Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants.

Data for: Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants. Authors: Daniel Barron, Mehraveh Salehi, Michael Browning, Catherine J Harmer, R. Todd Constable, Eugene Duff DATA: 268 features (column) for 306 subjects (rows). There is a separate groupings file that associates each row with each subject, clinical group, and experimental task. CODE_Python: takes the 268 features and uses a GBM classifier to predict group association. CODE_Matlab: takes output from GBM classifier and produces figures and computes Wilson confidence intervals of the accuracies Background. Clinically approved antidepressants modulate the brain’s emotional valence circuits, suggesting that the response of these circuits could serve as a biomarker for screening candidate antidepressant drugs. However, it is necessary that these modulations can be reliably detected. Here, we apply a cross-validated predictive model to classify emotional valence and pharmacologic effect across eleven task-based fMRI datasets (n=306) exploring the effect of antidepressant administration on emotional face processing. Methods. We created subject-level contrast of parameter estimates of the emotional faces task and used the Shen whole-brain parcellation scheme to define 268 subject-level features that trained a cross-validated gradient-boosting machine protocol to classify emotional valence (fearful vs happy face visual conditions) and pharmacologic effect (drug vs placebo administration) within and across studies. Results. We found patterns of brain activity that classify emotional valence with a statistically significant level of accuracy (70% across-all-subjects; range from 50-87% across-study). Our classifier failed to consistently discriminate drug from placebo. Subject population (healthy or unhealthy), treatment group (drug or placebo), and drug administration protocol (dose and duration) affected this accuracy with similar populations better predicting one another. Conclusions. We found limited evidence that antidepressants modulated brain response in a consistent manner, however found a consistent signature for emotional valence. Variable functional patterns across studies suggest that predictive modeling can inform biomarker development in mental health and in pharmacotherapy development. Our results suggest that case-controlled designs and more standardized protocols are required for functional imaging to provide robust biomarkers for drug development