Predicting Relapse and Psychosocial Functioning in Major Depressive Disorder: A Machine Learning Approach Using Clinical and Resting-State fMRI Data

BackgroundMachine learning approaches pave a promising avenue to advance individual predictions about psychiatric illnesses, possibly using biomarkers. Here, we investigate longitudinal individual-level predictions of depressive relapse and the level of psychosocial functioning. MethodsClinical variables (containing detailed symptom profile, previous disease course, as well as environmental and psychological protective and risk factors) and resting-state functional connectivity (rsFC) measures were used to predict relapse and the level of psychosocial functioning after a two-year follow-up interval in 346 patients (240 female) with Major Depressive Disorder (MDD). Random Forest machine learning models were computed to test the incremental predictive capability of clinical and rsFC data compared to a reference model containing confounding variables, and of a multimodal model (combining rsFC and clinical data) compared to the clinical model. ResultsClinical information significantly predicted future psychosocial functioning beyond the reference model (21% versus 12% explained variance, p < 0.001). Depression relapse can be predicted by clinical information, however not significantly better than by the reference model alone (64.64% versus 57.70% balanced accuracy, p = 0.062). Resting-state data did not yield above-chance accuracies on its own (12% explained variance and 51.72% balanced accuracy) and did not hold incremental predictive value compared to clinical variables for either outcome. ConclusionsBaseline clinical information can be used to predict individual future psychosocial functioning, while rsFC patterns fall short of predicting clinical trajectories in MDD over a two-year interval. Sample size, model complexity and methodological considerations are discussed as potential sources of poor translation of MDD biomarkers.