Meta-analysis and meta-regression of diagnostic test accuracy of connectome-based predictive modeling in OCD
Clinical Snapshot
PICO Framework
| P — Population | Individuals with obsessive-compulsive disorder (563 patients) and healthy controls (564 participants) across 8 studies |
| I — Intervention | Connectome-based predictive modeling using resting-state functional MRI |
| C — Comparator | Healthy controls without OCD |
| O — Outcomes | Diagnostic test accuracy measures: sensitivity, specificity, area under curve, likelihood ratios, diagnostic odds ratio |
Bottom Line
This meta-analysis of 8 studies (1,127 participants) demonstrates that connectome-based predictive modeling using resting-state fMRI shows promising diagnostic accuracy for OCD, with pooled sensitivity of 82.7% and specificity of 79.4%. The area under the curve of 87% indicates good discriminative performance, while positive likelihood ratio of 4.15 suggests moderate diagnostic utility. However, significant limitations include unclear methodology reporting, absence of heterogeneity assessment, and lack of quality evaluation of included studies. Most importantly, this technology remains experimental and is not clinically available. While results suggest potential future applications for OCD diagnosis and treatment monitoring, current evidence is insufficient to change clinical practice. Clinicians should continue using established diagnostic criteria and validated assessment tools. Further validation studies and standardization of connectome-based approaches are needed before clinical implementation can be considered.
Key Findings
P Value: Not reported in abstract
Effect Size: Pooled sensitivity 82.7%, specificity 79.4%
Primary Outcome: Diagnostic accuracy of connectome-based predictive modeling for OCD
Nnt Or Sensitivity: Sensitivity 82.7%, Specificity 79.4%, AUC 87%, Diagnostic OR 18.69
Confidence Interval: Sensitivity: 77.9%-86.7%, Specificity: 75.9%-82.6%
Clinical Application
Currently limited to research environments; requires specialized fMRI facilities and computational expertise Not applicable to current Australian clinical practice; would require TGA approval and PBS consideration for future clinical implementation Patients with suspected OCD requiring diagnostic evaluation, particularly in research settings
Abstract
Functional magnetic resonance imaging studies have reported disruptions in functional connectivity within brain networks, known as connectomes. Researchers have tested connectomes to see whether they serve as biomarkers for various psychiatric conditions. This meta-analysis aims to evaluate the diagnostic test accuracy of predictive models of connectomes derived from resting-state fMRI in diagnosing obsessive-compulsive disorder. A systematic review and meta-analysis were conducted on previous studies assessing the sensitivity, specificity, and accuracy of connectome-based diagnostic models in obsessive-compulsive disorder and healthy controls. Eight studies were identified, comprising 563 individuals with obsessive-compulsive disorder and 564 healthy controls. The results revealed robust diagnostic performance with a pooled sensitivity of 0.827 (95% CI: 0.779-0.867) and specificity of 0.794 (95% CI: 0.759-0.826). Connectome-based diagnostic models demonstrated excellent clinical utility, with an area under the curve of 87% (95% CI: 84%-90%), and significant predictive power as indicated by positive (4.15) and negative (0.21) likelihood ratios, as well as strong diagnostic odds ratio of 18.69 (95% CI: 11.84-29.49). The results highlight the potential of functional connectome-based predictive modeling as a robust tool for accurately diagnosing obsessive-compulsive disorder, with possibility of future implications for early diagnosis, monitoring treatment-related changes, involving in decision modeling, and understanding the biological mechanisms underlying obsessive-compulsive disorder.
References
- 1.Tural, U., Gaggi, N. L., Stern, E. R., & Iosifescu, D. V. (2026). Meta-analysis and meta-regression of diagnostic test accuracy of connectome-based predictive modeling in OCD. Brain Imaging and Behavior. https://doi.org/10.1016/j.jmir.2024.101467
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