Executive Function Heterogeneity in Pregnant Women and its Link to Differential Depression-Rumination Network Analysis: A Cross-Sectional Study

Executive Function Heterogeneity in Pregnant Women and its Link to Differential Depression-Rumination Network Analysis: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Executive Function Heterogeneity in Pregnant Women and its Link to Differential Depression-Rumination Network Analysis: A Cross-Sectional Study jingjing liu, xingxing Liu, Jiaqi Li, changyi Yan, haiyan Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9220765/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Pregnant women exhibit changes in executive function, which may modulate their psychological vulnerability. This study aimed to identify distinct executive function profiles among pregnant women and examine whether the network structure connecting depression and rumination symptoms differed across these profiles. Methods A sample of 1,770 pregnant women recruited from five tertiary referral hospitals in Guangdong, China, was evaluated using the Dysexecutive Questionnaire (DEX), Edinburgh Postnatal Depression Scale (EPDS) and Rumination Response Scale (RSS). Latent Profile analysis in Mplus 8.3 classified women by DEXs patterns, and network analysis was conducted using the bootnet package in R 4.3.2 to visualize complex interactions between Depression and Rumination by constructing network diagrams. Results Latent Profiles analysis identified three executive function profiles among pregnant women: Low-difficulty (40.3%), Moderate-difficulty (43.5%), and High-difficulty (16.1%). Network analysis revealed stronger overall connections between Depression and Rumination symptoms in the Moderate-difficulty profiles, with RSS2(Brooding) acted as a bridge symptom. EPDS8(sad or miserable) consistently emerged as the most central symptom. The association among Depression and Rumination was the strongest in Moderate -difficulties profiles. Conclusion The findings demonstrate significant heterogeneity in executive function among pregnant women, which is associated with differential patterns of depression-rumination symptom interactions. While core affective distress remains stable, the pathways linking cognitive and emotional symptoms differ by executive function profile. Clinically, interventions should be tailored: targeting brooding may be particularly crucial for women with moderate to high difficulty, whereas enhancing positive affect may benefit those with low difficulty. Pregnant Women Executive Function Depression Rumination Latent profile analysis Network comparison test Figures Figure 1 Figure 2 Figure 3 1. Introduction Pregnancy constitutes a critical neurodevelopmental window marked by profound hormonal, physiological, and psychological alterations. Evidence indicates these changes selectively impact cognitive domains, notably memory consolidation[ 1 ], attentional allocation[ 2 ], and executive function (EF)[ 3 ]. EF supports goal-directed behavior and includes inhibitory control, intentionality, knowledge-action dissociation, resistance, and social-behavioral modulation[ 4 ]. During pregnancy, EF is influenced by multiple factors[ 5 ]. Hormonal fluctuations affect brain activity[ 6 ], physical discomfort increase mental load[ 7 ], and psychosocial stressors[ 8 ] compound these effects. However, findings on cognitive changes in pregnant women remain inconsistent[ 3 ].This is largely because most studies center on population average effects, overlooking interindividual differences. Furthermore, few studies have established a link between executive function (EF) and emotional symptoms, leaving the characteristics of emotional regulation across different EF levels largely unelucidated. In the context of perinatal mental health, rumination involves a repetitive focus on negative emotions, adverse events, and personal experiences. This construct comprises three core dimensions [ 9 ], specifically depression-related rumination, brooding, and reflection. It is a common maladaptive cognitive-emotional response during pregnancy[ 10 ]. For pregnant women, depressive symptoms may trigger rumination, as negative emotions drive them to fixate on unpleasant feelings related to pregnancy or motherhood[ 11 ]. Conversely, rumination worsens depressive symptoms by inhibiting positive processing and amplifying negative experiences[ 12 ]. Thus, these constructs share a dynamic and reciprocal association rather than a unidirectional causal link[ 13 ]. Although the link between prenatal depression and rumination is well-documented, most studies explore their factors in isolation using traditional correlation analyses. To address this gap, the present study uses the network analysis to map the specific connection patterns within the depression-rumination symptom complex. In the context of prenatal mental health, executive function serves as a core mechanism of cognitive control[ 14 ]. It regulates the depression-rumination relationship by inhibiting negative cognitive fixation, managing attentional allocation, and disrupting ruminative cycles[ 14 ]. Impaired EF reduces the ability to inhibit rumination and thereby exacerbates the persistence and progression of depressive symptoms. This study hypothesize that the depression-rumination network varies according to individual differences in EF. Pregnant women with varying EF levels possess divergent cognitive control capacities, which may result in subtype-specific variations in network connectivity, central symptom nodes, and bridge symptoms. The current study addresses these critical gaps through an integrated methodological design that combines Latent Profile Analysis (LPA) -derived subgroup classification with contemporaneous symptom network analysis. Specifically, this study advances the field in three key ways. First, by applying LPA to identify latent heterogeneity in executive function among pregnant women, this study establish distinct cognitive profiles as the foundation for subsequent analyses. Second, by conducting contemporaneous network analysis of depression and rumination symptoms within each executive function subgroup, assessed via the Rumination Response Scale(RSS) and Edinburgh Postnatal Depression Scale (EPDS), examined whether distinct profiles exhibit differential symptom network architectures. This approach directly addresses the limitation of homogeneous sample assumptions in prior research and enables the detection of profile-specific central and bridge symptoms that would be invisible in aggregate analyses. Third, by comparing emotional states and network structures across subtypes, this study elucidate the dynamic interplay between executive function and mood symptomatology during pregnancy. 2. Materials and methods 2.1. Participants and procedures This multi-center cross-sectional study was conducted between June 2020 and January 2021 across five tertiary referral hospitals located in Guangdong, the most populous province in China. Participants were recruited from obstetrics departments in Guangzhou, Shenzhen, and Zhongshan. Due to differences in patient flow among the hospitals, the sample distribution varied across the five institutions, with a total sample size of 1770, distributed as 353, 60, 224, 702, and 431 respectively. Eligibility criteria included pregnant women between 13 and 40 weeks of gestation, aged 18 to 49 years, of Chinese nationality, and who provided informed consent. Exclusion criteria encompassed individuals with a personal or family history of psychiatric disorders, as well as those with impairments in hearing or speech. The study employed a multi-center cross-sectional design adhering to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. All investigators underwent standardized training on administering the questionnaire, and were responsible for informing participants about the study’s objectives, confidentiality protocols, and instructions for completing the questionnaire. Ethical approval was obtained from the Ethics Committee of Nanfang Hospital, Southern Medical University, Guangdong, China (Approval number: NFEC-2020-022), and written informed consent was secured from all eligible participants. 2.2. Measurement 2.2.1. Demographic information A portion of the questionnaire was developed based on a comprehensive review of the existing literature and subsequently evaluated by specialists in gynecology and epidemiology. The questionnaire encompassed the following domains: (1) social demographic characteristics, including age, occupation, educational attainment, monthly income, household registration status, and single-child family background; (2) obstetric information, such as parity, pregnancy planning status, and pregnancy-related medical conditions; and (3) social factors, including the quality of marital relationships and interactions with parents-in-law. 2.2.2. Executive Function The Chinese version of Dysexecutive Questionnaire (DEX) [ 15 ] was used to assess executive function, with a total of 20 items. All items are scored on a 5-Likert scale (0–4) and total score ranges from 0 to 80,was designed to estimate five areas of change: Inhibition, Intentionally, Dissociation of Knowing and Doing, Resistance and Social-Behavioral Modulation[ 16 ], with higher scores indicating more severe executive function[ 17 ]. The Cronbach’s alpha was 0.936 in the present sample. 2.2.3. Prenatal Depression The severity of depressive symptoms experienced during the preceding week was measured via the EPDS, a validated screening tool originally developed[ 18 ]. This scale consists of 10 items, with each item scoring 0–3 points and a total score ranging from 0 to 30 points. In this study, the Chinese version of the EPDS introduced by Lee et al. was used[ 19 ]. The reliability and validity of the Chinese version, and the applicability of its clinical cut-off scores for Chinese pregnant women, have been confirmed [ 20 ], the Cronbach’s alpha in the current study was 0.861. 2.2.4. Rumination The Rumination Response Scale(RSS)[ 9 ] was used to measure 2 dimensions of rumination. The scale consists of a total of 22 items, the depressed-symptom rumination dimension consisted of 12 items, the brooding dimension consisted of 5 items and the reflection dimension consisted of 5 items. Previous studies have demonstrated that this scale has reliable reliability and validity and is suitable for prenatal women[ 21 ]. All items are scored on a 4-Likert (1–4) scale and total score ranges from 22 to 88. The Cronbach’s alpha in the current study was 0.945. 2.3. Statistical analysis 2.3.1. Descriptive analyses Descriptive analyses were used to describe the socio-demographic and psychological characteristics. Participants with missing data on any of the key study variables (DEX, EPDS, RSS) were excluded from the analyses, resulting in a final analytic sample of 1,770 complete cases. Correlational analyses were used to explore correlations between continuous variables, and an Analysis of Variance (ANOVA) was used to compare differences in scores on the EPDS, RSS and the three dimensions of the DEX between the demographic variables. All analyses were performed using SPSS 25.0 software (Version 25 for Windows; IBM Corp. Released in 2016). 2.3.2. Common methods deviation test Self-report data collected in a uniform context may introduce common method bias[ 22 ]. This study applied Harman’s single-factor test, subjecting all items to unrotated exploratory factor analysis (EFA). Extraction of two or more factors with the first factor explaining ≤ 40% of variance indicates non-significant common method bias. 2.3.3. Latent profile analysis and factor analysis To delineate potential symptom profiles of executive function among prenatal women, this study employed latent profile analysis (LPA) using Mplus version 8.3 (Muthén & Muthén, Los Angeles, CA, USA). The analysis utilized individual item scores from measures of executive dysfunction scale as observed indicators. Beginning with a one-class model, the number of latent profiles was incrementally increased. The optimal number of profiles was determined by evaluating multiple model fit indices, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted BIC (aBIC), entropy, the Lo-Mendell-Rubin likelihood ratio test (LMR), and the bootstrap likelihood ratio test (BLRT). Lower values of AIC, BIC, and aBIC signify improved model fit, whereas entropy values approaching 1 indicate greater classification accuracy. Notably, an entropy value exceeding 0.80 corresponds to classification accuracy above 90% [ 23 ]. Furthermore, statistically significant p-values (p < 0.05) for the LMR and BLRT tests suggest that the K-class model accounts for significantly more variance than the (K-1)-class model[ 24 ]. 2.3.4. Network analysis To characterize the principal symptom clusters across distinct latent profiles of executive function, a concurrent symptom network analysis was performed incorporating 11 key variables: the items of EPDS, Depression-related Rumination, Brooding. This analysis was conducted using R version 4.4.2. Initially, the estimateNetwork function from the bootnet package[ 25 ]was employed to estimate the symptom networks. Subsequently, network regularization was applied via the least absolute shrinkage and selection operator (LASSO) method[ 26 ], which computes partial correlations and sets weaker or potentially spurious edge coefficients to zero, thereby reducing false positive connections[ 25 ]. The extended Bayesian information criterion (EBIC) was utilized to select the optimal model[ 25 ]. The stability of centrality indices was then evaluated through resampling procedures. In this study, expected influence(EI) was selected as the centrality metric, as it accounts for both positive and negative associations within the network, offering a more nuanced measure of a node’s overall influence compared to traditional centrality metrics [ 27 ]. A central variable is a node that has many correlations with other nodes in the network model and therefore has a greater impact on the network. The colour of the edge indicates the direction of the association. A green line indicates a positive association, while a red line indicates a negative correlation. Nodes are color-coded according to their originating questionnaire: orange for EPDS, light blue for the RSS. In the current study, four network models were generated. 2.3.5. Network comparison test To investigate potential differences in network structures associated with Executive Dysfunction Scale, the study utilized the network comparison test functionality provided by the NetworkComparisonTest package. Employing permutation-based methodologies, the study conducted comparative analyses of the Prenatal Depression, Rumination networks across groups at both global and local levels. The global analyses encompassed evaluations of global strength invariance and overall network structure invariance, while the local analyses focused on assessing invariance in node expected influence and edge connection strength. To control for the risk of Type I errors due to multiple comparisons, p-values were adjusted using the Holm-Bonferroni correction method[ 28 ]. 