Dysregulated connectivity configuration of functional network model in First- Episode, Treatment-Naive Adolescents with Major Depressive Disorder

Dysregulated connectivity configuration of functional network model in First- Episode, Treatment-Naive Adolescents with Major Depressive Disorder | 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 Dysregulated connectivity configuration of functional network model in First- Episode, Treatment-Naive Adolescents with Major Depressive Disorder Chan Zhang, Wenjie zhang, Jinji Bai, Xuan Deng, Jinyuan Zhao, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9042814/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Major depressive disorder (MDD) is a highly prevalent psychiatric condition that frequently emerges during adolescence, a critical developmental stage characterized by heightened vulnerability to emotional dysregulation. Despite increasing evidence of large-scale brain network dysfunction in adult MDD, the static and dynamic connectivity alterations underlying adolescent MDD remain poorly understood. Methods: We recruited 29 first-episode, treatment-naïve adolescents with MDD and 29 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) data were analyzed using group independent component analysis (ICA) combined with sliding-window clustering to evaluate both static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) across the default mode network (DMN), salience network (SN), central executive network (CEN), and dorsal attention network (DAN). Correlation analyses were performed between connectivity metrics and clinical severity assessed by the 17-item Hamilton Depression Rating Scale (HAMD-17). Results: Compared with HCs, adolescents with MDD exhibited significantly reduced intra- and inter-network connectivity within the DMN, SN, and CEN (all p < 0.05), alongside increased DAN–SN connectivity. Dynamic analyses revealed reduced state transition frequency, shorter dwell time in low-connectivity states (e.g., DMN–CEN–SN interactions), and longer dwell time in high-connectivity states (e.g., DAN–SN coupling). Clinical analyses demonstrated that weaker intra-DMN and intra-DAN connectivity, as well as reduced DMN–CEN and DMN–SN connectivity, were negatively correlated with HAMD-17 scores (r = − 0.296 to − 0.503, all p < 0.05). Conversely, prolonged dwell time in hyperconnected states positively correlated with greater symptom severity (r = 0.479, p < 0.001). Conclusion: Our findings highlight distinct static and dynamic network abnormalities in adolescent MDD, including disrupted DMN–CEN competitive balance and maladaptive DAN–SN hyperconnectivity. These alterations suggest developmental-stage–specific neuropathological mechanisms that differ from adult depression. Integrating static and dynamic FNC analyses may provide novel biomarkers for early detection and intervention strategies in adolescent MDD. Major depressive disorder adolescence resting-state fMRI functional network connectivity dynamic functional connectivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Major depressive disorder (MDD) is a prevalent psychiatric disorder characterized by persistent sadness, irritability, diminished concentration, and impaired social functioning, affecting more than 350 million individuals worldwide( 1 ). In China, MDD has become the second leading cause of disability, underscoring its substantial public health burden( 2 ). Adolescence represents a critical developmental window for emotional regulation and cognitive maturation, and is also a peak period for the onset of MDD( 3 – 5 ). Compared with adults, adolescents with MDD exhibit higher recurrence rates and distinctive clinical manifestations, including pronounced mood fluctuations and heightened sensitivity to social evaluation( 6 ). Furthermore, adolescent MDD is associated with increased risks of substance abuse, cognitive impairment, and academic difficulties( 7 ), highlighting the urgent need to clarify its underlying neurobiological mechanisms. Currently, the diagnosis of adolescent depression primarily relies on depression scale screenings, which is a symptom-based approach. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V), a diagnosis of depression can be made if a patient exhibits five or more of the following symptoms persistently over a two-week period: depressed mood, diminished interest or pleasure, significant weight or appetite changes, insomnia or hypersomnia, psychomotor retardation or agitation, fatigue, feelings of worthlessness or guilt, diminished concentration, or recurrent suicidal ideation or attempts( 8 ). However, these subjective approaches are vulnerable to inter-rater variability and limited diagnostic precision. Accordingly, there is a pressing need to identify objective biomarkers that can improve diagnostic accuracy and guide personalized treatment strategies. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive, radiation-free approach to explore large-scale brain network interactions( 9 ). This neuroimaging approach, which reflects the relationship between brain circuitry and human behavior, holds potential as a source of biological markers for psychiatric disorders( 10 ). Blood oxygen level-dependent fMRI (BOLD-fMRI) detects spontaneous low-frequency fluctuations in neural activity; which can be used to infer functional connectivity (FC) across distributed brain regions( 11 ). Functional connectivity has been demonstrated to reliably map reproducible resting-state brain networks at both individual and group levels( 12 ). The human brain, estimated to contain 100 to 1,000 trillion synapses, exhibits resting-state networks (RSNs) that play critical roles in brain function and disorders such as depression. Modern network theory highlights the suitability of studying this complex neural system from a network perspective, given its intricate organization( 13 ). Previous studies have consistently identified four core resting-state networks relevant to MDD pathophysiology: the default mode network (DMN), salience network (SN), central executive network (CEN), and dorsal attention network (DAN)( 14 ). However, the developmental specificity and dynamic characteristics of these functional network abnormalities in adolescents—a unique population—remain poorly understood. The Default Mode Network (DMN), dominant during rest and associated with self-referential thinking and episodic memory, shows hyperactivation in depression, strongly correlating with rumination and fixation on negative emotions( 15 ), Key DMN regions include the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), precuneus, and angular gyrus( 16 ). The Salience Network (SN), composed of the anterior insula (aINS) and dorsal anterior cingulate cortex (dACC), acts as a "switch" for internal and external stimuli, dynamically allocating attentional resources between the DMN (self-focused processing) and the CEN/DAN (task-oriented processing)( 17 , 18 ). Meanwhile, the CEN and DAN support higher-order cognitive control and spatial attention maintenance, respectively. Their weakened functionality may contribute to impaired executive function and attentional deficits( 19 ). Studies in adults with depression often report a "competitive imbalance" between the DMN and CEN, where DMN hyperactivation suppresses CEN-mediated cognitive control( 20 ). However, adolescence is characterized by ongoing neural maturation, particularly within prefrontal–limbic circuits, which may yield developmental-stage–specific network abnormalities( 21 ). Most prior studies have focused on static FNC (sFNC), reflecting averaged connectivity patterns across the entire scanning session. there remains a lack of systematic exploration into how dynamic functional network connectivity (dFNC)—such as state transition frequency (NT) and mean dwell time (MDT) of specific network states—influences depressive symptoms in adolescents. Dynamic functional network connectivity(dFNC)( 22 ), typically measured using sliding-window and clustering methods, captures temporal variations in network states and provides complementary insights into large-scale brain interactions. This approach enables the study of functional interactions between brain networks and their underlying architecture without restricting analysis to predefined regional connections. Research suggests that functional connectivity (FC) dynamics may become more pronounced during unrestricted mental processes in the resting state( 23 ). Some researchers propose that individuals freely engage in spontaneous mental activities during conscious rest( 24 ). From this perspective, brain functional connectivity can vary over short time scales rather than remaining static. Alterations in dynamic FC have been implicated in multiple psychiatric conditions, including Alzheimer’s disease( 25 ), schizophrenia( 26 ),and depression( 27 ).Investigating whole-brain dynamic network connectivity patterns could offer novel insights into abnormal brain communication in MDD. Static and dynamic FC processes are complementary, and both are indispensable for understanding cognitive function and its development. However, systematic investigations of dFC abnormalities in adolescents with MDD remain limited, leaving critical questions unanswered. For example, do adolescents with MDD exhibit unique temporal patterns of network dysfunction distinct from adults? How do these dynamic features relate to symptom severity? Therefore, this study aimed to investigate both static and dynamic functional network connectivity (FNC) in first-episode, treatment-naïve adolescents with MDD. We hypothesized that ( 1 ) adolescents with MDD would exhibit disrupted intra- and inter-network connectivity within the DMN, SN, CEN, and DAN; ( 2 ) dynamic functional connectivity would show altered state transitions and mean dwell times; and ( 3 ) these abnormalities would correlate with depressive symptom severity, reflecting developmental-stage–specific neural mechanisms distinct from adult MDD. 2. Methods 2.1 Participants We recruited 29 treatment-naïve adolescents (aged 10–19 years) with first-episode MDD from the Changzhi Mental Health Center between January 2020 and December 2022. 29 age- and sex-matched healthy controls (HCs) were enrolled through community advertisements and online platforms (e.g., WeChat). The study was approved by the Ethics Committee of Changzhi Medical College (Approval No. ChiCTR2000038210) according to the standards of the Declaration of Helsinki. Written informed consent was obtained from the parents or legal guardians of all participants, and written informed assent was provided by the adolescents themselves. 2.1.1 Inclusion and Exclusion Criteria Inclusion criteria for the MDD group were: ( 1 ) diagnosis of MDD based on the Chinese Classification of Mental Disorders, Version 3 (CCMD-3) and DSM-5 criteria; ( 2 ) HAMD-17 score ≥ 7; ( 3 ) first depressive episode and no prior treatment; ( 4 ) right-handedness; ( 5 ) no history of psychiatric disorders in first-degree relatives; ( 6 ) no neurological disorders, substance/alcohol abuse, or MRI contraindications. Exclusion criteria included: ( 1 ) comorbid psychiatric disorders (e.g., bipolar disorder, epilepsy); ( 2 ) history of significant head trauma or brain surgery; ( 3 ) pregnancy; and ( 4 ) left-handedness. 2.1.2 The clinical data collection Demographic and clinical data were systematically gathered by trained psychiatrists, encompassing variables such as age, sex, anthropometric measures (height and weight), handedness, educational attainment, prior medical records, personal and family psychiatric history, occupation, and ethnicity. Diagnosis of MDD was established independently by two senior clinical psychiatrists based on DSM-V criteria. To further ensure diagnostic accuracy and rule out other psychiatric conditions, all adolescent participants and their parents underwent the semi-structured Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (K-SADS-PL), which integrates reports from both the child and a parent to evaluate lifetime and current mental disorders. Depression severity was quantified using the 17-item Hamilton Depression Rating Scale (HAMD-17). 