Individualized functional brain mapping machine learning prediction of symptom-change resulting from selective kappa-opioid antagonism in an anhedonic sample from a Fast-Fail trial.

Background: Anhedonia remains a difficult-to-treat symptom and has been associated with poor clinical course transdiagnostically. Here, we applied machine learning models to individualized neural patches derived from fMRI data during the Monetary Incentive Delay Task in anhedonic participants (N = 67) recruited for a clinical trial examining K-opioid receptor (KOR) antagonism in the treatment of anhedonia. Methods: Nine ensemble models were estimated using cortical, subcortical, and combined cortical subcortical features from individualized functional topographies to predict changes in symptoms of overall psychopathology (anhedonia, depression, anxiety). Analyses were performed on the KOR (N = 33) and placebo (N = 34) group. Results: Initial models showed that only subcortical data predicting depression and anxiety symptom change had a significant Spearman correlation between veridical and predicted data ( Conclusion: Using an ensemble of machine learning approaches, we identified individual differences in subcortical individualized systems data that predicted clinical change that was specific to KOR antagonism.