3. Results 3.1. Descriptive analyses In the present sample, the mean maternal age was 29.48 ± 4.30 years (range = 20–45). Among the participants, 96.6% (n = 1709) were married, and 51.41% (n = 910) were primiparous. Regarding educational attainment, 10.16% (n = 182) held a master’s degree or higher, while 60.67% (n = 1074) were employed full-time. Furthermore, 51.41% (n = 573) reported unintended pregnancies, 1.69% (n = 30) had a history of smoking, and 4.23% (n = 75) reported alcohol consumption. A summary of these demographic characteristics is provided in Table 1 . Table 1 Demographic and clinical characteristics of the sample (n = 1770). Variables Total(n = 1770, 100%) Mean ± SD/n(%) Low-Difficulty Profile(n = 713, 40.3%)Mean ± SD/n(%)2 Moderate-Difficulty Profile(n = 773, 43.5%)Mean ± SD/n(%)1 High-Difficulty Profile(n = 284, 16.1%)Mean ± SD/n(%)3 X 2 /F Age(years) 29.48 ± 4.30 26.92 ± 2.04 25.30 ± 2.30 21.95 ± 5.12 0.602 Occupation 0.367 Full-time 1074(60.67%) 433(62.13%) 469(60.67%) 172(60.56%) Part-time 71(4.01%) 33(4.62%) 31(4.01%) 7(2.46%) Unemployed 378(21.35%) 150(21.03%) 161(20.82%) 67(23.59%) Other 247(13.95%) 85(11.92%) 94(12.16%) 35(12.32%) Marital status 0.209 Married 1709(96.55%) 691(96.91%) 744(96.24%) 274(96.74%) Single 61(3.44%) 22(3.08%) 29(3.75%) 10(3.52%) Education level 0.217 High school or below 121(6.83%) 45(6.31%) 47(6.08%) 21(7.39%) Secondary Vocational School 619(34.97%) 257(36.04%) 265(34.28%) 97(34.15%) Associate Degree 478(27.00%) 182(25.52%) 213(27.55%) 83(29.22%) Bachelor’s Degree 370(20.90%) 146(20.47%) 164(21.21%) 60(21.12%) Master’s degree or above 182(10.16%) 80(11.22%) 81(10.47%) 21(7.39%) Smoking status 1.006 YES 30(1.69%) 7(0.98%) 4(0.51%) 1(0.35%) Alcohol consumption 0.531 YES 75(4.23%) 19(2.66%) 23(2.97%) 4(1.40%) Parity 0.281 0 910(51.41%) 380(53.29%) 366(47.34%) 138(48.59%) > 1 860(48.58) 324(45.44%) 396(51.22%) 140(49.29%) Unintended pregnancy 0.030* YES 573(32.37) 215(30.15%) 258(33.37%) 100(35.21%) History of adverse obstetric events 0.201 YES 252(14.23) 105(14.72%) 117(15.13%) 29(10.21%) Fetal abnormalities 0.367 YES 146(8.24) 27(3.78%) 25(3.23%) 5(1.75%) DEX Score 16.36 ± 11.29 12.82 ± 10.43 17.98 ± 11.14 20.84 ± 11.21 70.504** a Statistic was based on one-way analysis of variance (ANOVA). *p < 0.05; **p < 0.001;SD, standard deviation. DEX, the Chinese version of Dysexecutive Questionnaire The analysis revealed that the mean DEX score was 16.36 ± 11.29. The mean prenatal depression score was 6.71 ± 4.84. Descriptive statistics, including means and standard deviations for each variable and factor, are detailed in Table 2 . The skewness values for the DEX factors ranged from 0.437 to 0.597, and kurtosis values ranged from − 0.17 to 0.46, suggesting that the data largely conformed to the assumptions of normality and thus were appropriate for subsequent analyses. Due to the non-normal distribution of the data, the relationships among the psychological variables were assessed using Kendall’s tau-b correlation coefficients presented in Table 3 , indicated consistently positive correlations. Table 2 Descriptive statistics and analysis of variance. Variable Mean ± SD Skewness Kurtosis DEX 16.36 ± 11.29 0.597 -0.078 Inhibition 5.70 ± 3.66 0.437 -0.145 Intentionality 3.84 ± 3.27 0.712 -0.083 Dissociation of Knowing 3.03 ± 2.59 0.713 -0.002 Resistance 2.07 ± 1.91 0.906 0.460 Social-Behavioral Modulation 1.71 ± 1.46 0.580 -0.172 EPDS 6.71 ± 4.84 0.574 0.06 RSS 32.94 ± 9.22 0.844 0.608 Depression-related Rumination 17.29 ± 5.10 1.028 1.121 Brooding 8.10 ± 2.43 0.687 0.534 Reflection 7.56 ± 2.35 0.803 0.296 Table 3 Correlations analyses for each psychological variable among pregnant women(n = 1770). Variable DEX Inhibition Intentionality Dissociation of Knowing Resistance Social-Behavioral Modulation EPDS RSS Depression- related Rumination Brooding Reflection DEX 1 Inhibition 0.741** 1 Intentionality 0.772** 0.583** 1 Dissociation of Knowing 0.744** 0.549** 0.641** 1 Resistance 0.692** 0.522** 0.563** 0.607** 1 Social- Behavioral Modulation 0.654** 0.489** 0.563** 0.552** 0.631** 1 EPDS 0.320** 0.277** 0.302** 0.315** 0.285** 0.267** 1 RSS 0.403** 0.343** 0.377** 0.376** 0.367** 0.335** 0.430** 1 Depression- related Rumination 0.428** 0.362** 0.407** 0.399** 0.389** 0.335** 0.449** 0.871** 1 Brooding 0.366** 0.318** 0.341** 0.340** 0.335** 0.315** 0.397** 0.806** 0.698** 1 Reflection 0.325** 0.282** 0.297** 0.314** 0.308** 0.271** 0.359** 0.759** 0.644** 0.641** 1 *p < 0.05; **p < 0.001 3.2. Common method deviation test The study employed Harman’s single-factor test to assess common method bias. The exploratory factor analysis (EFA) revealed twelve factors with eigenvalues greater than one, with the largest factor accounting for 26.308% of the total variance, which is below the critical threshold of 40%[ 22 ]. Consequently, the associations among the variables in this research are considered to be minimally influenced by common method bias. 3.3. Classification of the latent profile In this study, 20 items from the Chinese version of Dysexecutive Questionnaire for Pregnant women were used as explicit indicators. The present study conducted sequential models ranging from one to five latent profiles were evaluated. Detailed model fit statistics for each profile solution are presented in Table 3 . As the number of profiles increased from one to five, AIC, BIC, and aBIC values consistently decreased, with the highest entropy observed in the two-profiles model. The five-profile model was rejected due to non-significant LMR p-values ( p > 0.05). The four-profile model was also deemed sub-optimal because certain subgroups represented a minimal proportion of the sample and lacked clinical interpretability. Consequently, the three-profile model was selected as the optimal solution. Table 4 displays the classification probability matrix for the three latent profiles, with average posterior probabilities ranging from 95.9% to 96.9%, all exceeding the 90% threshold[ 23 ], thereby supporting the robustness of the classification. Table 4 Model fit indices for latent profile analysis (n = 1770). Class AIC BIC aBIC Entropy LMR( p ) BLRT( P ) Classification probabilites 1 113397.392 113627.499 113494.068 - - - - 2 102100.067 102450.706 102247.383 0.930 < 0.001 < 0.001 0.599/0.401 3 98157.434 98628.605 98355.390 0.920 < 0.001 < 0.001 0.435/0.403/0.161 4 97089.408 97681.112 97338.004 0.883 < 0.001 < 0.001 0.289/0.329/0.268/0.114 5 96613.504 97325.740 96912.740 0.868 0.6702 < 0.001 0.283/0.267/0.250/0.088/0.111 Note.AIC, Akaike’s Information Criterion; BIC, Bayesian Information Criterion; aBIC, adjusted Bayesian information Criterion; Entropy, Information Entropy; LMR, Lo-Mendell-Rubin Likelihood Ratio Test; BLRT, Bootstrap Likelihood Ratio Test. The three-class model represents the best-fitting latent profile solution. Figure 1 illustrates the mean DEX scores across the three profiles. Profile 3, comprising 284 participants (16.1%), demonstrated consistently low DEX scores and was designated the “High-Difficulty Profile.” Profile 2 included 713 participants (40.3%) with moderate DEX scores and was labeled the “Low-Difficulty Profile.” Profile 1 consisted of 773 participants (43.5%) who exhibited significantly elevated DEX scores and was termed the “Moderate-Difficulty Profile.” 3.4. Symptom network analysis across the latent profiles The symptom network of the entire sample and the symptom network structure of the three subgroups is shown in Fig. 2 . Table S1 -S4 present the expected influence values of the symptom network for the entire sample and three potential profiles. In the entire sample, the three nodes with the highest EI values were EPDS8 (sad or miserable)(r s =4.405),EPDS4(anxious or worried)(r s =3.985), EPDS7 (sleep difficulties) (r s =3.972), The two strongest edges were between EPDS1-EPDS2 (able to laugh–look forward with enjoyment to things) (weight = 0.710) and EPDS4–EPDS5 (anxious or worried–scared or panicky) (weight = 0.577). In the High-Difficulty Profile (n = 284), the three nodes with the highest EI values were EPDS8(sad or miserable) (r s =4.372), EPDS7(sleep difficulties)(r s =4.118), EPDS4 (anxious or worried)(r s =3.853), The two strongest edges were between EPDS1–EPDS2 (able to laugh–look forward with enjoyment to things) (weight = 0.72) and RSS1-RSS2 (Depression-related Rumination – Brooding) (weight = 0.581). In the Moderate -Difficulty Profile (n = 773), the three nodes with the highest EI values were EPDS8 (sad or miserable) (r s =4.288),EPDS7(sleep difficulties)(r s =3.951), EPDS5 (scared or panicky)(r s =3.909), The two strongest edges were between EPDS1–EPDS2 (able to laugh–look forward with enjoyment to things) (weight = 0.637) and EPDS4-EPDS5 (anxious or worried–scared or panicky) (weight = 0.547). In the Low-Difficulty Profile (n = 713), the three nodes with the highest EI values were EPDS8 (sad or miserable) (r s =4.157), EPDS3(blaming myself)(r s =3.692), EPDS6 (things getting to me)(r s =3.638) and EPDS7 (sleep difficulties)(r s =3.638), the two strongest edges were between EPDS1–EPDS2 (able to laugh–look forward with enjoyment to things) (weight = 0.721) and EPDS8-EPDS9(Crying–Self-harming) (weight = 0.595). The global strength, defined as the mean absolute edge weight, was 0.070–0.075 for the respective networks, indicating a moderate level of overall connectivity is shown in Fig. 3 . 3.5. Network comparisons test among the four profiles Network Comparison Tests (NCT) were performed to investigate differences in network structure and global strength across the four models. This analysis assessed both global and local variations among the networks. The global strength invariance test indicated no significant differences in overall network strength among the groups ( p > 0.05), suggesting comparable levels of overall connectivity. Similarly, the edge strength invariance test revealed no significant differences in the strength of individual connections across the four networks. These findings imply that, despite the distinct characteristics of each group, the fundamental network architecture remains stable and consistent. Detailed results are provided in Table 5 . Table 5 The Characteristics of Network Comparison Test Characteristic The entire sample(n = 1770, 100%) Moderate-Difficulty Profile(n = 773, 43.5%) Low-Difficulty Profile(n = 713, 40.3%) High-Difficulty Profile(n = 284, 16.1%) Global Strength 0.074 0.075 0.075 0.070 Network density 0.577 0.603 0.641 0.538 Strongest Edge EPDS1-EPDS2 (weight = 0.710) EPDS1–EPDS2 (weight = 0.637) EPDS1–EPDS2 (weight = 0.721) EPDS1–EPDS2 (weight = 0.720) Bridge Symptoms EPDS1(B s =0.041) RSS2(B s =0.0709) RSS2(B s =0.133) RSS2(B s =0.095) Central Symptoms EPDS8(r s =4.405) EPDS8 (r s =4.288) EPDS8(r s =4.157) EPDS8(r s =4.372) 4.Discussion The key findings of this study are threefold. First, this study identified a heterogeneous distribution of executive function within the sample: the majority of pregnant women (40.3%) were classified into a Low-Difficulty Profile, the majority of pregnant women (43.5%) were classified into a Moderate-Difficulty Profile, while the remainder (16.1%) fell into a High-Difficulty Profile. Second, despite these differing backgrounds, all subgroups shared a common central of severe symptoms, most prominently “Sad or miserable”. Third, beyond this commonality, each subgroup exhibited a unique symptom network structure, with specific central and bridging symptoms, and the Moderate-Difficulty Profiles and Low-Difficulty Profiles demonstrated a significantly greater overall connectivity global strength within and among the Depression and Rumination networks. 4.1. Executive Function Profiles: Heterogeneity and Clinical Relevance Latent profile analysis (LPA) identified three distinct subgroups of pregnant women based on their scores on the DEX: Low-difficulty (40.3%), Moderate-difficulty (43.5%), and High-difficulty (16.1%). Consistent with prior research, these findings confirm the heterogeneity in executive function among pregnant women[ 3 ]. In this sample, the overall Executive Function were relatively low (16.36 ± 11.29), yet the High-Difficulty Profile exhibited significantly elevated scores (20.84 ± 11.21) compared to population norms [ 29 , 30 ]. The observed heterogeneity may arise from several interacting sources. Individual differences in physiological and hormonal regulation, such as variable sensitivity to estrogen and cortisol fluctuations,could differentially impact prefrontal functioning across pregnant women[ 6 ]. Psychosocial factors, including education, social support, prenatal stress, and sleep quality, further modulate cognitive performance in a non-uniform manner[ 31 , 32 ]. Moreover, executive function comprises distinct subcomponents, inhibition, shifting, updating, and deficits may be more pronounced in specific domains, a nuance that aggregate scores often mask[ 16 , 17 ]. Notably, the High-Difficulty Profile, though proportionally smaller, likely represents a clinically significant subgroup characterized by cumulative risk where multiple vulnerabilities, e.g., high stress, low cognitive reserve, poor sleep, converge to amplify executive challenges[ 1 , 33 ]. Although identified in aging populations[ 34 ], this pattern of a high-difficulty subgroup may similarly exist in pregnant women, as our findings suggest underscores the importance of targeted screening and intervention for this minority, even within a generally resilient maternal population. The LPA further revealed that a distinct subgroup of pregnant women exhibited lower levels of executive function relative to the overall population. This suggests that only a subset of women experience altered executive function during pregnancy, as indicated by the presence of this subgroup with notably reduced executive performance. Nevertheless, the absence of longitudinal data or comparisons with control groups precludes definitive conclusions regarding executive function impairment during pregnancy based solely on these findings. 