2.2 MRI data acquisition All imaging was performed on a 3.0 T MAGNETOM Skyra scanner (Siemens Healthcare, Erlangen, Germany) equipped with a 32-channel head coil at the Peace Hospital affiliated to Changzhi Medical College. For both the adolescent MDD group and the HC group, scans were acquired using T1-weighted imaging (T1WI), T2-FLAIR, echo planar imaging (EPI) sequence and 3D-T1 sequence. First, using T1WI,T2-FLAIR excluding organic lesions. Functional MRI data was acquired using an echo-planar imaging sequence with the following parameters repetition time/echo time = 2000 ms / 30 ms. flip angle = 90◦,field of view = 224 × 224 mm 2 , matrix size = 64 × 64, isotropic voxel size = 3.5 × 3.5 × 3.5 mm, phase encoding direction = A ≫ P,240 slices at 3.5 mm thickness covering the whole brain For each participant, with a scanning duration of 8 min and 6 s. Structural MRI data was acquired through a 3D T1-weighted gradient echo sequence with repetition time/echo time = 25 ms / 2.98 ms; phase encoding direction = A ≫ P, field of view = 256 × 256mm 2 , flip angle = 90°, 192 sagittal slices covering the whole brain with isotropic voxel size of 1 mm × 1 mm × 1 mm and an acquisition time of 6 min and 3 s. Participants lay flat on the MRI scanning bed with their eyes closed and their head fixed with a foam head pad. Noise-canceling earplugs were given to the participants to reduce the MRI noise, and they were instructed to avoid thinking as much as possible. 2.3 data preprocessing Data preprocessing was performed using GRETNA 2.0 (Graph Theoretical Network Analysis). For each subject, the conversion of the original DICOM data file into NIFTI format. The first 10 volumes of the rs-fMRI dataset were discarded to allow for MR signal equilibrium; then we performed slice-timing correction before using rigid body motion correction to adjust subject head motion .All of the subjects in this study satisfied our criteria for head motion with displacement < 2.0 mm in any plane and rotation < 1.5° in any direction.After that, we resampled to 3 × 3 × 3 mm 3 using an echo planar imaging template in the standard Montreal Neurological Institute (MNI) space .Finally, we smoothed the fMRI images using a Gaussian kernel having a full-width at half-maximum (FWHM) of 8 mm. linear detrending, and band-pass filter (0.01–0.08 HZ).Nuisance covariates including global mean signals, white matter, and cerebrospinal fluid (CSF) signals were regressed out from the blood oxygen level-dependent (BOLD) signals. 2.4 Independent component analysis and State function connectivity networks analysis Functional network decomposition was carried out using group-level spatial independent component analysis (ICA) implemented in the GIFT software (v3.0b; http://mialab.mrn.org/software/gift/ ). To reduce computational load, a two-stage principal component analysis was applied: first, each subject's preprocessed fMRI data underwent temporal dimension reduction; second, the concatenated data from all subjects were further reduced in dimension. Subsequently, ICA with the Infomax algorithm was applied to the group-level data to extract 46 independent components (ICs), yielding both spatial maps and corresponding time courses for each IC. The stability and reliability of the IC decomposition were ensured by repeating the ICA 100 times via the ICASSO procedure. Subject-specific ICs were then obtained through a back-reconstruction approach and converted to z-score maps, which formed the basis for voxel-wise one-sample t-tests at the group level. The resulting group-level t-maps enabled identification of brain regions significantly associated with each IC. Then we identified ICs of interest by the visual screening based on previous reported template-matching. First, the spatial and temporal profiles of each component were visually examined and labelled as DMN, CEN, DAN and SN. Next, these labels were further determined by calculating spatial correlations between each component and the reference templates( 28 , 29 ). To evaluate inter-network functional connectivity, we computed pairwise Pearson’s correlations between the time courses of the four identified RSNs for each subject, yielding six unique connectivity pairs. These correlation coefficients were then entered into a multivariate analysis of covariance (MANCOVAN) within the GIFT toolbox to test for group differences in static functional network connectivity (sFNC). 2.5 Dynamic Functional Network Connectivity analysis 2.5.1 Sliding Window Analysis. We estimated the dynamic FNC by computing Pearson’s correlations between time courses of ICs using the sliding window method. We set a window size of 20 TRs (44 s) with a step size of 2 TR (4 s) for each participant in accordance with previous studies. The window length of 20 TRs was chosen because previous studies( 24 , 30 ) have shown that a window length of 20–30 TRs may provide a good trade off between the quality of the FNC estimate and the temporal resolution. These time windows were convolved with a Gaussian value of σ = 3 TRs and computed precision matrix. To obtain z values and stabilize variance for further analyses, Fisher’s z-transformation was applied to the functional connectivity matrices. We regressed out age, sex and mean FD values as covariates. 2.5.2 Clustering Analysis To identify recurring patterns of dynamic functional connectivity, we employed a k-means clustering algorithm (squared Euclidean distance) on all sliding-window connectivity matrices. The analysis was repeated 500 times with 150 replicates to ensure robust clustering. The optimal number of clusters was determined using the elbow criterion, which indicated four distinct connectivity states, each characterized by a unique pattern of inter-network interactions. 2.5.3 Temporal Properties and Connectivity Strength of Dynamics States To characterize the temporal dynamics of functional connectivity, we computed three metrics for each state: fractional time (the proportion of total windows assigned to that state), mean dwell time (the average duration that a state was maintained before switching to another), and number of transitions (total switches between states across the scanning session). Age and sex were included as covariates in subsequent group comparisons. 2.5.4 Validation Analysis Two additional window lengths (22 and 25 TRs) were applied to test the robustness of the sliding window analysis. Moreover, the numbers of clusters were set at 5 and 6 also used to verify the stability of our results. 2.6 Statistical analyses Demographic and clinical characteristics were analyzed using SPSS 26.0 software. Continuous variables including age, BMI, and HAMD-17 scores were compared between groups using two-sample independent t -tests and reported as mean ± SD. Categorical data were compared using the chi-square test. the strength of FNC and dynamic FNC parameters were analyzed in GIFT software were assessed with Mann-Whiteney U tests, the p value for the above analysis was set at a level of p < 0.05, false discovery rate (FDR) correction for multiple comparisons. Pearson’s correlation was conducted to assess the relationship between the strength of FNC, dynamic FNC measures and HAMD-17 scores, controlling for age, sex, and mean FD values were treated as covariates. 3. Results 3.1 Demographics and clinical characteristics The details of demographic and clinical characteristics are shown in Table 1 . We did not observe any significant differences in age, education level, gender between MDD patients and HCs. HAMD-17 scores were significantly higher in the adolescent MDD group compared to the HC group ( p < 0.001). Table 1 Demographics and clinical characteristics of all participants. Variables MDD (n = 29) HC (n = 29) t/c 2 value p value Age(years) 15.07 ± 1.49 15.79 ± 2.23 1.46 0.15 Sex(male/female) 5/24 11/18 -3.11 0.08 Education(years) 9.07 ± 1.49 9.79 ± 2.23 1.46 0.15 BMI 19.20 ± 2.59 21.53 ± 4.26 2.52 0.02 HAMD-17(score) 18.54 ± 6.00 1.03 ± 1.52 -14.21 < 0.001 MDD, major depressive disorder; HC, healthy controls, BMI, body mass index; HAMD-17, the 17-items Hamilton Depression Scale; The data are presented as the mean ± standard deviation (SD) or percentages. Group differences were assessed using either chi-square analysis or independent t-tests. A p value < 0.05 was considered to indicate statistical significance. 3.2 ICs of interest Of the 46 ICs identified by the group ICA,28 ICs were identified as noise components and then discarded. The 18 ICs of interest, DMN, CEN, DAN and SN, which were identified and categorized into four networks using group ICA (Figure S1 ). Table S1 lists the ICs’ labels and peak activation coordinates. 3.3 FNC differences between MDD and HC Figure 1 illustrate the ICs that matched to the four brain networks. IC14, IC27, IC38, IC45 belongs to DMN; IC10, IC15, IC19, IC36, IC34, IC5 belongs to CEN; IC3, IC9, IC30, IC39 belongs to DAN; IC33, IC44 belongs to SN. (Table S2 and Fig. 1 ) illustrate differences in connectivity between MDD and HC. Compares with HCs,The MDD group exhibited decrease connectivity in the following pairs: intra-DMN_DMN( z = -2.605, p < 0.05); inter-DMN_CEN( z = -3.227, p < 0.05);DMN_SN ( z = -4.735, p < 0.05) ; CEN_SN ( z = -3.025, p < 0.05);intra-SN_SN ( z = -3.056, p < 0.05).On the other hand, MDD exhibited increase connectivity than HC in the following: DAN_SN ( z = 1.925, p < 0.05) . 3.4 Dynamic FNC states and properties When windows TR = 20, Fig. 2 shows the four identified states with highly structured FC that recurred throughout individual scans and across subjects, as well as their occurrence time and percentage. The proportions of the four states are 1% (178), 31% (3786), 8% (996) and 59% (7220) respectively. In state 3, compared with HCs, the DMN had positive function connectivity with SN and CEN; at the same time, the CEN with DAN and SN had decreased functional connectivity; SN had decreased connectivity with DAN in MDD group, In State 1,2,4, the FNC within the four networks was very sparse. Moreover, we found positive connections intra SN; negative connections intra DAN (Fig. 3 ). For dynamic FNC properties (Mann-Whiteney U tests, p FDR < 0.05 ), Compared with the HC group, we found significant decreased fraction time( FT )、mean dwell time ( MDT ) in state2,state3 and number of transitions ( NT ) in MDD patients .However, we found increased FT、MDT in state4 (Table S3 and Fig. 4 ). 3.5 Correlation Analysis 3.5.1 FNC difference index and HAMD-17 scores There is a negative correlation between the FNC strength of intra/inter-network and the HAMD-17 score(Figure 5 ). The strength of intra-DMN with HAMD-17 score had negative correlation (r = − 0.296, p = 0.024). The strength of intra-DAN with HAMD-17 score had negative correlation (r = − 0.270, p = 0.040). At the same time, The strength of DMN_CEN ( r =-0.386, p = 0.003),DMN_SN( r =-0.503, p <0.001 ),CEN_SN ( r =-0.259, p = 0.049)with HAMD-17 score had negative correlation. 3.5.2 Dynamic FNC properties and HAMD-17 scores The mean dwell time was negatively correlated with HAMD-17 score in state3 ( r = − 0.276, p = 0.036); positive correlated with HAMD-17 score in state4( r = 0.292, p = 0.026) The fraction time was negatively correlated with HAMD-17 score in state2 ( r = − 0.301, p = 0.022), state3 ( r = − 0.357, p = 0.006); positive correlated with HAMD-17 score in state4 ( r = 0.479, p < 0.001). The number of transitions was negatively correlated with HAMD-17 score in state2 (r = − 0.318, p = 0.015) (Table 2 , Fig. 5 and Figure S6). Table 2 Dynamic state features correlated with HAMD-17 scores. Fraction time of State 3 r value p value 0.42 < 0.001* Fraction time of State 4 -0.34 0.01 Mean dwell time State 3 0.35 0.01 Mean dwell time State 4 -0.27 0.04 When TR = 20, State = 5, the correlations between the dynamic FNC difference index and the HAMD-17 scores. p <0.05 was considered statistically significant. *significant p <0.001 3.6 Validation Analysis In this study, we used two different sliding window lengths to verify our main results of dynamic FNC. The results of the sliding window lengths of 22 and 25 TRs were similar to the results of the 20 TR we found. This study selected cluster = 5, 6. conduct verification analysis, the results of the dynamic FNC analysis were consistent with our main results (Figure S2-5,Table S4-5). 