs, patients with PDM showed significantly higher pain intensity, sleep disturbance, pain catastrophizing, anxiety, and depression scores (all P < 0.001).

Table 1.

Demographic and Clinical Characteristics of Patients with Primary Dysmenorrhea and Healthy Controls

Characteristics	HC (n=105)	PDM (n=113)	P Value
Age (years)	25.06 (2.84)	24.42 (2.71)	0.076
Education (years)	16.89 (2.04)	17.12 (1.77)	0.663
Disease duration (years)	–	6.56 (3.53)	–
VAS score (0–10)	–	6.45 (1.36)	–
PSQI	3.41 (1.77)	5.92 (2.65)	<0.001
PCS	2.14 (2.04)	20.72 (11.69)	<0.001
SAS	27.98 (6.11)	34.77 (9.48)	<0.001
SDS	30.07 (7.98)	36.75 (9.98)	<0.001

Notes: Values are reported as mean (SD).

Abbreviations: HC, healthy control, PDM, primary dysmenorrhea; VAS, visual analogue scale; PSQI, pittsburgh sleep quality index; PCS, pain catastrophizing scale; SAS, self-rating anxiety scale; SDS, self-rating depression scale.

Contemporaneous Networks

The contemporaneous symptom network revealed a densely interconnected structure among insomnia, anxiety, and depression symptoms, with strong within- and between-domain associations. In contrast, pain catastrophizing items were largely clustered within their own domain and exhibited relatively fewer cross-domain connections (Figure 1). Edge inspection indicated that the strongest connections were primarily observed within affective and sleep-related domains, whereas connections involving pain catastrophizing were comparatively weaker and more isolated. EI analysis showed that SDS_2 had the highest centrality (z = 1.37), followed by PSQI_1 (z = 1.25) and SAS_1 (z = 1.10), indicating that these symptoms were highly connected and influential within the network. In contrast, several insomnia-related items, including PSQI_4 (z = −1.25) and PSQI_6 (z = −1.40), showed low EI values, suggesting a more peripheral role. Bridge Expected Influence analysis further identified SAS_1, SDS_2, and PSQI_1 as key bridge nodes linking insomnia with anxiety and depression domains, highlighting their potential role in cross-domain symptom interactions. Centrality stability analysis yielded a correlation stability coefficient of 0.29, which exceeded the minimum acceptable threshold of 0.25 but indicated only modest stability of the centrality estimates.

Figure 1.

The results of symptom network construction and centrality analysis. (A) One-step bridge expected influence of nodes representing sleep disturbance, depressive symptoms, anxiety symptoms, and pain catastrophizing in the cross-sectional symptom network. (B) Expected influence of the same nodes in the cross-sectional symptom network. Values in both panels are standardized as z-scores. (C) Cross-sectional network diagram of sleep, depression, anxiety and pain catastrophizing symptom nodes based on data.

Abbreviations: PSQI, pittsburgh sleep quality index; PCS, pain catastrophizing scale; SAS, self-rating anxiety scale; SDS, self-rating depression scale.

Functional Connectivity

Compared with HCs, patients with PDM exhibited increased FC between the left vAI and the left inferior frontal gyrus (IFG), left midcingulate cortex (MCC), and right inferior parietal lobule (IPL), as well as between the right vAI and the right anterior cingulate cortex (ACC) and right IPL, at the original within-seed voxel-wise FDR-corrected threshold of P < 0.05 (Figure 2 and Table 2). However, after applying a more stringent Bonferroni-adjusted within-seed FDR threshold of P < 0.00833 (0.05/6) to account for the six insular seed-based analyses, none of these five previously reported FC differences remained significant.

Figure 2.

The group differences in insula subregions rs-FC network. (A) Seeds within the left ventral anterior insula of patients with PDM showed increased rs-FC with the left IFG, left MCC and right IPL. (B) Seeds within the right ventral anterior insula showed increased rs-FC with both right ACC and right IPL.

Abbreviations: rs-FC, resting-state functional connectivity; PDM, primary dysmenorrhea; HCs, healthy controls; IFG, inferior frontal gyrus; IPL, inferior parietal lobule; MCC, midcingulate cortex; ACC, anterior cingulate cortex.

Table 2.

