Adolescence is a pivotal stage for brain development and a critical window for the emergence and transition of self-injury thoughts and behaviours (SITBs). However, the genetic and neurobiological mechanisms underlying SITBs transition during this developmental period are poorly understood.

This study investigates associations among genetic predispositions, brain abnormalities and SITBs transition during adolescence, and identifies potential neurobiological and clinical mediators of genetic effects.

This national retrospective cohort study analysed 5-year longitudinal data from the Adolescent Brain and Cognitive DevelopmentSM Study® (N = 11 868 children aged 9–10 years at baseline). Logistic regression models identified genetic susceptibility and neurobiological abnormalities associated with SITBs transition over a 4-year period. Generalised additive models characterised genetic risk trajectories and critical developmental periods. Mediation analyses examined neurobiological and clinical pathways linking genetic susceptibility to SITBs.

Our findings highlight a notable correlation between SITBs transition and genetic susceptibility, including polygenic risk scores for suicide attempt, ever contemplated self-harm and ever self-harm. The analysis indicates that ages 10–15 years may be a critical period during which genetic risk exerts its most pronounced influence. Structural and functional brain imaging detected some alterations, particularly in grey matter volume (GMV) of the left ventral posterior cingulate cortex, alongside disrupted resting-state functional connectivity in the dorsal attention and default mode networks. Mediation analysis suggests that the association between genetic susceptibility and SITBs transition over 4 years may be partially mediated by GMV changes in the left inferior frontal sulcus, altered resting-state connectivity between the auditory and sensorimotor hand networks and the p-factor.

These results may offer insights into integrating genetic, neurobiological and clinical data to enhance the accuracy of suicide risk stratification in adolescents, and inform the development of more nuanced and targeted early intervention strategies.

Recent neuroimaging studies have demonstrated that the heterogeneous antidepressant responsiveness in patients with major depressive disorder (MDD) is associated with diverse resting-state functional brain network (rsFBN) topology; however, only limited studies have explored the rsFBN using electroencephalography (EEG). In this study, we aimed to identify EEG-derived rsFBN-based biomarkers to predict pharmacotherapeutic responsiveness.

The resting-state EEG signals were acquired for demography-matched three groups: 98 patients with treatment-refractory MDD (trMDD), 269 those with good-responding MDD (grMDD), and 131 healthy controls (HCs). The source-level rsFBN was constructed using 31 sources as nodes and beta-band power envelope correlation (PEC) as edges. The degree centrality (DC) and clustering coefficients (CCs) were calculated for various sparsity levels. Network-based statistic and one-way analysis of variance models were employed for comparing PECs and network indices, respectively. The multiple comparisons were controlled by the false discovery rate.

Patients with trMDD were characterized by the altered dorsal attention network and salience network. Specifically, they exhibited hypoconnection between eye fields and right parietal regions (p = 0.0088), decreased DC in the right supramarginal gyrus (q = 0.0057), and decreased CC in the reward circuit (qs < 0.05). On the other hand, both MDD groups shared increased DC but decreased CC in the posterior cingulate cortex.

We confirmed that network topology was more severely deteriorated in patients with trMDD, particularly for the attention-regulatory networks. Our findings suggested that the altered rsFBN topologies could serve as potential pathologically interpretable biomarkers for predicting antidepressant responsiveness.