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Neuroimage Clin. 2018 Aug 11;20:407-414. doi: 10.1016/j.nicl.2018.08.016. eCollection 2018.

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

Author information

Yale University School of Medicine, New Haven, CT, USA; Yale University Department of Psychiatry, New Haven, CT, USA. Electronic address:
Department of Electrical Engineering, Yale University, New Haven, CT, USA; Yale Institute for Network Science, Yale University, New Haven, CT, USA.
Oxford University Department of Psychiatry, Oxford, United Kingdom; Oxford Health NHS Trust, Oxford, UK.
Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA.
Functional Magnetic Resonance Imaging of the Brain Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Paediatrics, University of Oxford, UK.



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.


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.


We found patterns of brain activity that classify emotional valence with a statistically significant level of accuracy (70% across-all-subjects; range from 50 to 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.


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.


Antidepressant; Drug development; Emotional valence; Machine learning; Predictive analysis; Task-based fMRI

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