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...Expand abstract
- University of Oxford
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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. ... Expand documentation
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Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants.
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