4.2. Network Structure: A Stable Core with Dynamic Bridges The network analysis revealed a critical pattern: while the affective symptom of “feeling sad or miserable” was the most central and stable node across all groups, the key bridge connecting depression to rumination shifted based on executive function. The persistent centrality of core sadness suggests it acts as a foundational hub in perinatal distress. In contrast, the primary bridging symptom changed from a general loss of positive affect in the overall sample to maladaptive brooding within each executive function subgroup. This indicates that although profound sadness may be a common endpoint, the pathways maintaining distress differ: for cognitively resilient women, low positive affect may be the key link, whereas for those under higher cognitive load, rigid rumination becomes the critical bridge. “Sadness or misery” emerged as the most central node across the entire sample and all three subgroups, showing the highest expected influence values. Network comparison tests confirmed this stability. These findings suggest that sad affect serves as a core organizing hub within perinatal depression-rumination networks. This pattern aligns with Fuchshuber affective centrality hypothesis[ 35 ], extending it to the context of perinatal mental health.. Consequently, directly targeting this core emotional state should be prioritized as a universal intervention strategy in perinatal mental health, regardless of cognitive profile. Early identification and treatment of this central symptom through cognitive restructuring or behavioral activation may deliver the broadest clinical benefits by disrupting the activation of wider symptom clusters[ 36 ]. In contrast to this stable affective core, the key bridging symptoms connecting the depression and rumination clusters exhibited significant variation dependent on executive function profiles. In the overall sample, diminished positive affect, as measured by “able to laugh”, served as the primary bridge. However, within each of the three identified subgroups, the maladaptive cognitive process of ruminative brooding, captured by “brooding”, became the predominant bridging symptom, with bridge strength values varying across the subgroups. This systematic shift indicates that “Sadness or misery” constitutes a common affective core, the primary pathways for the maintenance and propagation of distress differ fundamentally based on cognitive resource availability. For women with relatively intact executive resources, dysregulation may be primarily bridged by disruptions in the motivation and reward systems[ 37 ],although this mechanism requires further validation in pregnant populations. In contrast, for women experiencing higher cognitive load, a rigid, self-focused ruminative cycle becomes the critical link that tightly couples negative affect with cognitive processes[ 38 ]. This distinction carries substantial theoretical and clinical implications. Theoretically, it demonstrates that the foundational architecture of emotional suffering remains stably anchored to a core affective state, even as the specific interactive pathways between symptom clusters dynamically reorganize in response to varying cognitive demands. Clinically, it advocates for a dual-target intervention strategy. Universal efforts must prioritize alleviating the core affective state of sadness. Simultaneously, personalized intervention pathways should be selected based on an individual’s executive function profile: augmenting positive affect and behavioral engagement may be most effective for the Low-difficulty profile[ 39 ], whereas directly targeting and dismantling the ruminative process via metacognitive or cognitive flexibility training is a necessary first step for those in the Moderate- and High-difficulty profiles[ 40 , 41 ]. Furthermore, monitoring brooding levels in pregnant women reporting cognitive difficulties may provide superior early warning value for identifying a transition towards a more entrenched and treatment-resistant pathological state. 4.3. Implications This study yields clear implications for clinical practice and research. Theoretically, our findings refine models of perinatal distress by demonstrating a stable affective core coupled with profile-specific symptom pathways. This underscores that pregnancy-related cognitive changes are heterogeneous and directly shape the architecture of emotional suffering. Clinically, the results advocate for a stratified intervention model. Routine prenatal care should integrate brief screening for executive difficulties to identify high-risk women, particularly those in the High-Difficulty subgroup. For this group, initial treatment should prioritize targeting maladaptive rumination, e.g., with metacognitive therapy, to disrupt the core maintaining mechanism. For women with lower cognitive load, interventions can effectively focus on enhancing positive affect and behavioral engagement. Future research should employ longitudinal designs in pregnant cohorts to test the predictive value of these network features for clinical outcomes. Incorporating objective cognitive tasks and multi-modal assessment will further elucidate the biobehavioral mechanisms underlying these distinct profiles and help translate these insights into personalized preventive strategies. 4.4. Limitations This study had several limitations. First, the cross-sectional design prevents longitudinal tracking of symptom networks, offering no insight into how connections evolve over time. Future longitudinal studies using panel network analyses are needed to model these dynamic processes. Second, the use of convenience sampling, while practical for initial exploration, may limit the generalizability of our findings. Future studies should employ randomized or stratified probability sampling designs to ensure demographic representativeness and enhance the external validity of results across diverse populations. Third, the ratings used in this study were self-reported and may have been subject to recall bias and social desirability. Future research could incorporate objective measures or triangulate self-reports with interviews or observational methods to mitigate subjective reporting biases. 5. Conclusions In summary, pregnant women with different levels of daily executive function exhibited both similarities and unique features in their depression-rumination symptom networks “sad or miserable” served as the central symptom of Depression and Rumination among network groups. “able to laugh” served as the bridge symptom of Depression and Rumination in entire network groups.in three subgroups, “brooding” emerged as the main bridge symptom, with varying strengths. The association among Depression and Rumination was the strongest in Moderate -difficulties profiles. Abbreviations EF Executive Function DEX The Dysexecutive Questionnaire EPDS The Edinburgh Postnatal Depression Scale RSS The Rumination Response Scale LPA The Latent Profile analysis AIC Akaike Information Criterion BIC Bayesian Information Criterion aBIC adjusted BIC LMR Lo-Mendell-Rubin likelihood ratio test BLRT bootstrap likelihood ratio test LASSO least absolute shrinkage and selection operator EBIC extended Bayesian information criterion EI expected influence Declarations Ethical approval This study was reviewed and approved by the Ethics Committee of Southern Medical University (No.NFEC-2020-022). All procedures involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study also strictly adhered to the Chinese “Measures for the Ethical Review of Biomedical Research Involving Humans” and other relevant regulations. The participants provided their written informed consent to participate in this study. Funding The current study was supported by the National Natural Science Foundation of China (72574096 and 72274090), the Guangdong Province Undergraduate Teaching Quality and Teaching Reform Project (Yue Jiao Gao Han [2024] No. 30), the Guangdong Provincial Education Science Planning Project (2024GXJK321), and the Guangdong Province Graduate Education Innovation Plan Project (2025KCJS-022). Undergraduate Innovation and Entrepreneurship Training Program (S202412121164, S20251121143). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data, in the writing of the manuscript; or in the decision to publish the results. Consent to Publish declaration: Not applicable Author Contribution declaration Jingjing Liu: Conceptualisation, Investigation, Data curation, Formal analysis, Writing-original draft, and Project administration. Xingxing Liu: Investigation, Data curation, Formal analysis, Writing-original draft. Jiaqi Li: Conceptualisation, Investigation, Formal analysis, Writing- original draft, and Visualisation. Changyi Yan&Haiyan Liu: Investigation, Methodology, and Data curation. Yu Chen: Conceptualisation, Methodology, Project administration, Resources, Supervision, Writing-review & editing, and Funding acquisition. All authors approved the final manuscript for submission. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations of competing interest The authors declare that they have no competing interest. References Grattan DR, Ladyman SR. Neurophysiological and cognitive changes in pregnancy. Handb Clin Neurol. 2020;171:25–55. https://doi.org/10.1016/B978-0-444-64239-4.00002-3 . Raz S. Behavioral and neural correlates of cognitive-affective function during late pregnancy: an Event-Related Potentials study. Behav Brain Res. 2014;267:17–25. https://doi.org/10.1016/j.bbr.2014.03.021 . Younis J, Bleibel M, Masri JE, Ismail A, Abou–Abbas L. Exploring the influence of pregnancy on cognitive function in women: a systematic review. BMC Pregnancy Childbirth. 2025;25:88. https://doi.org/10.1186/s12884-025-07181-3 . Chan RCK. Dysexecutive symptoms among a non-clinical sample: A study with the use of the Dysexecutive Questionnaire. Br J Psychol. 2001;92:551–65. https://doi.org/10.1348/000712601162338 . Iranshahi S, Arab M, Arabgol F, Shekarriz-Foumani R, Nouri N, Ghavami B. Investigating the factors related to the brain executive function change during pregnancy. BMC Pregnancy Childbirth. 2025;25:1202. https://doi.org/10.1186/s12884-025-08050-9 . Henry JF, Sherwin BB. Hormones and cognitive functioning during late pregnancy and postpartum: a longitudinal study. Behav Neurosci. 2012;126:73–85. https://doi.org/10.1037/a0025540 . R S-F SIMAFA. Investigating the factors related to the brain executive function change during pregnancy. BMC Pregnancy Childbirth. 2025;25. https://doi.org/10.1186/s12884-025-08050-9 . Poćwierz-Marciniak I, Budrewicz J, Żelaskowska L, Walczak-Kozłowska T. Baby brain syndrome or functional reorganization of the brain? Specificity of cognitive functioning of pregnant women in the second trimester of pregnancy. Eur J Obstet Gynecol Reprod Biol. 2025;310:113933. https://doi.org/10.1016/j.ejogrb.2025.03.054 . Nolen-Hoeksema S, Davis CG. Thanks for sharing that: Ruminators and their social support networks. J Pers Soc Psychol. 1999;77:801–14. https://doi.org/10.1037/0022-3514.77.4.801 . Liang M, Chen Y, Liu Y, Xiong R. The mediating role of rumination in the relationship between negative cognitive styles and depression among pregnant women in Guangzhou, China. Front Psychiatry. 2024;15:1499061. https://doi.org/10.3389/fpsyt.2024.1499061 . Whisman MA, du Pont A, Butterworth P. Longitudinal associations between rumination and depressive symptoms in a probability sample of adults. J Affect Disord. 2020;260:680–6. https://doi.org/10.1016/j.jad.2019.09.035 . Kuo JR, Edge IG, Ramel W, Edge MD, Drabant EM, Dayton WM, et al. Trait Rumination Is Associated with Enhanced Recollection of Negative Words. Cogn Ther Res. 2012;36:722–30. https://doi.org/10.1007/s10608-011-9430-7 . Barnum SE, Woody ML, Gibb BE. Predicting changes in depressive symptoms from pregnancy to postpartum: the role of brooding rumination and negative inferential styles. Cogn Ther Res. 2013;37:71–7. https://doi.org/10.1007/s10608-012-9456-5 . du Pont A, Rhee SH, Corley RP, Hewitt JK, Friedman NP. Rumination and executive functions: Understanding cognitive vulnerability for psychopathology. J Affect Disord. 2019;256:550–9. https://doi.org/10.1016/j.jad.2019.06.026 . Chan RCK, Manly T. The application of dysexecutive syndrome measures across cultures: Performance and checklist assessment in neurologically healthy and traumatically brain-injured Hong Kong Chinese volunteers. J Int Neuropsychol Soc. 2002;8:771–80. https://doi.org/10.1017/S1355617702860052 . Rabbitt P, editor. Methodology Of Frontal And Executive Function. London: Routledge; 2004. https://doi.org/10.4324/9780203344187 . Simblett SK, Bateman A. Dimensions of the Dysexecutive Questionnaire (DEX) examined using Rasch analysis. Neuropsychol Rehabil. 2011;21:1–25. https://doi.org/10.1080/09602011.2010.531216 . Cox JL, Chapman G, Murray D, Jones P. Validation of the Edinburgh postnatal depression scale (EPDS) in non-postnatal women. J Affect Disord. 1996;39:185–9. https://doi.org/10.1016/0165-0327(96)00008-0 . Lee DT, Yip SK, Chiu HF, Leung TY, Chan KP, Chau IO, et al. Detecting postnatal depression in Chinese women. Validation of the Chinese version of the Edinburgh Postnatal Depression Scale. Br J Psychiatry J Ment Sci. 1998;172:433–7. https://doi.org/10.1192/bjp.172.5.433 . Liu W, Li W, Wang Y, Yin C, Xiao C, Hu J, et al. Comparison of the EPDS and PHQ-9 in the assessment of depression among pregnant women: Similarities and differences. J Affect Disord. 2024;351:774–81. https://doi.org/10.1016/j.jad.2024.01.219 . Yim IS, Stapleton LRT, Guardino CM, Hahn-Holbrook J, Schetter CD. Biological and Psychosocial Predictors of Postpartum Depression: Systematic Review and Call for Integration. Annu Rev Clin Psychol. 2015;11 Volume 11, 2015:99–137. https://doi.org/10.1146/annurev-clinpsy-101414-020426 Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J Appl Psychol. 2003;88:879–903. https://doi.org/10.1037/0021-9010.88.5.879 . Lubke G, Muthén BO. Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters. Struct Equ Model Multidiscip J. 2007;14:26–47. https://doi.org/10.1080/10705510709336735 . Tein J-Y, Coxe S, Cham H. Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis. Struct Equ Model Multidiscip J. 2013;20:640–57. https://doi.org/10.1080/10705511.2013.824781 . Epskamp S, Waldorp LJ, Mõttus R, Borsboom D. The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. Multivar Behav Res. 2018;53:453–80. https://doi.org/10.1080/00273171.2018.1454823 . Tibshirani R. Regression Shrinkage and Selection via The Lasso: A Retrospective. J R Stat Soc Ser B Stat Methodol. 2011;73:273–82. https://doi.org/10.1111/j.1467-9868.2011.00771.x . Robinaugh DJ, Millner AJ, McNally RJ. Identifying highly influential nodes in the complicated grief network. J Abnorm Psychol. 2016;125:747–57. https://doi.org/10.1037/abn0000181 . van Borkulo CD, van Bork R, Boschloo L, Kossakowski JJ, Tio P, Schoevers RA, et al. Comparing network structures on three aspects: A permutation test. Psychol Methods. 2023;28:1273–85. https://doi.org/10.1037/met0000476 . Chan RCK, Hoosain R, Lee TMC. Reliability and validity of the Cantonese version of the Test of Everyday Attention among normal Hong Kong Chinese: a preliminary report. Clin Rehabil. 2002;16:900–9. https://doi.org/10.1191/0269215502cr574oa . Gerstorf D, Siedlecki KL, Tucker-Drob EM, Salthouse TA. Executive Dysfunctions Across Adulthood: Measurement Properties and Correlates of the DEX Self-Report Questionnaire. Aging Neuropsychol Cogn. 2008;15:424–45. https://doi.org/10.1080/13825580701640374 . Grande LA, Olsavsky AK, Erhart A, Dufford AJ, Tribble R, Phan KL, et al. Postpartum Stress and Neural Regulation of Emotion among First-Time Mothers. Cogn Affect Behav Neurosci. 2021;21:1066–82. https://doi.org/10.3758/s13415-021-00914-9 . Wang Y, Fang F, Yang Y, Liu W, Gao Y, Chen Y et al. The mediating and moderating role of rumination and cognitive reappraisal between perceived stress and prenatal depression: a multicenter cross-sectional study in southeast China. 2023. https://doi.org/10.21203/rs.3.rs-3286622/v1 Cumming MM, Smith SW, O’Brien K. Perceived stress, executive function, perceived stress regulation, and behavioral outcomes of adolescents with and without significant behavior problems. Psychol Sch. 2019;56:1359–80. https://doi.org/10.1002/pits.22293 . Wang K, Chen XS, Zeng X, Wu B, Liu J, Daquin J, et al. Cognitive Trajectories and Associated Social and Behavioral Determinants Among Racial/Ethnic Minority Older Adults in the United States. Gerontologist. 2024;64:gnae147. https://doi.org/10.1093/geront/gnae147 . Fuchshuber J, Senra H, Löffler-Stastka H, Alexopolos J, Roithmeier L, Prandstätter T, et al. Investigating the network ties between affect, attachment, and psychopathology. J Affect Disord. 2024;367:263–73. https://doi.org/10.1016/j.jad.2024.08.219 . Ciharova M, Furukawa TA, Efthimiou O, Karyotaki E, Miguel C, Noma H, et al. Cognitive restructuring, behavioral activation and cognitive-behavioral therapy in the treatment of adult depression: A network meta-analysis. J Consult Clin Psychol. 