Observations on dynamic brain network properties of emotional information processing in patients with depression and sex differences

Abstract

BACKGROUND

Depression is a leading cause of disability worldwide and is associated with abnormal emotional information processing. Traditional static brain network analyses are not suited for capturing the dynamic features of emotional processing. By characterizing temporal fluctuations in inter-regional connectivity, dynamic brain network analyses can provide new perspectives for elucidating the neural mechanisms of depression.

AIM

To use dynamic brain network analysis to explore emotional processing and sex differences in depression.

METHODS

From October 2023 to March 2025, 90 patients with major depressive disorder (45 men/45 women) and 30 healthy controls (15 men/15 women) were enrolled. Resting-state functional magnetic resonance imaging data were collected and preprocessed, and dynamic functional connectivity matrices were constructed using the sliding window technique. Global topology, modularity, and key network connectivity [default mode network (DMN), salience network (SN)], and executive control network) were analyzed and compared between the depression and control groups.

RESULTS

The depression group showed lower global efficiency, local efficiency, small-worldness, number of modules, and within-module connectivity strength; higher mean dynamic functional connectivity variability; shorter state dwell time; reduced within-DMN and SN-limbic connectivity; and reduced executive control network hub involvement than the control group. Sex differences were observed; women had a lower global efficiency, more pronounced within-module deficits, stronger fluctuations, and more DMN inhibition; men showed enhanced intermodule segregation, more frequent state transitions, and prominent SN-limbic connectivity deficits.

CONCLUSION

Patients with depression showed impaired dynamic brain network efficiency, modularity, and emotional regulation. These impairments also showed sex differences; women showed enhanced DMN inhibition, and men showed SN-limbic segregation.

Key Words: Dynamic brain networks; Depression; Gender differences; Emotional information processing; Resting-state functional magnetic resonance imaging

Core Tip: This observational study included 90 patients with depression (45 men/45 women) and 30 healthy controls. Using resting-state functional magnetic resonance imaging and dynamic brain network analysis, we identified impaired network efficiency, modularity, and emotional regulation in depression, with sex-related patterns. Women showed enhanced default mode network inhibition, while men exhibited salience network-limbic segregation. These findings offer key neuroimaging evidence for sex-specific depression diagnostic criteria and treatments.

INTRODUCTION

Depression is a leading global cause of years lived with disability, affecting approximately 280 million people worldwide and imposing substantial economic and social burdens on individuals, families, and healthcare systems[1]. It is characterized by persistent low mood, anhedonia, and impaired emotional regulation, all of which are closely linked to abnormal emotional information processing in the brain. Recent advances in functional magnetic resonance imaging (fMRI) technology have transformed our understanding of depression by enabling non-invasive mapping of brain functional networks. Static brain network analyses, which average connectivity over the entire scan duration, have consistently identified hypo-connectivity within the default mode network (DMN) and salience network (SN), as well as disrupted functional coupling between these networks and the executive control network (ECN) in patients with depression[2,3]. However, emotional processing is inherently dynamic and context-dependent, involving rapid temporal fluctuations in inter-regional communication that static metrics, which assume stationary connectivity, fail to capture[4,5]. Dynamic brain network analysis, by contrast, characterizes time-varying patterns of functional connectivity using techniques such as sliding-window analysis, thereby allowing quantification of network flexibility, stability, and state transitions. This approach provides a more accurate reflection of the adaptive neural processes underlying emotional information processing and has emerged as a powerful tool for uncovering novel neurobiological mechanisms of depression[6].

Studies have confirmed abnormal dynamic connectivity in the prefrontal-limbic system (e.g., amygdala, hippocampus) in patients with depression in response to emotional stimuli[7,8]. Furthermore, some studies have suggested that sex differences may influence symptom presentation and changes in brain function, as evidenced by a higher depression prevalence and greater susceptibility to comorbid anxiety in women and more frequent agitation or substance abuse in men[9,10]. These clinical disparities suggest underlying sex-specific neural mechanisms, yet most existing neuroimaging studies have either pooled male and female participants or included small sex-stratified samples, limiting our understanding of how dynamic brain network abnormalities differ between sexes.

