Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis
Abstract Background Current interventions for major depressive disorder (MDD) demonstrate limited and heterogeneous efficacy, highlighting the need for improving the precision of treatment. Although findings have been mixed, resting-state functional connectivity (rsFC) at baseline shows promise as a...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
|
| Series: | BMC Psychiatry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12888-025-06728-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849737741693091840 |
|---|---|
| author | Yanyao Zhou Na Dong Letian Lei Dorita H. F. Chang Charlene L. M. Lam |
| author_facet | Yanyao Zhou Na Dong Letian Lei Dorita H. F. Chang Charlene L. M. Lam |
| author_sort | Yanyao Zhou |
| collection | DOAJ |
| description | Abstract Background Current interventions for major depressive disorder (MDD) demonstrate limited and heterogeneous efficacy, highlighting the need for improving the precision of treatment. Although findings have been mixed, resting-state functional connectivity (rsFC) at baseline shows promise as a predictive biomarker. This meta-analysis evaluates the evidence for baseline rsFC as a predictor of treatment outcomes of MDD interventions. Method We included MDD literature published between 2012 and 2024 that used antidepressants, non-invasive brain stimulation, and cognitive behavioral therapy. Pearson correlations or their equivalents were analyzed between baseline rsFC and treatment outcome. Nodes were categorized according to the type of brain networks they belong to, and pooled coefficients were generated for rsFC connections reported by more than three studies. Result Among the 16 included studies and 892 MDD patients, data from nine studies were used to generate pooled coefficients for the rsFC connection between the frontoparietal network (FPN) and default mode network (DMN), and within the DMN (six studies each, with three overlapping studies, involving 534 and 300 patients, respectively). The rsFC between the DMN and FPN had a pooled predictability of -0.060 (p = 0.171, fixed effect model), and the rsFC within the DMN had a pooled predictability of 0.207 (p < 0.001, fixed effect model). The rsFC between the DMN and FPN and the rsFC within the DMN had a larger effect in predicting the outcome of non-invasive brain stimulation (-0.215, p < 0.001, fixed effect model) and antidepressants (0.315, p < 0.001, fixed effect model), respectively. Heterogeneity was observed in both types of rsFC, study design, sample characteristics and data analysis pipeline. Conclusion Baseline rsFC within the DMN and between the DMN and FPN demonstrated a small but differential predictive effect on the outcome of antidepressants and non-invasive brain stimulation, respectively. The small predictability of rsFC suggested that rsFC between the FPN and DMN and the rsFC within the DMN might not be a good biomarker for predicting treatment outcome. Future research should focus on exploring treatment-specific predictions of baseline rsFC and its predictive utility for other types of MDD interventions. Trial registration The review was pre-registered at PROSPERO CRD42022370235 (33). |
| format | Article |
| id | doaj-art-0d5ac3e71b26486ca55610567446a3fd |
| institution | DOAJ |
| issn | 1471-244X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj-art-0d5ac3e71b26486ca55610567446a3fd2025-08-20T03:06:50ZengBMCBMC Psychiatry1471-244X2025-04-0125112310.1186/s12888-025-06728-0Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysisYanyao Zhou0Na Dong1Letian Lei2Dorita H. F. Chang3Charlene L. M. Lam4Laboratory of Clinical Psychology and Affective Neuroscience, The University of Hong KongLaboratory of Clinical Psychology and Affective Neuroscience, The University of Hong KongLaboratory of Clinical Psychology and Affective Neuroscience, The University of Hong KongThe State Key Laboratory of Brain and Cognitive Sciences, The University of Hong KongLaboratory of Clinical Psychology and Affective Neuroscience, The University of Hong KongAbstract Background Current interventions for major depressive disorder (MDD) demonstrate limited and heterogeneous efficacy, highlighting the need for improving the precision of treatment. Although findings have been mixed, resting-state functional connectivity (rsFC) at baseline shows promise as a predictive biomarker. This meta-analysis evaluates the evidence for baseline rsFC as a predictor of treatment outcomes of MDD interventions. Method We included MDD literature published between 2012 and 2024 that used antidepressants, non-invasive brain stimulation, and cognitive behavioral therapy. Pearson correlations or their equivalents were analyzed between baseline rsFC and treatment outcome. Nodes were categorized according to the type of brain networks they belong to, and pooled coefficients were generated for rsFC connections reported by more than three studies. Result Among the 16 included studies and 892 MDD patients, data from nine studies were used to generate pooled coefficients for the rsFC connection between the frontoparietal network (FPN) and default mode network (DMN), and within the DMN (six studies each, with three overlapping studies, involving 534 and 300 patients, respectively). The rsFC between the DMN and FPN had a pooled predictability of -0.060 (p = 0.171, fixed effect model), and the rsFC within the DMN had a pooled predictability of 0.207 (p < 0.001, fixed effect model). The rsFC between the DMN and FPN and the rsFC within the DMN had a larger effect in predicting the outcome of non-invasive brain stimulation (-0.215, p < 0.001, fixed effect model) and antidepressants (0.315, p < 0.001, fixed effect model), respectively. Heterogeneity was observed in both types of rsFC, study design, sample characteristics and data analysis pipeline. Conclusion Baseline rsFC within the DMN and between the DMN and FPN demonstrated a small but differential predictive effect on the outcome of antidepressants and non-invasive brain stimulation, respectively. The small predictability of rsFC suggested that rsFC between the FPN and DMN and the rsFC within the DMN might not be a good biomarker for predicting treatment outcome. Future research should focus on exploring treatment-specific predictions of baseline rsFC and its predictive utility for other types of MDD interventions. Trial registration The review was pre-registered at PROSPERO CRD42022370235 (33).https://doi.org/10.1186/s12888-025-06728-0Major depressive disorderResting-state functional connectivityPrediction |
| spellingShingle | Yanyao Zhou Na Dong Letian Lei Dorita H. F. Chang Charlene L. M. Lam Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis BMC Psychiatry Major depressive disorder Resting-state functional connectivity Prediction |
| title | Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis |
| title_full | Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis |
| title_fullStr | Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis |
| title_full_unstemmed | Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis |
| title_short | Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis |
| title_sort | predicting the treatment outcomes of major depressive disorder interventions with baseline resting state functional connectivity a meta analysis |
| topic | Major depressive disorder Resting-state functional connectivity Prediction |
| url | https://doi.org/10.1186/s12888-025-06728-0 |
| work_keys_str_mv | AT yanyaozhou predictingthetreatmentoutcomesofmajordepressivedisorderinterventionswithbaselinerestingstatefunctionalconnectivityametaanalysis AT nadong predictingthetreatmentoutcomesofmajordepressivedisorderinterventionswithbaselinerestingstatefunctionalconnectivityametaanalysis AT letianlei predictingthetreatmentoutcomesofmajordepressivedisorderinterventionswithbaselinerestingstatefunctionalconnectivityametaanalysis AT doritahfchang predictingthetreatmentoutcomesofmajordepressivedisorderinterventionswithbaselinerestingstatefunctionalconnectivityametaanalysis AT charlenelmlam predictingthetreatmentoutcomesofmajordepressivedisorderinterventionswithbaselinerestingstatefunctionalconnectivityametaanalysis |