2021;89:563–74. https://doi.org/10.1037/ccp0000654 . Toobaei M, Taghavi M, Jobson L. Understanding cognitive control in depression: the interactive role of emotion, expected efficacy and reward. BMC Psychiatry. 2025;25:406. https://doi.org/10.1186/s12888-025-06847-8 . Moberly NJ, Watkins ER. Ruminative self-focus and negative affect: an experience sampling study. J Abnorm Psychol. 2008;117:314–23. https://doi.org/10.1037/0021-843X.117.2.314 . Hawthorne BS, Slemp GR, Vella-Brodrick DA, Hattie J. The relationship between positive and painful emotions and cognitive load during an algebra learning task. Learn Individ Differ. 2025;117:102597. https://doi.org/10.1016/j.lindif.2024.102597 . Allen KJD, Elliott MV, Ronold EH, Mason L, Rajgopal N, Hammar Å, et al. Cognitive Training for Emotion-Related Impulsivity and Rumination: Protocol for a Pilot Randomized Waitlist-Controlled Trial. JMIR Res Protoc. 2025;14:e54221. https://doi.org/10.2196/54221 . Yang Y, Cao S, Shields GS, Teng Z, Liu Y. The relationships between rumination and core executive functions: A meta-analysis. Depress Anxiety. 2017;34:37–50. https://doi.org/10.1002/da.22539 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablematerial.docx SupplementaryFigurematerial.docx Supplementary3.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 28 Apr, 2026 Editor assigned by journal 26 Apr, 2026 Editor invited by journal 06 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 03 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9220765","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631709129,"identity":"5f427466-2076-4cff-a571-ffdd94df05f2","order_by":0,"name":"jingjing liu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"jingjing","middleName":"","lastName":"liu","suffix":""},{"id":631709130,"identity":"b849cdd8-71b4-42d4-8590-21c8031f37ae","order_by":1,"name":"xingxing Liu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"xingxing","middleName":"","lastName":"Liu","suffix":""},{"id":631709131,"identity":"ca8ef919-7af3-47c9-bff8-abfa05f8d476","order_by":2,"name":"Jiaqi Li","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Li","suffix":""},{"id":631709132,"identity":"d3405308-ded7-4e85-aa5e-dba634a3a550","order_by":3,"name":"changyi Yan","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"changyi","middleName":"","lastName":"Yan","suffix":""},{"id":631709133,"identity":"e5ec75a5-5479-48bc-9869-fe967a24ff3f","order_by":4,"name":"haiyan Liu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"haiyan","middleName":"","lastName":"Liu","suffix":""},{"id":631709134,"identity":"a72e4298-7aee-4a3c-a7bd-8cec0d52bc5f","order_by":5,"name":"yu chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACfvnjhx984Pkvx8/efIA4LZIzeNIMZ8gwG0v2HEsgTovBDQYDaR4b5sQNN3IMiHTZ7YYEA54cNsaZDTkfb7xhsJPTbSCgg3HOwQMPJM7wMPMznN1sOYch2djsAAEtzAwJCQaGPRJsko2926R5GA4kbiOkhY0hwUAi8Z8Bj8FhnmfEaeGRAGo5wJMgYXCMh404LRI8Z9IMG3gOGEj2sBlbzjEgwi/2x9sPP/7Dc6C+X/7xwxtvKuzkCGpBs5LYqEHSQqqOUTAKRsEoGBEAAHbsQ1TmyNpXAAAAAElFTkSuQmCC","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"yu","middleName":"","lastName":"chen","suffix":""}],"badges":[],"createdAt":"2026-03-25 09:09:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9220765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9220765/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108805235,"identity":"5af67063-9165-4fae-876b-90a1c917d4ec","added_by":"auto","created_at":"2026-05-08 15:25:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimated means of the 20 items from the daily executive functioning in each of the three profiles identified among pregnant women.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9220765/v1/f1e9bf65255fc15fad7bfa9c.png"},{"id":108806275,"identity":"5121c5df-4616-45c7-a1f7-0e14d4037ffa","added_by":"auto","created_at":"2026-05-08 15:28:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1819061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork structures of the four sample.Note: the entire sample(A), High-Difficulty Profile(B), Moderate-Difficulty Profile(C), Low-Difficulty Profile(D)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9220765/v1/f78b031eaf760d520e28d892.png"},{"id":108805501,"identity":"100b6e5f-1d90-4321-b3b9-396103319f8f","added_by":"auto","created_at":"2026-05-08 15:26:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1313111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentrality indices for the entire sample and three subgroups: A, entire sample; B, High-Difficulty Profile; C, Moderate-Difficulty Profile; and D, Low-Difficulty Profile.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9220765/v1/1a708255642057ffb4fe01ad.png"},{"id":108977074,"identity":"2b0e736f-d672-4b77-8f70-acd63aa06702","added_by":"auto","created_at":"2026-05-11 11:30:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4102754,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9220765/v1/927b4af3-7ee4-4d53-b4a7-bf6107a663be.pdf"},{"id":108648135,"identity":"48bbff15-dfe8-4e53-8146-ff8eba7cdb77","added_by":"auto","created_at":"2026-05-07 00:51:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16975,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablematerial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9220765/v1/803e829d78592492119e3ca2.docx"},{"id":108806250,"identity":"c69dd4eb-2752-4b07-b76e-f4b032ced6a0","added_by":"auto","created_at":"2026-05-08 15:28:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":133692,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigurematerial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9220765/v1/1b908a5ec30c7c09d2f71512.docx"},{"id":108805583,"identity":"2f425806-411d-4a21-91aa-406a4b2e7a8a","added_by":"auto","created_at":"2026-05-08 15:26:20","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17642,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9220765/v1/c993607334271e8bed971c3b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eExecutive Function Heterogeneity in Pregnant Women and its Link to Differential Depression-Rumination Network Analysis: A Cross-Sectional Study\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePregnancy constitutes a critical neurodevelopmental window marked by profound hormonal, physiological, and psychological alterations. Evidence indicates these changes selectively impact cognitive domains, notably memory consolidation[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], attentional allocation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and executive function (EF)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. EF supports goal-directed behavior and includes inhibitory control, intentionality, knowledge-action dissociation, resistance, and social-behavioral modulation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. During pregnancy, EF is influenced by multiple factors[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Hormonal fluctuations affect brain activity[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], physical discomfort increase mental load[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and psychosocial stressors[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] compound these effects. However, findings on cognitive changes in pregnant women remain inconsistent[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].This is largely because most studies center on population average effects, overlooking interindividual differences. Furthermore, few studies have established a link between executive function (EF) and emotional symptoms, leaving the characteristics of emotional regulation across different EF levels largely unelucidated.\u003c/p\u003e \u003cp\u003eIn the context of perinatal mental health, rumination involves a repetitive focus on negative emotions, adverse events, and personal experiences. This construct comprises three core dimensions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], specifically depression-related rumination, brooding, and reflection. It is a common maladaptive cognitive-emotional response during pregnancy[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For pregnant women, depressive symptoms may trigger rumination, as negative emotions drive them to fixate on unpleasant feelings related to pregnancy or motherhood[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Conversely, rumination worsens depressive symptoms by inhibiting positive processing and amplifying negative experiences[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, these constructs share a dynamic and reciprocal association rather than a unidirectional causal link[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Although the link between prenatal depression and rumination is well-documented, most studies explore their factors in isolation using traditional correlation analyses. To address this gap, the present study uses the network analysis to map the specific connection patterns within the depression-rumination symptom complex.\u003c/p\u003e \u003cp\u003eIn the context of prenatal mental health, executive function serves as a core mechanism of cognitive control[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It regulates the depression-rumination relationship by inhibiting negative cognitive fixation, managing attentional allocation, and disrupting ruminative cycles[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Impaired EF reduces the ability to inhibit rumination and thereby exacerbates the persistence and progression of depressive symptoms. This study hypothesize that the depression-rumination network varies according to individual differences in EF. Pregnant women with varying EF levels possess divergent cognitive control capacities, which may result in subtype-specific variations in network connectivity, central symptom nodes, and bridge symptoms.\u003c/p\u003e \u003cp\u003eThe current study addresses these critical gaps through an integrated methodological design that combines Latent Profile Analysis (LPA) -derived subgroup classification with contemporaneous symptom network analysis. Specifically, this study advances the field in three key ways. First, by applying LPA to identify latent heterogeneity in executive function among pregnant women, this study establish distinct cognitive profiles as the foundation for subsequent analyses. Second, by conducting contemporaneous network analysis of depression and rumination symptoms within each executive function subgroup, assessed via the Rumination Response Scale(RSS) and Edinburgh Postnatal Depression Scale (EPDS), examined whether distinct profiles exhibit differential symptom network architectures. This approach directly addresses the limitation of homogeneous sample assumptions in prior research and enables the detection of profile-specific central and bridge symptoms that would be invisible in aggregate analyses. Third, by comparing emotional states and network structures across subtypes, this study elucidate the dynamic interplay between executive function and mood symptomatology during pregnancy.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants and procedures\u003c/h2\u003e \u003cp\u003eThis multi-center cross-sectional study was conducted between June 2020 and January 2021 across five tertiary referral hospitals located in Guangdong, the most populous province in China. Participants were recruited from obstetrics departments in Guangzhou, Shenzhen, and Zhongshan. Due to differences in patient flow among the hospitals, the sample distribution varied across the five institutions, with a total sample size of 1770, distributed as 353, 60, 224, 702, and 431 respectively. Eligibility criteria included pregnant women between 13 and 40 weeks of gestation, aged 18 to 49 years, of Chinese nationality, and who provided informed consent. Exclusion criteria encompassed individuals with a personal or family history of psychiatric disorders, as well as those with impairments in hearing or speech. The study employed a multi-center cross-sectional design adhering to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. All investigators underwent standardized training on administering the questionnaire, and were responsible for informing participants about the study\u0026rsquo;s objectives, confidentiality protocols, and instructions for completing the questionnaire. Ethical approval was obtained from the Ethics Committee of Nanfang Hospital, Southern Medical University, Guangdong, China (Approval number: NFEC-2020-022), and written informed consent was secured from all eligible participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measurement\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Demographic information\u003c/h2\u003e \u003cp\u003eA portion of the questionnaire was developed based on a comprehensive review of the existing literature and subsequently evaluated by specialists in gynecology and epidemiology. The questionnaire encompassed the following domains: (1) social demographic characteristics, including age, occupation, educational attainment, monthly income, household registration status, and single-child family background; (2) obstetric information, such as parity, pregnancy planning status, and pregnancy-related medical conditions; and (3) social factors, including the quality of marital relationships and interactions with parents-in-law.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Executive Function\u003c/h2\u003e \u003cp\u003eThe Chinese version of Dysexecutive Questionnaire (DEX) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] was used to assess executive function, with a total of 20 items. All items are scored on a 5-Likert scale (0\u0026ndash;4) and total score ranges from 0 to 80,was designed to estimate five areas of change: Inhibition, Intentionally, Dissociation of Knowing and Doing, Resistance and Social-Behavioral Modulation[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], with higher scores indicating more severe executive function[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Cronbach\u0026rsquo;s alpha was 0.936 in the present sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Prenatal Depression\u003c/h2\u003e \u003cp\u003eThe severity of depressive symptoms experienced during the preceding week was measured via the EPDS, a validated screening tool originally developed[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This scale consists of 10 items, with each item scoring 0\u0026ndash;3 points and a total score ranging from 0 to 30 points. In this study, the Chinese version of the EPDS introduced by Lee et al. was used[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The reliability and validity of the Chinese version, and the applicability of its clinical cut-off scores for Chinese pregnant women, have been confirmed [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the Cronbach\u0026rsquo;s alpha in the current study was 0.861.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. Rumination\u003c/h2\u003e \u003cp\u003eThe Rumination Response Scale(RSS)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] was used to measure 2 dimensions of rumination. The scale consists of a total of 22 items, the depressed-symptom rumination dimension consisted of 12 items, the brooding dimension consisted of 5 items and the reflection dimension consisted of 5 items. Previous studies have demonstrated that this scale has reliable reliability and validity and is suitable for prenatal women[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. All items are scored on a 4-Likert (1\u0026ndash;4) scale and total score ranges from 22 to 88. The Cronbach\u0026rsquo;s alpha in the current study was 0.945.