4. Discussion In this study of first-episode, treatment-naïve adolescents with major depressive disorder (MDD), we identified distinct patterns of both static and dynamic functional network dysconnectivity. Statically, adolescents with MDD showed reduced connectivity within and between the default mode network (DMN), central executive network (CEN), and salience network (SN), alongside hyperconnectivity between the dorsal attention network (DAN) and SN. Dynamically, they exhibited reduced state flexibility and spent more time in a hyperconnected state that correlated with symptom severity. Crucially, these static and dynamic alterations were significantly correlated with depressive symptoms, highlighting their clinical relevance and offering insights into the neurobiology of early-onset depression. 4.1 Disrupted Static Network Architecture in Adolescent MDD The weakened static connectivity within the DMN and between the DMN and SN likely reflects a core deficit in regulating self-referential thought. In healthy states, the SN helps disengage the DMN's introspective processing; its failure to do so in MDD may trap adolescents in ruminative, negative self-evaluative loops, which aligns with clinical presentations of irritability and social withdrawal ( 15 ) ( 31 ).Furthermore, the reduced DMN-CEN connectivity suggests a disruption of the typical antagonistic balance required to shift between internal thought and external, task-focused cognition ( 32 ). This breakdown may directly contribute to the attentional and executive function deficits, such as academic difficulties, commonly observed in depressed adolescents ( 33 ).Conversely, the observed DAN-SN hyperconnectivity points toward a state of maladaptive hypervigilance. This suggests the SN persistently biases the dorsal attention system toward emotionally salient, often negative, stimuli, creating a cycle that reinforces attention to and memory of adverse information ( 34 ). 4.2 Altered Network Dynamics Suggest Cognitive and Emotional Inflexibility Our dynamic analysis complements these static findings by revealing a loss of network flexibility. The significantly lower number of transitions between brain states in the MDD group suggests a "neurodynamic rigidity," where the brain is less able to fluidly shift between different information processing configurations in response to changing demands ( 35 ). This inflexibility may be a neural correlate of the cognitive rigidity and emotional perseveration characteristic of depression. This rigidity was further characterized by altered temporal properties. Patients spent less time in sparsely connected states (e.g., State 3), which correlated with milder symptoms and may represent an adaptive or restful mode. Conversely, they spent significantly more time in a hyperconnected state (State 4), and this prolonged dwelling was positively correlated with greater symptom severity. This suggests that adolescent MDD involves getting "stuck" in a maladaptive, resource-intensive network configuration that reinforces pathological mood. 4.3 Clinical Correlations and Developmental Specificity The clinical relevance of these network disruptions is underscored by their direct correlations with HAMD-17 scores. Weaker static connectivity within the DMN and between the DMN, CEN, and SN was associated with more severe depression, supporting the Triple Network Model's premise that impaired coordination between self-referential, executive, and salience-detecting networks is central to MDD pathophysiology ( 14 ). The link between prolonged dwell time in a hyperconnected dynamic state and higher symptom severity further solidifies the connection between neurodynamic rigidity and the clinical expression of depression. Crucially, these findings must be interpreted through the developmental lens of adolescence—a period of high neuroplasticity but also profound vulnerability due to the ongoing maturation of prefrontal-limbic circuits ( 36 , 37 ). The reduced DMN-CEN connectivity we observed may not just be a symptom of depression, but a sign of a disrupted or delayed developmental trajectory, as this anticorrelation typically strengthens with age ( 38 ). This underlying neural immaturity could amplify the impact of adolescent-specific stressors, such as social evaluation, potentially driving the SN hypersensitivity and maladaptive DAN-SN hyperconnectivity seen in our cohort ( 20 ). Therefore, the dysfunctions identified here may represent a critical developmental derailment, offering a unique window for early intervention before these pathological network patterns become consolidated in adulthood. 5. Limitations Our study has several limitations. First, the relatively small sample size may limit the generalizability of our findings and precluded more complex subgroup analyses. Future studies with larger cohorts are needed to validate these results. Second, the cross-sectional design prevents us from drawing conclusions about the dynamic progression of the disease over time. Longitudinal studies are essential to track these network changes and their relationship to treatment outcomes. Third, our analysis was restricted to four major networks; finer-grained analyses of subnetworks may reveal more specific pathophysiological mechanisms. Finally, this study relied exclusively on neuroimaging; future work should integrate multi-omics approaches to provide a more comprehensive understanding of the pathogenesis of adolescent MDD. 6. Conclusion In conclusion, by integrating static and dynamic fMRI analyses, this study demonstrates that first-episode adolescent MDD is characterized by both stable network imbalances and a loss of dynamic flexibility. The disrupted interplay between the DMN, CEN, and SN, coupled with maladaptive DAN-SN hyperconnectivity, is directly linked to symptom severity and likely reflects a pathological deviation in the brain's neurodevelopmental trajectory. These findings underscore the potential of using integrated neuroimaging metrics as biomarkers for early detection and suggest that interventions aimed at restoring network balance and flexibility during the critical neuroplastic window of adolescence may hold unique therapeutic promise. Abbreviations MDD Major depressive disorder sFNC Static functional network connectivity dFNC Dynamic functional network connectivity DMN Default mode network CEN Control Executive network SN Salience network DAN Dorsal attention network HAMD-17 17-item Hamilton Depression Rating Scale DSM-V The Diagnostic and Statistical Manual of Mental Disorders FC functional connectivity ICA Independent component analysis Declarations Acknowledgements None. Author Contributions All authors provided have made strong, direct, effective contributions to this research, and agree to publish it. Conceptualization: K. Li, and J. Li ; Literature search：C. Zhang,W. Zhang, J. Bai, X. Deng, J. Ji; IRB approvals: J. Ji, T. Li, Y. Wang, and J. Li; Participant enrollment: C. Zhang,W. Zhang, J. Zhao, J. Cui, J. Bai, X. Deng, J. Ji, T. Li, and Y. Wang; Data analysis and visualization: C. Zhang, W. Zhang, J. Cui, J. Ji, T. Li, and Y. Wang; Project supervision: J. Li. Funding: J. Li, and K. Li; Manuscript-first draft: C. Zhang, W. Zhang, Manuscript-review & editing: All the authors. All the authors have approved the final manuscript. Funding This study was supported by the Natural Science Foundation of Shanxi Province (201901D221113), the Four “Batches” Innovation Project of Invigorating Medical through Science and Technology of Shanxi Province (2023XM016), and the Macao Polytechnic University fund (RP/FCA-14/2023). Data Availability The data that supports the findings of this study are available from the corresponding authors upon reasonable request. Ethics approval and consent to participate The study was approved by the Ethics Committee of Changzhi Medical College (Approval No. ChiCTR2000038210) according to the standards of the Declaration of Helsinki. Written informed consent was obtained from the parents or legal guardians of all participants, and written informed assent was provided by the adolescents themselves. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Jeon SW, Kim YK. The role of neuroinflammation and neurovascular dysfunction in major depressive disorder. J Inflamm Res. 2018;11:179–92. Lu J, Xu X, Huang Y, Li T, Ma C, Xu G, et al. Prevalence of depressive disorders and treatment in China: a cross-sectional epidemiological study. lancet Psychiatry. 2021;8(11):981–90. Kessler RC, Amminger GP, Aguilar-Gaxiola S, Alonso J, Lee S, Ustün TB. Age of onset of mental disorders: a review of recent literature. Curr Opin Psychiatry. 2007;20(4):359–64. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):593–602. Weissman MM, Bland RC, Canino GJ, Faravelli C, Greenwald S, Hwu HG, et al. Cross-national epidemiology of major depression and bipolar disorder. JAMA. 1996;276(4):293–9. Thapar A, Collishaw S, Pine DS, Thapar AK. Depression in adolescence. Lancet. 2012;379(9820):1056–67. Kieling C, Adewuya A, Fisher HL, Karmacharya R, Kohrt BA, Swartz JR, et al. Identifying depression early in adolescence. Lancet Child Adolesc health. 2019;3(4):211–3. Uher R, Payne JL, Pavlova B, Perlis RH. Major depressive disorder in DSM-5: implications for clinical practice and research of changes from DSM-IV. Depress Anxiety. 2014;31(6):459–71. Roffet F, Delrieux C, Patow G. Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory. Brain Sci. 2022;12(9). Bebko G, Bertocci MA, Fournier JC, Hinze AK, Bonar L, Almeida JR, et al. Parsing dimensional vs diagnostic category-related patterns of reward circuitry function in behaviorally and emotionally dysregulated youth in the Longitudinal Assessment of Manic Symptoms study. JAMA psychiatry. 2014;71(1):71–80. Jann K, Smith RX, Rios Piedra EA, Dapretto M, Wang DJ. Noise Reduction in Arterial Spin Labeling Based Functional Connectivity Using Nuisance Variables. Front NeuroSci. 2016;10:371. Yang J, Gohel S, Vachha B. Current methods and new directions in resting state fMRI. Clin Imaging. 2020;65:47–53. Yu M, Linn KA, Shinohara RT, Oathes DJ, Cook PA, Duprat R, et al. Childhood trauma history is linked to abnormal brain connectivity in major depression. Proc Natl Acad Sci USA. 2019;116(17):8582–90. Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15(10):483–506. Hamilton JP, Furman DJ, Chang C, Thomason ME, Dennis E, Gotlib IH. Default-mode and task-positive network activity in major depressive disorder: implications for adaptive and maladaptive rumination. Biol Psychiatry. 2011;70(4):327–33. Andrews-Hanna JR, Smallwood J, Spreng RN. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann N Y Acad Sci. 2014;1316(1):29–52. Sikora M, Heffernan J, Avery ET, Mickey BJ, Zubieta JK, Peciña M. Salience Network Functional Connectivity Predicts Placebo Effects in Major Depression. Biol psychiatry Cogn Neurosci neuroimaging. 2016;1(1):68–76. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J neuroscience: official J Soc Neurosci. 2007;27(9):2349–56. Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3(3):201–15. Silk JS, Siegle GJ, Lee KH, Nelson EE, Stroud LR, Dahl RE. Increased neural response to peer rejection associated with adolescent depression and pubertal development. Soc Cognit Affect Neurosci. 2014;9(11):1798–807. Sheline YI, Price JL, Yan Z, Mintun MA. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci USA. 2010;107(24):11020–5. Preti MG, Bolton TA, Van De Ville D. The dynamic functional connectome: State-of-the-art and perspectives. NeuroImage. 2017;160:41–54. Tang Q, Cui Q, Chen Y, Deng J, Sheng W, Yang Y, et al. Shared and distinct changes in local dynamic functional connectivity patterns in major depressive and bipolar depressive disorders. J Affect Disord. 