Early Treatment-related Changes in Dorsolateral Prefrontal Cortex Activity and Functional Connectivity as Potential Biomarkers for Antidepressant Response in Major Depressive Disorder

Early Treatment-related Changes in Dorsolateral Prefrontal Cortex Activity and Functional Connectivity as Potential Biomarkers for Antidepressant Response in 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 Article Early Treatment-related Changes in Dorsolateral Prefrontal Cortex Activity and Functional Connectivity as Potential Biomarkers for Antidepressant Response in Major Depressive Disorder Yuan Yang, Han Zhang, Cun Li, Ke Shi, Ye Xia, Yanhui Song, Jie Feng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4914286/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Oct, 2025 Read the published version in Translational Psychiatry → Version 1 posted 9 You are reading this latest preprint version Abstract Previous research has demonstrated that patients with major depressive disorder (MDD) exhibit cognitive deficits. As a crucial region within the executive control network, the dorsolateral prefrontal cortex (DLPFC) activity and its functional connectivity (FC) may serve as potential indicators of antidepressant response. This prospective cohort study recruited 115 MDD patients and 43 healthy controls. Psychological assessments, electroencephalogram and event-related potential recordings were performed at baseline and 1 week after venlafaxine treatment, with a 12-week follow-up. Group differences were analyzed using independent sample t-tests and Mann-Whitney U tests, while linear mixed-effects models and logistic regression evaluated associations between DLPFC activity/FC changes and clinical outcomes. The MDD group showed significantly reduced right DLPFC current density during the N2 time window evoked by oddball stimuli ( p = 0.028), which negatively correlated with 21-item Hamilton Depression Rating Scale (HAMD-21) scores ( p = 0.041) (n = 46). Furthermore, an early increase predicted remission at week 12 ( p = 0.005). Decreased beta-band FC between the left DLPFC and both the left posterior cingulate cortex (PCC) ( p = 0.003) and right PCC ( p = 0.004) predicted lower HAMD-21 scores (n = 71). Moreover, an early reduction in these connectivity measures (left: odds ratio (OR) = 0.534, 95% confidence interval (CI): 0.297–0.972, p = 0.036; right: OR = 0.533, 95% CI: 0.299–0.950, p = 0.033) predicted remission at week 12. Early changes in DLPFC activity and FC may serve as biomarkers for monitoring treatment efficacy and predicting clinical outcomes, informing personalized treatment approaches. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Major depressive disorder (MDD) is a widespread and debilitating condition that profoundly affects quality of life [ 1 ]. Due to its heterogeneous deficits in emotional, cognitive, and motor functioning, the effectiveness of standard antidepressant treatment is limited, with less than half of MDD patients achieving remission [ 1 – 3 ]. Current treatment options, including medications, psychotherapies, and somatic therapies, are predominantly selected based on their tolerability, leading to uncertainty about which patients will benefit from such treatment [ 4 , 5 ]. Given that therapeutic efficacy usually emerging after 4 to 8 weeks, many patients must endure multiple treatment trials before achieving relief, potentially receiving suboptimal treatment in the process [ 6 ]. This has generated significant interest in identifying biomarkers that could assist in medication selection or facilitate the early termination of ineffective trials. Recent conceptualizations consider MDD as a systems-level disorder arising from dysregulation among large-scale functional brain networks [ 7 ]. Altered emotion regulation and deficits in cognitive control have been reported in individuals with MDD [ 8 ]. The dorsolateral prefrontal cortex (DLPFC), an essential region of the executive control network (ECN), plays a vital role in emotion processing and cognitive control through its functional connectivity (FC) with other networks like the default mode network (DMN) and salience network (SN) [ 9 ]. Although there is some inconsistency, most functional magnetic resonance imaging (fMRI) findings support antidepressant treatment is associated with change in DLPFC activity in individuals with MDD, mainly characterized by increased activity [ 10 – 14 ]. For instance, one study demonstrated increased DLPFC activity during resting-state fMRI within days of treatment initiation [ 11 ], while another task-based fMRI study revealed heightened DLPFC activity in response to unattended fear-related stimuli in patients with MDD at week 8 [ 12 ]. However, other studies showed reduced right DLPFC activity during the N-back task after vortioxetine treatment [ 13 ], and reduced DLPFC activity during the Go/NoGo task in remitters compared to non-remitters [ 14 ]. Additionally, antidepressant treatment was found been associated with changes in DLPFC volumes and DLPFC-seed based FC based on fMRI [ 15 ]. Previous research has primarily focused on fMRI, which has proven invaluable in identifying structural and functional abnormalities in MDD [ 16 ]. However, the high temporal resolution and direct measurement of neuronal activity offered by electroencephalogram (EEG) could provide complementary insights into the rapid neural dynamics and network interactions underlying the cognitive and emotional dysregulation observed in MDD [ 17 , 18 ]. In addition, most studies predicting the efficacy of depression treatment based on neuroimaging have been conducted at baseline [ 16 , 19 ]. Nevertheless, treatment efficacy may be more closely associated with dynamic changes in brain function during treatment, which cannot be captured by a single baseline imaging session [ 15 ]. EEG signals have been shown to change within days after initiating antidepressant medication. By tracking these changes over time, researchers can investigate the temporal dynamics and trajectories of neural activity and FC patterns related to symptom improvement or treatment response, potentially identifying critical time windows or stages in the recovery process predictive of treatment outcomes [ 20 , 21 ]. To address the limitations of previous research, this longitudinal cohort study enrolled both patients with MDD and healthy controls (HCs). Psychological assessments, EEG, and event-related potential (ERP) data were collected at baseline and 1 week after initiating antidepressant treatment, with a 12-week follow-up. We hypothesized that: (1) patients with MDD would display abnormal DLPFC activity compared to HCs under the visual oddball paradigm; (2) DLPFC activity might change after treatment, potentially correlating with prognosis; and (3) changes in DLPFC FC with the DMN and SN would correlate with depressive symptoms and treatment prognosis. The posterior cingulate cortex (PCC) and insula cortex (IC) were selected as regions of interest (ROIs) representing the DMN and SN, respectively. The study aimed to determine the stability or recovery signs in DLPFC activity and DLPFC-seed based FC, shedding light on the impact of therapeutic interventions on brain activity and networks. Additionally, it sought to enhance understanding of the cognition-depression relationship and brain functional changes during treatment, providing insights into individual treatment responses. Materials and Methods Study Design All patients with MDD were treated with venlafaxine for 12 weeks. Depression severity was evaluated utilizing the 21-item Hamilton Depression Rating Scale (HAMD-21) [ 22 ] at baseline, week 1 and week 12. Clinical outcomes were evaluated by Visual Analogue Scale (VAS) and HAMD-21 scores. The criteria for clinical outcomes were defined as follows: 1) Remission: VAS score ≥ 5 and HAMD-21 score < 7 at week 12; 2) Response: VAS score ≥ 5 or a ≥ 50% reduction in HAMD-21 scores from baseline to week 12; 3) Non-response: VAS score < 5 or a < 50% reduction in HAMD-21 scores from baseline to week 12 [ 22 ]. EEG and ERP recordings were conducted at baseline and week 1. Participants This prospective longitudinal cohort study enrolled 115 untreated patients with MDD at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from March 2022 to September 2023. The inclusion criteria were as follows: (1) age 18–65 years; (2) meeting the diagnostic criteria for depressive disorder as outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V); (3) HAMD-21 score ≥ 17; (4) educational level of elementary school or higher; (5) right-handedness; and (6) native Chinese speaker. Exclusion criteria included: (1) history of mental disorders such as schizophrenia, severe depressive disorder with suicidal tendencies, alcoholism, or substance abuse; (2) presence of severe or unstable physical disorders; (3) evidence of current or prior head injury, central nervous system disease, or other disorders according to the International Classification of Diseases, Tenth Revision (ICD-10); (4) contraindications to antidepressants; (5) history of antidepressants or long-acting antipsychotic injections within the past month; (6) evidence of aphasia, deafness, blindness, or cognitive impairment; and (7) breastfeeding, pregnancy, or planning pregnancy during the trial. Additionally, 43 HCs matched for age, gender, and education level were recruited. Exclusion criteria for the HCs were the same as mentioned earlier, with the additional of no history of any psychiatric illness. The participant enrollment flowchart is presented in Fig. 1 . The informed consent was obtained in accordance with the Declaration of Helsinki. This study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20220205). The registration number for this trial was ChiCTR2200057365. The URL of the publicly accessible registered website is: http://www.chictr.org.cn/ . EEG Recording and Preprocessing Rest-state EEG signals, involving 12-minute trials with eyes-open and eyes-closed conditions, were recorded in a dimly illuminated, electrically shielded, and acoustically isolated chamber utilizing a 128-channel EEG system (BrainVision Recorder software, Brain Products