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Descriptive analyses\u003c/h2\u003e \u003cp\u003eDescriptive analyses were used to describe the socio-demographic and psychological characteristics. Participants with missing data on any of the key study variables (DEX, EPDS, RSS) were excluded from the analyses, resulting in a final analytic sample of 1,770 complete cases. Correlational analyses were used to explore correlations between continuous variables, and an Analysis of Variance (ANOVA) was used to compare differences in scores on the EPDS, RSS and the three dimensions of the DEX between the demographic variables. All analyses were performed using SPSS 25.0 software (Version 25 for Windows; IBM Corp. Released in 2016).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Common methods deviation test\u003c/h2\u003e \u003cp\u003eSelf-report data collected in a uniform context may introduce common method bias[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This study applied Harman\u0026rsquo;s single-factor test, subjecting all items to unrotated exploratory factor analysis (EFA). Extraction of two or more factors with the first factor explaining\u0026thinsp;\u0026le;\u0026thinsp;40% of variance indicates non-significant common method bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Latent profile analysis and factor analysis\u003c/h2\u003e \u003cp\u003eTo delineate potential symptom profiles of executive function among prenatal women, this study employed latent profile analysis (LPA) using Mplus version 8.3 (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, Los Angeles, CA, USA). The analysis utilized individual item scores from measures of executive dysfunction scale as observed indicators. Beginning with a one-class model, the number of latent profiles was incrementally increased. The optimal number of profiles was determined by evaluating multiple model fit indices, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted BIC (aBIC), entropy, the Lo-Mendell-Rubin likelihood ratio test (LMR), and the bootstrap likelihood ratio test (BLRT). Lower values of AIC, BIC, and aBIC signify improved model fit, whereas entropy values approaching 1 indicate greater classification accuracy. Notably, an entropy value exceeding 0.80 corresponds to classification accuracy above 90% [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, statistically significant p-values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the LMR and BLRT tests suggest that the K-class model accounts for significantly more variance than the (K-1)-class model[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Network analysis\u003c/h2\u003e \u003cp\u003eTo characterize the principal symptom clusters across distinct latent profiles of executive function, a concurrent symptom network analysis was performed incorporating 11 key variables: the items of EPDS, Depression-related Rumination, Brooding. This analysis was conducted using R version 4.4.2. Initially, the estimateNetwork function from the bootnet package[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]was employed to estimate the symptom networks. Subsequently, network regularization was applied via the least absolute shrinkage and selection operator (LASSO) method[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which computes partial correlations and sets weaker or potentially spurious edge coefficients to zero, thereby reducing false positive connections[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The extended Bayesian information criterion (EBIC) was utilized to select the optimal model[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The stability of centrality indices was then evaluated through resampling procedures. In this study, expected influence(EI) was selected as the centrality metric, as it accounts for both positive and negative associations within the network, offering a more nuanced measure of a node\u0026rsquo;s overall influence compared to traditional centrality metrics [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A central variable is a node that has many correlations with other nodes in the network model and therefore has a greater impact on the network. The colour of the edge indicates the direction of the association. A green line indicates a positive association, while a red line indicates a negative correlation. Nodes are color-coded according to their originating questionnaire: orange for EPDS, light blue for the RSS. In the current study, four network models were generated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5. Network comparison test\u003c/h2\u003e \u003cp\u003eTo investigate potential differences in network structures associated with Executive Dysfunction Scale, the study utilized the network comparison test functionality provided by the NetworkComparisonTest package. Employing permutation-based methodologies, the study conducted comparative analyses of the Prenatal Depression, Rumination networks across groups at both global and local levels. The global analyses encompassed evaluations of global strength invariance and overall network structure invariance, while the local analyses focused on assessing invariance in node expected influence and edge connection strength. To control for the risk of Type I errors due to multiple comparisons, p-values were adjusted using the Holm-Bonferroni correction method[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive analyses\u003c/h2\u003e \u003cp\u003eIn the present sample, the mean maternal age was 29.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30 years (range\u0026thinsp;=\u0026thinsp;20\u0026ndash;45). Among the participants, 96.6% (n\u0026thinsp;=\u0026thinsp;1709) were married, and 51.41% (n\u0026thinsp;=\u0026thinsp;910) were primiparous. Regarding educational attainment, 10.16% (n\u0026thinsp;=\u0026thinsp;182) held a master\u0026rsquo;s degree or higher, while 60.67% (n\u0026thinsp;=\u0026thinsp;1074) were employed full-time. Furthermore, 51.41% (n\u0026thinsp;=\u0026thinsp;573) reported unintended pregnancies, 1.69% (n\u0026thinsp;=\u0026thinsp;30) had a history of smoking, and 4.23% (n\u0026thinsp;=\u0026thinsp;75) reported alcohol consumption. A summary of these demographic characteristics is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of the sample (n\u0026thinsp;=\u0026thinsp;1770).\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;1770, 100%)\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD/n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-Difficulty Profile(n\u0026thinsp;=\u0026thinsp;713, 40.3%)Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD/n(%)2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate-Difficulty Profile(n\u0026thinsp;=\u0026thinsp;773, 43.5%)Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD/n(%)1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-Difficulty Profile(n\u0026thinsp;=\u0026thinsp;284, 16.1%)Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD/n(%)3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/F\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.95\u0026thinsp;\u0026plusmn;\u0026thinsp;5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1074(60.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e433(62.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e469(60.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172(60.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(4.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(4.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(4.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(2.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378(21.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150(21.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161(20.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67(23.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247(13.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(11.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94(12.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35(12.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1709(96.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e691(96.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e744(96.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e274(96.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61(3.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(3.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(3.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(3.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121(6.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(6.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(6.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(7.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary Vocational School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e619(34.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257(36.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e265(34.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97(34.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociate Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e478(27.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182(25.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213(27.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83(29.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e370(20.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146(20.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164(21.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60(21.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u0026rsquo;s degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182(10.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(11.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81(10.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(7.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(1.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(0.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(0.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(4.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(2.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(2.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(1.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e910(51.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380(53.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366(47.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138(48.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e860(48.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e324(45.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e396(51.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140(49.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnintended pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e573(32.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215(30.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258(33.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100(35.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of adverse obstetric events\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252(14.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105(14.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117(15.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(10.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFetal abnormalities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146(8.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(3.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(3.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(1.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDEX Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.82\u0026thinsp;\u0026plusmn;\u0026thinsp;10.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.98\u0026thinsp;\u0026plusmn;\u0026thinsp;11.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.84\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.504**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea Statistic was based on one-way analysis of variance (ANOVA).\u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.001;SD, standard deviation.\u003c/p\u003e \u003cp\u003eDEX, the Chinese version of Dysexecutive Questionnaire\u003c/p\u003e \u003cp\u003eThe analysis revealed that the mean DEX score was 16.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29. The mean prenatal depression score was 6.71\u0026thinsp;\u0026plusmn;\u0026thinsp;4.84. Descriptive statistics, including means and standard deviations for each variable and factor, are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The skewness values for the DEX factors ranged from 0.437 to 0.597, and kurtosis values ranged from \u0026minus;\u0026thinsp;0.17 to 0.46, suggesting that the data largely conformed to the assumptions of normality and thus were appropriate for subsequent analyses. Due to the non-normal distribution of the data, the relationships among the psychological variables were assessed using Kendall\u0026rsquo;s tau-b correlation coefficients presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, indicated consistently positive correlations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics and analysis of variance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDEX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntentionality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissociation of Knowing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial-Behavioral Modulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEPDS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.71\u0026thinsp;\u0026plusmn;\u0026thinsp;4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e32.94\u0026thinsp;\u0026plusmn;\u0026thinsp;9.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression-related Rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.29\u0026thinsp;\u0026plusmn;\u0026thinsp;5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrooding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReflection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations analyses for each psychological variable among pregnant women(n\u0026thinsp;=\u0026thinsp;1770).