2022;298(Pt A):43–50. Wang S, Cai H, Cao Z, Li C, Wu T, Xu F, et al. More Than Just Static: Dynamic Functional Connectivity Changes of the Thalamic Nuclei to Cortex in Parkinson's Disease With Freezing of Gait. Front Neurol. 2021;12:735999. Delamillieure P, Doucet G, Mazoyer B, Turbelin MR, Delcroix N, Mellet E, et al. The resting state questionnaire: An introspective questionnaire for evaluation of inner experience during the conscious resting state. Brain Res Bull. 2010;81(6):565–73. Jones DT, Vemuri P, Murphy MC, Gunter JL, Senjem ML, Machulda MM, et al. Non-stationarity in the resting brain's modular architecture. PLoS ONE. 2012;7(6):e39731. Sakoğlu U, Pearlson GD, Kiehl KA, Wang YM, Michael AM, Calhoun VD. A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. Volume 23. New York, NY: Magma; 2010. pp. 351–66. 5–6. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(3):1125–65. Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cerebral cortex (New York, NY: 1991). 2012;22(1):158 – 65. Leonardi N, Van De Ville D. On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage. 2015;104:430–6. Corr R, Glier S, Bizzell J, Pelletier-Baldelli A, Campbell A, Killian-Farrell C, et al. Triple Network Functional Connectivity During Acute Stress in Adolescents and the Influence of Polyvictimization. Biol psychiatry Cogn Neurosci neuroimaging. 2022;7(9):867–75. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA. 2005;102(27):9673–8. Liston C, Chen AC, Zebley BD, Drysdale AT, Gordon R, Leuchter B, et al. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol Psychiatry. 2014;76(7):517–26. Monti MM, Parsons LM, Osherson DN. Response to Tzourio-Mazoyer and Zago: yes, there is a neural dissociation between language and reasoning. Trends Cogn Sci. 2012;16(10):495–6. Kaiser RH, Whitfield-Gabrieli S, Dillon DG, Goer F, Beltzer M, Minkel J, et al. Dynamic Resting-State Functional Connectivity in Major Depression. Neuropsychopharmacology: official publication Am Coll Neuropsychopharmacol. 2016;41(7):1822–30. Uhlhaas PJ. The adolescent brain: implications for the understanding, pathophysiology, and treatment of schizophrenia. Schizophr Bull. 2011;37(3):480–3. Eiland L, Romeo RD. Stress and the developing adolescent brain. Neuroscience. 2013;249:162–71. Sherman LE, Rudie JD, Pfeifer JH, Masten CL, McNealy K, Dapretto M. Development of the default mode and central executive networks across early adolescence: a longitudinal study. Dev Cogn Neurosci. 2014;10:148–59. Additional Declarations No competing interests reported. Supplementary Files Supplementalmatrials.doc Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviews received at journal 29 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers invited by journal 20 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Editor invited by journal 19 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 17 Mar, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9042814","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611586343,"identity":"70e4d5e7-b3f5-4242-a6e9-e1a79d7230c1","order_by":0,"name":"Chan Zhang","email":"","orcid":"","institution":"Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China","correspondingAuthor":false,"prefix":"","firstName":"Chan","middleName":"","lastName":"Zhang","suffix":""},{"id":611586344,"identity":"a9b35c57-0914-441d-8454-af06cf53c903","order_by":1,"name":"Wenjie zhang","email":"","orcid":"","institution":"Department of Radiology, Rizhao Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"zhang","suffix":""},{"id":611586345,"identity":"5bce1ec6-f895-4426-a236-7c5288b1d131","order_by":2,"name":"Jinji Bai","email":"","orcid":"","institution":"Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China","correspondingAuthor":false,"prefix":"","firstName":"Jinji","middleName":"","lastName":"Bai","suffix":""},{"id":611586346,"identity":"1523754d-6cb8-4b33-b113-2f9b07534382","order_by":3,"name":"Xuan Deng","email":"","orcid":"","institution":"Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Deng","suffix":""},{"id":611586347,"identity":"be2604fd-0351-4ee5-88a6-eed7c8594d86","order_by":4,"name":"Jinyuan Zhao","email":"","orcid":"","institution":"Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China","correspondingAuthor":false,"prefix":"","firstName":"Jinyuan","middleName":"","lastName":"Zhao","suffix":""},{"id":611586348,"identity":"3f53f3e3-3db0-4c4a-95ef-e824cf8d61a4","order_by":5,"name":"Jiajing Cui","email":"","orcid":"","institution":"Changzhi Key Lab of Functional Imaging for Brain Diseases, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China","correspondingAuthor":false,"prefix":"","firstName":"Jiajing","middleName":"","lastName":"Cui","suffix":""},{"id":611586349,"identity":"dad7e114-18f8-446c-9894-59d31e71fba8","order_by":6,"name":"Junjun Ji","email":"","orcid":"","institution":"Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China","correspondingAuthor":false,"prefix":"","firstName":"Junjun","middleName":"","lastName":"Ji","suffix":""},{"id":611586350,"identity":"d19c5d59-9411-44a9-8418-47e617e199f8","order_by":7,"name":"Ting Li","email":"","orcid":"","institution":"Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Li","suffix":""},{"id":611586351,"identity":"96f7c06d-3a67-45d3-b4fb-05a5514f0db0","order_by":8,"name":"Yu Wang","email":"","orcid":"","institution":"Department of Psychiatry, Changzhi Mental Health Center","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":611586352,"identity":"280ee5e2-1ec5-4b78-97cc-83649c2cd969","order_by":9,"name":"Kefeng Li","email":"","orcid":"","institution":"Faculty of Applied Sciences, Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Kefeng","middleName":"","lastName":"Li","suffix":""},{"id":611586353,"identity":"dd1afb60-4a75-45bb-9446-55b119cb9d32","order_by":10,"name":"Junfeng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACPmaGBAYGGzCb8UFCRQ1hLWxgLWlgNrPBgzPHiNACJiFa2CQftjAToYWd4Zl0QYJNnrz78WcViQ1sDPzt3QmEHJYmPSMhrdjwTELajcQdMgwSZ85uIKyF98fhxI0zGI7dSDzDxmAgkUuEFp6E/0AtjG0FiW3MRGs5kDhfgpmNgVgtydY8CcmJG3jSmCUSzhzjIegXfv4zibd5EuwS57cff/jxR0WNHH97L34tDAw8CWDK4ACUS0A5CLBD1Mo3EKF2FIyCUTAKRiYAAMIXQZy2szRaAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Radiology, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China","correspondingAuthor":true,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-03-05 17:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9042814/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9042814/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105565976,"identity":"f23c1179-6f2b-4b64-a897-059f5385b916","added_by":"auto","created_at":"2026-03-27 12:54:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":465561,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Group differences in the selected FNC. The red line indicates an increased FNC ( \u003cem\u003ep \u003c/em\u003e＜ 0.05 ) ；the blue line indicates an decreased FNC( \u003cem\u003ep \u003c/em\u003e＜ 0.05 ). (B) The comparison of FNC between the MDD group and the HC group ( \u003cem\u003ep \u003c/em\u003e＜ 0.05)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9042814/v1/9f954c60ba2e9cf8f553c744.png"},{"id":105410016,"identity":"ea7b1f06-ee4e-49c0-ac6d-07740ddfcfc8","added_by":"auto","created_at":"2026-03-25 17:11:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76951,"visible":true,"origin":"","legend":"\u003cp\u003eThe centroid of each functional network connectivity state, and the total number and percentage of occurrence of each connectivity state. DMN, default mode network; CEN, central executive network; SN, Salience network; DAN, Dorsal attention network\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9042814/v1/7217957dd748331ce531eb36.png"},{"id":105410015,"identity":"b32bbd4d-6830-4e24-8c9c-a4930202117f","added_by":"auto","created_at":"2026-03-25 17:11:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55509,"visible":true,"origin":"","legend":"\u003cp\u003eCompared to the HC group, the MDD group showed statistically significant differences in functional connectivity between DMN, CEN, SN, and DAN in state 3.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9042814/v1/faf07ff2aa609df1993ba2c2.jpeg"},{"id":105565566,"identity":"00f57fd1-f1b9-4016-995d-e09019000d3c","added_by":"auto","created_at":"2026-03-27 12:53:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":103697,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic connectivity feature analysis for the HC and MDD groups. (A) The fraction of time the occurrence of FC state 2, state 3 and state 4 has significant between group difference (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). (B) The mean dwell time of FC state 2, state 3 and state 4 has significant between group difference (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). (C) The number of transitions between states has significant difference (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) between group. (D) cluster number. FT1, FT2, FT3, FT4 indicates the fraction of time in state1, state2, state3, state4, respectively. MDT1, MDT2, MDT3, MDT4 indicates the mean dwell time in state1, state2, state3, state4, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9042814/v1/e368f97dc160974a91ef032e.png"},{"id":105410014,"identity":"cb67fd03-e7f6-40ed-9262-e90085c3c54a","added_by":"auto","created_at":"2026-03-25 17:11:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":122169,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation among the strength of functional connectivity (FC) between networks, the dynamic FNC difference index, and HAMD-17 scores. (A) The correlations between the strength of functional connectivity (FC) between networks, and the HAMD-17 scores. (B) The correlations between the dynamic FNC difference index and the HAMD-17 scores. Pearson’s correlation was employed, with the correlation coefficient r values denoted using the color bar. * indicates\u003cem\u003e p ≤ 0.05,**\u003c/em\u003e indicates\u003cem\u003e p ≤ 0.01\u003c/em\u003e,\u003cem\u003e***\u003c/em\u003e indicates\u003cem\u003e p ≤ 0.001\u003c/em\u003e,\u003cem\u003e**\u003c/em\u003e indicates\u003cem\u003e p ≤ 0.0001.\u003c/em\u003e “_” indicates the strength of functional connectivity (FC). Abbreviations: HAMD-17: Hamilton Depression Rating Scale.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9042814/v1/3f51d30e421839c9accc401e.png"},{"id":105571029,"identity":"87d0fa1e-9434-4f1a-98ce-84ceeb3667c2","added_by":"auto","created_at":"2026-03-27 13:21:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1833652,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9042814/v1/4b5f1622-436b-46df-a2ea-e3b5aad811f7.pdf"},{"id":105410020,"identity":"612cf7ef-038f-47e9-ba1b-685e11befd12","added_by":"auto","created_at":"2026-03-25 17:11:57","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4544512,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmatrials.doc","url":"https://assets-eu.researchsquare.com/files/rs-9042814/v1/5d1c391ac3890cc4da29a0c0.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dysregulated connectivity configuration of functional network model in First- Episode, Treatment-Naive Adolescents with Major Depressive Disorder","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is a prevalent psychiatric disorder characterized by persistent sadness, irritability, diminished concentration, and impaired social functioning, affecting more than 350\u0026nbsp;million individuals worldwide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In China, MDD has become the second leading cause of disability, underscoring its substantial public health burden(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Adolescence represents a critical developmental window for emotional regulation and cognitive maturation, and is also a peak period for the onset of MDD(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Compared with adults, adolescents with MDD exhibit higher recurrence rates and distinctive clinical manifestations, including pronounced mood fluctuations and heightened sensitivity to social evaluation(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Furthermore, adolescent MDD is associated with increased risks of substance abuse, cognitive impairment, and academic difficulties(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), highlighting the urgent need to clarify its underlying neurobiological mechanisms.