Wenquan Yu
Yuchen Ying
Li Wang2
Yuning Pan- 1Department of Radiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China
- 2Ningbo College of Health Sciences, Ningbo, Zhejiang, China
Purpose: Patients with subthreshold depression (sD) carry a significant risk of developing major depressive disorder. However, few studies focus on the influence of Internet-based Cognitive Behavioral Therapy (ICBT) on brain network, particularly among Chinese undergraduates. This study aims to conduct resting-state functional MRI (rs-fMRI) to explore the effects of ICBT on neurofunctional network.
Methods: This short-term longitudinal study enrolled 30 sD patients and 24 healthy matched participants. We gathered the clinical measurements outcomes from sD patients. Baseline and post-intervention MRI scans were performed for the sD patients, additionally, a matched healthy controls group underwent baseline MRI scans, functional network matrix was established from the temporal rs-MRI series. Nodal efficiency (Enod) was quantified by calculating the area under the curve (AUC) of network metrics.
Results: The patients with sD exhibited reduced clinical measurement scores after ICBT intervention. Moreover, after ICBT intervention, functional network analysis revealed an increased Enod in the orbital part of the left inferior frontal gyrus, and decreased Enod in the left paracentral lobule and right precentral gyrus.
Conclusion: This study showed alterations in brain network in patients with sD after ICBT intervention. These findings shed light on the neurofunctional mechanisms underlying the effects of ICBT in sD patients.
1 Introduction
Subthreshold depression (sD), characterized by the presence of 2 to 4 depressive symptoms lasting more than two weeks, falls short of meeting the comprehensive criteria for Major Depressive Disorder (MDD) as delineated in the Diagnostic and Statistical Manual of Mental Disorders (DSM) (1, 2). Despite not reaching the severity of MDD, sD is a significant mental health concern due to its potential to progress into full-blown depression (3). This condition, is commonly observed across various age groups including university students at pivotal life stages, contributes not only to the individual neuropsychiatric symptomatology but also imposes a considerable socio-economic burden (4–6). The transition from sD to MDD underscores the critical need to elucidate its underlying neurophysiological mechanisms. A comprehensive understanding of these mechanisms is essential for the development and implementation of effective intervention strategies, which may aid in mitigating or even preventing the progression of sD to MDD.
MDD is a severe mental health challenge, persisting for at least two weeks and characterized by sustained sadness, loss of interest in enjoyable activities, and various physical ailments (7). Globally, MDD has a profound impact, affecting individuals in diverse regions with significant variation in prevalence rates, studies indicate a lifetime prevalence of about 16.6% in the United States and 12.8% in several European countries (8, 9). Additionally, another meta-analysis on subthreshold depression in the elderly report prevalence rates of 1.38–26.7% in Europe, 2.3–58.18% in the United States, and 3.5–56.89% in Asia (10). MDD is not limited to personal suffering, also often leads to social dysfunction, quality of life deterioration, and increased suicide risk (11). Furthermore, research suggests that a substantial number of MDD cases are preceded by a period of sD (12). Therefore, it is imperative to promptly identify and intervene in cases of sD to reduce the risk of its progression to MDD.
Cognitive Behavioral Therapy (CBT) has been recognized as an effective intervention for individuals experiencing depression. The efficacy of CBT has been demonstrated in numerous randomized controlled trials (RCTs) (13). However, the conventional face-to-face format of CBT poses limitations such as the need for in-person sessions and associated costs. These limitations are addressed by the development of Internet-based Cognitive Behavioral Therapy (ICBT), which enhances accessibility and convenience (14). Notably, evidence from a meta-analysis has demonstrated the effectiveness of ICBT in alleviating depressive symptoms in adults with subthreshold depression (sD) (15). Another study from a meta-analysis highlighted that ICBT showed enhanced short-term efficacy compared to control group in patients with sD (16).
ICBT provides several advantages over traditional CBT, such as flexibility in scheduling and the potential for anonymity, which can reduce stigma and discomfort associated with seeking mental health treatment (17). ICBT can be cost-effective and easily accessible, thus increasing the reach of mental health services to larger populations, including those in remote or underserved areas (18). As demonstrated in these studies, ICBT is not only a practical adaptation of traditional CBT but also an effective means of delivering therapy, especially for conditions like sD where early intervention is key to preventing the progression to more severe depressive disorders (15, 19, 20).
However, in China, the public health landscape is notably impacted by MDD and sD, with the latter particularly prevalent. Research indicates that the prevalence rates for MDD stand at 3.4% (21), moreover, a study identified a 14.7% prevalence of sD among adults in southern China, highlighting its substantial impact on mental health (22). Moreover, a study indicates that 30–40% of Chinese adolescents experience sD (23). Furthermore, In China, cultural and socio-economic barriers often prevent individuals from seeking conventional face-to-face mental health treatment. Fortunately, ICBT has become a valuable alternative, offering accessible care through a digital format (18, 21). Random clinical trials have affirmed the effectiveness of ICBT in managing sD within the Chinese population (14). Nevertheless, the mechanistic insights into ICBT are still profoundly lacking.
Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) has emerged as a pivotal tool in psychiatric research, offering valuable insights into intrinsic brain activities, which holds extensive applications in numerous neuroimaging studies (24–26). The values of rs-fMRI in elucidating the neural underpinnings of MDD are well-established in psychiatric research (27). Furthermore, studies have noted that CBT treatment influences the Default Mode Network (DMN) in patients with sD (28). However, its application in studying sD treated by ICBT especially among undergraduates is not as extensive (27, 29). In this research, we employ rs-fMRI to explore the neurobiological mechanisms involved in treating sD with ICBT. This investigation is directed toward augmenting the therapeutic efficacy of ICBT by exploring its impact at the neural level.
This study is designed to achieve these objectives: (1) Evaluating the efficacy of ICBT in depression management; (2) Elucidating the neurobiological mechanisms of ICBT’s therapeutic effects via rs-fMRI; (3) Providing empirical evidence to facilitate the widespread clinical implementation of ICBT in mental health care.