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDEX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntentionality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDissociation of Knowing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResistance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSocial-Behavioral Modulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEPDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDepression-\u003c/p\u003e \u003cp\u003erelated Rumination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBrooding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eReflection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.741**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntentionality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.772**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.583**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissociation of Knowing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.744**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.549**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.641**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.692**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.522**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.563**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.607**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial-\u003c/p\u003e \u003cp\u003eBehavioral Modulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.654**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.489**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.563**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.552**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.631**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEPDS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.320**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.277**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.302**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.315**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.285**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.267**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.403**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.343**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.377**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.376**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.367**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.335**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.430**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression-\u003c/p\u003e \u003cp\u003erelated Rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.428**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.362**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.407**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.399**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.389**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.335**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.449**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.871**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrooding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.366**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.318**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.341**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.340**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.335**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.315**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.397**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.806**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.698**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReflection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.325**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.282**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.314**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.271**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.359**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.759**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.644**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.641**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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\u003e3.2. Common method deviation test\u003c/h2\u003e \u003cp\u003eThe study employed Harman\u0026rsquo;s single-factor test to assess common method bias. The exploratory factor analysis (EFA) revealed twelve factors with eigenvalues greater than one, with the largest factor accounting for 26.308% of the total variance, which is below the critical threshold of 40%[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Consequently, the associations among the variables in this research are considered to be minimally influenced by common method bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Classification of the latent profile\u003c/h2\u003e \u003cp\u003eIn this study, 20 items from the Chinese version of Dysexecutive Questionnaire for Pregnant women were used as explicit indicators. The present study conducted sequential models ranging from one to five latent profiles were evaluated. Detailed model fit statistics for each profile solution are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As the number of profiles increased from one to five, AIC, BIC, and aBIC values consistently decreased, with the highest entropy observed in the two-profiles model. The five-profile model was rejected due to non-significant LMR p-values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The four-profile model was also deemed sub-optimal because certain subgroups represented a minimal proportion of the sample and lacked clinical interpretability. Consequently, the three-profile model was selected as the optimal solution. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the classification probability matrix for the three latent profiles, with average posterior probabilities ranging from 95.9% to 96.9%, all exceeding the 90% threshold[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], thereby supporting the robustness of the classification.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fit indices for latent profile analysis (n\u0026thinsp;=\u0026thinsp;1770).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMR(\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLRT(\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eClassification probabilites\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113397.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113627.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113494.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102100.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102450.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102247.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.599/0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98157.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98628.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98355.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.435/0.403/0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97089.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97681.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97338.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.289/0.329/0.268/0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96613.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97325.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96912.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.283/0.267/0.250/0.088/0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote.AIC, Akaike\u0026rsquo;s Information Criterion; BIC, Bayesian Information Criterion; aBIC, adjusted Bayesian information Criterion; Entropy, Information Entropy; LMR, Lo-Mendell-Rubin Likelihood Ratio Test; BLRT, Bootstrap Likelihood Ratio Test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe three-class model represents the best-fitting latent profile solution. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the mean DEX scores across the three profiles. Profile 3, comprising 284 participants (16.1%), demonstrated consistently low DEX scores and was designated the \u0026ldquo;High-Difficulty Profile.\u0026rdquo; Profile 2 included 713 participants (40.3%) with moderate DEX scores and was labeled the \u0026ldquo;Low-Difficulty Profile.\u0026rdquo; Profile 1 consisted of 773 participants (43.5%) who exhibited significantly elevated DEX scores and was termed the \u0026ldquo;Moderate-Difficulty Profile.\u0026rdquo;\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Symptom network analysis across the latent profiles\u003c/h2\u003e \u003cp\u003eThe symptom network of the entire sample and the symptom network structure of the three subgroups is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S4 present the expected influence values of the symptom network for the entire sample and three potential profiles. In the entire sample, the three nodes with the highest EI values were EPDS8 (sad or miserable)(r\u003csub\u003es\u003c/sub\u003e=4.405),EPDS4(anxious or worried)(r\u003csub\u003es\u003c/sub\u003e=3.985), EPDS7 (sleep difficulties) (r\u003csub\u003es\u003c/sub\u003e=3.972), The two strongest edges were between EPDS1-EPDS2 (able to laugh\u0026ndash;look forward with enjoyment to things) (weight\u0026thinsp;=\u0026thinsp;0.710) and EPDS4\u0026ndash;EPDS5 (anxious or worried\u0026ndash;scared or panicky) (weight\u0026thinsp;=\u0026thinsp;0.577).\u003c/p\u003e \u003cp\u003eIn the High-Difficulty Profile (n\u0026thinsp;=\u0026thinsp;284), the three nodes with the highest EI values were EPDS8(sad or miserable) (r\u003csub\u003es\u003c/sub\u003e=4.372), EPDS7(sleep difficulties)(r\u003csub\u003es\u003c/sub\u003e=4.118), EPDS4 (anxious or worried)(r\u003csub\u003es\u003c/sub\u003e=3.853), The two strongest edges were between EPDS1\u0026ndash;EPDS2 (able to laugh\u0026ndash;look forward with enjoyment to things) (weight\u0026thinsp;=\u0026thinsp;0.72) and RSS1-RSS2 (Depression-related Rumination \u0026ndash; Brooding) (weight\u0026thinsp;=\u0026thinsp;0.581).\u003c/p\u003e \u003cp\u003eIn the Moderate -Difficulty Profile (n\u0026thinsp;=\u0026thinsp;773), the three nodes with the highest EI values were EPDS8 (sad or miserable) (r\u003csub\u003es\u003c/sub\u003e=4.288),EPDS7(sleep difficulties)(r\u003csub\u003es\u003c/sub\u003e=3.951), EPDS5 (scared or panicky)(r\u003csub\u003es\u003c/sub\u003e=3.909), The two strongest edges were between EPDS1\u0026ndash;EPDS2 (able to laugh\u0026ndash;look forward with enjoyment to things) (weight\u0026thinsp;=\u0026thinsp;0.637) and EPDS4-EPDS5 (anxious or worried\u0026ndash;scared or panicky) (weight\u0026thinsp;=\u0026thinsp;0.547).\u003c/p\u003e \u003cp\u003eIn the Low-Difficulty Profile (n\u0026thinsp;=\u0026thinsp;713), the three nodes with the highest EI values were EPDS8 (sad or miserable) (r\u003csub\u003es\u003c/sub\u003e=4.157), EPDS3(blaming myself)(r\u003csub\u003es\u003c/sub\u003e=3.692), EPDS6 (things getting to me)(r\u003csub\u003es\u003c/sub\u003e=3.638) and EPDS7 (sleep difficulties)(r\u003csub\u003es\u003c/sub\u003e=3.638), the two strongest edges were between EPDS1\u0026ndash;EPDS2 (able to laugh\u0026ndash;look forward with enjoyment to things) (weight\u0026thinsp;=\u0026thinsp;0.721) and EPDS8-EPDS9(Crying\u0026ndash;Self-harming) (weight\u0026thinsp;=\u0026thinsp;0.595). The global strength, defined as the mean absolute edge weight, was 0.070\u0026ndash;0.075 for the respective networks, indicating a moderate level of overall connectivity is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Network comparisons test among the four profiles\u003c/h2\u003e \u003cp\u003eNetwork Comparison Tests (NCT) were performed to investigate differences in network structure and global strength across the four models. This analysis assessed both global and local variations among the networks. The global strength invariance test indicated no significant differences in overall network strength among the groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting comparable levels of overall connectivity. Similarly, the edge strength invariance test revealed no significant differences in the strength of individual connections across the four networks. These findings imply that, despite the distinct characteristics of each group, the fundamental network architecture remains stable and consistent. Detailed results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Characteristics of Network Comparison Test\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 \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe entire sample(n\u0026thinsp;=\u0026thinsp;1770, 100%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate-Difficulty Profile(n\u0026thinsp;=\u0026thinsp;773, 43.5%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-Difficulty Profile(n\u0026thinsp;=\u0026thinsp;713, 40.3%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-Difficulty Profile(n\u0026thinsp;=\u0026thinsp;284, 16.1%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal Strength\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNetwork density\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStrongest Edge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEPDS1-EPDS2\u003c/p\u003e \u003cp\u003e(weight\u0026thinsp;=\u0026thinsp;0.710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEPDS1\u0026ndash;EPDS2\u003c/p\u003e \u003cp\u003e(weight\u0026thinsp;=\u0026thinsp;0.637)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEPDS1\u0026ndash;EPDS2\u003c/p\u003e \u003cp\u003e(weight\u0026thinsp;=\u0026thinsp;0.721)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEPDS1\u0026ndash;EPDS2\u003c/p\u003e \u003cp\u003e(weight\u0026thinsp;=\u0026thinsp;0.720)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBridge Symptoms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEPDS1(B\u003csub\u003es\u003c/sub\u003e=0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRSS2(B\u003csub\u003es\u003c/sub\u003e=0.0709)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRSS2(B\u003csub\u003es\u003c/sub\u003e=0.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRSS2(B\u003csub\u003es\u003c/sub\u003e=0.095)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCentral Symptoms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEPDS8(r\u003csub\u003es\u003c/sub\u003e=4.405)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEPDS8 (r\u003csub\u003es\u003c/sub\u003e=4.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEPDS8(r\u003csub\u003es\u003c/sub\u003e=4.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEPDS8(r\u003csub\u003es\u003c/sub\u003e=4.372)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.Discussion","content":"\u003cp\u003eThe key findings of this study are threefold. First, this study identified a heterogeneous distribution of executive function within the sample: the majority of pregnant women (40.3%) were classified into a Low-Difficulty Profile, the majority of pregnant women (43.5%) were classified into a Moderate-Difficulty Profile, while the remainder (16.1%) fell into a High-Difficulty Profile. Second, despite these differing backgrounds, all subgroups shared a common central of severe symptoms, most prominently \u0026ldquo;Sad or miserable\u0026rdquo;. Third, beyond this commonality, each subgroup exhibited a unique symptom network structure, with specific central and bridging symptoms, and the Moderate-Difficulty Profiles and Low-Difficulty Profiles demonstrated a significantly greater overall connectivity global strength within and among the Depression and Rumination networks.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Executive Function Profiles: Heterogeneity and Clinical Relevance\u003c/h2\u003e \u003cp\u003eLatent profile analysis (LPA) identified three distinct subgroups of pregnant women based on their scores on the DEX: Low-difficulty (40.3%), Moderate-difficulty (43.5%), and High-difficulty (16.1%). Consistent with prior research, these findings confirm the heterogeneity in executive function among pregnant women[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In this sample, the overall Executive Function were relatively low (16.