Against this backdrop, this study constructed dynamic brain networks from resting-state fMRI data to examine the connectivity strength, modularity, and temporal variability of brain regions involved in emotional information processing and to compare differences between men and women. By revealing sex-specific patterns in the dynamic brain networks of patients with depression, this study aimed to provide neuroimaging evidence for the early identification of high-risk individuals, the development of sex-differentiated treatment strategies (e.g., medication selection, psychological intervention approaches), the advancement of precision medicine for depression, and ultimately the improvement of patient outcomes.

MATERIALS AND METHODS

Study participants

This observational study included 90 patients with depression (45 men and 45 women) and 30 healthy individuals (15 men and 15 women) who were enrolled consecutively from October 2023 to March 2025. All participants provided written informed consent prior to enrollment. This study was reviewed and approved by the Institutional Review Board of the Second Affiliated Hospital of Xinxiang Medical University (2023-52-1).

Sample size calculation

The sample size was determined using G*Power 3.1 software (Heinrich-Heine-Universität Düsseldorf, Germany), with reference to recent evidence on resting-state dynamic functional connectivity and dynamic brain network alterations in major depressive disorder[11,12]. We assumed a medium effect size of 0.25, a significance level of α = 0.05, and statistical power of 1 - β = 0.90. The calculation indicated that at least 78 patients with depression were required; therefore, 90 patients were enrolled to account for potential fMRI data artifacts or dropouts.

Inclusion and exclusion criteria

For the patient group, the inclusion criteria were: (1) Meeting the criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition[13]; (2) Age 18-55 years; (3) Hamilton Depression Rating Scale (HAMD-17) total score ≥ 18; (4) Acceptable resting-state fMRI data (head motion translation < 2 mm, rotation < 2°); and (5) Right-handedness. The exclusion criteria were: (1) Concurrent psychiatric disorders (e.g., schizophrenia, bipolar affective disorder); (2) History of organic brain disease (e.g., stroke, tumor) or neurological conditions; (3) Use of medications that affect the central nervous system (e.g., antipsychotics, corticosteroids) within the past 3 months; (4) History of substance dependence or substance use disorder; and (5) fMRI data artifacts impeding analysis.

For the control group, the inclusion criteria were: (1) Age 18-55 years; (2) Right-handedness; (3) No history or family history of psychiatric disorders; (4) HAMD-17 total score < 7; and (5) Acceptable resting-state fMRI data. The exclusion criteria were the same as those for the depression group.

Data collection

Demographic characteristics, clinical data, and fMRI data were prospectively collected following enrollment: Age, sex, years of education, body mass index (BMI), disease duration, number of episodes, prior treatment history (pharmacotherapy/psychotherapy), and HAMD-17[14] scores (assessing depression severity).

fMRI data

Resting-state fMRI data were acquired (Siemens Prisma 3.0T; Siemens Healthineers, Erlangen, Germany) using the following parameters: Repetition time (TR), 2000 milliseconds; echo time, 30 milliseconds; voxel size, 3 mm × 3 mm × 3 mm; and scan duration, 8 minutes (240 volumes). The participants were instructed to keep their eyes closed and relax while remaining awake.

Data processing and dynamic brain network construction

Using the DPABI software, head motion correction, spatial normalization (Montreal Neurological Institute brain standard template), removal of linear drift, bandpass filtering (0.01-0.08 Hz), and covariate regression (white matter, cerebrospinal fluid signals) were performed. Using the sliding time-window technique (window width, 40 TRs; step size, 2 TRs), the entire brain was divided into 90 regions of interest (based on the Automated Anatomical Labeling atlas). Pearson correlation coefficients were calculated between the regions of interest within each window to construct a dynamic functional connectivity matrix.

Outcome measures

Differences were assessed between the depression and control groups for the following indicators: (1) Demographic and clinical characteristics; (2) Global network topology properties: Global efficiency (GE), local efficiency (LE), clustering coefficient (CC), small-worldness (σ = GE/GE of random network); (3) Network modularity characteristics: Number of modules (N_mod), coefficient of variation in module size (CV_size), mean within-module connectivity strength (W_in), and mean inter-module connectivity (W_out); (4) Dynamic functional connectivity variability: Mean dynamic functional connectivity variability (dFCV), state transition frequency, and state dwell time; and (5) Key network dynamic connectivity: DMN internal connectivity, SN-limbic system connectivity, and involvement of ECN hub nodes. Additionally, subgroup analyses were conducted according to sex (male, female).

