Han Lu, Xiaoqian Yan, Benjamin Becker, Andreas Heinz, Barbara J Sahakian, Christelle Langley, Zhaoyu Zuo, Luolong Cao, Zuo Zhang, Lauren Robinson, Nilakshi Vaidya, Jeanne Winterer, Sinead King, Charlotte Walton, Tobias Banaschewski, Gareth J Barker, Arun L W Bokde, Rüdiger Brühl, Herta Flor, Hugh Garavan, Penny Gowland, Antoine Grigis, Herve Lemaitre, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Luise Poustka, Hedi Kebir, Ulrike Schmidt, Julia Sinclair, Michael N Smolka, Sarah Hohmann, Nathalie Holz, Henrik Walter, Robert Whelan, Sylvane Desrivières, Gunter Schumann, Qiang Luo, STRATIFY/ESTRA Consortium, IMAGEN Consortium
Early detection and prevention of psychiatric disorders, particularly depression, remain as major global health challenges, yet reliable tools for identifying individuals before symptom onset are lacking. Here, we combine functional neuroimaging with computational modeling to identify a mechanistic biomarker of depression risk. In a population-based adolescent cohort (IMAGEN, N = 1332), we found that weakened neural representations of emotional signals were linked to depressive symptoms. Perturbation experiments in a brain-aligned deep learning model showed that this deficit reflects overregularized emotion perception, producing a negative perceptual bias. A neurocomputational signature of this mechanism predicted depression symptom onset up to 4 years later at the IMAGEN follow-up (N = 725), was associated with both a genetic-risk variant and polygenic risk for depression, and improved depression classification in a patient cohort (STRATIFY, N = 411). These findings suggest a possible mechanism linking genetic vulnerability to altered emotion perception and future depression, and propose a predictive computational marker with potential for early detection and prevention.