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29), yet the High-Difficulty Profile exhibited significantly elevated scores (20.84\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21) compared to population norms [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe observed heterogeneity may arise from several interacting sources. Individual differences in physiological and hormonal regulation, such as variable sensitivity to estrogen and cortisol fluctuations,could differentially impact prefrontal functioning across pregnant women[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Psychosocial factors, including education, social support, prenatal stress, and sleep quality, further modulate cognitive performance in a non-uniform manner[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, executive function comprises distinct subcomponents, inhibition, shifting, updating, and deficits may be more pronounced in specific domains, a nuance that aggregate scores often mask[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, the High-Difficulty Profile, though proportionally smaller, likely represents a clinically significant subgroup characterized by cumulative risk where multiple vulnerabilities, e.g., high stress, low cognitive reserve, poor sleep, converge to amplify executive challenges[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Although identified in aging populations[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], this pattern of a high-difficulty subgroup may similarly exist in pregnant women, as our findings suggest underscores the importance of targeted screening and intervention for this minority, even within a generally resilient maternal population.\u003c/p\u003e \u003cp\u003eThe LPA further revealed that a distinct subgroup of pregnant women exhibited lower levels of executive function relative to the overall population. This suggests that only a subset of women experience altered executive function during pregnancy, as indicated by the presence of this subgroup with notably reduced executive performance. Nevertheless, the absence of longitudinal data or comparisons with control groups precludes definitive conclusions regarding executive function impairment during pregnancy based solely on these findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Network Structure: A Stable Core with Dynamic Bridges\u003c/h2\u003e \u003cp\u003eThe network analysis revealed a critical pattern: while the affective symptom of \u0026ldquo;feeling sad or miserable\u0026rdquo; was the most central and stable node across all groups, the key bridge connecting depression to rumination shifted based on executive function. The persistent centrality of core sadness suggests it acts as a foundational hub in perinatal distress. In contrast, the primary bridging symptom changed from a general loss of positive affect in the overall sample to maladaptive brooding within each executive function subgroup. This indicates that although profound sadness may be a common endpoint, the pathways maintaining distress differ: for cognitively resilient women, low positive affect may be the key link, whereas for those under higher cognitive load, rigid rumination becomes the critical bridge.\u003c/p\u003e \u003cp\u003e\u0026ldquo;Sadness or misery\u0026rdquo; emerged as the most central node across the entire sample and all three subgroups, showing the highest expected influence values. Network comparison tests confirmed this stability. These findings suggest that sad affect serves as a core organizing hub within perinatal depression-rumination networks. This pattern aligns with Fuchshuber affective centrality hypothesis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], extending it to the context of perinatal mental health.. Consequently, directly targeting this core emotional state should be prioritized as a universal intervention strategy in perinatal mental health, regardless of cognitive profile. Early identification and treatment of this central symptom through cognitive restructuring or behavioral activation may deliver the broadest clinical benefits by disrupting the activation of wider symptom clusters[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to this stable affective core, the key bridging symptoms connecting the depression and rumination clusters exhibited significant variation dependent on executive function profiles. In the overall sample, diminished positive affect, as measured by \u0026ldquo;able to laugh\u0026rdquo;, served as the primary bridge. However, within each of the three identified subgroups, the maladaptive cognitive process of ruminative brooding, captured by \u0026ldquo;brooding\u0026rdquo;, became the predominant bridging symptom, with bridge strength values varying across the subgroups. This systematic shift indicates that \u0026ldquo;Sadness or misery\u0026rdquo; constitutes a common affective core, the primary pathways for the maintenance and propagation of distress differ fundamentally based on cognitive resource availability. For women with relatively intact executive resources, dysregulation may be primarily bridged by disruptions in the motivation and reward systems[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e],although this mechanism requires further validation in pregnant populations. In contrast, for women experiencing higher cognitive load, a rigid, self-focused ruminative cycle becomes the critical link that tightly couples negative affect with cognitive processes[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis distinction carries substantial theoretical and clinical implications. Theoretically, it demonstrates that the foundational architecture of emotional suffering remains stably anchored to a core affective state, even as the specific interactive pathways between symptom clusters dynamically reorganize in response to varying cognitive demands. Clinically, it advocates for a dual-target intervention strategy. Universal efforts must prioritize alleviating the core affective state of sadness. Simultaneously, personalized intervention pathways should be selected based on an individual\u0026rsquo;s executive function profile: augmenting positive affect and behavioral engagement may be most effective for the Low-difficulty profile[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], whereas directly targeting and dismantling the ruminative process via metacognitive or cognitive flexibility training is a necessary first step for those in the Moderate- and High-difficulty profiles[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, monitoring brooding levels in pregnant women reporting cognitive difficulties may provide superior early warning value for identifying a transition towards a more entrenched and treatment-resistant pathological state.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Implications\u003c/h2\u003e \u003cp\u003eThis study yields clear implications for clinical practice and research. Theoretically, our findings refine models of perinatal distress by demonstrating a stable affective core coupled with profile-specific symptom pathways. This underscores that pregnancy-related cognitive changes are heterogeneous and directly shape the architecture of emotional suffering.\u003c/p\u003e \u003cp\u003eClinically, the results advocate for a stratified intervention model. Routine prenatal care should integrate brief screening for executive difficulties to identify high-risk women, particularly those in the High-Difficulty subgroup. For this group, initial treatment should prioritize targeting maladaptive rumination, e.g., with metacognitive therapy, to disrupt the core maintaining mechanism. For women with lower cognitive load, interventions can effectively focus on enhancing positive affect and behavioral engagement.\u003c/p\u003e \u003cp\u003eFuture research should employ longitudinal designs in pregnant cohorts to test the predictive value of these network features for clinical outcomes. Incorporating objective cognitive tasks and multi-modal assessment will further elucidate the biobehavioral mechanisms underlying these distinct profiles and help translate these insights into personalized preventive strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Limitations\u003c/h2\u003e \u003cp\u003eThis study had several limitations. First, the cross-sectional design prevents longitudinal tracking of symptom networks, offering no insight into how connections evolve over time. Future longitudinal studies using panel network analyses are needed to model these dynamic processes. Second, the use of convenience sampling, while practical for initial exploration, may limit the generalizability of our findings. Future studies should employ randomized or stratified probability sampling designs to ensure demographic representativeness and enhance the external validity of results across diverse populations. Third, the ratings used in this study were self-reported and may have been subject to recall bias and social desirability. Future research could incorporate objective measures or triangulate self-reports with interviews or observational methods to mitigate subjective reporting biases.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, pregnant women with different levels of daily executive function exhibited both similarities and unique features in their depression-rumination symptom networks \u0026ldquo;sad or miserable\u0026rdquo; served as the central symptom of Depression and Rumination among network groups. \u0026ldquo;able to laugh\u0026rdquo; served as the bridge symptom of Depression and Rumination in entire network groups.in three subgroups, \u0026ldquo;brooding\u0026rdquo; emerged as the main bridge symptom, with varying strengths. The association among Depression and Rumination was the strongest in Moderate -difficulties profiles.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExecutive Function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDEX\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Dysexecutive Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEPDS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Edinburgh Postnatal Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Rumination Response Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLPA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Latent Profile analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eaBIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eadjusted BIC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLMR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLo-Mendell-Rubin likelihood ratio test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBLRT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebootstrap likelihood ratio test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLASSO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleast absolute shrinkage and selection operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEBIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eextended Bayesian information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eexpected influence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Ethics Committee of Southern Medical University (No.NFEC-2020-022). All procedures involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study also strictly adhered to the Chinese \u0026ldquo;Measures for the Ethical Review of Biomedical Research Involving Humans\u0026rdquo; and other relevant regulations. The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study was supported by the National Natural Science Foundation of China (72574096 and 72274090), the Guangdong Province Undergraduate Teaching Quality and Teaching Reform Project (Yue Jiao Gao Han [2024] No. 30), the Guangdong Provincial Education Science Planning Project (2024GXJK321), and the Guangdong Province Graduate Education Innovation Plan Project (2025KCJS-022).\u0026nbsp;Undergraduate Innovation and Entrepreneurship Training Program (S202412121164, S20251121143). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data, in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJingjing Liu: Conceptualisation, Investigation, Data curation, Formal analysis, Writing-original draft, and Project administration. Xingxing Liu: Investigation, Data curation, Formal analysis, Writing-original draft. Jiaqi Li: Conceptualisation, Investigation, Formal analysis, Writing- original draft, and Visualisation. Changyi Yan\u0026amp;Haiyan Liu: Investigation, Methodology, and Data curation. Yu Chen: Conceptualisation, Methodology, Project administration, Resources, Supervision, Writing-review \u0026amp; editing, and Funding acquisition. All authors approved the final manuscript for submission.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDeclarations of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGrattan DR, Ladyman SR. Neurophysiological and cognitive changes in pregnancy. Handb Clin Neurol. 2020;171:25\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-444-64239-4.00002-3\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-444-64239-4.00002-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaz S. Behavioral and neural correlates of cognitive-affective function during late pregnancy: an Event-Related Potentials study. Behav Brain Res. 2014;267:17\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbr.2014.03.021\u003c/span\u003e\u003cspan address=\"10.1016/j.bbr.2014.03.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYounis J, Bleibel M, Masri JE, Ismail A, Abou\u0026ndash;Abbas L. Exploring the influence of pregnancy on cognitive function in women: a systematic review. BMC Pregnancy Childbirth. 2025;25:88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12884-025-07181-3\u003c/span\u003e\u003cspan address=\"10.1186/s12884-025-07181-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan RCK. Dysexecutive symptoms among a non-clinical sample: A study with the use of the Dysexecutive Questionnaire. Br J Psychol. 2001;92:551\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1348/000712601162338\u003c/span\u003e\u003cspan address=\"10.1348/000712601162338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIranshahi S, Arab M, Arabgol F, Shekarriz-Foumani R, Nouri N, Ghavami B. Investigating the factors related to the brain executive function change during pregnancy. BMC Pregnancy Childbirth. 2025;25:1202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12884-025-08050-9\u003c/span\u003e\u003cspan address=\"10.1186/s12884-025-08050-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenry JF, Sherwin BB. Hormones and cognitive functioning during late pregnancy and postpartum: a longitudinal study. Behav Neurosci. 2012;126:73\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/a0025540\u003c/span\u003e\u003cspan address=\"10.1037/a0025540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR S-F SIMAFA. Investigating the factors related to the brain executive function change during pregnancy. BMC Pregnancy Childbirth. 2025;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12884-025-08050-9\u003c/span\u003e\u003cspan address=\"10.1186/s12884-025-08050-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoćwierz-Marciniak I, Budrewicz J, Żelaskowska L, Walczak-Kozłowska T. Baby brain syndrome or functional reorganization of the brain? Specificity of cognitive functioning of pregnant women in the second trimester of pregnancy. Eur J Obstet Gynecol Reprod Biol. 2025;310:113933. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejogrb.2025.03.054\u003c/span\u003e\u003cspan address=\"10.1016/j.ejogrb.2025.03.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNolen-Hoeksema S, Davis CG. Thanks for sharing that: Ruminators and their social support networks. J Pers Soc Psychol. 1999;77:801\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0022-3514.77.4.801\u003c/span\u003e\u003cspan address=\"10.1037/0022-3514.77.4.801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang M, Chen Y, Liu Y, Xiong R. The mediating role of rumination in the relationship between negative cognitive styles and depression among pregnant women in Guangzhou, China. Front Psychiatry. 2024;15:1499061. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyt.2024.1499061\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2024.1499061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhisman MA, du Pont A, Butterworth P. Longitudinal associations between rumination and depressive symptoms in a probability sample of adults. J Affect Disord. 2020;260:680\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2019.09.035\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2019.09.035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuo JR, Edge IG, Ramel W, Edge MD, Drabant EM, Dayton WM, et al. Trait Rumination Is Associated with Enhanced Recollection of Negative Words. Cogn Ther Res. 2012;36:722\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10608-011-9430-7\u003c/span\u003e\u003cspan address=\"10.1007/s10608-011-9430-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnum SE, Woody ML, Gibb BE. Predicting changes in depressive symptoms from pregnancy to postpartum: the role of brooding rumination and negative inferential styles. Cogn Ther Res. 2013;37:71\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10608-012-9456-5\u003c/span\u003e\u003cspan address=\"10.1007/s10608-012-9456-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edu Pont A, Rhee SH, Corley RP, Hewitt JK, Friedman NP. Rumination and executive functions: Understanding cognitive vulnerability for psychopathology. J Affect Disord. 2019;256:550\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2019.06.026\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2019.06.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan RCK, Manly T. The application of dysexecutive syndrome measures across cultures: Performance and checklist assessment in neurologically healthy and traumatically brain-injured Hong Kong Chinese volunteers. J Int Neuropsychol Soc. 2002;8:771\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1355617702860052\u003c/span\u003e\u003cspan address=\"10.1017/S1355617702860052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabbitt P, editor. Methodology Of Frontal And Executive Function. London: Routledge; 2004. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9780203344187\u003c/span\u003e\u003cspan address=\"10.4324/9780203344187\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimblett SK, Bateman A. Dimensions of the Dysexecutive Questionnaire (DEX) examined using Rasch analysis. Neuropsychol Rehabil. 2011;21:1\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09602011.2010.531216\u003c/span\u003e\u003cspan address=\"10.1080/09602011.2010.531216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox JL, Chapman G, Murray D, Jones P. Validation of the Edinburgh postnatal depression scale (EPDS) in non-postnatal women. J Affect Disord. 1996;39:185\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0165-0327(96)00008-0\u003c/span\u003e\u003cspan address=\"10.1016/0165-0327(96)00008-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee DT, Yip SK, Chiu HF, Leung TY, Chan KP, Chau IO, et al. Detecting postnatal depression in Chinese women. Validation of the Chinese version of the Edinburgh Postnatal Depression Scale. Br J Psychiatry J Ment Sci. 1998;172:433\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1192/bjp.172.5.433\u003c/span\u003e\u003cspan address=\"10.1192/bjp.172.5.433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu W, Li W, Wang Y, Yin C, Xiao C, Hu J, et al. Comparison of the EPDS and PHQ-9 in the assessment of depression among pregnant women: Similarities and differences. J Affect Disord. 2024;351:774\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2024.01.219\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2024.01.219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYim IS, Stapleton LRT, Guardino CM, Hahn-Holbrook J, Schetter CD. Biological and Psychosocial Predictors of Postpartum Depression: Systematic Review and Call for Integration. Annu Rev Clin Psychol. 2015;11 Volume 11, 2015:99\u0026ndash;137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-clinpsy-101414-020426\u003c/span\u003e\u003cspan address=\"10.1146/annurev-clinpsy-101414-020426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePodsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J Appl Psychol. 2003;88:879\u0026ndash;903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0021-9010.88.5.879\u003c/span\u003e\u003cspan address=\"10.1037/0021-9010.88.5.879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLubke G, Muth\u0026eacute;n BO. Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters. Struct Equ Model Multidiscip J. 2007;14:26\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10705510709336735\u003c/span\u003e\u003cspan address=\"10.1080/10705510709336735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTein J-Y, Coxe S, Cham H. Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis. Struct Equ Model Multidiscip J. 2013;20:640\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10705511.2013.824781\u003c/span\u003e\u003cspan address=\"10.1080/10705511.2013.824781\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpskamp S, Waldorp LJ, M\u0026otilde;ttus R, Borsboom D. The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. Multivar Behav Res. 2018;53:453\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00273171.2018.1454823\u003c/span\u003e\u003cspan address=\"10.1080/00273171.2018.1454823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTibshirani R. Regression Shrinkage and Selection via The Lasso: A Retrospective. J R Stat Soc Ser B Stat Methodol. 2011;73:273\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1467-9868.2011.00771.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-9868.2011.00771.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinaugh DJ, Millner AJ, McNally RJ. Identifying highly influential nodes in the complicated grief network. J Abnorm Psychol. 2016;125:747\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/abn0000181\u003c/span\u003e\u003cspan address=\"10.1037/abn0000181\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Borkulo CD, van Bork R, Boschloo L, Kossakowski JJ, Tio P, Schoevers RA, et al. Comparing network structures on three aspects: A permutation test. Psychol Methods. 2023;28:1273\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/met0000476\u003c/span\u003e\u003cspan address=\"10.1037/met0000476\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan RCK, Hoosain R, Lee TMC. Reliability and validity of the Cantonese version of the Test of Everyday Attention among normal Hong Kong Chinese: a preliminary report. Clin Rehabil. 2002;16:900\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1191/0269215502cr574oa\u003c/span\u003e\u003cspan address=\"10.1191/0269215502cr574oa\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerstorf D, Siedlecki KL, Tucker-Drob EM, Salthouse TA. Executive Dysfunctions Across Adulthood: Measurement Properties and Correlates of the DEX Self-Report Questionnaire. Aging Neuropsychol Cogn. 2008;15:424\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13825580701640374\u003c/span\u003e\u003cspan address=\"10.1080/13825580701640374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrande LA, Olsavsky AK, Erhart A, Dufford AJ, Tribble R, Phan KL, et al. Postpartum Stress and Neural Regulation of Emotion among First-Time Mothers. Cogn Affect Behav Neurosci. 2021;21:1066\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3758/s13415-021-00914-9\u003c/span\u003e\u003cspan address=\"10.3758/s13415-021-00914-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Fang F, Yang Y, Liu W, Gao Y, Chen Y et al. The mediating and moderating role of rumination and cognitive reappraisal between perceived stress and prenatal depression: a multicenter cross-sectional study in southeast China. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-3286622/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-3286622/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCumming MM, Smith SW, O\u0026rsquo;Brien K. Perceived stress, executive function, perceived stress regulation, and behavioral outcomes of adolescents with and without significant behavior problems. Psychol Sch. 2019;56:1359\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pits.22293\u003c/span\u003e\u003cspan address=\"10.1002/pits.22293\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang K, Chen XS, Zeng X, Wu B, Liu J, Daquin J, et al. Cognitive Trajectories and Associated Social and Behavioral Determinants Among Racial/Ethnic Minority Older Adults in the United States. Gerontologist. 2024;64:gnae147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/geront/gnae147\u003c/span\u003e\u003cspan address=\"10.1093/geront/gnae147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuchshuber J, Senra H, L\u0026ouml;ffler-Stastka H, Alexopolos J, Roithmeier L, Prandst\u0026auml;tter T, et al. Investigating the network ties between affect, attachment, and psychopathology. J Affect Disord. 2024;367:263\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2024.08.219\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2024.08.219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiharova M, Furukawa TA, Efthimiou O, Karyotaki E, Miguel C, Noma H, et al. Cognitive restructuring, behavioral activation and cognitive-behavioral therapy in the treatment of adult depression: A network meta-analysis. J Consult Clin Psychol. 2021;89:563\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/ccp0000654\u003c/span\u003e\u003cspan address=\"10.1037/ccp0000654\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToobaei M, Taghavi M, Jobson L. Understanding cognitive control in depression: the interactive role of emotion, expected efficacy and reward. BMC Psychiatry. 2025;25:406. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12888-025-06847-8\u003c/span\u003e\u003cspan address=\"10.1186/s12888-025-06847-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoberly NJ, Watkins ER. Ruminative self-focus and negative affect: an experience sampling study. J Abnorm Psychol. 2008;117:314\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0021-843X.117.2.314\u003c/span\u003e\u003cspan address=\"10.1037/0021-843X.117.2.314\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHawthorne BS, Slemp GR, Vella-Brodrick DA, Hattie J. The relationship between positive and painful emotions and cognitive load during an algebra learning task. Learn Individ Differ. 2025;117:102597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lindif.2024.102597\u003c/span\u003e\u003cspan address=\"10.1016/j.lindif.2024.102597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen KJD, Elliott MV, Ronold EH, Mason L, Rajgopal N, Hammar \u0026Aring;, et al. Cognitive Training for Emotion-Related Impulsivity and Rumination: Protocol for a Pilot Randomized Waitlist-Controlled Trial. JMIR Res Protoc. 2025;14:e54221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/54221\u003c/span\u003e\u003cspan address=\"10.2196/54221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Cao S, Shields GS, Teng Z, Liu Y. The relationships between rumination and core executive functions: A meta-analysis. Depress Anxiety. 2017;34:37\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/da.22539\u003c/span\u003e\u003cspan address=\"10.1002/da.22539\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pregnant Women, Executive Function, Depression, Rumination, Latent profile analysis, Network comparison test","lastPublishedDoi":"10.21203/rs.3.rs-9220765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9220765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePregnant women exhibit changes in executive function, which may modulate their psychological vulnerability. This study aimed to identify distinct executive function profiles among pregnant women and examine whether the network structure connecting depression and rumination symptoms differed across these profiles.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA sample of 1,770 pregnant women recruited from five tertiary referral hospitals in Guangdong, China, was evaluated using the Dysexecutive Questionnaire (DEX), Edinburgh Postnatal Depression Scale (EPDS) and Rumination Response Scale (RSS). Latent Profile analysis in Mplus 8.3 classified women by DEXs patterns, and network analysis was conducted using the bootnet package in R 4.3.2 to visualize complex interactions between Depression and Rumination by constructing network diagrams.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLatent Profiles analysis identified three executive function profiles among pregnant women: Low-difficulty (40.3%), Moderate-difficulty (43.5%), and High-difficulty (16.1%). Network analysis revealed stronger overall connections between Depression and Rumination symptoms in the Moderate-difficulty profiles, with RSS2(Brooding) acted as a bridge symptom. EPDS8(sad or miserable) consistently emerged as the most central symptom. The association among Depression and Rumination was the strongest in Moderate -difficulties profiles.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings demonstrate significant heterogeneity in executive function among pregnant women, which is associated with differential patterns of depression-rumination symptom interactions. While core affective distress remains stable, the pathways linking cognitive and emotional symptoms differ by executive function profile. Clinically, interventions should be tailored: targeting brooding may be particularly crucial for women with moderate to high difficulty, whereas enhancing positive affect may benefit those with low difficulty.\u003c/p\u003e","manuscriptTitle":"Executive Function Heterogeneity in Pregnant Women and its Link to Differential Depression-Rumination Network Analysis: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 00:51:31","doi":"10.21203/rs.3.rs-9220765/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-28T13:21:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-26T22:03:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T14:47:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-03T06:07:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-04-03T05:56:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb0ac000-860f-490c-aa1e-e649458536b0","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T00:51:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 00:51:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9220765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9220765","identity":"rs-9220765","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}