Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis

Abstract

No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.

Fig. 1. Flow diagram of the study inclusion process.

n number.

Fig. 2. Overall accuracy measures: area under the curve 0.84 (95% CI 0.81–0.87), sensitivity 77% (95% CI 71–82), specificity 79% (95% CI 73–84).

Reitsma bivariate SROC model of the receiver operating characteristic curve. Summary of sensitivity and false-positive rate (1 − specificity) is indicated in black, sensitivity and false-positive rates for different interventions are gray-scale. ECT electroconvulsive therapy, rTMS repetitive transcranial magnetic stimulation, pharmacological pharmacotherapeutic antidepressive interventions.

Fig. 3. Univariate random-effect forest plot of natural logarithm of diagnostic odds ratios.

Summary estimates for odds ratios are computed assuming normal distribution. CI confidence interval, rTMS repetitive transcranial magnetic stimulation, ECT electroconvulsive therapy.