GmbH, Germany). Participants maintained stillness, minimizing blinks and eye movements, while fixating on a central cross during eyes-open conditions. Electrodes were positioned according to the standard international 10/5 system at a sampling frequency of 1000Hz, with the FCz electrode serving as the default reference electrode. Electrode impedance was meticulously maintained below 20 KΩ during recording. EEG data preprocessing was underwent using BrainVision Analyzer software (version 2.2, Brain Products GmbH, Germany) (see Supplemental Methods). Visual Oddball Paradigm and Event-related Potential The visual oddball paradigm was conducted in a soundproof chamber. It consisted of two visual stimuli: a red car as the oddball stimulus and a blue car as the standard stimulus. The experimental process is illustrated in Fig. 2 A and Supplemental Methods. In the study, the ERP analysis focused primarily on the stimulus-associated N1, P2, N2, and P3 components. The selection of time windows and electrodes was guided by a prior study [ 23 ], as shown in Supplemental Table S1 . Source Localization Analyses Source localization analyses were conducted using the built-in low resolution electromagnetic tomography analysis (LORETA) within the BrainVision Analyzer software. LORETA calculates the current density (i.e., the amount of electrical current flowing through a volume; unit: µA/mm 2 ) of intracranial sources responsible for scalp-recorded EEG signals [ 24 ]. The study focused on the current density within the DLPFC during the visual oddball paradigm. The DLPFC ROI was defined by the following coordinates (x, y, z): left DLPFC (-45, 32, 20) and right DLPFC (47, 32, 19). Region-of-Interest Selection To examine the longitudinal changes in FC between the DLPFC and other brain networks (DMN and SN), and their association with alternations in depression symptoms, we identified the PCC and IC as ROIs for DMN and SN, respectively, according to findings by Whitton et al [ 21 ]. The coordinates (x, y, z) for the ROIs were as follows: left PCC (-7, -48, 33), right PCC (7, -47, 33), left IC (-38, -4, 5), and right IC (40, -5, 7). The locations of the DLPFC, PCC and IC ROIs are showed in Fig. 3 A. Finally, as described above, the intracortical current density within each ROI was computed using LORETA. Source-Based Functional Connectivity Given the practical constraints inherent in measuring FC using scalp electrodes, which are vulnerable to volume conduction effects, this study employed the phase lag index (PLI) to compute FC between sources [ 25 ]. As a phase-based connectivity analysis method, PLI relies on the distribution of phase angle differences between two sources [ 25 ]. The underlying concept is that oscillatory sequences synchronize in phase when neural clusters are functionally coupled. PLI was calculated for the delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz) and beta (12–30 Hz) frequency bands. Statistical analysis The general characteristics of the study population were described as percentages for categorical variables, means ± standard deviations (SDs) for continuous variables with normal distributions, and medians (interquartile ranges, IQRs) for continuous variables without normal distributions. Z-score standardization was performed for DLPFC current density and DLPFC-seed based FC due to their relatively small and scattered nature. Group comparisons were conducted using chi-square tests, independent samples t-tests, and Wilcoxon rank-sum tests. All statistical analyses were performed using SPSS for Windows, version 26.0, with two- tailed tests set at a significance level of α = 0.05. Pearson correlation analysis was employed to examine the bivariate associations between the changes in DLPFC current density and DLPFC-seed based FC, and the changes in HAMD-21 scores. The changes of these variables were calculated by subtracting the week 1 values from the baseline. To further investigate these associations while accounting for multiple factors, a linear mixed-effects model (LMM) based on maximum likelihood estimation (MLE) was conducted. LME, a statistical model suitable for hierarchical or repeated measures data with fixed and random effects, allows for more accurate characterization of variability without distribution restrictions and is widely utilized across various fields [ 26 ]. In the current study, participants were treated as random effects, while age, gender and DLPFC current density or DLPFC-seed based FC were designated as fixed effects, with HAMD-21 score as the dependent variable. Binary logistic regression analysis was utilized to examine the association between early changes in DLPFC current density and DLPFC-seed based FC, and clinical outcomes at week 12, with corrected odds ratio (OR) and its corresponding 95% confidence interval (CI) serving as the effect size. The variables were entered into the regression model in the following steps: Step1: Age, sex, and baseline HAMD-21 score included as control variables. Step 2: Early change in DLPFC activity and DLPFC-seed based FC, calculated by subtracting the week 1 values from baseline, were entered as the independent variable. Step 3: Clinical outcomes (remission or no-remission) at week 12 were used as dependent variables. Results Baseline Characteristics A total of 69 patients completed two ERP recordings, out of which 46 patients participated in the follow-up stage. Additionally, 93 patients underwent two EEG recordings, with 71 of these patients completing the follow-up stage. The detailed screening process is depicted in Fig. 1 . As shown in Table 1 , there were no significant differences between the MDD group and the HC group in terms of age, gender, education level, and marriage status ( p > 0.05). After treatment, 58.7% of patients who completed ERP and 64.8% of patients who completed EEG met the remission criteria. No significant differences were observed between remitters and non-remitters concerning age, gender, and baseline HAMD-21 scores ( p > 0.05) (Supplemental Table S2). Table 1 Comparison of baseline characteristics between patients with MDD and HCs HC group (n = 43) MDD group (n = 115) p Age (years) 51 ± 7 54 ± 10 0.092 Female (n, %) 33 (76.7) 84 (73.0) 0.637 Educational level 0.128 Elementary school or lower 7 (16.3) 37 (32.2) Secondary school or vocational school 25 (58.1) 57 (49.6) College or higher 11 (25.6) 21 (18.3) Marriage status 0.220 Married 41 (95.1) 100 (87.0) Single, divorced or widowed 2 (4.7) 15 (13.0) HAMD-21 scores 1 (0–2) 21 (17–24) < 0.001 Note. MDD = major depressive disorder; HC = healthy control; HAMD-21 = 21-item Hamilton Depression Rating Scale. DLPFC activity difference in the MDD group and the HC group As showed in Supplemental Table S3, the current density of the right DLPFC during the N2 ((-5.46) × 10 ^(−5) µA/mm 2 vs. 60.28 × 10 ^(−5) µA/mm 2 , p = 0.028) and P3 (14.68 × 10 ^(−5) µA/mm 2 vs. 60.58 × 10 ^(−5) µA/mm 2 , p = 0.037) time windows under the oddball stimulus were significantly lower in the MDD group compared to the HC group. DLPFC activity and DLFPC seed-based functional connectivity predicted HAMD-21 scores Pearson correlation analysis and LMM were performed to assess the associations between DLPFC activity, DLPFC seed-based FC, and depression symptoms across 1 week of treatment, as reported in Figs. 2 – 3 and Supplemental Table S4-S5. As shown in Fig. 2 B, the changes in right DLPFC current density during the N1 (r = -0.464, p = 0.001), P2 (r = -0.424, p = 0.003) and N2 (r = -0.348, p = 0.018) time windows were negatively associated with change in HAMD-21scores. Furthermore, after controlling for age and gender, heightened right DLPFC current density during the N1 (β = -1.447, 95% CI: -2.524-(-0.369), p = 0.009), P2 (β = -1.260, 95% CI: -2.416-(-0.103), p = 0.033), and N2 (β = -1.243, 95% CI: -2.433-(-0.054), p = 0.041) time windows under the oddball stimuli predicted lower HAMD-21 scores across 1 week of treatment (Table S3). Additionally, as shown in Fig. 3 B, the changes in theta-band FC between the left DLPFC and both the left IC (r = -0.340, p = 0.004) and the right IC (r = -0.285, p = 0.015) exhibited negative associations with change in HAMD-21 scores, while change in alpha-band FC between the right DLPFC and the right IC showed a positive association with change in HAMD-21 scores (r = 0.382, p = 0.001). Moreover, as reported in Table S4, after controlling for age and gender, elevated theta-band FC between the left DLPFC and both the left PCC (β = 1.001, 95% CI: 0.032–1.970, p = 0.043) and right PCC (β = 1.289, 95% CI: 0.384–2.194, p = 0.006), alpha-band FC between the right DLPFC and right IC (β = 1.152, 95% CI: 0.179–2.125, p = 0.021), and beta-band FC between the left DLPFC and both the left PCC (β = 1.326, 95% CI: 0.446–2.207, p = 0.003) and right PCC (β = 1.415, 95% CI: 0.473–2.357, p = 0.004) predicted higher HAMD-21 scores across 1 week of treatment. Conversely, theta-band FC between the left DLPFC and right IC predicted lower HAMD-21 scores across 1 week of treatment (β = -1.154, 95% CI: -2.094-(-0.214), P = 0.017). DLPFC activity and DLFPC seed-based functional connectivity predicted remission status Binary logistic regression analysis was conducted to examine the associations between changes in DLPFC activity and DLPFC seed-based FC from baseline to week 1, and remission status at week 12. As demonstrated in Supplemental Table S6, after controlling for age, gender, and baseline HAMD-21 scores, increases in right DLPFC current density during the N1 (OR = 4.295, 95% CI: 1.437–12.833, p = 0.009), P2 (OR = 4.748, 95% CI: 1.527–14.763, p = 0.007), N2 (OR = 5.235, 95% CI: 1.638–16.730, p = 0.005), and P3 (OR = 4.499, 95% CI: 1.457–13.893, p = 0.009) time windows under the oddball stimuli predicted a higher likelihood of achieving remission at week 12. As illustrated in Fig. 4 A, changes in right DLPFC current density during these time windows were significantly higher in remitters compared to non-remitters. Moreover, as reported in Supplemental Table S7, after controlling for age, gender, and baseline HAMD-21 scores, reductions in beta-band FC between the left DLPFC and both the left PCC (OR = 0.534, 95% CI: 