2 Materials and methods
2.1 Research design
This research conducted as a prospective, short-term longitudinal study. Participants were briefed on the examination procedures and methods prior to the commencement of the study. The ICBT group engaged in a four-week, self-administered ICBT program without direct psychiatric supervision. Baseline and post-treatment MRI scans were conducted for this group, supplemented by a suite of behavioral and cognitive evaluations after MRI scans. A gender, age and degree of education matched healthy control (HC) group, was also subjected to MRI scans at baseline. The study participants were thus categorized into three distinct groups: pre-intervention ICBT (pre-ICBT), post- intervention ICBT (post-ICBT), and the HC group. Conducted under the ethical guidelines of the Medical Ethics Committee of Ningbo University (No. NBU-2020-139), the study ensured comprehensive briefing on its objectives and methodologies to each participant, securing written informed consent before commencement.
2.2 Participants and recruitment
From December 2021 to June 2023, a comprehensive recruitment process involving telephone, WeChat, and direct interactions was implemented at diverse colleges in Ningbo in Zhejiang Province, China. The sample size was determined using G*Power (version 3.1.9.7), based on a previously reported RCT survey with an effect size of 0.52 (30). With a one-tailed alpha level of 0.05 and modest power (1-beta = 0.8), the minimum required sample size was 25 sD patients. To account for an estimated 20% attrition rate, the final target sample size was set at 30 sD participants. This recruitment process was conducted using an online questionnaire designed to gather demographic information, as well as scores from the Patient Health Questionnaire-9 (PHQ-9) and the Center for Epidemiological Studies Depression Scale (CES-D). All recruits were college undergraduates, ensuring homogeneity in educational background. Inclusion criteria for the ICBT group were: (1) Chinese undergraduates (included junior college students), (2) CES-D scores ≥ 16, indicating sD (31), (3) PHQ-9 score ≥ 9, (4) age above 18 years, and (5) adeptness in using smart devices.
Exclusion criteria were meticulously set to include: (1) PHQ-9 item 9 score above 2, indicative of suicidal inclinations, (2) medical history of neurological or psychiatric conditions, (3) cerebral anomalies like trauma, tumors, or infections, (4) disorders potentially impacting brain functionality such as mania, severe depression, or bipolar disorders, (5) background of substance misuse, (6) alcohol dependency, and (7) contraindications to MRI or inability to undergo the scanning process. Ultimately, this study enrolled 30 participants in the ICBT group (aged 18–20 years, 25 females/5 males) and 24 matched individuals in the HC group (aged 19–22 years, 20 female/4 males).
2.3 Clinical intervention
In current study, the ICBT was developed based on the Chinese version of the Cognitive Behavioral Therapy Manual. This program comprising five lessons, included extensive case narratives to complement the CBT skills taught. The ICBT was conducted via a Healthy Psychological Station integrated into a WeChat mini-program, spanning five weeks under clinical supervision. Each week focused on a specific CBT technique: emotional awareness and relationship building (Week 1), cognitive restructuring (Week 2), behavioral activation (Week 3), core belief modification (Week 4), and skill consolidation with future planning (Week 5). Lessons were sequentially unlocked, included real-life case stories, and were reinforced through interactive assignments, clinician support, and automated reminders to enhance engagement and adherence. More detailed exposition of the intervention’s structure and methodology, refer to the corresponding articles (14, 32).
2.4 Measures
2.4.1 Primary clinical measurements
The Chinese version of the CES-D: The CES-D comprises 20 items, each item assessed on a scale from 0 to 3. The cumulative score of scale can range from 0 to 60, where a higher score signifies an increased likelihood of experiencing depressive symptoms. The reliability and validity have been validated in Chinese populations (33).
2.4.2 Secondary clinical outcome measures
The PHQ-9 and the Chinese version of the Generalized Anxiety Disorder-7 (GAD-7) were utilized to evaluate symptoms of depression and anxiety respectively, and both have exhibited notable reliability and validity in Chinese populations (34–36).
The Cognitive Behavioral Therapy Skills Questionnaire (CBTSQ) and the Competencies of Cognitive Therapy Scale – Therapist Report version (CCTS-TR) was utilized to assess the competence of participants in CBT skills usage, emphasizing the evaluation of both cognitive and behavioral competencies (37, 38). Notably, the CCTS-TR scale assessments were conducted by CBT therapists, who remain blinded to other research elements to ensure objectivity. The Frequency of Actions and Thoughts Scale (FATS) evaluated the frequency of CBT skills usage across four different dimensions (20).
2.5 fMRI data acquisition
The fMRI data were collected by utilizing 3.0 T Siemens MAGNETOM Vida (Siemens, USA) with a 64-channel phased-array head coil. Before performing fMRI sequences, the T1WI, T2WI, T2-FLAIR and DWI imaging were collected, these images were evaluated by experienced radiologists to exclude participants with structural brain abnormalities. For each participant, rs-fMRI scan was conducted over approximately 10 min (54 slices, FOV = 216mmx216mm, TR = 2000 ms, TE = 30 ms, flip angle = 90°, matrix = 72 × 72, thickness = 3 mm, voxel = 3x3x3mm3, 200 volumes). Participants were positioned supine with their heads immobilized using foam pads, and earplugs were provided to mitigate noise during the rs-fMRI scanning process, meanwhile, the participants were advised to keep awake, remain calm and to avoid thinking about anything specific. Structural 3D-T1 weighted images were obtained by applying a 3D-MPRAGE sequence (192 slices, FOV = 256mmx256mm, TR = 2,300 ms, TE = 2.98 ms, flip angle = 9°, matrix = 256 × 256, thickness = 1 mm, voxel = 1x1x1mm3). For the ICBT group, MRI scans were completed within 48 h at baseline and after the ICBT intervention.
2.6 fMRI data preprocessing
All rs-fMRI data were preprocessed in Resting-State fMRI Data Processing Toolkit Plus (RESTplus, version 1.271) and Data Processing Assistant for Resting-State fMRI (DPARSF, version7.02), which is based on the Statistical Parametric Mapping (SPM12) (39). The preprocessing for rs-fMRI data was executed through several specialized steps: (1) To facilitate the achievement of steady-state magnetization and to acclimatize participants to the scanning environment, the first 10 time points were discarded, (2) realigned the time series of functional images to the initial frame to correct for any head movements, participants exceeding 3 mm in translation or 3° in rotation being exclude, (3) co-registered with each participant’s high-resolution structural T1 images, followed by segmentation and alignment to a standard brain template based on deformation field mapping, (4) an isotropic Gaussian kernel with a 6 mm full-width at half-maximum (FWHM) was applied for spatial smoothing to reduce noise and improve signal quality, (5) removing linear trends, addressing potential biases introduced by MRI equipment, (6) removal of nuisance covariates, encompassing Friston’s 24-parameter motion model and signals from white matter and cerebrospinal fluid, (7) to eradicate the confounding influences of ultra-low frequency drift, white matter-derived signals, and high-frequency physiological noises, a frequency filter range of 0.01–0.08 Hz was applied.