Statistical analysis

Data processing was performed using SPSS software (version 28.0; IBM Corp., Armonk, NY, United States). Measurement data are expressed as mean ± SD, and count data are presented as n (%), and continuous variables were analyzed using the independent samples t-test, and categorical variables were analyzed using the χ² test. For correlation analysis, Pearson correlation for normally distributed data. Multivariate linear regression models were used to explore the associations between potential confounding factors and key dynamic brain network indicators, and collinearity was tested using the variance inflation factor (VIF), with a VIF < 3 indicating no significant collinearity. The threshold for statistical significance was set at P < 0.05.

RESULTS

Demographic and clinical characteristics

In the present sample, the depression and control groups were similar in age (P = 0.564). As expected, patients with depression had markedly higher HAMD-17 scores than healthy controls (27.00 ± 4.85 vs 3.03 ± 1.67, P < 0.001). BMI, history of substance abuse, and years of education did not differ significantly between the two groups (P > 0.05; Table 1).

Table 1 Demographic characteristics of the study participants, mean ± SD/n (%).

Groups	Age	BMI (kg/m2)	HAMD-17	Course of the disease (months)	History of substance abuse, yes/no	Years of education, high school/≥ high school
Control group (n = 30)	39.30 ± 10.90	22.08 ± 2.78	3.03 ± 1.67	-	5 (16.67)/25 (83.33)	17 (56.67)/13 (43.33)
Depression group (n = 90)	38.07 ± 9.83	21.19 ± 2.85	27.00 ± 4.85	19.01 ± 8.44	25 (27.78)/65 (72.22)	61 (67.78)/29 (32.22)
t	0.579	1.479	26.504	-	1.481	1.221
P value	0.564	0.142	< 0.001	-	0.224	0.269

BMI: Body mass index; HAMD-17: Hamilton Depression Rating Scale-17 items.

Among patients with depression, women had higher HAMD-17 scores than men (28.11 ± 4.96 vs 25.89 ± 4.51, P = 0.029). Women also had lower BMI and a lower proportion of substance abuse history than men (both P < 0.05), while age, disease duration, and years of education were comparable between the two sex subgroups (Table 2).

Table 2 Differences in demographic and clinical characteristics between male and female patients with depression, mean ± SD/n (%).

Subgroup	Age	BMI (kg/m2)	HAMD-17	Course of the disease (months)	History of substance abuse, yes/no	Years of education, high school/≥ high school
Men (n = 45)	37.22 ± 9.31	21.94 ± 2.95	25.89 ± 4.51	19.71 ± 8.69	17 (37.78)/28 (62.22)	32 (71.11)/13 (28.89)
Women (n = 45)	38.91 ± 10.36	20.44 ± 2.57	28.11 ± 4.96	18.31 ± 8.22	8 (17.78)/37 (82.22)	29 (64.44)/16 (35.56)
t	0.813	2.564	2.223	0.785	4.486	0.458
P value	0.418	0.012	0.029	0.435	0.034	0.499

BMI: Body mass index; HAMD-17: Hamilton Depression Rating Scale-17 items.

Comparison of global network topology properties

For global network topology, patients with depression had lower GE than healthy controls (0.42 ± 0.05 vs 0.49 ± 0.04; P < 0.001). LE, CC, and σ were also significantly lower in the depression group (P < 0.001; Table 3).

Table 3 Comparison of global network topology properties, mean ± SD.

Groups	GE	LE	CC	σ
Control group (n = 30)	0.49 ± 0.04	0.42 ± 0.04	0.39 ± 0.02	2.08 ± 0.12
Depression group (n = 90)	0.42 ± 0.05	0.37 ± 0.05	0.35 ± 0.05	1.86 ± 0.16
t	7.078	4.712	3.493	7.006
P value	< 0.001	< 0.001	< 0.001	< 0.001

GE: Global efficiency; LE: Local efficiency; CC: Clustering coefficient; σ: Small-worldness.

In the sex-stratified analysis, women had lower GE than men (0.40 ± 0.04 vs 0.44 ± 0.05, P < 0.001), whereas σ was higher in women than in men (P < 0.001). LE and CC did not differ significantly between male and female patients (Table 4).

Table 4 Differences in global network topology properties between male and female patients with depression, mean ± SD.