0.297–0.972, p = 0.036) and right PCC (OR = 0.533, 95% CI: 0.299–0.950, p = 0.033) from baseline to week 1 were predictive of a greater probability of achieving remission at week 12. As depicted in Fig. 4 B, alterations in beta-band FC between the left DLPFC and both the left PCC and the right PCC were significantly lower in remitters compared to non-remitters. Discussion Numerous studies have emphasized the aberrant nature of the DLPFC and its FC with large-scale brain networks in MDD. However, prior research has predominantly focused on MRI methodologies, with EEG and ERP receiving limited attention. Notably, EEG changes shortly after initiating antidepressant medication, whereas baseline resting-state EEG may offer a more trait-like biomarker for predicting treatment response and subtyping depression, early treatment related EEG changes reflect state-dependent alterations in brain activity and connectivity patterns. These state-dependent biomarkers may be more sensitive to the specific neural changes induced by the drug, potentially making them better predictors of treatment response compared to trait-like baseline measures. The present cohort study aimed to better elucidate the neural mechanisms underlying MDD by leveraging the ability of high-density EEG to directly measure neural activity and to assess the early dynamic changes in brain function associated with treatment response. This approach allows for the identification of state-dependent biomarkers that may be more closely linked to the specific neural changes induced by antidepressant medication, in contrast to the more trait-like baseline measures examined in prior research. Compared to HCs, patients with MDD exhibited significantly diminished current density in the right DLPFC during the N2 and P3 time windows under the oddball stimulus. Importantly, heightened right DLPFC current density during the N1, P2 and N2 time windows predicted lower HAMD-21 scores across 1 week of treatment. Furthermore, remitters showed a significantly greater increase in the right DLPFC current density during the N1, P2, N2 and P3 time windows compared to non-remitters. These findings indicate that DLPFC activity patterns are closely linked to depressive symptoms, and increased current density in certain time windows is associated with better treatment outcomes for MDD patients. The N1 and P2 components represent early sensory processes elicited by stimuli, reflecting the reflecting the detection, initial perceptual characterization, and inhibition of the stimuli [ 27 – 30 ]. In contrast, the N2 and P3 components constitute mid- to late-stage negative components evoked by oddball stimuli, reflecting automatic attentional mechanisms and cognitive control [ 31 ]. The observed changes in these ERP components provide insights into the cognitive and neural processes that are impaired in MDD and those that are involved in the recovery or remission of depressive symptoms. Prior studies on repetitive transcranial magnetic stimulation (rTMS) or electrical stimulation to activate the DLPFC has shown promise for ameliorating clinical symptoms in patients with MDD, further indicating that increasing DLPFC activity may help alleviate depressive symptoms [ 32 , 33 ]. However, the discovery of increased DLPFC activity in remitters during the visual oddball paradigm in the present study contradicts findings from a previous task-based fMRI study, which reported decreased DLPFC activity in remitters during the Go/NoGo paradigm [ 14 ]. This discrepancy may reflect divergent cognitive and neurobiological processes across different task paradigms. Notably, ERPs can capture distinct stages of cognitive processing, such as early attentional orienting and later cognitive control, whereas fMRI primarily reflects average activity levels across the entire task duration, making it difficult to differentiate activity patterns at different cognitive stages [ 34 , 35 ]. Additionally, the differences in the temporal measurement time windows of ERPs and fMRI during task performance may also contribute to the observed discrepancies. Further investigating the influence of DLPFC activity on the DMN and SN, we discovered that higher theta-band FC between the left DLPFC and the right IC, as well as lower alpha-band FC between the right DLPFC and right IC, predicted lower HAMD-21 scores after one week of treatment. This finding suggests that enhanced communication between the DLPFC, which is associated with cognitive control processes, and the right IC, a key node in the SN responsible for processing salient stimuli and regulating attention, may facilitate more rapid alleviation of depressive symptoms during the initial stages of treatment. This result is partly in line with prior fMRI studies which have documented decreased FC between the ECN and SN in patients with MDD [ 10 ]. Additionally, a body of fMRI literature has consistently indicated that MDD primarily manifests as dysfunctions within the prefrontal-limbic circuitry [ 9 , 36 , 37 ]. Notably, the SN has been shown to exhibit increased connectivity with the DMN but reduced connectivity with the ECN individuals with MDD [ 10 ]. This pattern may reflect an over-allocation of attention towards internal negative information coupled with a neglect of external stimuli, as well as reduced recruitment of ECN resources for cognitive control processes [ 10 ]. Finally, the present study observed that higher FC in the theta-band and beta-band between the left DLPFC and both the left PCC and right PCC predicted higher HAMD-21 scores over 1 week of treatment. Furthermore, an early decrease in the beta-band FC between the left DLPFC and both the left PCC and right PCC predicted a greater probability of achieving remission at week 12. These findings align with previous fMRI studies indicating increased FC between the ECN and DMN in MDD [ 10 , 38 , 39 ]. Reduced DLPFC activity may impair cognitive control and emotion regulation abilities in MDD patients, the increased FC between the DLPFC and DMN regions in the beta-band may be a compensatory mechanism adopted by the brain to cope with the reduced DLPFC function, aiming to maintain the regulation of DMN activity [ 40 , 41 ]. However, further research is needed to validate this hypothesized mechanism. Strengths and limitations This study offers several key strengths compared to prior research in this area. Firstly, this prospective longitudinal study encompassed both MDD patients and HCs, and collected psychological assessment, EEG and ERP data at baseline and week 1 post-treatment, enabling continuous observation of dynamic alternations in brain network and depressive symptoms, as well as evolving associations between brain networks alterations and symptoms progression. Secondly, by examining changes in brain activity states and FC before and after treatment, this study reduces the impact of individual variances and the inherent diversity of MDD symptomatology. Thirdly, the concurrent examination of DLPFC activity and DLPFC seed-based FC provided further insights into the pathophysiological mechanisms of MDD. Fourthly, unlike previous research predominantly relying on MRI, this study utilized EEG, offering several advantages, including higher temporal resolution, lower cost, greater portability, and insensitivity to motion artifacts. Additionally, the source seed-based FC analysis of the EEG data enhanced interpretability compared to direct calculations between scalp channels, facilitating a more comprehensive understanding of brain areas interactions. Lastly, and importantly, this study revealed that the EEG changes observed soon after initiating antidepressant medication reflect state-dependent modifications in brain function and connectivity. By investigating early treatment response and long-term remission, this study provides crucial insights that have the potential to optimize treatment selection and outcomes for MDD patients, thereby reducing morbidity and economic burden by facilitating timely transitions to more effective interventions when initial antidepressants are predicted to have limited benefit. However, future research should address several limitations. Firstly, the single-center recruitment and relatively small sample size necessitate further validation. Secondly, limited analyzable EEG and ERP data at week 12 due to participant refusal and poor quality highlight the need for more comprehensive treatment assessment over time. The long-term effectiveness of antidepressants warrants further investigation, including 6- and 12-month follow-ups. Moreover, the exclusive use of SNRI medications limits generalizability. Additionally, while EEG offers high temporal resolution, cost-effectiveness, and portability compared to MRI, its spatial resolution remains limited. Finally, individual differences in symptoms and treatment response should be carefully considered and controlled for in future research. Conclusions In summary, this study indicates that the activity of the DLPFC and its FC with the DMN and SN are linked to depression symptoms. Specifically, for patients with MDD receiving treatment with venlafaxine, early alterations in DLPFC activity and its beta-band FC with the DMN could potentially act as predictive biomarkers of improvement in depressive symptoms. Utilizing these early changes detected via EEG during the treatment course may reflect individual variations in neural response to this specific antidepressant medication, enabling more tailored predictions of treatment outcomes based on each patient's unique neural pattern changes. Declarations ACKNOWLEDGMENTS AND DISCLOSURES Ethics approval and consent to participate This study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20220205). The registration number for this trial was ChiCTR2200057365. All methods were performed in accordance with the Declaration of Helsinki. All participants signed informed consent to participate. Consent for publication All participants signed informed content for publication. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Conflict of interest The authors report there are no conflict of interest to declare. Funding The study was supported by the National Natural Science Foundation of China (82090034). Authors' contributions YY, HZ, and KW were responsible for the conceptualization and design of this research. CL, KS, YX, YHS, JF, and ZWW collaborated in data collection. CL performed the data analysis and wrote the initial draft of the manuscript. HZ and YY provided comprehensive revisions to the manuscript. All authors participated in the manuscript revisions and approved the final version for submission. Acknowledgements The authors would like to thank the participants and their family for participation and referring physicians. References Weitz ES, Hollon SD, Twisk J, van Straten A, Huibers MJ, David D , et al . Baseline Depression Severity as Moderator of Depression Outcomes Between Cognitive Behavioral Therapy vs Pharmacotherapy: An Individual Patient Data Meta-analysis. JAMA Psychiatry 2015;72:1102-1109. Dunlop BW, Kelley ME, Aponte-Rivera V, Mletzko-Crowe T, Kinkead B, Ritchie JC , et al . 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Measures of Connectivity and Dorsolateral Prefrontal Cortex Volumes and Depressive Symptoms Following Treatment With Selective Serotonin Reuptake Inhibitors in Adolescents. JAMA Netw Open 2023;6:e2327331. Kang SG, Cho SE. Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder. Int J Mol Sci 2020;21. Keren H, O'Callaghan G, Vidal-Ribas P, Buzzell GA, Brotman MA, Leibenluft E , et al . Reward Processing in Depression: A Conceptual and Meta-Analytic Review Across fMRI and EEG Studies. Am J Psychiatry 2018;175:1111-1120. Fingelkurts AA, Fingelkurts AA. Altered structure of dynamic electroencephalogram oscillatory pattern in major depression. Biol Psychiatry 2015;77:1050-1060. Cohen SE, Zantvoord JB, Wezenberg BN, Bockting CLH, van Wingen GA. Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis. Transl Psychiatry 2021;11:168. 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Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Res 1999;90:169-179. Stam CJ, Nolte G, Daffertshofer A. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 2007;28:1178-1193. Detry MA, Ma Y. Analyzing Repeated Measurements Using Mixed Models. JAMA 2016;315:407-408. Vogel EK, Luck SJ. The visual N1 component as an index of a discrimination process. Psychophysiology 2000;37:190-203. Bidet-Caulet A, Mikyska C, Knight RT. Load effects in auditory selective attention: evidence for distinct facilitation and inhibition mechanisms. Neuroimage 2010;50:277-284. Chait M, de Cheveigne A, Poeppel D, Simon JZ. Neural dynamics of attending and ignoring in human auditory cortex. Neuropsychologia 2010;48:3262-3271. Tong Y, Melara RD, Rao A. P2 enhancement from auditory discrimination training is associated with improved reaction times. Brain Res 2009;1297:80-88. Kayser J, Bruder GE, Tenke CE, Stewart JE, Quitkin FM. Event-related potentials (ERPs) to hemifield presentations of emotional stimuli: differences between depressed patients and healthy adults in P3 amplitude and asymmetry. Int J Psychophysiol 2000;36:211-236. O'Reardon JP, Solvason HB, Janicak PG, Sampson S, Isenberg KE, Nahas Z , et al . Efficacy and safety of transcranial magnetic stimulation in the acute treatment of major depression: a multisite randomized controlled trial. Biol Psychiatry 2007;62:1208-1216. Boggio PS, Rigonatti SP, Ribeiro RB, Myczkowski ML, Nitsche MA, Pascual-Leone A , et al . A randomized, double-blind clinical trial on the efficacy of cortical direct current stimulation for the treatment of major depression. Int J Neuropsychopharmacol 2008;11:249-254. Woodman GF. A brief introduction to the use of event-related potentials in studies of perception and attention. Atten Percept Psychophys 2010;72:2031-2046. Poldrack RA. The future of fMRI in cognitive neuroscience. Neuroimage 2012;62:1216-1220. Lui S, Wu Q, Qiu L, Yang X, Kuang W, Chan RC , et al . Resting-state functional connectivity in treatment-resistant depression. Am J Psychiatry 2011;168:642-648. Diener C, Kuehner C, Brusniak W, Ubl B, Wessa M, Flor H. A meta-analysis of neurofunctional imaging studies of emotion and cognition in major depression. Neuroimage 2012;61:677-685. Mulders PC, van Eijndhoven PF, Schene AH, Beckmann CF, Tendolkar I. Resting-state functional connectivity in major depressive disorder: A review. Neurosci Biobehav Rev 2015;56:330-344. Dutta A, McKie S, Deakin JF. Resting state networks in major depressive disorder. Psychiatry Res 2014;224:139-151. Sheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ , et al . The default mode network and self-referential processes in depression. Proc Natl Acad Sci U S A 2009;106:1942-1947. Pizzagalli DA. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology 2011;36:183-206. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 06 Oct, 2025 Read the published version in Translational Psychiatry → Version 1 posted Editorial decision: revise 13 Mar, 2025 Reviewer # 3 agreed at journal 07 Mar, 2025 Reviewer # 2 agreed at journal 05 Mar, 2025 Review # 1 received at journal 19 Sep, 2024 Reviewer # 1 agreed at journal 17 Sep, 2024 Reviewers invited by journal 02 Sep, 2024 Submission checks completed at journal 15 Aug, 2024 Editor assigned by journal 14 Aug, 2024 First submitted to journal 14 Aug, 2024 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. 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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-4914286","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":348583509,"identity":"b282d547-d3ee-474d-a3d6-1db4c8a92004","order_by":0,"name":"Yuan Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDAD+8PMx8AMNnai9RxvS2NgSABqYSZay5kzZmAtDIS0GNxIfvaYp+aOXeOMnG8PPv7YJs/HzMD44WMOPi1p5sY8x54lN0vkbjeckXDbsI2ZgVly5jZ8WhLMpHnYDiezSeRuk+ZJuM0I1MLGzItXS/o3aZ5/h5N5JHKegbTYE6Elx0yat+2wnQTPGTaQlkSCWiTPvCmTnNt3OMGAvc1Mckba7eQ2ZsZmvH7hO56+TeLNt8P2BszMzyQ+2Ny2nd/efPDDRzxaFA4wMDDxMDAkNiDEGBtwKIYAeaA04w9gesGrahSMglEwCkY2AABl7FC1aJksfwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4511-8083","institution":"Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Yang","suffix":""},{"id":348583510,"identity":"380de7c7-115c-457f-b315-cccdf54e80ff","order_by":1,"name":"Han Zhang","email":"","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology,","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhang","suffix":""},{"id":348583511,"identity":"a119918a-bceb-4f69-828f-2b2c79ef6caa","order_by":2,"name":"Cun Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Cun","middleName":"","lastName":"Li","suffix":""},{"id":348583512,"identity":"cc922da5-8124-4a3e-b4fe-61029aeb358b","order_by":3,"name":"Ke Shi","email":"","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology,","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Shi","suffix":""},{"id":348583513,"identity":"c25df55e-f355-4282-a148-9615c71c31bc","order_by":4,"name":"Ye Xia","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Xia","suffix":""},{"id":348583514,"identity":"7f349a18-1dc8-441a-bc20-ca9d8732a1cc","order_by":5,"name":"Yanhui Song","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yanhui","middleName":"","lastName":"Song","suffix":""},{"id":348583515,"identity":"d5c7d02a-dec0-481a-9fb0-b76d5b84ef8a","order_by":6,"name":"Jie Feng","email":"","orcid":"","institution":"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology,","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Feng","suffix":""},{"id":348583516,"identity":"95e9fbb8-46e2-4af4-99e5-e4f94fc43e72","order_by":7,"name":"Ziwei Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ziwei","middleName":"","lastName":"Wang","suffix":""},{"id":348583517,"identity":"c18dbac5-251d-4404-a5e2-86f5fb0623f4","order_by":8,"name":"Kai Wang","email":"","orcid":"","institution":"Department of Medical Psychology, Anhui Medical University, Hefei, PR China","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-08-14 14:17:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4914286/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4914286/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41398-025-03576-0","type":"published","date":"2025-10-06T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67097389,"identity":"d798b41c-0a9f-480d-a9ca-567fce18173f","added_by":"auto","created_at":"2024-10-21 07:40:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1462658,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of individuals from enrollment to electroencephalogram (EEG) and event-related potential (ERP) analyses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e MDD = major depressive disorder; HC = healthy control.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4914286/v1/bdb38da6dfe612284eb84884.png"},{"id":67099090,"identity":"74e76be9-209a-41a8-a531-0512f1a8c1dc","added_by":"auto","created_at":"2024-10-21 07:56:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1823134,"visible":true,"origin":"","legend":"\u003cp\u003eA: The process of the visual oddball paradigm task; Figure 2B: Correlations between change in right dorsolateral prefrontal cortex (DLPFC) current density (μA/mm\u003csup\u003e2\u003c/sup\u003e) during different time windows under the oddball stimulus and change in 21-item Hamilton Depression Rating Scale (HAMD-21) scores in patients with major depressive disorder (MDD).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4914286/v1/9117ebd81f273971f9f813d1.png"},{"id":67097393,"identity":"816fd20e-9d35-4afb-afbf-603b5ed9da72","added_by":"auto","created_at":"2024-10-21 07:40:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2658334,"visible":true,"origin":"","legend":"\u003cp\u003eA: Regions of interest (ROIs) that were created in the dorsolateral prefrontal cortex (DLPFC), posterior cingulate cortex (PCC), and insula cortex (IC); Figure 3B: Correlations between changes in DLPFC- IC functional connectivity (FC) in the theta-band and alpha-band and change in 21-item Hamilton Depression Rating Scale (HAMD-21) scores in patients with major depressive disorder (MDD).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003eL= left; R = right.