Brain Regions with Increased Seed-Based Rs-FC Values in Patients with PDM Compared to HCs

Seed	Contrast	Brain Region	Voxel Size	MNI Coordinates			Peak Z Score
				X	Y	Z
Left vAI	HC>PDM	None
	HC<PDM	L. Inferior Frontal Gyrus	63	−48	30	9	4.34
		L. Midcingulate Cortex	69	−6	15	42	4.36
		R. Inferior Parietal Lobule	61	57	−24	27	4.71
Right vAI	HC>PDM	None
	HC<PDM	R. Anterior Cingulate Cortex	79	3	24	24	4.40
		R. Inferior Parietal Lobule	60	57	−24	27	4.68

Abbreviations: HC, healthy control, PDM, primary dysmenorrhea; rs-FC, resting-state functional connectivity; MNI, montreal neurological institute; R, right; L, left; vAI, ventral anterior insula.

Correlation Analysis

Consistent with the symptom network findings highlighting the importance of sleep and affective symptoms, exploratory Spearman correlation analyses revealed nominal positive associations between right vAI -right ACC functional connectivity and PSQI scores (ρ = 0.21, uncorrected P = 0.026; Figure 3a) and SAS scores (ρ = 0.26, uncorrected P = 0.005; Figure 3b) in patients with PDM. However, neither association remained statistically significant after Benjamini–Hochberg FDR correction across the 15 symptom-connectivity correlation tests (FDR-adjusted P = 0.194 and 0.075, respectively). No associations survived FDR correction.

Figure 3.

Correlation between increased rs-FC network values and clinical indicators. (a) Association between right vAI - ACC functional connectivity and PSQI scores (Spearman’s ρ = 0.21, uncorrected P = 0.026). (b) Association between right vAI-ACC functional connectivity and SAS scores (Spearman’s ρ = 0.26, uncorrected P = 0.005). Neither association survived Benjamini–Hochberg false discovery rate correction across 15 symptom - connectivity correlation tests.

Abbreviations: vAI, ventral anterior insula; ACC, anterior cingulate cortex; PSQI, Pittsburgh Sleep Quality Index; SAS, Self-Rating Anxiety Scale; FDR, false discovery rate.

Discussion

In this study, we combined symptom network analysis with resting-state functional connectivity to characterize neural correlations of the multidimensional symptom profile in PDM. Symptom network analysis identified sleep disturbance and affective symptoms as central and bridging nodes within the clinical symptom network, highlighting the interrelationships among pain, sleep, and emotional domains. In parallel, imaging analyses revealed increased functional connectivity between the ventral anterior insula and cortical regions implicated in salience and cognitive-emotional processing in patients with PDM. Exploratory correlation analyses showed nominal positive associations between right vAI-ACC connectivity and sleep disturbance and anxiety, however, these associations were modest and did not survive false discovery rate correction for multiple comparisons. Together, these findings integrate symptom-level and network-level evidence and delineate an anterior insula–centered connectivity may be related to the co-occurrence of pain, sleep disturbance, and affective symptoms in PDM.

The insula is a key integrative cortical hub involved in interoception, salience detection, and the coordination of sensory, affective, and cognitive processes.³¹ Recent neuroimaging studies have demonstrated its pivotal role in pain processing and regulation.^32,33 Previous studies indicate that the insula is functionally heterogeneous and can be subdivided into ventral anterior, dorsal anterior, and posterior subregions, each contributing to distinct aspects of pain-related processing.³⁴ Specifically, the vAI has been linked to affective and motivational aspects of pain,³⁵ the dAI participates in salience detection and attentional control,^36,37 and the PI is more closely related to primary sensory representations of nociceptive input.³⁸ However, the involvement of insular subregions in chronic visceral pain conditions such as PDM remains incompletely characterized. In the present study, we identified enhanced functional connectivity between the vAI and key salience and emotion-related regions in patients with PDM, indicating a potential involvement of vAI-centered connectivity in menstrual pain and its associated affective and sleep disturbances.

Specifically, enhanced connectivity was observed between the vAI and anterior/medial cingulate cortex. The cingulate cortex is known to plays a central role in pain appraisal, affective motivational processing, and the integration of nociceptive information with emotional responses.^39,40 Consistent with these functional findings, previous structural and functional neuroimaging studies have reported cingulate abnormalities across chronic pelvic and visceral pain conditions. For example, Sawsan As-Sanie et al demonstrated reduced gray matter volume in the cingulate cortex in women with chronic pelvic pain,⁴¹ and Chang et al reported altered anterior cingulate activation in patients with irritable bowel syndrome.⁴² In the context of dysmenorrhea, abnormal interactions and altered network dynamics within the cingulate-insular-frontal network have also been observed during both painful and pain-free periods.⁴³ These observations are consistent with the enhanced vAI-ACC/MCC connectivity identified in the present study and together highlight insula-cingulate coupling as a network-level feature associated with pain and negative emotional symptoms in PDM.

