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 = 6...
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Elsevier
2025-09-01
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| Series: | Journal of Mood and Anxiety Disorders |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950004425000239 |
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| author | Matthew D. Sacchet Joseph L. Valenti Poorvi Keshava Shane W. Walsh Moria J. Smoski Andrew D. Krystal Diego A. Pizzagalli |
| author_facet | Matthew D. Sacchet Joseph L. Valenti Poorvi Keshava Shane W. Walsh Moria J. Smoski Andrew D. Krystal Diego A. Pizzagalli |
| author_sort | Matthew D. Sacchet |
| collection | DOAJ |
| description | 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 (rho = 0.480 and rho = 0.415 respectively). Next, leave-one-out-cross-validation (LOOCV) showed that the best-performing models comprised only the subcortical individualized systems data, which correlated with clinical change for depression and anxiety scores for the KOR group with significantly higher accuracy (rho = 0.634 and rho = 0.562, respectively) compared to the placebo group (rho = 0.294 and rho = 0.034, respectively). Further, 25 subcortical neural features were identified based on correlation and ensemble determined importance in driving prediction. Final models for both depression and anxiety showed an overall higher representation of the dorsal attention network. Cortical and combined cortical-subcortical feature data showed no significant improvement in prediction of clinical change between the two groups. 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. |
| format | Article |
| id | doaj-art-3304fc5c4d1d4920bc08cb5e7fb3ecb1 |
| institution | DOAJ |
| issn | 2950-0044 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Mood and Anxiety Disorders |
| spelling | doaj-art-3304fc5c4d1d4920bc08cb5e7fb3ecb12025-08-20T03:08:17ZengElsevierJournal of Mood and Anxiety Disorders2950-00442025-09-011110012610.1016/j.xjmad.2025.100126Individualized functional brain mapping machine learning prediction of symptom-change resulting from selective kappa-opioid antagonism in an anhedonic sample from a Fast-Fail trialMatthew D. Sacchet0Joseph L. Valenti1Poorvi Keshava2Shane W. Walsh3Moria J. Smoski4Andrew D. Krystal5Diego A. Pizzagalli6Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA; Correspondence to: 149 13th St., Charlestown, MA 02129-4522, USA.Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAMeditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAMeditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAPsychiatry and Behavioral Sciences, Duke University, Durham, NC, USADepartment of Psychiatry, University of California, San Francisco, San Francisco, CA, USACenter for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USABackground: 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 (rho = 0.480 and rho = 0.415 respectively). Next, leave-one-out-cross-validation (LOOCV) showed that the best-performing models comprised only the subcortical individualized systems data, which correlated with clinical change for depression and anxiety scores for the KOR group with significantly higher accuracy (rho = 0.634 and rho = 0.562, respectively) compared to the placebo group (rho = 0.294 and rho = 0.034, respectively). Further, 25 subcortical neural features were identified based on correlation and ensemble determined importance in driving prediction. Final models for both depression and anxiety showed an overall higher representation of the dorsal attention network. Cortical and combined cortical-subcortical feature data showed no significant improvement in prediction of clinical change between the two groups. 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.http://www.sciencedirect.com/science/article/pii/S2950004425000239Individualized brain mappingMachine learningSelective kappa-opioid antagonismFunctional brain systemsTreatment predictionAnhedonia |
| spellingShingle | Matthew D. Sacchet Joseph L. Valenti Poorvi Keshava Shane W. Walsh Moria J. Smoski Andrew D. Krystal Diego A. Pizzagalli 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 Journal of Mood and Anxiety Disorders Individualized brain mapping Machine learning Selective kappa-opioid antagonism Functional brain systems Treatment prediction Anhedonia |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Individualized brain mapping Machine learning Selective kappa-opioid antagonism Functional brain systems Treatment prediction Anhedonia |
| url | http://www.sciencedirect.com/science/article/pii/S2950004425000239 |
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