2.7 Cerebral functional network construction and analysis
For functional brain network construction, we formulated a brain network by defining nodes as specific brain regions on account of the Automated Anatomical Labeling (AAL) atlas, which represented a standard in graph theory analysis, resulting in 90 anatomical regions, inclusive of 78 cortical (each hemisphere contains 39 regions) and 12 subcortical areas (each hemisphere contains 6 regions). The nodes represented various brain regions, and the edges symbolized the connections between these nodes. Subsequently, the partial correlation coefficients of average time series from each region were calculated, while controlling the impacts of remaining 88 regions (representing the conditional dependency), thus, for each participant, a comprehensive 90 × 90 partial correlation matrix was created. To facilitate further analysis, according to the predefined sparsity thresholds (0.10–0.34, interval was 0.01), this sparsity thresholds generated sparse networks that accurately captured small-world properties while reducing spurious edges (40), these partial correlation matrices were transformed into binary matrices. Finally, for each subject, neurofunctional networks under specified sparsity threshold, we employed Graph Theoretical Network Analysis Tool Box (GRETNA3) for network analysis, and calculated the area under the curve (AUC) of whole network metrics that offered topological properties. Subsequently, we calculated the AUC of small-world properties of the whole-brain topological network at the predefined sparsity threshold, including global efficiency (Eg), clustering coefficient (Cp), shortest path length (Lp), normalized clustering coefficient (γ), normalized characteristic path length (λ), small-worldness (σ), local efficiency (Eloc), nodal betweenness centrality, nodal degree centrality, and nodal efficiency (Enod).
Enod quantified the capability of a node to disseminate information to another nodes in brain network, serving as a metric for assessing changes in functional connectivity across different brain regions (41). Elevated nodal efficiency indicates a node’s pivotal function in information transfer within the neural network, marking it as a central hub in facilitating efficient communication, and quantitatively measuring the information transformation of neural networks (42).
2.8 Statistical analysis
In order to examine the impact of depression on brain functional dynamics, we performed two-sample T-tests to evaluate intergroup differences in Enod and global brain attributes between the pre-ICBT and HC groups, as well as between the post-ICBT and HC groups. Subsequently, to ascertain the modulatory effects of ICBT on neural connectivity, we conducted a paired T-test comparing intra-group variations in global properties and nodal attributes between pre-ICBT and post-ICBT group. The threshold for determining statistical significance was set p ≤ 0.05 (FDR corrected, two-tailed). GRETNA facilitated these graph-theoretic statistical analyses.
The analysis of other variables and their correlations was conducted using IBM SPSS (version 26), the normality of distributions for each variable was ascertained using the Shapiro–Wilk test. Comparative analyses between the HC group and the pre-ICBT group regarding demographic parameters (such as age, height, weight, and BMI) were conducted employing either a two-sample T-test or the Mann–Whitney U test, according to the characteristics of data distribution. Chi-square tests evaluated gender differences. Additionally, intra-group variations in clinical measurement pre-and post-ICBT group were analyzed through paired T-test or Wilcoxon signed-rank test. p ≤ 0.05 (two-tailed) was considered to indicate statistical significance. For multiple comparisons, we applied FDR correction, p ≤ 0.05 (FDR corrected, two-tailed) was considered as statistically significant difference.
Subsequently, we employed partial correlation or Spearman correlation analyses to investigate the relationship between clinical outcomes and brain network properties. Additionally, mediation analysis using the PROCESS macro (43) of SPSS to assess the indirect effects of changed CBT skill usage (X) on clinical outcomes variation (Y) after ICBT intervention, mediated by changes in functional brain network properties (M),as shown in Figure 1.
Figure 1. The mediation analyze model applied for this study. (a) The Path e represents the total effect of the independent variable (X) on the outcomes (Y); (b) the Path a represents the effect of the independent variable (X) on the mediator variable (M). Path b measures the effect of the mediator variable (M) on the dependent variable (Y). Path c’ represents the indirect effect of X on Y, adjusted for mediation by (Μ).
3 Results
3.1 Primary outcomes
3.1.1 Demographic characteristics and clinical outcomes
As illustrated in Table 1, there were no significant statistical differences in demographic variables between the HC group and pre-ICBT group. Furthermore, Table 2 presents the clinical measurement outcomes, including CES-D, PHQ-P, GAD-7, CBTSQ, CCTS-TR, FATS, and their respective sub-scales, both pre-and post-ICBT intervention, notably, the significant statistical differences were observed between the pre-ICBT group and post-ICBT group in all clinical outcomes except for the CBTSQ-Behavioral Activation (CBTSQ-BhA) and the FATS-Activity Scheduling (FATS-AS).
Table 1. Demographic variables differences between HC and pre-ICBT groups.
Table 2. Clinical measurements differences between pre- and post-ICBT groups.
3.1.2 Globally functional brain network results
In our study, initially, we compared the global network properties (including Cp, Lp, γ, λ, σ, Eg, and Eloc) among the pre-ICBT, post-ICBT, and HC groups. As shown in Table 3, no significant differences were identified in the global topological features (PFDR ≥ 0.05). Subsequently, nodal properties analysis revealed that after the ICBT intervention, the post-ICBT group demonstrated a significant increased Enod in the orbital part of the left inferior frontal gyrus (ORBinf.L; PFDR ≤ 0.05). Additionally, significant decreases in Enod were noted in the left paracentral lobule (PCL.L; PFDR ≤ 0.05) and the right precentral gyrus (PCG.R; PFDR ≤ 0.05), as detailed in Table 4 and Figure 2. These findings indicate that ICBT can promote brain network reorganization in patients with sD by strengthening or weakening connections between certain nodes, thereby altering the topological characteristics of the brain network in patients with sD.
Table 3. Group comparisons of global properties AUC: pre-ICBT vs. HC, post-ICBT vs. HC, and pre- vs. post-ICBT.
Table 4. Brain regions with significantly different nodal-efficiency in the post-ICBT group compared with the pre-ICBT group.