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4914286/v1/bf9012b199a599099415e860.png"},{"id":67097394,"identity":"2f9fedf3-b75d-4976-b383-feb6ab2c132a","added_by":"auto","created_at":"2024-10-21 07:40:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3094674,"visible":true,"origin":"","legend":"\u003cp\u003eA: Comparison of changes in right dorsolateral prefrontal cortex (DLPFC) current density (μA/mm\u003csup\u003e2\u003c/sup\u003e) during different time windows under the oddball stimulus between remitters and non-remitters; Figure 4B: Comparison of changes in beta-band functional connectivity (FC) between the left DLPFC and both the left posterior cingulate cortex (PCC) and right PCC between remitters and non-remitters.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4914286/v1/909be37ba0066ff1713a5213.png"},{"id":92921903,"identity":"974957a0-f127-4c9a-ae0c-49bdab63bab6","added_by":"auto","created_at":"2025-10-07 07:10:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9849742,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4914286/v1/03fdf334-a0a7-412d-bb09-5a0c990f3c72.pdf"},{"id":67097752,"identity":"d6e5a67b-135c-4842-9524-bbf3414f37b1","added_by":"auto","created_at":"2024-10-21 07:48:51","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":40338,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4914286/v1/fcf56397a1f8b564fb6bab3a.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Early Treatment-related Changes in Dorsolateral Prefrontal Cortex Activity and Functional Connectivity as Potential Biomarkers for Antidepressant Response in Major Depressive Disorder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is a widespread and debilitating condition that profoundly affects quality of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to its heterogeneous deficits in emotional, cognitive, and motor functioning, the effectiveness of standard antidepressant treatment is limited, with less than half of MDD patients achieving remission [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Current treatment options, including medications, psychotherapies, and somatic therapies, are predominantly selected based on their tolerability, leading to uncertainty about which patients will benefit from such treatment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given that therapeutic efficacy usually emerging after 4 to 8 weeks, many patients must endure multiple treatment trials before achieving relief, potentially receiving suboptimal treatment in the process [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This has generated significant interest in identifying biomarkers that could assist in medication selection or facilitate the early termination of ineffective trials.\u003c/p\u003e \u003cp\u003eRecent conceptualizations consider MDD as a systems-level disorder arising from dysregulation among large-scale functional brain networks [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Altered emotion regulation and deficits in cognitive control have been reported in individuals with MDD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The dorsolateral prefrontal cortex (DLPFC), an essential region of the executive control network (ECN), plays a vital role in emotion processing and cognitive control through its functional connectivity (FC) with other networks like the default mode network (DMN) and salience network (SN) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although there is some inconsistency, most functional magnetic resonance imaging (fMRI) findings support antidepressant treatment is associated with change in DLPFC activity in individuals with MDD, mainly characterized by increased activity [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For instance, one study demonstrated increased DLPFC activity during resting-state fMRI within days of treatment initiation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while another task-based fMRI study revealed heightened DLPFC activity in response to unattended fear-related stimuli in patients with MDD at week 8 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, other studies showed reduced right DLPFC activity during the N-back task after vortioxetine treatment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and reduced DLPFC activity during the Go/NoGo task in remitters compared to non-remitters [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, antidepressant treatment was found been associated with changes in DLPFC volumes and DLPFC-seed based FC based on fMRI [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious research has primarily focused on fMRI, which has proven invaluable in identifying structural and functional abnormalities in MDD [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the high temporal resolution and direct measurement of neuronal activity offered by electroencephalogram (EEG) could provide complementary insights into the rapid neural dynamics and network interactions underlying the cognitive and emotional dysregulation observed in MDD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, most studies predicting the efficacy of depression treatment based on neuroimaging have been conducted at baseline [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nevertheless, treatment efficacy may be more closely associated with dynamic changes in brain function during treatment, which cannot be captured by a single baseline imaging session [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. EEG signals have been shown to change within days after initiating antidepressant medication. By tracking these changes over time, researchers can investigate the temporal dynamics and trajectories of neural activity and FC patterns related to symptom improvement or treatment response, potentially identifying critical time windows or stages in the recovery process predictive of treatment outcomes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address the limitations of previous research, this longitudinal cohort study enrolled both patients with MDD and healthy controls (HCs). Psychological assessments, EEG, and event-related potential (ERP) data were collected at baseline and 1 week after initiating antidepressant treatment, with a 12-week follow-up. We hypothesized that: (1) patients with MDD would display abnormal DLPFC activity compared to HCs under the visual oddball paradigm; (2) DLPFC activity might change after treatment, potentially correlating with prognosis; and (3) changes in DLPFC FC with the DMN and SN would correlate with depressive symptoms and treatment prognosis. The posterior cingulate cortex (PCC) and insula cortex (IC) were selected as regions of interest (ROIs) representing the DMN and SN, respectively. The study aimed to determine the stability or recovery signs in DLPFC activity and DLPFC-seed based FC, shedding light on the impact of therapeutic interventions on brain activity and networks. Additionally, it sought to enhance understanding of the cognition-depression relationship and brain functional changes during treatment, providing insights into individual treatment responses.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eAll patients with MDD were treated with venlafaxine for 12 weeks. Depression severity was evaluated utilizing the 21-item Hamilton Depression Rating Scale (HAMD-21) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] at baseline, week 1 and week 12. Clinical outcomes were evaluated by Visual Analogue Scale (VAS) and HAMD-21 scores. The criteria for clinical outcomes were defined as follows: 1) Remission: VAS score\u0026thinsp;\u0026ge;\u0026thinsp;5 and HAMD-21 score\u0026thinsp;\u0026lt;\u0026thinsp;7 at week 12; 2) Response: VAS score\u0026thinsp;\u0026ge;\u0026thinsp;5 or a\u0026thinsp;\u0026ge;\u0026thinsp;50% reduction in HAMD-21 scores from baseline to week 12; 3) Non-response: VAS score\u0026thinsp;\u0026lt;\u0026thinsp;5 or a\u0026thinsp;\u0026lt;\u0026thinsp;50% reduction in HAMD-21 scores from baseline to week 12 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. EEG and ERP recordings were conducted at baseline and week 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis prospective longitudinal cohort study enrolled 115 untreated patients with MDD at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from March 2022 to September 2023. The inclusion criteria were as follows: (1) age 18\u0026ndash;65 years; (2) meeting the diagnostic criteria for depressive disorder as outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V); (3) HAMD-21 score\u0026thinsp;\u0026ge;\u0026thinsp;17; (4) educational level of elementary school or higher; (5) right-handedness; and (6) native Chinese speaker. Exclusion criteria included: (1) history of mental disorders such as schizophrenia, severe depressive disorder with suicidal tendencies, alcoholism, or substance abuse; (2) presence of severe or unstable physical disorders; (3) evidence of current or prior head injury, central nervous system disease, or other disorders according to the International Classification of Diseases, Tenth Revision (ICD-10); (4) contraindications to antidepressants; (5) history of antidepressants or long-acting antipsychotic injections within the past month; (6) evidence of aphasia, deafness, blindness, or cognitive impairment; and (7) breastfeeding, pregnancy, or planning pregnancy during the trial. Additionally, 43 HCs matched for age, gender, and education level were recruited. Exclusion criteria for the HCs were the same as mentioned earlier, with the additional of no history of any psychiatric illness. The participant enrollment flowchart is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e The informed consent was obtained in accordance with the Declaration of Helsinki. This study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20220205). The registration number for this trial was ChiCTR2200057365. The URL of the publicly accessible registered website is: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.chictr.org.cn/\u003c/span\u003e\u003cspan address=\"http://www.chictr.org.