Background Early diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents. Methods We obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls. Results We found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight. Conclusions Our findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.

Neuroticism is a robust personality trait associated with multiple mental disorders. Heretofore, research on the relationship among genes, brain, and behavior to explore individual differences in neuroticism is scarce. Hence, in this study (N = 630), genetic data, self‐reported neuroticism, and brain structural data were combined to explore whether the cortical thickness (CT) of brain regions mediated the relationship between the polygenic risk score (PRS) of neuroticism and NEO neuroticism (NEO‐N), and the enrichment analysis was performed to reveal the underlying mechanism of their relationship. Results showed that the PRSs were significantly associated with NEO‐N scores (p < .05). The CT of left rostral middle frontal gyrus was negatively related to the best PRS in PRSice (PRSbest) or the PRS at 0.05 threshold (PRS0.05) (corrected p < .05), which was also found to mediate the association between the PRS and NEO‐N (PRSbest: ab = .012, p < .05; PRS0.05: ab = .012, p < .05). Enrichment analysis revealed that these genes were mainly involved in biological adhesion, cell adhesion, neuron part, and synapse part, which were associated with the abnormal thickness of frontal cortex. By integrating genetic, brain imaging, and behavioral data, our research initially revealed the neurogenetic underpinnings of neuroticism, which is helpful for understanding individual differences in neuroticism.

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