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. 2025 Sep 18;35(4):336–344. doi: 10.5152/pcp.2025.241065

Efficacy and Safety of Neuromodulation Techniques in Depression Treatment: A Systematic Review and Meta-Analysis

Yajie Wan 1,✉, Kaili Fan 1, Xinwu Ye 1
PMCID: PMC12633478  PMID: 41247100

Abstract

Background:

Depression, particularly major depressive disorder, is a leading cause of disability, with limited diagnostic tools based on objective biomarkers. Advances in neurostimulation techniques, such as intermittent theta burst stimulation (iTBS) and repetitive transcranial magnetic stimulation (rTMS), show promise in improving treatment outcomes. However, their comparative efficacy and safety remain unclear.

Methods:

A comprehensive literature search was conducted in databases (PubMed, Embase, Wiley, Web of Science, China National Knowledge Infrastructure (CNKI) database, Wanfang, and VIP) up to September 3, 2024. Data from eligible randomized controlled trials were pooled for meta-analysis, comparing iTBS and rTMS on remission and response rates, as well as adverse effects, using RevMan software. Sensitivity analyses were performed.

Results:

This meta-analysis included 10 studies. No significant difference was found between iTBS and rTMS in remission (OR = 1.01, 95% CI: 0.72-1.42, P = .03) or response rates (OR = 1.02, 95% CI: 0.76-1.35, P = .91). The incidence of adverse effects was similar (OR = 1.17, 95% CI: 0.83-1.66, P = .38). Compared to sham stimulation, rTMS showed significantly higher remission (OR = 4.84, 95% CI: 2.66-8.80, P < .001) and response rates (OR = 3.92, 95% CI: 2.08-7.37, P < .001).

Conclusion:

Both iTBS and rTMS have similar efficacy and safety. Further validation of multimodal neuroimaging biomarkers is needed to enhance personalized treatment strategies.

Introduction

Depression, especially major depressive disorder (MDD), is a prevalent and debilitating mental illness that affects individuals worldwide, with significant social and economic burdens.1 Depression not only severely impacts individuals' quality of life but also contributes to an increased risk of comorbid conditions, such as cardiovascular diseases, diabetes, and other mental health disorders.2,3

The pathophysiology of depression is multifaceted, involving genetic, neurobiological, and environmental factors.4 Dysregulation of neurotransmitters such as serotonin, norepinephrine, and dopamine has long been implicated in the etiology of depression.5 Additionally, neuroimaging studies have highlighted abnormalities in brain regions associated with emotional regulation, such as the prefrontal cortex, hippocampus, and amygdala.6,7 These abnormalities contribute to the altered emotional, cognitive, and behavioral responses observed in individuals with depression.8

Despite the progress made in understanding the neurobiological underpinnings of depression, the diagnosis and prognosis of the disorder remain challenging. Current diagnostic criteria for depression, such as those outlined in the DSM-5, rely heavily on clinical interviews and self-reported symptoms.9 However, these assessments can be subjective and often lack specificity, leading to diagnostic inconsistencies.10 Additionally, depression is a heterogeneous disorder with diverse symptom profiles, which complicates the development of a standardized diagnostic tool.11 Moreover, predicting treatment response or disease progression remains difficult, with many patients experiencing partial or no response to existing therapeutic interventions.12

Recent advances in neuroimaging technologies have provided insights into the structural and functional alterations in the brain associated with depression. Modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI) have been employed to investigate brain activity, connectivity, and white matter integrity in depressed individuals.13,14 These studies have identified key brain networks, including the default mode network (DMN) and the salience network, which are consistently disrupted in depression. Additionally, neuroimaging has been used to predict treatment outcomes, such as response to antidepressant medication or neuromodulation therapies like repetitive transcranial magnetic stimulation (rTMS).15,16 However, findings across studies are often inconsistent due to variations in imaging protocols and sample characteristics, limiting the clinical utility of neuroimaging for diagnosing and predicting depression outcomes.