\u003c/p\u003e \u003cp\u003eCurrently, the diagnosis of adolescent depression primarily relies on depression scale screenings, which is a symptom-based approach. According to the \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition\u003c/em\u003e (DSM-V), a diagnosis of depression can be made if a patient exhibits five or more of the following symptoms persistently over a two-week period: depressed mood, diminished interest or pleasure, significant weight or appetite changes, insomnia or hypersomnia, psychomotor retardation or agitation, fatigue, feelings of worthlessness or guilt, diminished concentration, or recurrent suicidal ideation or attempts(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, these subjective approaches are vulnerable to inter-rater variability and limited diagnostic precision. Accordingly, there is a pressing need to identify objective biomarkers that can improve diagnostic accuracy and guide personalized treatment strategies.\u003c/p\u003e \u003cp\u003eResting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive, radiation-free approach to explore large-scale brain network interactions(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This neuroimaging approach, which reflects the relationship between brain circuitry and human behavior, holds potential as a source of biological markers for psychiatric disorders(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Blood oxygen level-dependent fMRI (BOLD-fMRI) detects spontaneous low-frequency fluctuations in neural activity; which can be used to infer functional connectivity (FC) across distributed brain regions(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Functional connectivity has been demonstrated to reliably map reproducible resting-state brain networks at both individual and group levels(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The human brain, estimated to contain 100 to 1,000 trillion synapses, exhibits resting-state networks (RSNs) that play critical roles in brain function and disorders such as depression. Modern network theory highlights the suitability of studying this complex neural system from a network perspective, given its intricate organization(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have consistently identified four core resting-state networks relevant to MDD pathophysiology: the default mode network (DMN), salience network (SN), central executive network (CEN), and dorsal attention network (DAN)(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, the developmental specificity and dynamic characteristics of these functional network abnormalities in adolescents\u0026mdash;a unique population\u0026mdash;remain poorly understood. The Default Mode Network (DMN), dominant during rest and associated with self-referential thinking and episodic memory, shows hyperactivation in depression, strongly correlating with rumination and fixation on negative emotions(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), Key DMN regions include the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), precuneus, and angular gyrus(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The Salience Network (SN), composed of the anterior insula (aINS) and dorsal anterior cingulate cortex (dACC), acts as a \"switch\" for internal and external stimuli, dynamically allocating attentional resources between the DMN (self-focused processing) and the CEN/DAN (task-oriented processing)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Meanwhile, the CEN and DAN support higher-order cognitive control and spatial attention maintenance, respectively. Their weakened functionality may contribute to impaired executive function and attentional deficits(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Studies in adults with depression often report a \"competitive imbalance\" between the DMN and CEN, where DMN hyperactivation suppresses CEN-mediated cognitive control(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, adolescence is characterized by ongoing neural maturation, particularly within prefrontal\u0026ndash;limbic circuits, which may yield developmental-stage\u0026ndash;specific network abnormalities(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost prior studies have focused on static FNC (sFNC), reflecting averaged connectivity patterns across the entire scanning session. there remains a lack of systematic exploration into how dynamic functional network connectivity (dFNC)\u0026mdash;such as state transition frequency (NT) and mean dwell time (MDT) of specific network states\u0026mdash;influences depressive symptoms in adolescents. Dynamic functional network connectivity(dFNC)(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), typically measured using sliding-window and clustering methods, captures temporal variations in network states and provides complementary insights into large-scale brain interactions. This approach enables the study of functional interactions between brain networks and their underlying architecture without restricting analysis to predefined regional connections. Research suggests that functional connectivity (FC) dynamics may become more pronounced during unrestricted mental processes in the resting state(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Some researchers propose that individuals freely engage in spontaneous mental activities during conscious rest(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). From this perspective, brain functional connectivity can vary over short time scales rather than remaining static. Alterations in dynamic FC have been implicated in multiple psychiatric conditions, including Alzheimer\u0026rsquo;s disease(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), schizophrenia(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e),and depression(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).Investigating whole-brain dynamic network connectivity patterns could offer novel insights into abnormal brain communication in MDD. Static and dynamic FC processes are complementary, and both are indispensable for understanding cognitive function and its development. However, systematic investigations of dFC abnormalities in adolescents with MDD remain limited, leaving critical questions unanswered. For example, do adolescents with MDD exhibit unique temporal patterns of network dysfunction distinct from adults? How do these dynamic features relate to symptom severity?\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to investigate both static and dynamic functional network connectivity (FNC) in first-episode, treatment-na\u0026iuml;ve adolescents with MDD. We hypothesized that (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) adolescents with MDD would exhibit disrupted intra- and inter-network connectivity within the DMN, SN, CEN, and DAN; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) dynamic functional connectivity would show altered state transitions and mean dwell times; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) these abnormalities would correlate with depressive symptom severity, reflecting developmental-stage\u0026ndash;specific neural mechanisms distinct from adult MDD.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eWe recruited 29 treatment-na\u0026iuml;ve adolescents (aged 10\u0026ndash;19 years) with first-episode MDD from the Changzhi Mental Health Center between January 2020 and December 2022. 29 age- and sex-matched healthy controls (HCs) were enrolled through community advertisements and online platforms (e.g., WeChat). The study was approved by the Ethics Committee of Changzhi Medical College (Approval No. ChiCTR2000038210) according to the standards of the Declaration of Helsinki. Written informed consent was obtained from the parents or legal guardians of all participants, and written informed assent was provided by the adolescents themselves.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria for the MDD group were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) diagnosis of MDD based on the Chinese Classification of Mental Disorders, Version 3 (CCMD-3) and DSM-5 criteria; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) HAMD-17 score\u0026thinsp;\u0026ge;\u0026thinsp;7; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) first depressive episode and no prior treatment; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) right-handedness; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) no history of psychiatric disorders in first-degree relatives; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) no neurological disorders, substance/alcohol abuse, or MRI contraindications.\u003c/p\u003e \u003cp\u003eExclusion criteria included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) comorbid psychiatric disorders (e.g., bipolar disorder, epilepsy); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) history of significant head trauma or brain surgery; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) pregnancy; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) left-handedness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 The clinical data collection\u003c/h2\u003e \u003cp\u003eDemographic and clinical data were systematically gathered by trained psychiatrists, encompassing variables such as age, sex, anthropometric measures (height and weight), handedness, educational attainment, prior medical records, personal and family psychiatric history, occupation, and ethnicity. Diagnosis of MDD was established independently by two senior clinical psychiatrists based on DSM-V criteria. To further ensure diagnostic accuracy and rule out other psychiatric conditions, all adolescent participants and their parents underwent the semi-structured Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children\u0026mdash;Present and Lifetime Version (K-SADS-PL), which integrates reports from both the child and a parent to evaluate lifetime and current mental disorders. Depression severity was quantified using the 17-item Hamilton Depression Rating Scale (HAMD-17).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 MRI data acquisition\u003c/h2\u003e \u003cp\u003eAll imaging was performed on a 3.0 T MAGNETOM Skyra scanner (Siemens Healthcare, Erlangen, Germany) equipped with a 32-channel head coil at the Peace Hospital affiliated to Changzhi Medical College. For both the adolescent MDD group and the HC group, scans were acquired using T1-weighted imaging (T1WI), T2-FLAIR, echo planar imaging (EPI) sequence and 3D-T1 sequence. First, using T1WI,T2-FLAIR excluding organic lesions. Functional MRI data was acquired using an echo-planar imaging sequence with the following parameters repetition time/echo time\u0026thinsp;=\u0026thinsp;2000 ms / 30 ms. flip angle\u0026thinsp;=\u0026thinsp;90◦,field of view\u0026thinsp;=\u0026thinsp;224 \u0026times; 224 mm\u003csup\u003e2\u003c/sup\u003e, matrix size\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, isotropic voxel size\u0026thinsp;=\u0026thinsp;3.5 \u0026times; 3.5 \u0026times; 3.5 mm, phase encoding direction\u0026thinsp;=\u0026thinsp;A ≫ P,240 slices at 3.5 mm thickness covering the whole brain For each participant, with a scanning duration of 8 min and 6 s. Structural MRI data was acquired through a 3D T1-weighted gradient echo sequence with repetition time/echo time\u0026thinsp;=\u0026thinsp;25 ms / 2.98 ms; phase encoding direction\u0026thinsp;=\u0026thinsp;A ≫ P, field of view\u0026thinsp;=\u0026thinsp;256 \u0026times; 256mm\u003csup\u003e2\u003c/sup\u003e, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, 192 sagittal slices covering the whole brain with isotropic voxel size of 1 mm \u0026times; 1 mm \u0026times; 1 mm and an acquisition time of 6 min and 3 s.