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEEG Recording and Preprocessing\u003c/h2\u003e \u003cp\u003eRest-state EEG signals, involving 12-minute trials with eyes-open and eyes-closed conditions, were recorded in a dimly illuminated, electrically shielded, and acoustically isolated chamber utilizing a 128-channel EEG system (BrainVision Recorder software, Brain Products GmbH, Germany). Participants maintained stillness, minimizing blinks and eye movements, while fixating on a central cross during eyes-open conditions. Electrodes were positioned according to the standard international 10/5 system at a sampling frequency of 1000Hz, with the FCz electrode serving as the default reference electrode. Electrode impedance was meticulously maintained below 20 KΩ during recording. EEG data preprocessing was underwent using BrainVision Analyzer software (version 2.2, Brain Products GmbH, Germany) (see Supplemental Methods).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVisual Oddball Paradigm and Event-related Potential\u003c/h2\u003e \u003cp\u003eThe visual oddball paradigm was conducted in a soundproof chamber. It consisted of two visual stimuli: a red car as the oddball stimulus and a blue car as the standard stimulus. The experimental process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Supplemental Methods. In the study, the ERP analysis focused primarily on the stimulus-associated N1, P2, N2, and P3 components. The selection of time windows and electrodes was guided by a prior study [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], as shown in Supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSource Localization Analyses\u003c/h2\u003e \u003cp\u003eSource localization analyses were conducted using the built-in low resolution electromagnetic tomography analysis (LORETA) within the BrainVision Analyzer software. LORETA calculates the current density (i.e., the amount of electrical current flowing through a volume; unit: \u0026micro;A/mm\u003csup\u003e2\u003c/sup\u003e) of intracranial sources responsible for scalp-recorded EEG signals [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The study focused on the current density within the DLPFC during the visual oddball paradigm. The DLPFC ROI was defined by the following coordinates (x, y, z): left DLPFC (-45, 32, 20) and right DLPFC (47, 32, 19).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRegion-of-Interest Selection\u003c/h2\u003e \u003cp\u003eTo examine the longitudinal changes in FC between the DLPFC and other brain networks (DMN and SN), and their association with alternations in depression symptoms, we identified the PCC and IC as ROIs for DMN and SN, respectively, according to findings by Whitton et al [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The coordinates (x, y, z) for the ROIs were as follows: left PCC (-7, -48, 33), right PCC (7, -47, 33), left IC (-38, -4, 5), and right IC (40, -5, 7). The locations of the DLPFC, PCC and IC ROIs are showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. Finally, as described above, the intracortical current density within each ROI was computed using LORETA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSource-Based Functional Connectivity\u003c/h2\u003e \u003cp\u003eGiven the practical constraints inherent in measuring FC using scalp electrodes, which are vulnerable to volume conduction effects, this study employed the phase lag index (PLI) to compute FC between sources [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. As a phase-based connectivity analysis method, PLI relies on the distribution of phase angle differences between two sources [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The underlying concept is that oscillatory sequences synchronize in phase when neural clusters are functionally coupled. PLI was calculated for the delta (1\u0026ndash;4 Hz), theta (4\u0026ndash;8 Hz), alpha (8\u0026ndash;12 Hz) and beta (12\u0026ndash;30 Hz) frequency bands.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe general characteristics of the study population were described as percentages for categorical variables, means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) for continuous variables with normal distributions, and medians (interquartile ranges, IQRs) for continuous variables without normal distributions. Z-score standardization was performed for DLPFC current density and DLPFC-seed based FC due to their relatively small and scattered nature. Group comparisons were conducted using chi-square tests, independent samples t-tests, and Wilcoxon rank-sum tests.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS for Windows, version 26.0, with two- tailed tests set at a significance level of α\u0026thinsp;=\u0026thinsp;0.05. Pearson correlation analysis was employed to examine the bivariate associations between the changes in DLPFC current density and DLPFC-seed based FC, and the changes in HAMD-21 scores. The changes of these variables were calculated by subtracting the week 1 values from the baseline. To further investigate these associations while accounting for multiple factors, a linear mixed-effects model (LMM) based on maximum likelihood estimation (MLE) was conducted. LME, a statistical model suitable for hierarchical or repeated measures data with fixed and random effects, allows for more accurate characterization of variability without distribution restrictions and is widely utilized across various fields [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the current study, participants were treated as random effects, while age, gender and DLPFC current density or DLPFC-seed based FC were designated as fixed effects, with HAMD-21 score as the dependent variable.\u003c/p\u003e \u003cp\u003eBinary logistic regression analysis was utilized to examine the association between early changes in DLPFC current density and DLPFC-seed based FC, and clinical outcomes at week 12, with corrected odds ratio (OR) and its corresponding 95% confidence interval (CI) serving as the effect size. The variables were entered into the regression model in the following steps: Step1: Age, sex, and baseline HAMD-21 score included as control variables. Step 2: Early change in DLPFC activity and DLPFC-seed based FC, calculated by subtracting the week 1 values from baseline, were entered as the independent variable. Step 3: Clinical outcomes (remission or no-remission) at week 12 were used as dependent variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eA total of 69 patients completed two ERP recordings, out of which 46 patients participated in the follow-up stage. Additionally, 93 patients underwent two EEG recordings, with 71 of these patients completing the follow-up stage. The detailed screening process is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there were no significant differences between the MDD group and the HC group in terms of age, gender, education level, and marriage status (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). After treatment, 58.7% of patients who completed ERP and 64.8% of patients who completed EEG met the remission criteria. No significant differences were observed between remitters and non-remitters concerning age, gender, and baseline HAMD-21 scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Supplemental Table S2).\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\u003eComparison of baseline characteristics between patients with MDD and HCs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC group (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDD group (n\u0026thinsp;=\u0026thinsp;115)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\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\u003e51\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (76.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school or vocational school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle, divorced or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMD-21 scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (17\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e MDD\u0026thinsp;=\u0026thinsp;major depressive disorder; HC\u0026thinsp;=\u0026thinsp;healthy control; HAMD-21\u0026thinsp;=\u0026thinsp;21-item Hamilton Depression Rating Scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDLPFC activity difference in the MDD group and the HC group\u003c/h2\u003e \u003cp\u003eAs showed in Supplemental Table S3, the current density of the right DLPFC during the N2 ((-5.46) \u0026times; 10\u003csup\u003e^(\u0026minus;5)\u003c/sup\u003e \u0026micro;A/mm\u003csup\u003e2\u003c/sup\u003e vs. 60.28 \u0026times; 10\u003csup\u003e^(\u0026minus;5)\u003c/sup\u003e \u0026micro;A/mm\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) and P3 (14.68 \u0026times; 10\u003csup\u003e^(\u0026minus;5)\u003c/sup\u003e \u0026micro;A/mm\u003csup\u003e2\u003c/sup\u003e vs. 60.58 \u0026times; 10\u003csup\u003e^(\u0026minus;5)\u003c/sup\u003e \u0026micro;A/mm\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) time windows under the oddball stimulus were significantly lower in the MDD group compared to the HC group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDLPFC activity and DLFPC seed-based functional connectivity predicted HAMD-21 scores\u003c/h2\u003e \u003cp\u003ePearson correlation analysis and LMM were performed to assess the associations between DLPFC activity, DLPFC seed-based FC, and depression symptoms across 1 week of treatment, as reported in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplemental Table S4-S5. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, the changes in right DLPFC current density during the N1 (r = -0.464, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), P2 (r = -0.424, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and N2 (r = -0.348, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) time windows were negatively associated with change in HAMD-21scores. Furthermore, after controlling for age and gender, heightened right DLPFC current density during the N1 (β = -1.447, 95% CI: -2.524-(-0.369), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), P2 (β = -1.260, 95% CI: -2.416-(-0.103), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), and N2 (β = -1.243, 95% CI: -2.433-(-0.054), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) time windows under the oddball stimuli predicted lower HAMD-21 scores across 1 week of treatment (Table S3).