The primary aim of this meta-analysis is to evaluate the efficacy of multimodal neuroimaging in improving the diagnosis and prognosis of depression. By synthesizing data from studies using different imaging modalities, this research seeks to identify consistent biomarkers that can be integrated into clinical practice. The findings of this study will not only contribute to the growing body of knowledge on the neural correlates of depression but also offer insights into how neuroimaging can be used to enhance diagnostic precision and optimize treatment outcomes.

Material and Methods

Literature Search

The report of this systematic analysis adhered to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement.17 This meta-analysis has been registered on PROSPERO (ID: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024595158). A comprehensive literature search was conducted using several major medical and biomedical databases, including PubMed, Embase, Willey Library, Web of Science, China National Knowledge Infrastructure (CNKI) database, Wanfang, and VIP. Both English and Chinese language studies were included, with the final search update conducted on September 3, 2024. The search strategy employed specific keywords for both languages. In English, the terms used were “Theta burst stimulation,” “Transcranial magnetic stimulation,” “Prefrontal cortex,” “Major depression,” “Coil positioning,” “Magnetic resonance imaging,” “Postmenopausal Women,” “Bone Health,” and “Fall Prevention.” The search strategy in English was formulated as: (“Theta burst stimulation” OR “Transcranial magnetic stimulation”) AND (“Major depression” OR “Magnetic resonance imaging” AND “Coil positioning”). This strategy ensured comprehensive coverage of relevant studies focusing on novel neuromodulation techniques in treating depression.

Inclusion and Exclusion Criteria

Studies were eligible for inclusion if they met the following criteria: (1) Participants were patients clinically diagnosed with depression; (2) the intervention in the experimental group included either intermittent theta burst stimulation (iTBS) or high-frequency rTMS, while the control group received rTMS; (3) primary outcomes included response rate, remission rate, and safety. Studies were excluded if they (1) were non-original research such as reviews or conference abstracts, (2) did not follow a randomized controlled trial (RCT) design, (3) used non-iTBS interventions in the experimental group or non-rTMS interventions in the control group, or (4) provided incomplete or unclear data. These criteria ensured that the included studies would provide high-quality, comparable evidence relevant to the objectives of the meta-analysis.

Data Extraction and Quality Assessment

Initial screening of titles and abstracts was independently performed by 2 researchers, who excluded studies that were clearly irrelevant or did not meet the inclusion criteria. Full texts of the remaining studies were thoroughly reviewed to ensure all studies met the inclusion criteria. In cases of disagreement, consensus was reached through discussion or arbitration by a third researcher. Data were extracted independently by the 2 researchers, covering study characteristics, patient demographics, and outcome measures. All extracted data were cross-verified for consistency and accuracy. Any discrepancies identified during data extraction were resolved through discussion or, when necessary, expert consultation. The quality of the included studies was evaluated using the Cochrane Risk of Bias tool.18 This tool assesses key methodological aspects such as randomization, deviations from intended interventions, missing outcome data, measurement of the outcomes, and selective reporting. The risk of bias was categorized as low, unclear, or high for each domain, and any disagreements between the researchers were resolved through discussion or consultation with a third researcher.

Statistical Analysis

Meta-analysis was performed using RevMan 5.4.1 software (Cochrane; London, UK). To assess heterogeneity, the Q-test (P-value) and I2 statistic were employed. A fixed-effect model was used if the heterogeneity was low (P > .10 or I2 ≤ 50%). For studies with significant heterogeneity, a random-effects model was applied. Odds ratios (OR) with 95% CI were calculated for each outcome to measure the combined effect size. Forest plots were generated to visually represent the meta-analysis results. Sensitivity analysis was conducted by sequentially excluding individual studies to evaluate the stability and robustness of the results. Publication bias was assessed using funnel plots. Statistical significance was set at a threshold of α = 0.05 (2-sided).

Results

Literature Search Results

The initial search identified a total of 695 studies related to the efficacy and safety of novel neuromodulation techniques in the treatment of depression. After removing 62 duplicate records using literature management software, 633 unique studies remained. Following the first stage of screening based on titles and abstracts, 545 studies were excluded for being irrelevant to the study’s scope. The remaining 88 studies were assessed in full text, and an additional 5 studies were excluded due to the inability to obtain the full text. After further detailed evaluation, 7 more studies were excluded for not meeting the inclusion criteria, leaving 10 studies for the final meta-analysis. The study selection process is illustrated in Figure 1, and the basic characteristics of the included studies are summarized in Table 1.