Subgroup	GE	LE	CC	σ
Men (n = 45)	0.44 ± 0.05	0.38 ± 0.04	0.36 ± 0.04	1.78 ± 0.10
Women (n = 45)	0.40 ± 0.04	0.37 ± 0.05	0.35 ± 0.06	1.94 ± 0.16
t	3.448	1.624	1.046	5.374
P value	< 0.001	0.108	0.299	< 0.001

GE: Global efficiency; LE: Local efficiency; CC: Clustering coefficient; σ: Small-worldness.

Comparison of network modularity characteristics

The depression group showed significantly lower N_mod (P = 0.006), higher CV_size (P < 0.001), and lower W_in (P < 0.001) and W_out (P < 0.001) than the control group, indicating impaired network modularity and insufficient cross-module information integration. In sex-stratified analyses, women with depression had significantly lower W_in (P < 0.001) and higher W_out (P < 0.001) than men (Tables 5 and 6).

Table 5 Comparison of network modularity characteristics, mean ± SD.

Groups	N_mod	CV_size	W_in	W_out
Control group (n = 30)	5.86 ± 0.83	0.31 ± 0.06	0.36 ± 0.04	0.18 ± 0.03
Depression group (n = 90)	5.18 ± 1.23	0.45 ± 0.10	0.28 ± 0.06	0.12 ± 0.03
t	2.816	6.860	7.115	8.576
P value	0.006	< 0.001	< 0.001	< 0.001

N_mod: Number of modules; CV_size: Coefficient of variation in module size; W_in: Mean within-module connectivity strength; W_out: Mean inter-module connectivity.

Table 6 Differences in network modularity characteristics between male and female patients with depression, mean ± SD.

Subgroup	N_mod	CV_size	W_in	W_out
Men (n = 45)	5.23 ± 1.31	0.45 ± 0.10	0.31 ± 0.04	0.11 ± 0.02
Women (n = 45)	5.13 ± 1.15	0.44 ± 0.10	0.25 ± 0.06	0.14 ± 0.03
t	0.417	0.718	5.314	7.095
P value	0.678	0.475	< 0.001	< 0.001

N_mod: Number of modules; CV_size: Coefficient of variation in module size; W_in: Mean within-module connectivity strength; W_out: Mean inter-module connectivity.

Comparison of dynamic functional connectivity variability

The depression group showed significantly higher mean dFCV (0.22 ± 0.05 vs 0.17 ± 0.04, P < 0.001), lower state transition frequency (1.96 ± 0.39 times/minute vs 2.40 ± 0.67 times/minute, P < 0.001), and shorter state dwell time (25.06 ± 6.11 seconds vs 34.87 ± 7.67 seconds, P < 0.001) than the control group, indicating abnormal dynamic flexibility of brain networks characterized by excessive fluctuation and reduced stability. In sex-stratified analyses, women with depression had significantly higher mean dFCV (0.25 ± 0.03 vs 0.19 ± 0.04, P < 0.001) and lower state transition frequency (1.80 ± 0.40 times/minute vs 2.11 ± 0.32 times/minute, P < 0.001) than men, demonstrating more intense network fluctuations in women and more frequent state switching in men (Tables 7 and 8).

Table 7 Comparison of dynamic functional connectivity variability, mean ± SD.

Groups	dFCV	State transition frequency (times/minute)	State residence time (second)
Control group (n = 30)	0.17 ± 0.04	2.40 ± 0.67	34.87 ± 7.67
Depression group (n = 90)	0.22 ± 0.05	1.96 ± 0.39	25.06 ± 6.11
t	5.014	4.405	7.128
P value	< 0.001	< 0.001	< 0.001

dFCV: Dynamic functional connectivity variability.

Table 8 Differences in dynamic functional connectivity variability between male and female patients with depression, mean ± SD.

Subgroup	dFCV	State transition frequency (times/minute)	State residence time (second)
Men (n = 45)	0.19 ± 0.04	2.11 ± 0.32	24.76 ± 6.41
Women (n = 45)	0.25 ± 0.03	1.80 ± 0.40	25.36 ± 5.86
t	7.692	4.057	0.464
P value	< 0.001	< 0.001	0.644

dFCV: Dynamic functional connectivity variability.