Figure 2. (a,b) Regions of significant nodal efficiency differences between the pre-ICBT group and post-ICBT group. (b) The red and blue balls represent increased and decreased nodal efficiencies in post-ICBT group with efficiency compared to pre-ICBT group. Pre/post-ICBT, pre/post-intervention Internet-based Cognitive Behavioral Therapy; PCL.L, left paracentral lobule; PCG.R, right precentral gyrus; ORBinf.L, orbital part of the left inferior frontal gyrus.
3.2 Secondary outcomes
3.2.1 Correlations between Enod and clinical outcomes for depression and anxiety
We analyzed the associations between brain network properties and clinical symptoms in patients with sD. Specifically, we examined the correlations between Enod in brain regions showing differences before and after ICBT intervention with scores on the CES-D, PHQ-9, and GAD-7 scales, In the pre-ICBT group, no significant correlations were observed between Enod in brain regions and clinical outcome measures. However, as depicted in Figure 3, a positive correlation was identified in the post-ICBT group between Enod in PCL.L and post-intervention CES-D scores (r = 0.382, p = 0.037).
Figure 3. Association analysis between CES-D and nodal efficiency of PCL.L in post-ICBT group. CES-D, Center for Epidemiological Studies Depression Scale; PCLL, left paracentral lobule; post-ICBT, post-intervention Internet-based Cognitive Behavioral Therapy.
Then we analyzed the correlations between the changes in Enod (post-ICBT minus pre-ICBT) across various brain regions and changes in CES-D, PHQ-9, and GAD-7 scores (post-ICBT minus pre-ICBT), the results indicated no significant associations between the variations in Enod and the changes in clinical outcome measures.
3.2.2 Associations between Enod and CBT skills usage
A similar correlation analysis was conducted to explore associations between brain network dynamics and CBT skills usage before and after ICBT intervention. As shown in Figure 4, in the pre-ICBT group, Enod in the PCL.L was positively correlated with FATS-cognitive restructuring (FATS-CoR; r = 0.398, p = 0.030) and negatively with CCTS-TR - behavioral activation (CCTS-TR-BA; r = −0.600, p < 0.001). Further analyses assessed the correlations between changes in Enod and variations in CBTSQ, FATS, and CCTS-TR scores. Findings indicated a negative correlation between changes in Enod within the ORBinf.L and CBTSQ-cognitive restructuring (CBTSQ-CR; r = −0.378, p = 0.039), detailed in Figure 5.
Figure 4. Association analysis among FATS-CoR, CCTS-TR-BA and nodal efficiency of PCL.L. in pre-ICBT group. FATS-CoR, Cognitive Restructuring in Frequency of Actions and Thoughts Scale; CCTS-TR-BA, Behavioral Activation in Competencies of Cognitive Therapy Scale Therapist Report version; PCL.L., left paracentral lobule; pre-ICBT, pre-intervention Internet-based Cognitive Behavioral Therapy.
Figure 5. Correlation between changed scores of CBTSQ-CR and altered nodal efficiency in ORBinf.L. CBTSQ-CR, Cognitive restructuring of Cognitive Behavioral Therapy Skills Questionnaire, ORBinf.L orbital part of the left inferior frontal gyrus.
3.2.3 Mediation analysis results
Building on previous correlation analyses in this study, changes in Enod within PCL.L and ORBinf.L were used as mediators to assess the indirect effects of CBT skills on clinical outcomes, as demonstrated in Table 5, reveal that modifications in brain network properties do not mediate the effects of CBT skills on clinical measures.
Table 5. Mediation analysis of clinical measures through the usage of CBT skills mediated by the changes in the properties of functional brain networks.
4 Discussion
This research primarily focused on exploring neurofunctional alterations in undergraduates with sD in China, specifically assessing the impact of ICBT on their brain network connectivity. Our study integrates neuroimaging with clinical psychometrics to assess the specific neurofunctional impacts of ICBT, particularly in a demographic that is increasingly at risk for depressive disorders but less likely to seek traditional forms of mental health care (21). Furthermore, this study provides a neuroimaging basis for the digital-network-based cognitive therapy approach.
Initially, the results indicated a significant reduction in clinical measurement scores across various measures (CES-D, PHQ-9, GAD-7) and enhanced proficiency in CBT skills from post-ICBT to pre-ICBT, suggesting that ICBT interventions can effectively alleviate the clinical symptoms of sD, moreover, trained CBT skill usage could improve the prognosis for sD patients (37, 38, 44). These findings support the efficacy of ICBT in alleviating depressive symptoms and improving mental health outcomes in young adults, even reducing the risk of suicide among school students (45), highlighting its effectiveness in enhancing emotional regulation capabilities essential for managing depressive symptoms, which were consistent with other ICBT research (46, 47).
In contrast to initial expectations, there were no significant neurofunctional disparities between pre-ICBT, post-ICBT participants and the matched HC group. Previous studies have reported no significant differences in brain function between the pre-treatment ICBT group and healthy controls in patients with depression, suggesting that neural network reorganization may not yet have emerged in certain individuals prior to ICBT intervention, potentially reflecting heterogeneity in disease progression and compensatory mechanisms (48–50). In addition, another neuroimaging study on female university students with sD utilized seed-based functional connectivity analysis with seeds in right inferior frontal gyrus (IFG), right middle frontal gyrus (MFG), and the left precentral or postcentral cortex, the study indicated no significant differences in functional connectivity between the attentional bias modification (ABM) group and the active control (AC) group before and after training (44). Another short-term fMRI study focusing on patients with mild depression identified a post-training reduction in the amplitude of low-frequency fluctuations (ALFF) exclusively in the right MFG (45). That suggests subtle functional connectivity alterations in sD even mild depression may be not detected by conventional fMRI analysis.