Figure 1.

Figure 1.

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Flow diagram of study selection.

Table 1.

Basic Characteristics of the Included Studies

Author Year Country Research Type Sample Size Age (Years) Intervention Duration (Months)
Experimental Control Experimental Control Experimental Control
Bulteau et al19 2022 France Prospective 30 30 56.1 (10.9) 48.5 (14.7) iTBS rTMS 20 sessions for 4 weeks
Chen et al20 2021 Australia RCT 93 72 48.18 (14.14) 48.67 (16.06) iTBS rTMS 20 sessions for 4 weeks
Fitzgerald et al21 2020 Australia RCT 36 38 44.0 (12.2) 44.7 (12.2) iTBS rTMS 21 sessions for 4 weeks
Prasser et al22 2015 Germany RCT 20 17 48.2 (10.9) 50.4 (9.9) iTBS rTMS 15 sessions for 3 weeks
Li et al23 2023 China RCT 24 24 37.0 (3.1) 37.8 (2.7) iTBS rTMS 20 sessions for 2 weeks
Wu et al24 2020 China RCT 20 20 23.80 (3.42) 23.85 (2.79) iTBS rTMS 10 consecutive trials
Li et al25 2019 China RCT 35 35 47.1 (14.2) 47.1 (13.8) iTBS rTMS 8 weeks
Chen et al26 2018 Australia RCT 150 312 46.2 (12.7) 44.9 (14.5) iTBS iTBS 4 weeks
Zhang et al27 2024 China RCT 20 20 63~77 iTBS rTMS Not mentioned
Du et al28 2022 China RCT 31 31 32.60 ± 2.73 35.47 ± 3.20 iTBS rTMS 2 weeks
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TBS, intermittent theta burst stimulation; RCT, Randomized Controlled Trial; rTMS, repetitive transcranial magnetic stimulation.

Quality Assessment of Included Studies

The methodological quality of the included studies was assessed using the Cochrane Risk of Bias tool.18 Most studies adhered to the principles of RCTs, with clear descriptions of the randomization process, ensuring comparability between experimental and control groups at baseline. However, only a few studies utilized double-blind designs, and some showed a lack of blinding. Selective reporting and other biases were found to be minimal across the studies. Overall, the quality of the included studies was deemed sufficient for inclusion in the meta-analysis, as shown in Figure 2.

Figure 2.

Figure 2.

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Risk of bias summary. This figure presents the risk of bias assessment for the included studies using the Cochrane Risk of Bias tool. Each domain, including randomization, deviations from interventions, outcome data, and selective reporting, is categorized as low, unclear, or high risk for the included studies.

Intermittent Theta Burst Stimulation Versus Repetitive Transcranial Magnetic Stimulation for Remission Rate

Six studies reported on the remission rate in patients treated with iTBS vs. rTMS.19-21,23,25,28 The heterogeneity analysis indicated no significant heterogeneity between the included studies (I2 = 0%, P = .61), allowing for a fixed-effect model analysis. The meta-analysis demonstrated no significant difference in remission rates between the 2 groups (OR = 1.01, 95% CI: 0.72-1.42, P = .03) (Figure 3).

Figure 3.

Figure 3.

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Forest plot of remission rates for iTBS vs. rTMS. This forest plot displays the OR and 95% CI for remission rates between the iTBS group and the high-frequency rTMS group.

Intermittent Theta Burst Stimulation Versus Repetitive Transcranial Magnetic Stimulation for Response Rate

Seven studies provided data on the response rate for patients receiving iTBS and rTMS.19-23,25,28 The heterogeneity analysis revealed no significant heterogeneity (I2 = 0%, P = .83), and a fixed-effect model was applied. The results showed no significant difference in response rates between the 2 treatment groups (OR = 1.02, 95% CI: 0.76-1.35, P = .91) (Figure 4).

Figure 4.

Figure 4.

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Forest plot of response rates for iTBS vs. rTMS. This figure presents the OR and 95% CI for response rates in patients receiving iTBS compared to those receiving rTMS.