Key network dynamic connectivity

The depression group exhibited significantly weaker DMN internal connectivity (0.35 ± 0.07 vs 0.48 ± 0.07, P < 0.001) and SN-limbic system connectivity (0.28 ± 0.06 vs 0.42 ± 0.07, P < 0.001) than the control group, along with reduced involvement of ECN hub nodes (dorsolateral prefrontal cortex) (0.62 ± 0.06 vs 0.42 ± 0.06, P < 0.001). In sex-stratified analyses, women with depression showed significantly weaker DMN internal connectivity (0.31 ± 0.06 vs 0.39 ± 0.05, P < 0.001), indicating more pronounced DMN spontaneous activity suppression. However, men with depression showed significantly weaker SN-limbic system connectivity (0.25 ± 0.04 vs 0.31 ± 0.06, P < 0.001), reflecting more prominent deficits in the emotion regulation network (Tables 9 and 10).

Table 9 Comparison of key network dynamic connectivity, mean ± SD.

Groups	DMN internal connectivity	SN-limbic system connectivity	ECN hub nodes
Control group (n = 30)	0.48 ± 0.07	0.42 ± 0.07	0.42 ± 0.06
Depression group (n = 90)	0.35 ± 0.07	0.28 ± 0.06	0.62 ± 0.06
t	9.319	10.431	15.213
P value	< 0.001	< 0.001	< 0.001

DMN: Default mode network; SN: Salience network; ECN: Executive control network.

Table 10 Differences in key network dynamic connectivity between male and female patients with depression, mean ± SD.

Subgroup	DMN internal connectivity	SN-limbic system connectivity	ECN hub nodes
Men (n = 45)	0.39 ± 0.05	0.25 ± 0.04	0.61 ± 0.06
Women (n = 45)	0.31 ± 0.06	0.31 ± 0.06	0.63 ± 0.07
t	7.200	5.198	1.701
P value	< 0.001	< 0.001	0.093

DMN: Default mode network; SN: Salience network; ECN: Executive control network.

Analysis of influencing factors

Multiple linear regression was performed with depression as the independent variable, and indicators with significant differences in the above analyses were used as independent variables. HAMD-17 scores were excluded because of their significant collinearity (VIF = 4.528). Analysis of the other independent variables showed that GE (P = 0.031), σ (P < 0.001), CV_size (P = 0.026), W_in (P = 0.017), W_out (P < 0.001), state transition frequency (P = 0.004), SN-limbic system connectivity (P = 0.004), and ECN hub nodes (P < 0.001) were independent factors affecting depression (Table 11).

Table 11 Influencing factors of dynamic brain network indicators.

Factors	Non-standardized coefficient		Standardized coefficient	t	P value	Collinearity statistic
	B	SE	β			Tolerance	VIF
HAMD-17	0.017	0.002	0.438	9.117	< 0.001	0.221	4.528
GE	-0.472	0.216	-0.06	-2.185	0.031	0.677	1.477
LE	-0.053	0.231	-0.006	-0.23	0.818	0.712	1.405
CC	-0.252	0.222	-0.028	-1.133	0.260	0.817	1.224
σ	-0.284	0.074	-0.115	-3.832	< 0.001	0.571	1.752
N_mod	-0.007	0.009	-0.019	-0.801	0.425	0.864	1.157
CV_size	0.258	0.114	0.064	2.256	0.026	0.640	1.562
W_in	-0.477	0.196	-0.072	-2.432	0.017	0.588	1.701
W_out	-1.298	0.354	-0.116	-3.664	< 0.001	0.506	1.978
dFCV	0.308	0.239	0.036	1.288	0.201	0.65	1.538
State transition frequency	-0.064	0.022	-0.076	-2.949	0.004	0.766	1.306
State residence time	-0.001	0.002	-0.013	-0.457	0.648	0.624	1.603
DMN internal connectivity	-0.309	0.166	-0.063	-1.861	0.066	0.452	2.214
SN-limbic system connectivity	-0.514	0.177	-0.1	-2.905	0.004	0.428	2.335
ECN hub nodes	0.611	0.156	0.152	3.925	< 0.001	0.338	2.958

HAMD-17: Hamilton Depression Rating Scale-17 items; GE: Global efficiency; LE: Local efficiency; CC: Clustering coefficient; σ: Small-worldness; N_mod: Number of modules; CV_size: Coefficient of variation in module size; W_in: Mean within-module connectivity strength; W_out: Mean inter-module connectivity; dFCV: Dynamic functional connectivity variability; DMN: Default mode network; SN: Salience network; ECN: Executive control network; β: Standardized regression coefficient; VIF: Variance inflation factor.

