Accurate and Interpretable Prediction of Antidepressant Treatment Response from Receptor-informed Neuroimaging
Hanna M. Tolle, Andrea I. Luppi, Timothy Lawn, Leor Roseman, David Nutt, Robin Carhart-Harris, Pedro A. M. Mediano
bioRxiv (Cold Spring Harbor Laboratory) preprint DOI: 10.1101/2025.07.02.662710 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Clinical trial |
|---|---|
| Population | Patients with major depressive disorder |
| Interventions | Psilocybin Escitalopram |
| Topics | Psychedelic-assisted therapy |
| Keywords | Depression treatment Antidepressant therapy Mdd treatment Psilocybin therapy Escitalopram Mental health treatment Predictive biomarkers Treatment response prediction Prognostic markers Brain imaging biomarkers Serotonin system biomarkers Neural predictors Precision medicine Personalized care Individualized treatment Treatment selection Tailored therapy Stratified medicine Neuroimaging Functional brain imaging Neural imaging Brain scans |
| Citations | 1 |
| Registration | NCT03429075 |
| Key findings | The graphTRIP model accurately predicts post-treatment depression severity using pretreatment data and identifies that reduced functional coupling within serotonin systems and broader serotonergic integration with sensory-motor networks are linked to better outcomes, with psilocybin showing a group-level advantage over escitalopram but escitalopram benefiting individuals with specific stress-related neuromodulatory profiles. |
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.
Comparable studies
Other non-randomized and open-label trials on psychedelic-assisted therapy, most cited first.
Citations in the library
Cites 20
- Psilocybin produces substantial and sustained decreases in depression and anxiety in patients with life-threatening cancer: A randomized double-blind trial Journal of Psychopharmacology November 30, 2016
- Rapid and sustained symptom reduction following psilocybin treatment for anxiety and depression in patients with life-threatening cancer: a randomized controlled trial Journal of Psychopharmacology November 30, 2016
- Psilocybin-assisted treatment for alcohol dependence: A proof-of-concept study Journal of Psychopharmacology January 13, 2015
- Quality of Acute Psychedelic Experience Predicts Therapeutic Efficacy of Psilocybin for Treatment-Resistant Depression Frontiers in Pharmacology January 17, 2018
- The Therapeutic Potential of Psychedelic Drugs: Past, Present, and Future. Neuropsychopharmacology April 26, 2017
and 15 more in the library