After the ICBT intervention, increased Enod was observed in the ORBinf.L, along with a negative correlation between altered Enod in ORBinf.L and the altered CBTSQ-CR score. Previous studies reported that OBRinf.L was a crucial language center responsible for semantic and syntactic processing (51, 52), several researches highlights that impairments in language functions, such as aphasia or reduced desire to communicate, are crucial factors contributing to depression (53, 54). In our study, following ICBT intervention, increased Enod in the ORBinf.L and a negative correlation between altered Enod in ORBinf.L and the altered CBTSQ-CR score were observed, suggesting restructuring of brain network that potentially improve language processing and thereby affect communication and may not directly enhance the cognitive restruction in sD patients. Extensive prior researches have consistently shown alterations in the brain function of the limbic system (e.g. orbitofrontal cortex and amygdala) in patients with MDD (55, 56). Although this study did not directly detect changes in the neural network of the limbic system in subthreshold depression, previous studies have substantiated the significant role of the functional network connectivity between the inferior frontal gyrus (IFG) and the orbitofrontal cortex (OFC) in emotion regulation and decision-making (57–60). Furthermore, the left inferior frontal gyrus is critical for emotional regulation (61), and emotional dyscontrol is a significant characteristic of depression. Accordingly, the observed reorganization of nodal properties in the inferior frontal gyrus in subthreshold depression may represent an important biomarker for predicting the progression of the disorder, and indicates ICBT may alleviate depressive symptoms by simultaneously enhancing linguistic processing and emotional regulation.
Additionally, our study findings indicate a decreased Enod in the PCL.L and PCG.R, these regions integral to the sensorimotor network (SMN). As we know, patients with depression experience impairments in both internal and external sensory processing, such as diminished capacity to process non-verbal auditory stimuli and increased pain tolerance, along with a general slowing of psychomotor functions (62–64). Previous study reported that depression severity correlated with alteration in the sensorimotor network, specifically a reduction in exteroceptive activities and an enhancement of interoceptive processes as symptoms escalate (65). Our findings suggested that ICBT may affect the sensorimotor network by potentially inhibiting the interoceptive stimulus threshold, thereby alleviating depressive symptoms. The results enhance our understanding of the neurobiological mechanisms underlying the effects of ICBT on sD.
Correlation analysis revealed that, in the pre-ICBT group, the Enod of the PCL.L was positively correlated with FATS-CoR scores and negatively correlated with CCTS-TR-BA scores, suggesting a potential mediating role of PCL.L in the relationship between CBT skills application and clinical outcomes. However, subsequent mediation analysis did not support this hypothesis, indicating the need for further large-scale studies to validate this assumption. In the post-ICBT group, a positive correlation between Enod of PCL.L and CES-D scores was observed, suggesting that reduced PCL.L network activity was associated with symptom alleviation. Although this study could not identify additional brain region changes associated with clinical measures. However, larger-scale studies in the future may provide more meaningful insights and further elucidate these relationships. They may provide valuable insights into the neural mechanisms underlying ICBT and offer a foundation for further exploration of its effects on brain function in sD patients. Moreover, these results may inform refinements in ICBT interventions, ultimately enhancing their efficacy in improving clinical outcomes and prognosis for individuals with sD.
This study suggests that ICBT may alleviate clinical symptoms in sD by modulating emotion regulation regions, the SMN, and cognitive-motor pathways. Previous studies have verified the left and right prefrontal cortices play distinct roles in emotional processing, with the left hemisphere associated with positive emotions and the right hemisphere primarily involved in negative emotions (66). Previous research has reported altered functional connectivity between ORBinf.L, PCL.L, and the limbic system in depression (55, 60, 67). Our findings revealed changes in Enod within the ORBinf.L, PCL.L, and PGC.R, suggesting that ICBT primarily enhances positive emotion regulation to improve clinical symptoms, while its effect on negative emotion processing is less pronounced. This provides further insight into the mechanisms through which ICBT modulates neural networks to alleviate symptoms in patients with sD.
4.1 Limitation
Despite this study revealing the impact of ICBT on brain function, this study presents several limitations. First, the research was conducted with a restricted sample size and focused exclusively on Chinese university students, which may restrict the applicability of the findings, future research is planned to broaden the sample size to further explore the impact of ICBT on changes in brain activities and assess the comparative advantages of ICBT over traditional CBT approaches. Moreover, this study did not examine the differential effects of CBT and ICBT on brain function, an aspect that should be addressed in future research to enhance the comprehensiveness of these findings. Furthermore, the specific ICBT features utilized in this study might not capture the entire range of internet-based CBT interventions, limiting the generalizability to other ICBT formats. Additionally, the HC group in this study underwent MRI scanning only at baseline, which may limit the ability to assess potential longitudinal changes in brain network properties among healthy individuals, future studies should consider incorporating longitudinal assessments of healthy controls to better delineate the specific effects of ICBT on brain networks in sD patients. Finally, the short-term of this four-week longitudinal study primarily captures the immediate neurofunctional changes following ICBT. To investigate the enduring neurobiological impacts of ICBT, in the future, extended longitudinal studies are necessary.
5 Conclusion
Our study investigated the neurofunctional changes in patients with sD after ICBT, highlighting significant alterations in brain activity during the resting state that correlate with clinical symptoms. Notably, it demonstrated alterations in the brain network properties of brain regions including the ORBinf.L, the PCL.L, and the PCG.R after ICBT intervention, reflecting the impact of ICBT on the language system, emotional regulation network, and the SMN. Moreover, correlations between Enod in ORBinf.L and PCL.L with clinical symptoms and CBT skill utilization were identified, illustrating the intricate relationships between brain network modifications and clinical outcomes. Overall, this research provides valuable insights into the neurofunctional effects of ICBT on patients with sD, offering pathways for further research and clinical application.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Medical Ethics Committee of Ningbo University (No. NBU-2020-139). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author contributions
WY: Data curation, Investigation, Writing – original draft, Writing – review & editing. YuY: Data curation, Funding acquisition, Investigation, Project administration, Writing – review & editing. LW: Data curation, Funding acquisition, Investigation, Writing – review & editing. YiY: Data curation, Funding acquisition, Investigation, Writing – review & editing. LZ: Data curation, Funding acquisition, Investigation, Writing – review & editing. YW: Data curation, Formal analysis, Methodology, Software, Writing – review & editing. ZZ: Data curation, Formal analysis, Methodology, Software, Writing – review & editing. HZ: Data curation, Investigation, Writing – review & editing. YP: Data curation, Funding acquisition, Project administration, Supervision, Validation, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This study was funded by the Zhejiang Provincial Natural Science Foundation of China (grant number LGF21H090004), Zhejiang Provincial Philosophy and Social Sciences Planning Project (grant number 24NDQN137YBM), Research Project of Innovation and Development Center of Ideological and Political Work in Universities of Ministry of Education (LiaoNing Finance and Trade College) (grant number SZZXKT2023022), Humanities and Social Science Research Special Task Project of the Ministry of Education (College Counselor Research) (grant number 23JDSZ3178), and Clinical Research Center for Medical Imaging (grant number 2022LY-KEYB03).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Footnotes
References
1. Cuijpers, P, Koole, SL, van Dijke, A, Roca, M, Li, J, and Reynolds, CF 3rd. Psychotherapy for subclinical depression: meta-analysis. Br J Psychiatry. (2014) 205:268–74. doi: 10.1192/bjp.bp.113.138784
2. Kroenke, K. When and how to treat subthreshold depression. J Am Med Assoc. (2017) 317:702–4. doi: 10.1001/jama.2017.0233
3. Cuijpers, P, and Smit, F. Subthreshold depression as a risk indicator for major depressive disorder: a systematic review of prospective studies. Acta Psychiatr Scand. (2004) 109:325–31. doi: 10.1111/j.1600-0447.2004.00301.x
4. Cuijpers, P, Smit, F, Oostenbrink, J, de Graaf, R, ten Have, M, and Beekman, A. Economic costs of minor depression: a population-based study. Acta Psychiatr Scand. (2007) 115:229–36. doi: 10.1111/j.1600-0447.2006.00851.x
5. Kong, F, Yu, L, Hou, Y, Zhu, L, Zhou, J, Huang, L, et al. Efficacy of Internet-based Cognitive Behavioral Therapy for subthreshold depression among older adults in institutional long-term care settings: pragmatic randomized controlled trial. J Med Internet Res. (2024) 26:e40187. eng. Epub 2024/03/01. doi: 10.2196/40187
6. Sakata, M, Toyomoto, R, Yoshida, K, Luo, Y, Nakagami, Y, Uwatoko, T, et al. Components of smartphone cognitive-behavioural therapy for subthreshold depression among 1093 university students: a factorial trial. Evid Based Ment Health. (2022) 25:e18–25. doi: 10.1136/ebmental-2022-300455
7. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Washington, DC: American Psychiatric Association (2013).