We further observed enhanced connectivity between the left vAI and inferior frontal gyrus. A recent meta-analysis of musculoskeletal pain reported heightened insula-IFG connectivity and implicated IFG in cognitive-emotional evaluation and inhibitory control during pain processing.⁴⁴ Similar vAI-prefrontal intrinsic coupling has also been reported in migraine patients without aura, providing convergent anatomical and functional support for the vAI-IFG strengthening observed in PDM.¹⁴ In this context, the increased vAI-IFG coupling observed in PDM may reflect greater engagement of top-down regulatory processes during pain. In addition, our prior research identified abnormalities within the cognitive control network in PDM patients,²⁰ suggesting that enhanced vAI-IFG connectivity may be associated with compensatory engagement in response to pain-related emotional/attentional interference.

Chronic pain is frequently accompanied by multiple comorbid symptoms, with negative emotional states and sleep disturbances being particularly prevalent.⁴⁵ Consistent with this clinical profile, our symptom network analysis highlighted close interrelations among pain, sleep, and affective symptoms. Our centrality analysis identified somatic depressive complaints and psychomotor agitation as highly influential nodes. Moreover, daytime dysfunction emerged as a key bridging symptom linking sleep related and affective domains, highlighting its potential role in transmitting the impact of nocturnal sleep impairment to emotional distress. Previous research has demonstrated a bidirectional relationship between pain and sleep, whereby poor sleep quality exacerbates pain and pain, in turn, disrupts sleep.^46,47 Chronic pain is also commonly accompanied by comorbid anxiety and depression.^48,49 The network topology observed in the present study suggests that pain may interact with sleep and emotional symptoms through shared pathways of heightened somatic vigilance and dysregulated arousal, rather than via isolated symptom-specific mechanisms.

Finally, exploratory correlation analyses showed nominal positive associations between right vAI-ACC functional connectivity and both sleep disturbance severity and anxiety levels in patients with PDM. However, these associations were modest and did not survive FDR correction for multiple comparisons. Previous studies have demonstrated bidirectional interactions between pain and sleep quality,^50,51 as well as between pain and negative emotional states, potentially in association with alterations in salience network connectivity.^52,53 These observations are consistent with the pattern of associations identified in the present PDM study. Furthermore, a recent review has proposed insular abnormalities across chronic pain conditions, highlighting the insula as a central hub for pain, emotion, and viscerosensory integration.⁵⁴ Together, these findings provide trans-diagnostic theoretical support for the co-occurrence of pain, sleep disturbance, and affective symptoms observed in PDM.

Limitations

Several limitations of the present study should be acknowledged. First, the cross-sectional design precludes causal inference regarding the associations among insular subregional functional connectivity, symptom network properties, and clinical manifestations of primary dysmenorrhea. Second, symptom network analyses were derived from self-reported measures collected at a single time point and thus do not allow examination of temporal trajectories or directional relationships among symptoms. Third, the seed-based, hypothesis-driven imaging approach focused on predefined insular subregions. While this strategy was guided by prior theoretical and empirical evidence, it may not capture additional network-level alterations that could emerge from fully data-driven analyses. Finally, the relatively homogeneous sample of young women recruited from a single center may limit the generalizability of the findings. Moreover, although the CS coefficient of 0.29 exceeded the minimum acceptable threshold of 0.25, it indicated only modest centrality stability. Therefore, further research is needed in future independent and diverse large samples.

Conclusion

This study identified increased anterior insula functional connectivity in PDM and characterized symptom relationships using symptom network analysis. Modest associations between right vAI-ACC connectivity and sleep disturbance and anxiety did not survive FDR correction and should therefore be interpreted as exploratory. In summary, symptom networks and neuroimaging findings provide complementary perspectives for the multidimensional symptom features of PDM, but independent repeatability and longitudinal studies are still needed to validate these findings and clarify their clinical relevance.

Funding Statement

The study was supported by the National Key R&D Program Young Scientist Project (SQ2025YFC3500670), the China National Postdoctoral Program for Innovative Talent (No. BX20190046), the Sichuan Provincial Science and Technology Department project in China (2024NSFSC1850, 2025ZNSFSC1849, 2026NSFSCZY0146), the Tianfu Qingcheng Plan Youth Science and Technology Talent Program, The Rongcheng Talent Program Innovative Young Talent Project, the National Natural Science Foundation of China (8230153988).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare no conflicts of interest.