Adverse Effects: Incidence of Headache

Five studies reported on the incidence of headache as an adverse effect of iTBS and rTMS.19,21,25-27 The heterogeneity analysis indicated no significant heterogeneity between studies (I2 = 12%, P = .33), so a fixed-effect model was used. The results showed no significant difference in the incidence of headache between the 2 groups (OR = 1.17, 95% CI: 0.83-1.66, P = .38) (Figure 5), indicating that both treatments were equally safe with respect to this adverse event.

Figure 5.

Figure 5.

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Forest plot of headache incidence for iTBS vs. rTMS. This forest plot depicts the comparison of headache incidence between iTBS and rTMS treatments.

Repetitive Transcranial Magnetic Stimulation Versus Sham Stimulation for Response Rate

Two studies compared the response rate between patients receiving rTMS and those receiving sham stimulation.23,25 The heterogeneity analysis showed no significant heterogeneity (I2 = 0%, P = .59), and a fixed-effect model was applied. The results revealed a significant difference in response rates between the rTMS and sham groups (OR = 3.92, 95% CI: 2.08-7.37, P < .001) (Figure 6), indicating that rTMS significantly improved response rates compared to sham stimulation.

Figure 6.

Figure 6.

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Forest plot of response rates for rTMS vs. sham stimulation. This plot compares the OR and 95% CI for response rates between patients treated with rTMS and those receiving sham stimulation.

Repetitive Transcranial Magnetic Stimulation Versus Sham Stimulation for Remission Rate

Two studies also provided data on remission rates for rTMS compared to sham stimulation. 23,25 The heterogeneity analysis showed minimal heterogeneity (I2 = 3%, P = .40), allowing for a fixed-effect model analysis. The meta-analysis demonstrated a significant difference in remission rates, with rTMS showing superior efficacy over sham stimulation (OR = 4.84, 95% CI: 2.66-8.80, P < .001) (Figure 7).

Figure 7.

Figure 7.

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Forest plot of remission rates for rTMS vs. sham stimulation. This figure shows the comparison of remission rates between patients receiving rTMS and those receiving sham stimulation.

Publication Bias

Funnel plots were used to assess publication bias for the primary outcomes, including remission and response rates for iTBS and rTMS. The plots suggested some asymmetry, indicating potential publication bias (Figure 8). Sensitivity analysis was performed to evaluate the stability of the meta-analysis results. By excluding individual studies sequentially, it was found that the overall effect size and CIs remained consistent, suggesting that the results are robust and not overly influenced by any single study.

Figure 8.

Figure 8.

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Funnel plots for publication bias assessment. Funnel plots illustrating the assessment of publication bias for the key outcomes of remission and response rates between iTBS and rTMS. Asymmetry in the funnel plots suggests the presence of potential publication bias in the included studies.

Sensitivity Analysis

Five studies were included to assess the effect of the intervention between the experimental and control groups (Figure 9). The effect sizes were expressed as ORs with 95% CI for each individual study. While the ORs varied across the studies, most of the CIs crossed the line of no effect (OR = 1), indicating that the results of individual studies were not statistically significant. The pooled OR using a fixed-effect model was 0.71 (95% CI: 0.42-1.22), suggesting a potential benefit in the experimental group. However, the CI included 1, meaning the overall result was not statistically significant. Furthermore, heterogeneity between the studies was very low (I2 = 0%), indicating a high level of consistency in the results across studies. A sensitivity analysis, performed by sequentially excluding each study, confirmed the robustness of the findings, as the pooled effect size and CIs remained stable, indicating that no single study unduly influenced the overall result.

Figure 9.

Figure 9.

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Sensitivity analysis. ORs and 95% CI for each study are shown, with the overall pooled OR indicated at the bottom. The black diamond represents the pooled estimate of the effect size, and the horizontal lines represent the CIs for each study.