8. Alonso, J, Angermeyer, MC, Bernert, S, Bruffaerts, R, Brugha, IS, Bryson, H, et al. Prevalence of mental disorders in Europe: results from the European study of the epidemiology of mental disorders (ESEMeD) project. Acta Psychiatr Scand. (2004) 109:21–7. doi: 10.1111/j.1600-0047.2004.00327.x
9. Kessler, RC, Berglund, P, Demler, O, Jin, R, and Walters, EE. Lifetime prevalence and age-of-onset distributions' of DSM-IV disorders in the national comorbidity survey replication. Arch Gen Psychiatry. (2005) 62:593–602. doi: 10.1001/archpsyc.62.6.593
10. Zhao, X, Zhang, L, Sáenz, AA, Zhang, X, Sun, J, Zhong, Q, et al. Prevalence of subthreshold depression in older adults: a systematic review and meta-analysis. Asian J Psychiatr. (2024) 102:104253. doi: 10.1016/j.ajp.2024.104253
11. Kessler, RC, DuPont, RL, Berglund, P, and Wittchen, HU. Impairment in pure and comorbid generalized anxiety disorder and major depression at 12 months in two national surveys. Am J Psychiatry. (1999) 156:1915–23. doi: 10.1176/ajp.156.12.1915
12. Frank, E, Prien, RF, Jarrett, RB, Keller, MB, Kupfer, DJ, Lavori, PW, et al. Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence. Arch Gen Psychiatry. (1991) 48:851–5. doi: 10.1001/archpsyc.1991.01810330075011
13. Peng, Y, Fang, JJ, Huang, W, and Qin, S. Efficacy of cognitive behavioral therapy for heart failure a meta-analysis of randomized controlled studies. Int Heart J. (2019) 60:665–70. doi: 10.1536/ihj.18-408
14. Ying, YC, Ji, YX, Kong, FQ, Wang, MY, Chen, QQ, Wang, L, et al. Efficacy of an Internet-based Cognitive Behavioral Therapy for subthreshold depression among Chinese adults: a randomized controlled trial. Psychol Med. (2023) 53:3932–42. doi: 10.1017/s0033291722000599
15. Reins, JA, Buntrock, C, Zimmermann, J, Grund, S, Harrer, M, Lehr, D, et al. Efficacy and moderators of internet-based interventions in adults with subthreshold depression: an individual participant data Meta-analysis of randomized controlled trials. Psychother Psychosom. (2021) 90:94–106. doi: 10.1159/000507819
16. Zhou, T, Li, X, Pei, Y, Gao, JA, and Kong, JH. Internet-based cognitive behavioural therapy for subthreshold depression: a systematic review and meta-analysis. BMC Psychiatry. (2016) 16:356. doi: 10.1186/s12888-016-1061-9
17. Andersson, G, Titov, N, Dear, BF, Rozental, A, and Carlbring, P. Internet-delivered psychological treatments: from innovation to implementation. World Psychiatry. (2019) 18:20–8. doi: 10.1002/wps.20610
18. Lu, J, Xu, X, Huang, Y, Li, T, Ma, C, Xu, G, et al. Prevalence of depressive disorders and treatment in China: a cross-sectional epidemiological study. Lancet Psychiatry. (2021) 8:981–90. doi: 10.1016/S2215-0366(21)00251-0
19. Karyotaki, E, Efthimiou, O, Miguel, C, Bermpohl, FMG, Furukawa, TA, and Cuijpers, P. Individual patient data M-A. Internet-based Cognitive Behavioral Therapy for depression a systematic review and individual patient data network meta-analysis. JAMA Psychiatry. (2021) 78:361–71. doi: 10.1001/jamapsychiatry.2020.4364
20. Terides, MD, Dear, BF, Fogliati, VJ, Gandy, M, Karin, E, Jones, MP, et al. Increased skills usage statistically mediates symptom reduction in self-guided internet-delivered cognitive-behavioural therapy for depression and anxiety: a randomised controlled trial. Cogn Behav Ther. (2018) 47:43–61. doi: 10.1080/16506073.2017.1347195
21. Huang, YQ, Wang, Y, Wang, H, Liu, ZR, Yu, X, Yan, J, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry. (2019) 6:211–24. doi: 10.1016/s2215-0366(18)30511-x
22. Liao, D-D, Dong, M, Ding, K-R, Hou, C-L, Tan, W-Y, Ke, Y-F, et al. Prevalence and patterns of major depressive disorder and subthreshold depressive symptoms in South China. J Affect Disord. (2023) 329:131–40. doi: 10.1016/j.jad.2023.02.069
23. Liu, HN, Zeng, WX, and Li, XW. Impacts of attentional training on attention bias of sub-clinical depressed undergraduates. Chinese J Behav Med Brain Sci. (2016) 25:60–4. doi: 10.3760/cma.j.issn.1674-6554.2016.01.013
