Accurate and Interpretable Prediction of Antidepressant Treatment Response from Receptor-informed Neuroimaging
Bibliographic record
Abstract
Conventional antidepressants show moderate efficacy in treating major depressive disorder. Psychedelic-assisted therapy holds promise, yet individual responses vary, underscoring the need for predictive tools to guide treatment selection. Here, we present graphTRIP (graph-based Treatment Response Interpretability and Prediction) – a geometric deep learning architecture that enables three advances: 1) accurate prediction of post-treatment depression severity using only pretreatment clinical and neuroimaging data; 2) identification of robust biomarkers; and 3) causal analysis of treatment effects and underlying mechanisms. Trained on data from a clinical trial comparing psilocybin and escitalopram ( NCT03429075 ), graphTRIP achieves strong predictive accuracy ( r = 0.72, p = 6.8 ×10 −8 ), and shows clear generalization to both an independent dataset and across brain atlases. The model identifies stronger functional connectivity within sensory networks as a robust predictor of poorer response across both treatments. In contrast, causal analysis implicates frontoparietal and default mode networks as key moderators of differential response, with stronger 5-HT1A- and 5-HT2A-related signalling in the frontoparietal network predicting escitalopram response but psilocybin resistance. Overall, this work advances precision medicine and biomarker discovery in depression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".