\u003c/p\u003e \u003cp\u003eParticipants lay flat on the MRI scanning bed with their eyes closed and their head fixed with a foam head pad. Noise-canceling earplugs were given to the participants to reduce the MRI noise, and they were instructed to avoid thinking as much as possible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 data preprocessing\u003c/h2\u003e \u003cp\u003eData preprocessing was performed using GRETNA 2.0 (Graph Theoretical Network Analysis). For each subject, the conversion of the original DICOM data file into NIFTI format. The first 10 volumes of the rs-fMRI dataset were discarded to allow for MR signal equilibrium; then we performed slice-timing correction before using rigid body motion correction to adjust subject head motion .All of the subjects in this study satisfied our criteria for head motion with displacement\u0026thinsp;\u0026lt;\u0026thinsp;2.0 mm in any plane and rotation\u0026thinsp;\u0026lt;\u0026thinsp;1.5\u0026deg; in any direction.After that, we resampled to 3 \u0026times; 3 \u0026times; 3 mm\u003csup\u003e3\u003c/sup\u003e using an echo planar imaging template in the standard Montreal Neurological Institute (MNI) space .Finally, we smoothed the fMRI images using a Gaussian kernel having a full-width at half-maximum (FWHM) of 8 mm. linear detrending, and band-pass filter (0.01\u0026ndash;0.08 HZ).Nuisance covariates including global mean signals, white matter, and cerebrospinal fluid (CSF) signals were regressed out from the blood oxygen level-dependent (BOLD) signals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Independent component analysis and State function connectivity networks analysis\u003c/h2\u003e \u003cp\u003eFunctional network decomposition was carried out using group-level spatial independent component analysis (ICA) implemented in the GIFT software (v3.0b; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mialab.mrn.org/software/gift/\u003c/span\u003e\u003cspan address=\"http://mialab.mrn.org/software/gift/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To reduce computational load, a two-stage principal component analysis was applied: first, each subject's preprocessed fMRI data underwent temporal dimension reduction; second, the concatenated data from all subjects were further reduced in dimension. Subsequently, ICA with the Infomax algorithm was applied to the group-level data to extract 46 independent components (ICs), yielding both spatial maps and corresponding time courses for each IC. The stability and reliability of the IC decomposition were ensured by repeating the ICA 100 times via the ICASSO procedure. Subject-specific ICs were then obtained through a back-reconstruction approach and converted to z-score maps, which formed the basis for voxel-wise one-sample t-tests at the group level. The resulting group-level t-maps enabled identification of brain regions significantly associated with each IC. Then we identified ICs of interest by the visual screening based on previous reported template-matching. First, the spatial and temporal profiles of each component were visually examined and labelled as DMN, CEN, DAN and SN. Next, these labels were further determined by calculating spatial correlations between each component and the reference templates(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). To evaluate inter-network functional connectivity, we computed pairwise Pearson\u0026rsquo;s correlations between the time courses of the four identified RSNs for each subject, yielding six unique connectivity pairs. These correlation coefficients were then entered into a multivariate analysis of covariance (MANCOVAN) within the GIFT toolbox to test for group differences in static functional network connectivity (sFNC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Dynamic Functional Network Connectivity analysis\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Sliding Window Analysis.\u003c/h2\u003e \u003cp\u003eWe estimated the dynamic FNC by computing Pearson\u0026rsquo;s correlations between time courses of ICs using the sliding window method. We set a window size of 20 TRs (44 s) with a step size of 2 TR (4 s) for each participant in accordance with previous studies. The window length of 20 TRs was chosen because previous studies(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) have shown that a window length of 20\u0026ndash;30 TRs may provide a good trade off between the quality of the FNC estimate and the temporal resolution. These time windows were convolved with a Gaussian value of σ\u0026thinsp;=\u0026thinsp;3 TRs and computed precision matrix. To obtain z values and stabilize variance for further analyses, Fisher\u0026rsquo;s z-transformation was applied to the functional connectivity matrices. We regressed out age, sex and mean FD values as covariates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Clustering Analysis\u003c/h2\u003e \u003cp\u003eTo identify recurring patterns of dynamic functional connectivity, we employed a k-means clustering algorithm (squared Euclidean distance) on all sliding-window connectivity matrices. The analysis was repeated 500 times with 150 replicates to ensure robust clustering. The optimal number of clusters was determined using the elbow criterion, which indicated four distinct connectivity states, each characterized by a unique pattern of inter-network interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Temporal Properties and Connectivity Strength of Dynamics States\u003c/h2\u003e \u003cp\u003eTo characterize the temporal dynamics of functional connectivity, we computed three metrics for each state: fractional time (the proportion of total windows assigned to that state), mean dwell time (the average duration that a state was maintained before switching to another), and number of transitions (total switches between states across the scanning session). Age and sex were included as covariates in subsequent group comparisons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Validation Analysis\u003c/h2\u003e \u003cp\u003eTwo additional window lengths (22 and 25 TRs) were applied to test the robustness of the sliding window analysis. Moreover, the numbers of clusters were set at 5 and 6 also used to verify the stability of our results.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analyses\u003c/h2\u003e \u003cp\u003eDemographic and clinical characteristics were analyzed using SPSS 26.0 software. Continuous variables including age, BMI, and HAMD-17 scores were compared between groups using two-sample independent \u003cem\u003et\u003c/em\u003e-tests and reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Categorical data were compared using the chi-square test. the strength of FNC and dynamic FNC parameters were analyzed in GIFT software were assessed with Mann-Whiteney U tests, the \u003cem\u003ep\u003c/em\u003e value for the above analysis was set at a level of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, false discovery rate (FDR) correction for multiple comparisons. Pearson\u0026rsquo;s correlation was conducted to assess the relationship between the strength of FNC, dynamic FNC measures and HAMD-17 scores, controlling for age, sex, and mean FD values were treated as covariates.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographics and clinical characteristics\u003c/h2\u003e \u003cp\u003eThe details of demographic and clinical characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We did not observe any significant differences in age, education level, gender between MDD patients and HCs. HAMD-17 scores were significantly higher in the adolescent MDD group compared to the HC group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eDemographics and clinical characteristics of all participants.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDD (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et/c\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003evalue\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep value\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\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMD-17(score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.54\u0026thinsp;\u0026plusmn;\u0026thinsp;6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-14.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMDD, major depressive disorder; HC, healthy controls, BMI, body mass index; HAMD-17, the 17-items Hamilton Depression Scale; The data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or percentages. Group differences were assessed using either chi-square analysis or independent t-tests. A \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\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 ICs of interest\u003c/h2\u003e \u003cp\u003eOf the 46 ICs identified by the group ICA,28 ICs were identified as noise components and then discarded. The 18 ICs of interest, DMN, CEN, DAN and SN, which were identified and categorized into four networks using group ICA (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e lists the ICs\u0026rsquo; labels and peak activation coordinates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 FNC differences between MDD and HC\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrate the ICs that matched to the four brain networks. IC14, IC27, IC38, IC45 belongs to DMN; IC10, IC15, IC19, IC36, IC34, IC5 belongs to CEN; IC3, IC9, IC30, IC39 belongs to DAN; IC33, IC44 belongs to SN. (Table S2 and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) illustrate differences in connectivity between MDD and HC. Compares with HCs,The MDD group exhibited decrease connectivity in the following pairs: intra-DMN_DMN( z = -2.605, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); inter-DMN_CEN( z = -3.227, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05);DMN_SN ( z = -4.735, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) ; CEN_SN ( z = -3.025, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05);intra-SN_SN ( z = -3.056, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).On the other hand, MDD exhibited increase connectivity than HC in the following: DAN_SN ( z\u0026thinsp;=\u0026thinsp;1.925, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Dynamic FNC states and properties\u003c/h2\u003e \u003cp\u003eWhen windows TR\u0026thinsp;=\u0026thinsp;20, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the four identified states with highly structured FC that recurred throughout individual scans and across subjects, as well as their occurrence time and percentage. The proportions of the four states are 1% (178), 31% (3786), 8% (996) and 59% (7220) respectively. In state 3, compared with HCs, the DMN had positive function connectivity with SN and CEN; at the same time, the CEN with DAN and SN had decreased functional connectivity; SN had decreased connectivity with DAN in MDD group, In State 1,2,4, the FNC within the four networks was very sparse. Moreover, we found positive connections intra SN; negative connections intra DAN (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For dynamic FNC properties (Mann-Whiteney U tests, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026lt; 0.05 ), Compared with the HC group, we found significant decreased fraction time( FT )、mean dwell time ( MDT ) in state2,state3 and number of transitions ( NT ) in MDD patients .However, we found increased FT、MDT in state4 (Table S3 and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.5 Correlation Analysis\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 FNC difference index and HAMD-17 scores\u003c/h2\u003e \u003cp\u003eThere is a negative correlation between the FNC strength of intra/inter-network and the HAMD-17 score(Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The strength of intra-DMN with HAMD-17 score had negative correlation (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.296, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024). The strength of intra-DAN with HAMD-17 score had negative correlation (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.270, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040). At the same time, The strength of DMN_CEN ( r =-0.386, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003),DMN_SN( r =-0.503, \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001 ),CEN_SN ( r =-0.259, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049)with HAMD-17 score had negative correlation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Dynamic FNC properties and HAMD-17 scores\u003c/h2\u003e \u003cp\u003eThe mean dwell time was negatively correlated with HAMD-17 score in state3 ( r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.276, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036); positive correlated with HAMD-17 score in state4( r\u0026thinsp;=\u0026thinsp;0.292, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) The fraction time was negatively correlated with HAMD-17 score in state2 ( r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.301, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), state3 ( r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.357, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006); positive correlated with HAMD-17 score in state4 ( r\u0026thinsp;=\u0026thinsp;0.479, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001). The number of transitions was negatively correlated with HAMD-17 score in state2 (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.318, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Figure S6).