24. Ferreira, LK, and Busatto, GF. Resting-state functional connectivity in normal brain aging. Neurosci Biobehav Rev. (2013) 37:384–400. doi: 10.1016/j.neubiorev.2013.01.017
25. Sheline, YI, and Raichle, ME. Resting state functional connectivity in preclinical Alzheimer's disease. Biol Psychiatry. (2013) 74:340–7. doi: 10.1016/j.biopsych.2012.11.028
26. Tessitore, A, Cirillo, M, and De Micco, R. Functional connectivity signatures of Parkinson's disease. J Parkinsons Dis. (2019) 9:637–52. doi: 10.3233/jpd-191592
27. Macêdo, MA, Sato, JR, Bressan, RA, and Pan, PM. Adolescent depression and resting-state fMRI brain networks: a scoping review of longitudinal studies. Brazilian J Psychiatry. (2022) 44:420–33. doi: 10.47626/1516-4446-2021-2032
28. Yokoyama, S, Okamoto, Y, Takagaki, K, Okada, G, Takamura, M, Mori, A, et al. Effects of behavioral activation on default mode network connectivity in subthreshold depression: a preliminary resting-state fMRI study. J Affect Disord. (2018) 227:156–63. doi: 10.1016/j.jad.2017.10.021
29. Huang, LN, Huang, GF, Ding, QG, Liang, P, Hu, CH, Zhang, HQ, et al. Amplitude of low-frequency fluctuation (ALFF) alterations in adults with subthreshold depression after physical exercise: a resting-state fMRI study. J Affect Disord. (2021) 295:1057–65. doi: 10.1016/j.jad.2021.08.094
30. Ying, Y, Ji, Y, Kong, F, Wang, M, Chen, Q, Wang, L, et al. Efficacy of an Internet-based Cognitive Behavioral Therapy for subthreshold depression among Chinese adults: a randomized controlled trial. Psychol Med. (2023) 53:3932–42. doi: 10.1017/S0033291722000599
31. Radloff, LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. (1977) 1:385–401. doi: 10.1177/014662167700100306
32. Ying, YC, Ji, YX, Kong, FQ, Chen, QQ, Lv, YE, Hou, YB, et al. Internet-based Cognitive Behavioral Therapy for psychological distress in older adults without cognitive impairment living in nursing homes during the COVID-19 pandemic: a feasibility study. Internet Intervent. (2021) 26:26. doi: 10.1016/j.invent.2021.100461
33. Jiang, LJ, Wang, Y, Zhang, YN, Li, R, Wu, HL, Li, CY, et al. The reliability and validity of the Center for Epidemiologic Studies Depression Scale (CES-D) for Chinese university students. Front Psych. (2019) 10:315. doi: 10.3389/fpsyt.2019.00315
34. Bian, CD, Li, CB, Duan, QL, and Wu, H. Reliability and validity of patient health questionnaire: depressive syndrome module for outpatients. Sci Res Essays. (2011) 6:278–82. doi: 10.1186/s12888-020-02885-6
35. Wang, WZ, Bian, Q, Zhao, Y, Li, X, Wang, WW, Du, J, et al. Reliability and validity of the Chinese version of the patient health questionnaire (PHQ-9) in the general population. Gen Hosp Psychiatry. (2014) 36:539–44. doi: 10.1016/j.genhosppsych.2014.05.021
36. Yu, W, Singh, SS, Calhoun, S, Zhang, H, Zhao, XH, and Yang, FC. Generalized anxiety disorder in urban China: prevalence, awareness, and disease burden. J Affect Disord. (2018) 234:89–96. doi: 10.1016/j.jad.2018.02.012
37. Jacob, KL, Christopher, MS, and Neuhaus, ECJBM. Development and validation of the cognitive-behavioral therapy skills questionnaire. Behav Modif. (2011) 35:595–618. doi: 10.1177/0145445511419254
38. Strunk, DR, Hollars, SN, Adler, AD, Goldstein, LA, and Braun, JD. Assessing Patients' cognitive therapy skills: initial evaluation of the competencies of cognitive therapy scale. Cogn Ther Res. (2014) 38:559–69. doi: 10.1007/s10608-014-9617-9
39. Chao-Gan, Y, and Yu-Feng, Z. DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Front Syst Neurosci. (2010) 2010:13. doi: 10.3389/fnsys.2010.00013
40. Zhang, JR, Wang, JH, Wu, QZ, Kuang, WH, Huang, XQ, He, Y, et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry. (2011) 70:334–42. doi: 10.1016/j.biopsych.2011.05.018
41. Bullmore, E, and Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. (2009) 10:186–98. doi: 10.1038/nrn2575
42. Onoda, K, and Yamaguchi, S. Dissociative contributions of the anterior cingulate cortex to apathy and depression: topological evidence from resting-state functional MRI. Neuropsychologia. (2015) 77:10–8. doi: 10.1016/j.neuropsychologia.2015.07.030
