Turbulent-like Dynamics Predict Treatment Outcome in Major Depressive Disorder

Major depressive disorder is a prevalent and debilitating disease. Finding effective treatment is a lengthy trial-and-error process. Thus, identifying predictors of treatment outcome is key to reducing disease burden. Brain turbulence-like dynamic measures on transmission of information might be a potential methodological strategy for identifying informative predictors of antidepressant treatment response.

We analyzed data of the EMBARC study in which 296 adult outpatients were randomized to eight weeks of sertraline or placebo treatment. Resting state functional MRI scans were acquired at baseline and one week after treatment initiation. Turbulence-like dynamic measures were computed. Generalized linear models were used to predict week 8 response and symptom severity scores. Primary performance metrics were the area under the receiver operating characteristic (AUROC) and the root mean squared error (RMSE) respectively. Permutation testing was performed to test for significance against chance. Internal leave-one-out cross-validation was performed on the sertraline-treated patients (sample A) and external validation on the placebo arm (sample B) and on placebo-treated, non-responding patients who later switched to sertraline (sample C).

226 patients were analyzed (age 37.9±13.4 years; 148 [65.5%] female). Sample A included 109 participants, sample B 121, and sample C 61. Internal cross-validation results were significantly better than chance at predicting response (AUROC=0.71, balanced accuracy=69.5%, p=0.008) and symptom severity (RMSE=4.7, p=0.025). External validation in sample B did not yield performance significantly better than chance, and in sample C only prediction of response did (AUROC=0.65, balanced accuracy=60.3%, p=0.038).

The turbulence framework is a suitable paradigm for predicting sertraline response in major depressive disorder. Prediction specificity to sertraline treatment was limited. Non-responders to sertraline were characterized by increased long-distance and reduced short-distance information cascade flow.