Accurate and Interpretable Prediction of Antidepressant Treatment Response from Receptor-informed Neuroimaging
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Source: CrossRef
Summary
Predicting individual depression treatment response is challenging. New predictive tools, using pre-treatment brain imaging and clinical data, accurately forecast antidepressant response in major depressive disorder. This advanced approach achieved strong accuracy, identifying biomarkers tied to better outcomes in serotonin systems. Comparing psychedelic-assisted therapy with psilocybin and escitalopram, psilocybin showed a group-level advantage. It also pinpointed specific brain profiles suggesting who would benefit more from escitalopram, enhancing treatment selection. This advances precision medicine and biomarker discovery, enabling more personalized care.
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, patient-specific 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 = 0.75, p < 10−8), and generalises both to an independent dataset and across brain atlases. The model links better outcomes to reduced functional coupling within serotonin systems, and broader serotonergic integration with sensory-motor networks. Finally, causal analysis reveals a group-level advantage of psilocybin over escitalopram, but also identifies individuals with specific stress-related neuromodulatory profiles who may benefit more from escitalopram. Overall, this work advances precision medicine and biomarker discovery in depression.