\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\u003eDynamic state features correlated with HAMD-17 scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFraction time of State 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFraction time of State 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean dwell time State 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean dwell time State 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eWhen TR\u0026thinsp;=\u0026thinsp;20, State\u0026thinsp;=\u0026thinsp;5, the correlations between the dynamic FNC difference index and the HAMD-17 scores. \u003cem\u003ep\u003c/em\u003e \u0026lt;0.05 was considered statistically significant. *significant \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Validation Analysis\u003c/h2\u003e \u003cp\u003eIn this study, we used two different sliding window lengths to verify our main results of dynamic FNC. The results of the sliding window lengths of 22 and 25 TRs were similar to the results of the 20 TR we found. This study selected cluster\u0026thinsp;=\u0026thinsp;5, 6. conduct verification analysis, the results of the dynamic FNC analysis were consistent with our main results (Figure S2-5,Table S4-5).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study of first-episode, treatment-na\u0026iuml;ve adolescents with major depressive disorder (MDD), we identified distinct patterns of both static and dynamic functional network dysconnectivity. Statically, adolescents with MDD showed reduced connectivity within and between the default mode network (DMN), central executive network (CEN), and salience network (SN), alongside hyperconnectivity between the dorsal attention network (DAN) and SN. Dynamically, they exhibited reduced state flexibility and spent more time in a hyperconnected state that correlated with symptom severity. Crucially, these static and dynamic alterations were significantly correlated with depressive symptoms, highlighting their clinical relevance and offering insights into the neurobiology of early-onset depression.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Disrupted Static Network Architecture in Adolescent MDD\u003c/h2\u003e \u003cp\u003eThe weakened static connectivity within the DMN and between the DMN and SN likely reflects a core deficit in regulating self-referential thought. In healthy states, the SN helps disengage the DMN's introspective processing; its failure to do so in MDD may trap adolescents in ruminative, negative self-evaluative loops, which aligns with clinical presentations of irritability and social withdrawal (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).Furthermore, the reduced DMN-CEN connectivity suggests a disruption of the typical antagonistic balance required to shift between internal thought and external, task-focused cognition (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This breakdown may directly contribute to the attentional and executive function deficits, such as academic difficulties, commonly observed in depressed adolescents (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).Conversely, the observed DAN-SN hyperconnectivity points toward a state of maladaptive hypervigilance. This suggests the SN persistently biases the dorsal attention system toward emotionally salient, often negative, stimuli, creating a cycle that reinforces attention to and memory of adverse information (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Altered Network Dynamics Suggest Cognitive and Emotional Inflexibility\u003c/h2\u003e \u003cp\u003eOur dynamic analysis complements these static findings by revealing a loss of network flexibility. The significantly lower number of transitions between brain states in the MDD group suggests a \"neurodynamic rigidity,\" where the brain is less able to fluidly shift between different information processing configurations in response to changing demands (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This inflexibility may be a neural correlate of the cognitive rigidity and emotional perseveration characteristic of depression.\u003c/p\u003e \u003cp\u003eThis rigidity was further characterized by altered temporal properties. Patients spent less time in sparsely connected states (e.g., State 3), which correlated with milder symptoms and may represent an adaptive or restful mode. Conversely, they spent significantly more time in a hyperconnected state (State 4), and this prolonged dwelling was positively correlated with greater symptom severity. This suggests that adolescent MDD involves getting \"stuck\" in a maladaptive, resource-intensive network configuration that reinforces pathological mood.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Clinical Correlations and Developmental Specificity\u003c/h2\u003e \u003cp\u003eThe clinical relevance of these network disruptions is underscored by their direct correlations with HAMD-17 scores. Weaker static connectivity within the DMN and between the DMN, CEN, and SN was associated with more severe depression, supporting the Triple Network Model's premise that impaired coordination between self-referential, executive, and salience-detecting networks is central to MDD pathophysiology (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The link between prolonged dwell time in a hyperconnected dynamic state and higher symptom severity further solidifies the connection between neurodynamic rigidity and the clinical expression of depression.\u003c/p\u003e \u003cp\u003eCrucially, these findings must be interpreted through the developmental lens of adolescence\u0026mdash;a period of high neuroplasticity but also profound vulnerability due to the ongoing maturation of prefrontal-limbic circuits (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The reduced DMN-CEN connectivity we observed may not just be a symptom of depression, but a sign of a disrupted or delayed developmental trajectory, as this anticorrelation typically strengthens with age (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This underlying neural immaturity could amplify the impact of adolescent-specific stressors, such as social evaluation, potentially driving the SN hypersensitivity and maladaptive DAN-SN hyperconnectivity seen in our cohort (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Therefore, the dysfunctions identified here may represent a critical developmental derailment, offering a unique window for early intervention before these pathological network patterns become consolidated in adulthood.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eOur study has several limitations. First, the relatively small sample size may limit the generalizability of our findings and precluded more complex subgroup analyses. Future studies with larger cohorts are needed to validate these results. Second, the cross-sectional design prevents us from drawing conclusions about the dynamic progression of the disease over time. Longitudinal studies are essential to track these network changes and their relationship to treatment outcomes. Third, our analysis was restricted to four major networks; finer-grained analyses of subnetworks may reveal more specific pathophysiological mechanisms. Finally, this study relied exclusively on neuroimaging; future work should integrate multi-omics approaches to provide a more comprehensive understanding of the pathogenesis of adolescent MDD.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn conclusion, by integrating static and dynamic fMRI analyses, this study demonstrates that first-episode adolescent MDD is characterized by both stable network imbalances and a loss of dynamic flexibility. The disrupted interplay between the DMN, CEN, and SN, coupled with maladaptive DAN-SN hyperconnectivity, is directly linked to symptom severity and likely reflects a pathological deviation in the brain's neurodevelopmental trajectory. These findings underscore the potential of using integrated neuroimaging metrics as biomarkers for early detection and suggest that interventions aimed at restoring network balance and flexibility during the critical neuroplastic window of adolescence may hold unique therapeutic promise.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMDD \u0026nbsp; \u0026nbsp; \u0026nbsp; Major depressive disorder\u003c/p\u003e\n\u003cp\u003esFNC \u0026nbsp; \u0026nbsp; \u0026nbsp; Static functional network connectivity\u003c/p\u003e\n\u003cp\u003edFNC \u0026nbsp; \u0026nbsp; \u0026nbsp; Dynamic functional network connectivity\u003c/p\u003e\n\u003cp\u003eDMN \u0026nbsp; \u0026nbsp; \u0026nbsp; Default mode network\u003c/p\u003e\n\u003cp\u003eCEN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Control Executive network\u003c/p\u003e\n\u003cp\u003eSN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Salience network\u003c/p\u003e\n\u003cp\u003eDAN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Dorsal attention network\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHAMD-17 \u0026nbsp; \u0026nbsp;17-item Hamilton Depression Rating Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDSM-V \u0026nbsp; \u0026nbsp; \u0026nbsp;The Diagnostic and Statistical Manual of Mental Disorders\u003c/p\u003e\n\u003cp\u003eFC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;functional connectivity\u003c/p\u003e\n\u003cp\u003eICA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Independent component analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors provided have made strong, direct, effective contributions to this research, and agree to publish it. Conceptualization: K. Li, and J. Li ; Literature search：C. Zhang,W. Zhang, J. Bai, X. Deng, J. Ji; IRB approvals: J. Ji, T. Li, Y. Wang, and J. Li; Participant enrollment: C. Zhang,W. Zhang, J. Zhao, J. Cui, J. Bai, X. Deng, J. Ji, T. Li, and Y. Wang; Data analysis and visualization: C. Zhang, W. Zhang, J. Cui, J. Ji, T. Li, and Y. Wang; Project supervision: J. Li. Funding: J. Li, and K. Li; Manuscript-first draft: C. Zhang, W. Zhang, Manuscript-review \u0026amp; editing: All the authors. All the authors have approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation of Shanxi Province (201901D221113), the Four \u0026ldquo;Batches\u0026rdquo; Innovation Project of Invigorating Medical through Science and Technology of Shanxi Province (2023XM016), and the Macao Polytechnic University fund (RP/FCA-14/2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Changzhi Medical College (Approval No. ChiCTR2000038210) according to the standards of the Declaration of Helsinki. Written informed consent was obtained from the parents or legal guardians of all participants, and written informed assent was provided by the adolescents themselves.