DISCUSSION

In this study, we analyzed dynamic brain network properties in patients with depression. The depression group showed lower global and local efficiency, altered modularity, higher dynamic functional connectivity variability, lower DMN and SN-limbic connectivity, and higher ECN hub node involvement than the control group. In the sex subgroup analysis, female patients showed lower GE, lower W_in, higher dFCV, and lower DMN connectivity. Male patients showed lower W_out, higher state transition frequency, and lower SN-limbic system connectivity. These results indicate that the network changes in depression were not exactly the same between men and women.

When compared with the control group, the depression group showed lower GE, LE, and σ values, indicating impaired information transmission efficiency and integration capacity. These results align with those of previous static network studies[15], showing that functional decoupling between the DMN and ECN in patients with depression can be further validated through dynamic network analysis. In our subgroup analysis, female patients had lower GE, while male patients had lower σ, this finding should not be simply understood as one sex having more severe network damage than the other. It may indicate that different aspects of network topology were affected. Estrogen strengthens network integration capacity by modulating synaptic plasticity, and Holmes et al[16] showed that female patients may exhibit reduced estrogen receptor sensitivity, resulting in more pronounced declines in network efficiency.

For modularity, patients with depression had lower N_mod, W_in, and W_out, and higher CV_size, these results suggest that the modular structure was less regular in the depression group. As we all know, emotional regulation depends on the cooperation of several brain networks, including the DMN, SN, ECN, and limbic regions, so these modular changes may be related to emotional and cognitive symptoms in depression[17]. This is also consistent with the findings of Nie et al[18], and the reduced N_mod and W_in reflect emotional circuit module dysfunction. In this study, women demonstrated more pronounced within-module connectivity deficits, whereas men showed enhanced within-module segregation, this disparity may reflect sex-specific neurodevelopmental trajectories, such as finer modular specialization within emotional circuits (e.g., the DMN) in women and heightened within-module competition in cognitive control circuits (e.g., the ECN) in men[19].

The dynamic connectivity results showed that patients with depression had higher dFCV and shorter state dwell time, this means that functional connectivity changed more strongly over time, and stable network states lasted for a shorter time. At the same time, the state transition frequency was lower in the depression group, so the abnormality should not be simply interpreted as more frequent switching, it may reflect an unstable but inefficient temporal pattern of brain networks. Previous studies have linked dynamic connectivity abnormalities with anterior cingulate-limbic regulation and emotional symptoms in depression[20,21]. In the sex subgroup analysis, women had higher dFCV, while men had higher state transition frequency. Therefore, women and men may show different forms of dynamic network instability. Hormone- and stress-related factors may contribute to this difference[22], but this study did not directly test them.

In the key network analysis, we observed weaker DMN internal connectivity and weaker SN-limbic system connectivity in patients with depression. These differences may be related to sex-specific developmental patterns of the amygdala-prefrontal circuit; the amygdala in men is larger and more closely connected with the dorsolateral prefrontal cortex[23], so connectivity defects may have a more direct impact on emotional regulation. Conversely, excessive DMN inhibition in women may cause more severe impairment to the neural basis of rumination[24,25].

Finally, the regression analysis showed that GE, σ, CV_size, W_in, W_out, state transition frequency, SN-limbic connectivity, and ECN hub node involvement were associated with depression status. However, they should not be described as diagnostic biomarkers at present, because sample size was not large, and the regression model was not tested in another cohort.

Not only that, we included only right-handed participants, this reduced the possible influence of hemispheric lateralization, but it also limited the generalizability of the results. Secondly, this was a single-center study, and all patients were outpatients, so the results of the study may not apply to hospitalized patients or patients with severe or treatment-resistant depression. Finally, although we analyzed sex differences, we did not fully examine hormone levels, genetic factors, medication history, or other possible mediating factors, so future studies should include larger samples, longitudinal follow-up, and multimodal imaging.

CONCLUSION

This study showed that patients with depression had abnormalities in network efficiency, modular organization, temporal stability, and key emotion-related networks. Female patients showed more obvious DMN-related and within-module abnormalities, while male patients showed more obvious SN-limbic and between-module connectivity abnormalities.