Childhood Electroencephalographic Signatures Predict Distinct Developmental Trajectories to Adolescent Anxiety and...

Adolescence is a vulnerable period for the onset of anxiety and depression, yet their neurodevelopmental origins remain unclear.

In this 7-year prospective longitudinal study (N = 64), we recorded resting-state electroencephalography (EEG) data in children at ages 7, 9, and 11 years, followed by functional magnetic resonance imaging (fMRI) and symptom assessments at age 13. Using connectome-based predictive modeling, we examined whether childhood EEG patterns could predict adolescent symptoms, with rigorous control analyses and external validation in the Healthy Brain Network (HBN) dataset (N = 384). We further characterized the developmental trajectories of these predictive networks. To mechanistically ground these electrophysiological markers, we conducted mediation analyses to test whether the amygdala-seeded circuits mediate the link between childhood EEG dynamics and adolescent symptom severity.

We identified dissociable EEG indicators emerging at age 9 that predicted adolescent anxiety (alpha, 8-12 Hz) and depression (beta-1, 12-18 Hz). Importantly, the dynamic maturation of these EEG networks highlighted distinct neurodevelopmental susceptibilities, in which longitudinal EEG shifts between ages 9 and 11 predicted symptom severity in adolescence. The divergent developmental trajectories of EEG-based networks were characterized by opposing hemispheric lateralization: rightward for anxiety and leftward for depression. Mechanistically, these predictive associations were mediated by lateralized amygdala-ventrolateral prefrontal cortex (vlPFC) circuits, with the right and left vlPFC pathways selectively mediating anxiety risk and depression risk, respectively. These models generalized robustly to the independent HBN cohort.

Our findings highlight early neurobiological indicators of distinct developmental trajectories and susceptibilities for anxiety and depression, providing a foundation for early risk stratification and targeted precision prevention.