Adolescence represents a developmental phase marked by profound and rapidly shifting emotional states. While some adolescents exhibit pronounced, day-to-day mood variability, others remain comparatively stable. Although emotional fluctuation has long been considered a core feature with implications for health and well-being, the determinants underlying these substantial individual differences remain insufficiently understood. To address this, we conducted a longitudinal study using daily app-based assessments, aiming to explore whether perceived stress and self-esteem dynamics help explain why some adolescents show greater mood variability than others. We asked N = 70 adolescents to rate their self-esteem, positive, and negative mood once per day for 15 days. For data analyses, we used path analytic mediation modeling. Fluctuation of self-esteem (FSE) significantly predicted both fluctuation of positive mood (FPM; b = 0.282, 95% CI [0.101, 0.464], p = .0.002) and fluctuation of negative mood (FNM; b = 0.395, 95% CI [0.147, 0.644], p = 0.002) indicating that greater variability in self-esteem is associated with greater mood fluctuation. Moreover, perceived stress (PS) significantly predicted FSE (b = 0.057, 95% CI [0.012, 0.102], p = 0.013), suggesting that higher perceived stress was associated with greater self-esteem fluctuation. Significant indirect effect emerged for PS on FPM (b = 0.016, 95% CI [0.003, 0.030], p = 0.019) and on FNM (b = 0.023, 95% CI [0.002, 0.043], p = 0 .028) via FSE, confirming a mediating role of self-esteem fluctuations. This study showed that perceived stress was associated with fluctuations in self-esteem, which were related to mood variability among adolescents. These findings suggest that stress reduction could be explored as a potential avenue for future interventions.
C. Borzikowsky, M. Prignitz, Stella Guldner et al.· Scientific Reports· 0 citations
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.
Han Lu, Xiaoqian Yan, Benjamin Becker et al.· Science Advances· 0 citations
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