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Poster D in Poster Session D: Wednesday, August 5, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms

Accurate and Interpretable Prediction of Antidepressant Treatment Response from Receptor-informed Neuroimaging

Hanna M. Tolle¹, Andrea I. Luppi², Timothy Lawn¹, Leor Roseman¹, David Nutt¹, Robin L. Carhart-Harris³, Pedro A. M. Mediano¹; ¹Imperial College London, ²University of Oxford, ³University of California, San Francisco

Presenter: Hanna M. Tolle

Conventional antidepressants show moderate efficacy in treating major depressive disorder, 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, 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. These results demonstrate the potential of combining geometric deep learning and causal modeling for precision psychiatry.

Topic Area: Development, Individual Differences & Clinical Populations