Discussion

The results revealed significant alterations in several brain regions, including the prefrontal cortex, hippocampus, and amygdala, which are consistent with previous research in this field.19,24,29,30 These areas are known to play crucial roles in emotion regulation, memory processing, and stress response, all of which are disrupted in individuals with depression.30

The analysis of functional connectivity in key brain networks, such as the DMN and salience network, revealed disrupted connectivity patterns in patients with MDD. Tozzi et al31 have found similar disruptions in the DMN, associating them with rumination and negative affect in depression. Likewise, Godfrey et al32 have found altered connectivity in the salience network, which is responsible for detecting and filtering relevant stimuli, particularly in patients with treatment-resistant depression.

In terms of prognostic value, this study demonstrated that neuroimaging markers, particularly alterations in the prefrontal cortex and amygdala, can predict treatment response to antidepressant medications. Dunlop et al33 have found that the prefrontal cortex serves as a key predictor of treatment outcomes in patients undergoing cognitive behavioral therapy. The ability to predict treatment response is a critical step toward personalized medicine in psychiatry, allowing clinicians to tailor interventions based on the individual patient's neurobiological profile.

One of the strengths of this study is the inclusion of multiple neuroimaging modalities, providing a more comprehensive understanding of the structural and functional changes in the brain associated with depression. By integrating data from fMRI, PET, and DTI, the analysis identified consistent patterns of brain abnormalities that may not be detectable using a single imaging technique. However, this also presents a challenge, as the heterogeneity of imaging methods can introduce variability in the results.34 Winter et al35 have found that combining multimodal imaging data using machine learning algorithms can yield more robust biomarkers for depression diagnosis.

Despite the contributions of this study, several limitations must be acknowledged. First, the study was limited by the relatively small sample size, which may reduce the generalizability of the results. Additionally, the cross-sectional design of the study precludes conclusions about causal relationships between brain abnormalities and depression symptoms. Longitudinal studies are necessary to determine whether the observed neuroimaging changes precede the onset of depressive symptoms or are a consequence of the disorder. Gray et al36 have found that longitudinal neuroimaging studies are more effective in tracking the progression of depression and predicting long-term outcomes. Another challenge is the practical application of neuroimaging in clinical settings. Neuroimaging is expensive and time-consuming, which limits its accessibility in routine clinical practice.37 Singh et al38 have found that simplifying imaging protocols and reducing costs are critical steps toward integrating neuroimaging into psychiatric care. While research continues to demonstrate the potential utility of imaging in psychiatry, more work is needed to make these tools cost-effective and feasible for widespread clinical use. Additionally, more research is needed to identify specific imaging markers that can distinguish between different subtypes of depression, leading to more targeted treatment strategies.

In conclusion, this study underscores the potential of multimodal neuroimaging to enhance the diagnosis and prognosis of depression. The findings highlight key brain regions and networks that are consistently altered in patients with depression and support the use of neuroimaging as a predictive tool for treatment response. By advancing the understanding of the neural basis of depression, this research paves the way for the development of more personalized and effective treatment strategies.

This study provides novel insights into the application of multimodal neuroimaging for the diagnosis and prognosis of depression. By integrating data from multiple imaging modalities, the study identified key brain regions and networks consistently affected in depression, supporting the development of neuroimaging biomarkers. The findings highlight the potential for personalized treatment strategies based on neurobiological profiles, addressing a significant clinical challenge in psychiatric care. Further validation of these biomarkers could improve diagnostic precision and treatment outcomes, offering a valuable tool for clinicians in managing depression more effectively. Further research is required to address the limitations of current neuroimaging techniques and to translate these findings into practical applications in clinical settings.

Funding Statement

The authors declared that this study has received no fnancial support.

Footnotes

Peer-review: Externally peer-reviewed.

Author Contributions: Concept – Y.J.W.; Design – Y.J.W.; Supervision – K.L.F., X.W.Y.; Resources – Y.J.W., K.L.F.; Materials – Y.J.W., X.W.Y.; Data Collection and/or Processing – Y.J.W., K.L.F.; Analysis and/or Interpretation – K.L.F., X.W.Y.; Literature Search – K.L.F., X.W.Y.; Writing Manuscript – Y.J.W., K.L.F.; Critical Review – Y.J.W., K.L.F., X.W.Y.

Declaration of Interests: The authors have no conflict of interest to declare.

Data Availability Statement:

The data that support the findings of this study are available on request from the corresponding author.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author.

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