Predicting Treatment Outcomes in MDD using Baseline Resting-state Data: A Meta-Analysis
Poster No:
456
Submission Type:
Abstract Submission
Authors:
Yanyao Zhou1, Charlene Lam1
Institutions:
1The University of Hong Kong, Hong Kong, Hong Kong
First Author:
Co-Author:
Introduction:
Current pharmacological and psychotherapeutic interventions for major depressive disorder (MDD) demonstrate limited and heterogeneous efficacy, benefiting some patients. Past studies have attempted to find biomarkers, such as genetic variants, to improve treatment efficacy and better predict patient response to the treatments.
Resting-state functional connectivity (rsFC) at the baseline may offer particular promise as a predictive biomarker of the treatment outcome for MDD interventions. However, findings regarding the effect of baseline rsFC on predicting the treatment outcome have been mixed. In order to draw more robust conclusions regarding rsFC's potential as a biomarker for MDD treatment response, this meta-analysis systematically evaluates the evidence for baseline rsFC as a predictor of treatment outcomes in MDD interventions.
Resting-state functional connectivity (rsFC) at the baseline may offer particular promise as a predictive biomarker of the treatment outcome for MDD interventions. However, findings regarding the effect of baseline rsFC on predicting the treatment outcome have been mixed. In order to draw more robust conclusions regarding rsFC's potential as a biomarker for MDD treatment response, this meta-analysis systematically evaluates the evidence for baseline rsFC as a predictor of treatment outcomes in MDD interventions.
Methods:
We targeted MDD literature published between 2013 and 2023. Articles were included if they: 1) examined adult patients (>18 years old) with MDD; 2) tested antidepressants, psychotherapies like cognitive behavioral therapy, or non-invasive brain stimulation treatments; and 3) reported correlation coefficients indicating the relationship between baseline between-network and/or within-network rsFC and treatment outcomes. We generated a pooled predictive coefficient for different types of rsFC connections, if such type of rsFC connection contains predictive coefficients extracted from at least three different samples.
Results:
From 15 included studies, pooled coefficients were generated for three rsFC connections: 1) between the frontoparietal network (FPN) and the default mode network (DMN); 2) between the FPN and the ventral attention network (VAN); and 3) within the DMN.
The rsFC between the FPN and VAN emerged as the strongest predictor of treatment outcomes in MDD interventions, demonstrating an overall moderate to large effect. The effect size was 0.41 (95% CI: 0.23 – 0.59) for fixed effects and 0.45 (95% CI: 0.13 – 0.78) for random effects. The other two types of rsFC connections also showed predictive value, albeit with small to moderate effects. The effect size of the rsFC connection between the DMN and FPN was -0.17 (95% CI: -0.26 – -0.07) for fixed effects and -0.21 (95% CI: -0.46 – 0.05) for random effects. As for the rsFC connection within the DMN, the effect size was 0.19 (95% CI: 0.07 – 0.32) for fixed effects and 0.14 (95% CI: -0.13 – 0.41) for random effects.
Both the rsFC between the FPN and VAN and the rsFC within the DMN exhibited positive associations with treatment outcomes. In contrast, the rsFC between the FPN and DMN showed a negative association with treatment outcomes. Notably, substantial heterogeneity was observed across the three connection types, as well as in study design and data analysis approaches.
The rsFC between the FPN and VAN emerged as the strongest predictor of treatment outcomes in MDD interventions, demonstrating an overall moderate to large effect. The effect size was 0.41 (95% CI: 0.23 – 0.59) for fixed effects and 0.45 (95% CI: 0.13 – 0.78) for random effects. The other two types of rsFC connections also showed predictive value, albeit with small to moderate effects. The effect size of the rsFC connection between the DMN and FPN was -0.17 (95% CI: -0.26 – -0.07) for fixed effects and -0.21 (95% CI: -0.46 – 0.05) for random effects. As for the rsFC connection within the DMN, the effect size was 0.19 (95% CI: 0.07 – 0.32) for fixed effects and 0.14 (95% CI: -0.13 – 0.41) for random effects.
Both the rsFC between the FPN and VAN and the rsFC within the DMN exhibited positive associations with treatment outcomes. In contrast, the rsFC between the FPN and DMN showed a negative association with treatment outcomes. Notably, substantial heterogeneity was observed across the three connection types, as well as in study design and data analysis approaches.
Conclusions:
Significant heterogeneity was observed in effect size and direction of prediction, as well as the design and analysis pipelines across studies. However, rsFC still predicted outcomes with at least a small to moderate effect across different intervention types. RsFC demonstrates potential as a predictive biomarker; however, further research is needed to explore its full capabilities. This includes conducting studies with larger sample sizes, incorporating a wider range of MDD interventions, and employing more precise and consistent methodologies.
Brain Stimulation:
Non-invasive Electrical/tDCS/tACS/tRNS
Non-invasive Magnetic/TMS
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Task-Independent and Resting-State Analysis
Keywords:
Affective Disorders
Data analysis
DISORDERS
FUNCTIONAL MRI
Meta- Analysis
Psychiatric Disorders
Treatment
Other - Functional connectivity; Non-invasive brain stimulation; Antidepressant
1|2Indicates the priority used for review
·Figure 1. Pooled Pearson Correlation Coefficient for Different Types of rsFC Connection
·Figure 2. Nodes Involved in the rsFC Connection Predicting Treatment Outcomes with Different Directions
Provide references using author date format
Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D., & Pizzagalli, D. A. (2015). Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA psychiatry, 72(6), 603–611.
Long, Z., Du, L., Zhao, J., Wu, S., Zheng, Q., & Lei, X. (2020). Prediction on treatment improvement in depression with resting state connectivity: a coordinate-based meta-analysis. Journal of Affective Disorders, 276, 62–68.
Nord, C. L. (2021). Predicting response to brain stimulation in depression: a roadmap for biomarker discovery. Current Behavioral Neuroscience Reports, 8(1), 11–19.