\u003c/p\u003e \u003cp\u003eAdditionally, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, the changes in theta-band FC between the left DLPFC and both the left IC (r = -0.340, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and the right IC (r = -0.285, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) exhibited negative associations with change in HAMD-21 scores, while change in alpha-band FC between the right DLPFC and the right IC showed a positive association with change in HAMD-21 scores (r\u0026thinsp;=\u0026thinsp;0.382, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Moreover, as reported in Table S4, after controlling for age and gender, elevated theta-band FC between the left DLPFC and both the left PCC (β\u0026thinsp;=\u0026thinsp;1.001, 95% CI: 0.032\u0026ndash;1.970, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) and right PCC (β\u0026thinsp;=\u0026thinsp;1.289, 95% CI: 0.384\u0026ndash;2.194, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), alpha-band FC between the right DLPFC and right IC (β\u0026thinsp;=\u0026thinsp;1.152, 95% CI: 0.179\u0026ndash;2.125, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), and beta-band FC between the left DLPFC and both the left PCC (β\u0026thinsp;=\u0026thinsp;1.326, 95% CI: 0.446\u0026ndash;2.207, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and right PCC (β\u0026thinsp;=\u0026thinsp;1.415, 95% CI: 0.473\u0026ndash;2.357, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) predicted higher HAMD-21 scores across 1 week of treatment. Conversely, theta-band FC between the left DLPFC and right IC predicted lower HAMD-21 scores across 1 week of treatment (β = -1.154, 95% CI: -2.094-(-0.214), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDLPFC activity and DLFPC seed-based functional connectivity predicted remission status\u003c/h2\u003e \u003cp\u003eBinary logistic regression analysis was conducted to examine the associations between changes in DLPFC activity and DLPFC seed-based FC from baseline to week 1, and remission status at week 12. As demonstrated in Supplemental Table S6, after controlling for age, gender, and baseline HAMD-21 scores, increases in right DLPFC current density during the N1 (OR\u0026thinsp;=\u0026thinsp;4.295, 95% CI: 1.437\u0026ndash;12.833, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), P2 (OR\u0026thinsp;=\u0026thinsp;4.748, 95% CI: 1.527\u0026ndash;14.763, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), N2 (OR\u0026thinsp;=\u0026thinsp;5.235, 95% CI: 1.638\u0026ndash;16.730, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), and P3 (OR\u0026thinsp;=\u0026thinsp;4.499, 95% CI: 1.457\u0026ndash;13.893, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) time windows under the oddball stimuli predicted a higher likelihood of achieving remission at week 12. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, changes in right DLPFC current density during these time windows were significantly higher in remitters compared to non-remitters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, as reported in Supplemental Table S7, after controlling for age, gender, and baseline HAMD-21 scores, reductions in beta-band FC between the left DLPFC and both the left PCC (OR\u0026thinsp;=\u0026thinsp;0.534, 95% CI: 0.297\u0026ndash;0.972, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) and right PCC (OR\u0026thinsp;=\u0026thinsp;0.533, 95% CI: 0.299\u0026ndash;0.950, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033) from baseline to week 1 were predictive of a greater probability of achieving remission at week 12. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, alterations in beta-band FC between the left DLPFC and both the left PCC and the right PCC were significantly lower in remitters compared to non-remitters.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eNumerous studies have emphasized the aberrant nature of the DLPFC and its FC with large-scale brain networks in MDD. However, prior research has predominantly focused on MRI methodologies, with EEG and ERP receiving limited attention. Notably, EEG changes shortly after initiating antidepressant medication, whereas baseline resting-state EEG may offer a more trait-like biomarker for predicting treatment response and subtyping depression, early treatment related EEG changes reflect state-dependent alterations in brain activity and connectivity patterns. These state-dependent biomarkers may be more sensitive to the specific neural changes induced by the drug, potentially making them better predictors of treatment response compared to trait-like baseline measures.\u003c/p\u003e \u003cp\u003eThe present cohort study aimed to better elucidate the neural mechanisms underlying MDD by leveraging the ability of high-density EEG to directly measure neural activity and to assess the early dynamic changes in brain function associated with treatment response. This approach allows for the identification of state-dependent biomarkers that may be more closely linked to the specific neural changes induced by antidepressant medication, in contrast to the more trait-like baseline measures examined in prior research.\u003c/p\u003e \u003cp\u003eCompared to HCs, patients with MDD exhibited significantly diminished current density in the right DLPFC during the N2 and P3 time windows under the oddball stimulus. Importantly, heightened right DLPFC current density during the N1, P2 and N2 time windows predicted lower HAMD-21 scores across 1 week of treatment. Furthermore, remitters showed a significantly greater increase in the right DLPFC current density during the N1, P2, N2 and P3 time windows compared to non-remitters. These findings indicate that DLPFC activity patterns are closely linked to depressive symptoms, and increased current density in certain time windows is associated with better treatment outcomes for MDD patients. The N1 and P2 components represent early sensory processes elicited by stimuli, reflecting the reflecting the detection, initial perceptual characterization, and inhibition of the stimuli [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In contrast, the N2 and P3 components constitute mid- to late-stage negative components evoked by oddball stimuli, reflecting automatic attentional mechanisms and cognitive control [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The observed changes in these ERP components provide insights into the cognitive and neural processes that are impaired in MDD and those that are involved in the recovery or remission of depressive symptoms.\u003c/p\u003e \u003cp\u003ePrior studies on repetitive transcranial magnetic stimulation (rTMS) or electrical stimulation to activate the DLPFC has shown promise for ameliorating clinical symptoms in patients with MDD, further indicating that increasing DLPFC activity may help alleviate depressive symptoms [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, the discovery of increased DLPFC activity in remitters during the visual oddball paradigm in the present study contradicts findings from a previous task-based fMRI study, which reported decreased DLPFC activity in remitters during the Go/NoGo paradigm [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This discrepancy may reflect divergent cognitive and neurobiological processes across different task paradigms. Notably, ERPs can capture distinct stages of cognitive processing, such as early attentional orienting and later cognitive control, whereas fMRI primarily reflects average activity levels across the entire task duration, making it difficult to differentiate activity patterns at different cognitive stages [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, the differences in the temporal measurement time windows of ERPs and fMRI during task performance may also contribute to the observed discrepancies.\u003c/p\u003e \u003cp\u003eFurther investigating the influence of DLPFC activity on the DMN and SN, we discovered that higher theta-band FC between the left DLPFC and the right IC, as well as lower alpha-band FC between the right DLPFC and right IC, predicted lower HAMD-21 scores after one week of treatment. This finding suggests that enhanced communication between the DLPFC, which is associated with cognitive control processes, and the right IC, a key node in the SN responsible for processing salient stimuli and regulating attention, may facilitate more rapid alleviation of depressive symptoms during the initial stages of treatment. This result is partly in line with prior fMRI studies which have documented decreased FC between the ECN and SN in patients with MDD [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, a body of fMRI literature has consistently indicated that MDD primarily manifests as dysfunctions within the prefrontal-limbic circuitry [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Notably, the SN has been shown to exhibit increased connectivity with the DMN but reduced connectivity with the ECN individuals with MDD [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This pattern may reflect an over-allocation of attention towards internal negative information coupled with a neglect of external stimuli, as well as reduced recruitment of ECN resources for cognitive control processes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, the present study observed that higher FC in the theta-band and beta-band between the left DLPFC and both the left PCC and right PCC predicted higher HAMD-21 scores over 1 week of treatment. Furthermore, an early decrease in the beta-band FC between the left DLPFC and both the left PCC and right PCC predicted a greater probability of achieving remission at week 12. These findings align with previous fMRI studies indicating increased FC between the ECN and DMN in MDD [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Reduced DLPFC activity may impair cognitive control and emotion regulation abilities in MDD patients, the increased FC between the DLPFC and DMN regions in the beta-band may be a compensatory mechanism adopted by the brain to cope with the reduced DLPFC function, aiming to maintain the regulation of DMN activity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, further research is needed to validate this hypothesized mechanism.