43. Hayes, AF. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Publications (2013).
44. Terides, MD, Dear, BF, Karin, E, Jones, MP, Gandy, M, Fogliati, VJ, et al. The frequency of actions and thoughts scale: development and psychometric validation of a measure of adaptive behaviours and cognitions. Cogn Behav Ther. (2016) 45:196–216. doi: 10.1080/16506073.2016.1149876
45. Robinson, J, Hetrick, S, Cox, G, Bendall, S, Yung, A, Yuen, HP, et al. The development of a randomised controlled trial testing the effects of an online intervention among school students at risk of suicide. BMC Psychiatry. (2014) 14:14. doi: 10.1186/1471-244x-14-155
46. Grieve, P, Egan, SJ, Andersson, G, Carlbring, P, Shafran, R, and Wade, TD. The impact of internet-based cognitive behaviour therapy for perfectionism on different measures of perfectionism: a randomised controlled trial. Cogn Behav Ther. (2022) 51:130–42. doi: 10.1080/16506073.2021.1928276
47. Singh, P, Otero, JM, Howe, C, Feinstein, RT, Gupta, K, Gladstone, TRG, et al. Internet-based Cognitive Behavioral Therapy intervention for youth with type 1 diabetes and depressive symptoms: a pilot and feasibility study. mHealth. (2024) 10:6. doi: 10.21037/mhealth-23-33
48. Hanuka, S, Olson, EA, Admon, R, Webb, CA, Killgore, WDS, Rauch, SL, et al. Reduced anhedonia following internet-based cognitive-behavioral therapy for depression is mediated by enhanced reward circuit activation. Psychol Med. (2023) 53:4345–54. doi: 10.1017/S0033291722001106
49. Kaiser, RH, Andrews-Hanna, JR, Wager, TD, and Pizzagalli, DA. Large-scale network dysfunction in major depressive disorder: a Meta-analysis of resting-state functional connectivity. JAMA Psychiatry. (2015) 72:603–11. doi: 10.1001/jamapsychiatry.2015.0071
50. Dichter, GS, Gibbs, D, and Smoski, MJ. A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder. J Affect Disord. (2015) 172:8–17. doi: 10.1016/j.jad.2014.09.028
51. Nisbet, K, Kostiw, A, Huynh, TKT, Saggu, SK, Patel, D, and Cummine, J. A volumetric asymmetry study of gray matter in individuals with and without dyslexia. J Neurosci Res. (2024) 102:e25305. doi: 10.1002/jnr.25305
52. Rolls, ET, Cheng, W, Du, JN, Wei, DT, Qiu, J, Dai, D, et al. Functional connectivity of the right inferior frontal gyrus and orbitofrontal cortex in depression. Soc Cogn Affect Neurosci. (2020) 15:75–86. doi: 10.1093/scan/nsaa014
53. Mohr, B, Stahl, B, Berthier, ML, and Pulvermüller, F. Intensive communicative therapy reduces symptoms of depression in chronic nonfluent aphasia. Neurorehabil Neural Repair. (2017) 31:1053–62. doi: 10.1177/1545968317744275
54. Sullivan, SA, Hollen, L, Wren, Y, Thompson, AD, Lewis, G, and Zammit, S. A longitudinal investigation of childhood communication ability and adolescent psychotic experiences in a community sample. Schizophr Res. (2016) 173:54–61. doi: 10.1016/j.schres.2016.03.005
55. Du, J, Rolls, ET, Cheng, W, Li, Y, Gong, W, Qiu, J, et al. Functional connectivity of the orbitofrontal cortex, anterior cingulate cortex, and inferior frontal gyrus in humans. Cortex. (2020) 123:185–99. doi: 10.1016/j.cortex.2019.10.012
56. Hamilton, JP, Etkin, A, Furman, DJ, Lemus, MG, Johnson, RF, and Gotlib, IH. Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of baseline activation and neural response data. Am J Psychiatry. (2012) 169:693–703. doi: 10.1176/appi.ajp.2012.11071105
57. Bechara, A, Damasio, H, and Damasio, AR. Emotion, decision making and the orbitofrontal cortex. Cereb Cortex. (2000) 10:295–307. doi: 10.1093/cercor/10.3.295
58. Banks, SJ, Eddy, KT, Angstadt, M, Nathan, PJ, and Phan, KL. Amygdala–frontal connectivity during emotion regulation. Soc Cogn Affect Neurosci. (2007) 2:303–12. doi: 10.1093/scan/nsm029
59. Hampshire, A, Chamberlain, SR, Monti, MM, Duncan, J, and Owen, AM. The role of the right inferior frontal gyrus: inhibition and attentional control. Neuroimage. (2010) 50:1313–9. doi: 10.1016/j.neuroimage.2009.12.109
60. Heather Hsu, C-C, Rolls, ET, Huang, C-C, Chong, ST, Zac Lo, C-Y, Feng, J, et al. Connections of the human orbitofrontal cortex and inferior frontal gyrus. Cereb Cortex. (2020) 30:5830–43. doi: 10.1093/cercor/bhaa160
61. Urgesi, C, Mattiassi, ADA, Buiatti, T, and Marini, A. Tell it to a child! A brain stimulation study of the role of left inferior frontal gyrus in emotion regulation during storytelling. NeuroImage. (2016) 136:26–36. doi: 10.1016/j.neuroimage.2016.05.039
62. Bubl, E, Kern, E, Ebert, D, Bach, M, and Tebartz van Elst, L. Seeing gray when feeling blue? Depression can be measured in the eye of the diseased. Biol Psychiatry. (2010) 68:205–8. doi: 10.1016/j.biopsych.2010.02.009
63. Mumford, D. On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biol Cybern. (1992) 66:241–51. doi: 10.1007/bf00198477
64. Zwanzger, P, Zavorotnyy, M, Diemer, J, Ruland, T, Domschke, K, Christ, M, et al. Auditory processing in remitted major depression: a long-term follow-up investigation using 3T-fMRI. J Neural Transm. (2012) 119:1565–73. doi: 10.1007/s00702-012-0871-2
65. Ray, D, Bezmaternykh, D, Mel'nikov, M, Friston, KJ, and Das, M. Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression. Proc Natl Acad Sci USA. (2021) 118:e2105730118. doi: 10.1073/pnas.2105730118
66. Canli, T, Desmond, JE, Zhao, Z, Glover, G, and Gabrieli, JDE. Hemispheric asymmetry for emotional stimuli detected with fMRI. NeuroReport. (1998) 9:3233–3239. doi: 10.1097/00001756-199810050-00019
Keywords: cognitive behavioral therapy, nodal efficiency, left paracentral lobule, functional magnetic resonance imaging, brain network analysis
Citation: Yu W, Ying Y, Wang L, Yang Y, Zhang L, Wang Y, Zhu Z, Zhang H and Pan Y (2025) Short-term alterations of brain network properties in subthreshold depression: the impact of Internet-based Cognitive Behavioral Therapy. Front. Neurol. 16:1474339. doi: 10.3389/fneur.2025.1474339
Edited by:
Paolo Taurisano, University of Bari Aldo Moro, ItalyReviewed by:
Didem Gokcay, Istanbul Technical University, TürkiyeAnoushiravan Zahedi, University of Münster, Germany
Copyright © 2025 Yu, Ying, Wang, Yang, Zhang, Wang, Zhu, Zhang and Pan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yuning Pan, Znl5cGFueXVuaW5nQG5idS5lZHUuY24=
†These authors have contributed equally to this work and share first authorship