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJeon SW, Kim YK. The role of neuroinflammation and neurovascular dysfunction in major depressive disorder. J Inflamm Res. 2018;11:179\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu J, Xu X, Huang Y, Li T, Ma C, Xu G, et al. Prevalence of depressive disorders and treatment in China: a cross-sectional epidemiological study. lancet Psychiatry. 2021;8(11):981\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKessler RC, Amminger GP, Aguilar-Gaxiola S, Alonso J, Lee S, Ust\u0026uuml;n TB. Age of onset of mental disorders: a review of recent literature. Curr Opin Psychiatry. 2007;20(4):359\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):593\u0026ndash;602.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeissman MM, Bland RC, Canino GJ, Faravelli C, Greenwald S, Hwu HG, et al. Cross-national epidemiology of major depression and bipolar disorder. JAMA. 1996;276(4):293\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThapar A, Collishaw S, Pine DS, Thapar AK. Depression in adolescence. Lancet. 2012;379(9820):1056\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKieling C, Adewuya A, Fisher HL, Karmacharya R, Kohrt BA, Swartz JR, et al. Identifying depression early in adolescence. Lancet Child Adolesc health. 2019;3(4):211\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUher R, Payne JL, Pavlova B, Perlis RH. Major depressive disorder in DSM-5: implications for clinical practice and research of changes from DSM-IV. Depress Anxiety. 2014;31(6):459\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoffet F, Delrieux C, Patow G. Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory. Brain Sci. 2022;12(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBebko G, Bertocci MA, Fournier JC, Hinze AK, Bonar L, Almeida JR, et al. Parsing dimensional vs diagnostic category-related patterns of reward circuitry function in behaviorally and emotionally dysregulated youth in the Longitudinal Assessment of Manic Symptoms study. JAMA psychiatry. 2014;71(1):71\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJann K, Smith RX, Rios Piedra EA, Dapretto M, Wang DJ. Noise Reduction in Arterial Spin Labeling Based Functional Connectivity Using Nuisance Variables. Front NeuroSci. 2016;10:371.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Gohel S, Vachha B. Current methods and new directions in resting state fMRI. Clin Imaging. 2020;65:47\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu M, Linn KA, Shinohara RT, Oathes DJ, Cook PA, Duprat R, et al. Childhood trauma history is linked to abnormal brain connectivity in major depression. Proc Natl Acad Sci USA. 2019;116(17):8582\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15(10):483\u0026ndash;506.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton JP, Furman DJ, Chang C, Thomason ME, Dennis E, Gotlib IH. Default-mode and task-positive network activity in major depressive disorder: implications for adaptive and maladaptive rumination. Biol Psychiatry. 2011;70(4):327\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrews-Hanna JR, Smallwood J, Spreng RN. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann N Y Acad Sci. 2014;1316(1):29\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSikora M, Heffernan J, Avery ET, Mickey BJ, Zubieta JK, Peci\u0026ntilde;a M. Salience Network Functional Connectivity Predicts Placebo Effects in Major Depression. Biol psychiatry Cogn Neurosci neuroimaging. 2016;1(1):68\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J neuroscience: official J Soc Neurosci. 2007;27(9):2349\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3(3):201\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilk JS, Siegle GJ, Lee KH, Nelson EE, Stroud LR, Dahl RE. Increased neural response to peer rejection associated with adolescent depression and pubertal development. Soc Cognit Affect Neurosci. 2014;9(11):1798\u0026ndash;807.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheline YI, Price JL, Yan Z, Mintun MA. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci USA. 2010;107(24):11020\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePreti MG, Bolton TA, Van De Ville D. The dynamic functional connectome: State-of-the-art and perspectives. NeuroImage. 2017;160:41\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang Q, Cui Q, Chen Y, Deng J, Sheng W, Yang Y, et al. Shared and distinct changes in local dynamic functional connectivity patterns in major depressive and bipolar depressive disorders. J Affect Disord. 2022;298(Pt A):43\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Cai H, Cao Z, Li C, Wu T, Xu F, et al. More Than Just Static: Dynamic Functional Connectivity Changes of the Thalamic Nuclei to Cortex in Parkinson's Disease With Freezing of Gait. Front Neurol. 2021;12:735999.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelamillieure P, Doucet G, Mazoyer B, Turbelin MR, Delcroix N, Mellet E, et al. The resting state questionnaire: An introspective questionnaire for evaluation of inner experience during the conscious resting state. Brain Res Bull. 2010;81(6):565\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones DT, Vemuri P, Murphy MC, Gunter JL, Senjem ML, Machulda MM, et al. Non-stationarity in the resting brain's modular architecture. PLoS ONE. 2012;7(6):e39731.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakoğlu U, Pearlson GD, Kiehl KA, Wang YM, Michael AM, Calhoun VD. A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. Volume 23. New York, NY: Magma; 2010. pp. 351\u0026ndash;66. 5\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(3):1125\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cerebral cortex (New York, NY: 1991). 2012;22(1):158\u0026thinsp;\u0026ndash;\u0026thinsp;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeonardi N, Van De Ville D. On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage. 2015;104:430\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorr R, Glier S, Bizzell J, Pelletier-Baldelli A, Campbell A, Killian-Farrell C, et al. Triple Network Functional Connectivity During Acute Stress in Adolescents and the Influence of Polyvictimization. Biol psychiatry Cogn Neurosci neuroimaging. 2022;7(9):867\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA. 2005;102(27):9673\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eListon C, Chen AC, Zebley BD, Drysdale AT, Gordon R, Leuchter B, et al. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol Psychiatry. 2014;76(7):517\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonti MM, Parsons LM, Osherson DN. Response to Tzourio-Mazoyer and Zago: yes, there is a neural dissociation between language and reasoning. Trends Cogn Sci. 2012;16(10):495\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaiser RH, Whitfield-Gabrieli S, Dillon DG, Goer F, Beltzer M, Minkel J, et al. Dynamic Resting-State Functional Connectivity in Major Depression. Neuropsychopharmacology: official publication Am Coll Neuropsychopharmacol. 2016;41(7):1822\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUhlhaas PJ. The adolescent brain: implications for the understanding, pathophysiology, and treatment of schizophrenia. Schizophr Bull. 2011;37(3):480\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEiland L, Romeo RD. Stress and the developing adolescent brain. Neuroscience. 2013;249:162\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSherman LE, Rudie JD, Pfeifer JH, Masten CL, McNealy K, Dapretto M. Development of the default mode and central executive networks across early adolescence: a longitudinal study. Dev Cogn Neurosci. 2014;10:148\u0026ndash;59.\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-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Major depressive disorder, adolescence, resting-state fMRI, functional network connectivity, dynamic functional connectivity","lastPublishedDoi":"10.21203/rs.3.rs-9042814/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9042814/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eMajor depressive disorder (MDD) is a highly prevalent psychiatric condition that frequently emerges during adolescence, a critical developmental stage characterized by heightened vulnerability to emotional dysregulation. Despite increasing evidence of large-scale brain network dysfunction in adult MDD, the static and dynamic connectivity alterations underlying adolescent MDD remain poorly understood.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe recruited 29 first-episode, treatment-na\u0026iuml;ve adolescents with MDD and 29 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) data were analyzed using group independent component analysis (ICA) combined with sliding-window clustering to evaluate both static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) across the default mode network (DMN), salience network (SN), central executive network (CEN), and dorsal attention network (DAN). Correlation analyses were performed between connectivity metrics and clinical severity assessed by the 17-item Hamilton Depression Rating Scale (HAMD-17).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eCompared with HCs, adolescents with MDD exhibited significantly reduced intra- and inter-network connectivity within the DMN, SN, and CEN (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), alongside increased DAN\u0026ndash;SN connectivity. Dynamic analyses revealed reduced state transition frequency, shorter dwell time in low-connectivity states (e.g., DMN\u0026ndash;CEN\u0026ndash;SN interactions), and longer dwell time in high-connectivity states (e.g., DAN\u0026ndash;SN coupling). Clinical analyses demonstrated that weaker intra-DMN and intra-DAN connectivity, as well as reduced DMN\u0026ndash;CEN and DMN\u0026ndash;SN connectivity, were negatively correlated with HAMD-17 scores (r = \u0026minus;\u0026thinsp;0.296 to \u0026minus;\u0026thinsp;0.503, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, prolonged dwell time in hyperconnected states positively correlated with greater symptom severity (r\u0026thinsp;=\u0026thinsp;0.479, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eOur findings highlight distinct static and dynamic network abnormalities in adolescent MDD, including disrupted DMN\u0026ndash;CEN competitive balance and maladaptive DAN\u0026ndash;SN hyperconnectivity. These alterations suggest developmental-stage\u0026ndash;specific neuropathological mechanisms that differ from adult depression. Integrating static and dynamic FNC analyses may provide novel biomarkers for early detection and intervention strategies in adolescent MDD.\u003c/p\u003e","manuscriptTitle":"Dysregulated connectivity configuration of functional network model in First- Episode, Treatment-Naive Adolescents with Major Depressive Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 17:11:50","doi":"10.21203/rs.3.rs-9042814/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-16T12:57:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T09:52:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T21:14:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117350392952946764955066002109607034637","date":"2026-03-29T20:47:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310579499271054265989001213031739759887","date":"2026-03-27T20:43:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T17:22:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T17:14:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-19T09:15:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-18T01:34:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-03-17T18:36:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e543a5c0-90e7-46b8-b32e-6ca5d1880427","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T14:40:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 17:11:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9042814","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9042814","identity":"rs-9042814","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}