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study offers several key strengths compared to prior research in this area. Firstly, this prospective longitudinal study encompassed both MDD patients and HCs, and collected psychological assessment, EEG and ERP data at baseline and week 1 post-treatment, enabling continuous observation of dynamic alternations in brain network and depressive symptoms, as well as evolving associations between brain networks alterations and symptoms progression. Secondly, by examining changes in brain activity states and FC before and after treatment, this study reduces the impact of individual variances and the inherent diversity of MDD symptomatology. Thirdly, the concurrent examination of DLPFC activity and DLPFC seed-based FC provided further insights into the pathophysiological mechanisms of MDD. Fourthly, unlike previous research predominantly relying on MRI, this study utilized EEG, offering several advantages, including higher temporal resolution, lower cost, greater portability, and insensitivity to motion artifacts. Additionally, the source seed-based FC analysis of the EEG data enhanced interpretability compared to direct calculations between scalp channels, facilitating a more comprehensive understanding of brain areas interactions. Lastly, and importantly, this study revealed that the EEG changes observed soon after initiating antidepressant medication reflect state-dependent modifications in brain function and connectivity. By investigating early treatment response and long-term remission, this study provides crucial insights that have the potential to optimize treatment selection and outcomes for MDD patients, thereby reducing morbidity and economic burden by facilitating timely transitions to more effective interventions when initial antidepressants are predicted to have limited benefit.\u003c/p\u003e \u003cp\u003eHowever, future research should address several limitations. Firstly, the single-center recruitment and relatively small sample size necessitate further validation. Secondly, limited analyzable EEG and ERP data at week 12 due to participant refusal and poor quality highlight the need for more comprehensive treatment assessment over time. The long-term effectiveness of antidepressants warrants further investigation, including 6- and 12-month follow-ups. Moreover, the exclusive use of SNRI medications limits generalizability. Additionally, while EEG offers high temporal resolution, cost-effectiveness, and portability compared to MRI, its spatial resolution remains limited. Finally, individual differences in symptoms and treatment response should be carefully considered and controlled for in future research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study indicates that the activity of the DLPFC and its FC with the DMN and SN are linked to depression symptoms. Specifically, for patients with MDD receiving treatment with venlafaxine, early alterations in DLPFC activity and its beta-band FC with the DMN could potentially act as predictive biomarkers of improvement in depressive symptoms. Utilizing these early changes detected via EEG during the treatment course may reflect individual variations in neural response to this specific antidepressant medication, enabling more tailored predictions of treatment outcomes based on each patient's unique neural pattern changes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eACKNOWLEDGMENTS AND DISCLOSURES\u003c/h2\u003e\n\u003ch4\u003eEthics approval and consent to participate\u003c/h4\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20220205). The registration number for this trial was ChiCTR2200057365. All methods were performed in accordance with the Declaration of Helsinki. All participants signed informed consent to participate.\u003c/p\u003e\n\u003ch4\u003eConsent for publication\u003c/h4\u003e\n\u003cp\u003eAll participants signed informed content for publication.\u003c/p\u003e\n\u003ch4\u003eAvailability of data and materials\u003c/h4\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003ch4\u003eConflict\u0026nbsp;of interest\u003c/h4\u003e\n\u003cp\u003eThe authors report there are no conflict of interest to declare.\u003c/p\u003e\n\u003ch4\u003eFunding\u003c/h4\u003e\n\u003cp\u003eThe study was supported by the National Natural Science Foundation of China (82090034).\u003c/p\u003e\n\u003ch4\u003eAuthors\u0026apos; contributions\u003c/h4\u003e\n\u003cp\u003eYY, HZ, and KW were responsible for the conceptualization and design of this research. CL, KS, YX, YHS, JF, and ZWW collaborated in data collection. CL performed the data analysis and wrote the initial draft of the manuscript. HZ and YY provided comprehensive revisions to the manuscript. All authors participated in the manuscript revisions and approved the final version for submission.\u003c/p\u003e\n\u003ch4\u003eAcknowledgements\u003c/h4\u003e\n\u003cp\u003eThe authors would like to thank the participants and their family for participation and referring physicians.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeitz ES, Hollon SD, Twisk J, van Straten A, Huibers MJ, David D\u003cem\u003e, et al\u003c/em\u003e. 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Resting-state functional connectivity in major depressive disorder: A review. \u003cem\u003eNeurosci Biobehav Rev\u003c/em\u003e 2015;56:330-344.\u003c/li\u003e\n\u003cli\u003eDutta A, McKie S, Deakin JF. Resting state networks in major depressive disorder. \u003cem\u003ePsychiatry Res\u003c/em\u003e 2014;224:139-151.\u003c/li\u003e\n\u003cli\u003eSheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ\u003cem\u003e, et al\u003c/em\u003e. The default mode network and self-referential processes in depression. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e 2009;106:1942-1947.\u003c/li\u003e\n\u003cli\u003ePizzagalli DA. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. \u003cem\u003eNeuropsychopharmacology\u003c/em\u003e 2011;36:183-206.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4914286/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4914286/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious research has demonstrated that patients with major depressive disorder (MDD) exhibit cognitive deficits. As a crucial region within the executive control network, the dorsolateral prefrontal cortex (DLPFC) activity and its functional connectivity (FC) may serve as potential indicators of antidepressant response. This prospective cohort study recruited 115 MDD patients and 43 healthy controls. Psychological assessments, electroencephalogram and event-related potential recordings were performed at baseline and 1 week after venlafaxine treatment, with a 12-week follow-up. Group differences were analyzed using independent sample t-tests and Mann-Whitney U tests, while linear mixed-effects models and logistic regression evaluated associations between DLPFC activity/FC changes and clinical outcomes. The MDD group showed significantly reduced right DLPFC current density during the N2 time window evoked by oddball stimuli (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), which negatively correlated with 21-item Hamilton Depression Rating Scale (HAMD-21) scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) (n\u0026thinsp;=\u0026thinsp;46). Furthermore, an early increase predicted remission at week 12 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). Decreased beta-band FC between the left DLPFC and both the left posterior cingulate cortex (PCC) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and right PCC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) predicted lower HAMD-21 scores (n\u0026thinsp;=\u0026thinsp;71). Moreover, an early reduction in these connectivity measures (left: odds ratio (OR)\u0026thinsp;=\u0026thinsp;0.534, 95% confidence interval (CI): 0.297\u0026ndash;0.972, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036; right: OR\u0026thinsp;=\u0026thinsp;0.533, 95% CI: 0.299\u0026ndash;0.950, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033) predicted remission at week 12. Early changes in DLPFC activity and FC may serve as biomarkers for monitoring treatment efficacy and predicting clinical outcomes, informing personalized treatment approaches.\u003c/p\u003e","manuscriptTitle":"Early Treatment-related Changes in Dorsolateral Prefrontal Cortex Activity and Functional Connectivity as Potential Biomarkers for Antidepressant Response in Major Depressive Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-21 07:40:47","doi":"10.21203/rs.3.rs-4914286/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-03-13T09:33:51+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-03-07T11:16:29+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-03-06T04:09:19+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-09-19T21:24:10+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-09-17T21:53:01+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-09-02T20:02:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-15T10:48:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-14T14:14:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2024-08-14T14:14:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e440ce69-62a4-4bb4-a2c8-60120b11cdf1","owner":[],"postedDate":"October 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-07T07:10:07+00:00","versionOfRecord":{"articleIdentity":"rs-4914286","link":"https://doi.org/10.1038/s41398-025-03576-0","journal":{"identity":"translational-psychiatry","isVorOnly":false,"title":"Translational Psychiatry"},"publishedOn":"2025-10-06 04:00:00","publishedOnDateReadable":"October 6th, 2025"},"versionCreatedAt":"2024-10-21 07:40:47","video":"","vorDoi":"10.1038/s41398-025-03576-0","vorDoiUrl":"https://doi.org/10.1038/s41398-025-03576-0","workflowStages":[]},"version":"v1","identity":"rs-4914286","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4914286","identity":"rs-4914286","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}