Personality traits predict treatment outcome of an antidepressant in untreated adolescents with depression: An 8-week, open-label, flexible-dose study.

BACKGROUND: Antidepressant response in adults with major depressive disorder (MDD) is probably influenced by personality dimensions. However, personality dimensions in depression and their association with antidepressant treatment in adolescents are relatively unknown. We sought to investigate whether personality traits (PTs) can influence antidepressant treatment response in adolescents with depression. METHODS: Eighty-two adolescents with MDD who had completed the 8 weeks of treatment with selective serotonin reuptake inhibitors (SSRI) were enrolled. The Revised NEO Five-Factor Inventory (NEO-FFI-R) was used to measure their personality at baseline, and the 17-item Hamilton Depression Rating Scale (HAMD-17) and Children's Depression Rating Scale-Revised (CDRS-R) were used to evaluate depressive symptoms at baseline and 8 weeks. Moreover, logistic regression was performed to investigate the relationship between personality dimensions and antidepressant response. Receiver operating characteristic analyses were employed to determine the accuracy of a PT-based model in predicting the antidepressant response rate. RESULTS: Adolescents with MDD had significantly different PTs at baseline. Multivariable logistic regression analysis showed that extroversion scores were associated with response to antidepressant treatment, the lower the extroversion score, the better the response to antidepressant treatment, after correcting for variables with significant differences and trends or all potential confounding variables. It was also found that the combination of disease duration, extraversion-gregariousness, and agreeableness-trust effectively predicted antidepressant response in adolescents with MDD, with a sensitivity of 79.4 % and specificity of 68.7 %. CONCLUSION: Personality dysfunction in adolescents is associated with MDD. The antidepressant treatment response is influenced by the degree of extroversion in adolescents with MDD.

EEG microstate analysis reveals large-scale brain network alterations in depressed adolescents with suicidal ideation.

BACKGROUND: Accumulating evidence showed abnormalities in brain network connectivity in depressive individuals with suicidal ideation (SI). We aimed to investigate the large-scale brain network dynamics in adolescents with SI and major depressive disorder (MDD). METHODS: We recruited 47 first-episode drug-naïve adolescents with MDD and SI, 26 depressed adolescents without SI (noSI), and 26 age-matched healthy controls (HC). The Columbia Suicidal Ideation Severity Scale (C-SSRS) was utilized to assess suicide ideation. We acquired 64-channel resting-state EEG recordings from all subjects and used microstate analysis to investigate the large-scale brain network dynamics. RESULTS: We observed a significant reduction in the occurrence and coverage of microstate B within the SI group when contrasted with the noSI group. Conversely, there was a significant increase in the occurrence and coverage of microstate A in the SI group as compared to the HC group. Additionally, we observed heightened transition probabilities from microstates D and C to microstate A in the SI group; meanwhile, transitions from microstate D to B were more prevalent in the noSI group. Furthermore, the noSI group exhibited a significant decline in the transition probabilities from microstate D to microstate C. LIMITATIONS: The cross-sectional nature limits the capacity to determine whether microstate dynamics have prognostic significance for SI. CONCLUSION: We provided evidence that depressed adolescents with SI have a distinct pattern in microstate dynamics compared to those without SI. These findings suggest that microstate dynamics might serve as a potential neurobiomarker for identifying SI in depressed adolescents.

A resting-state electroencephalographic microstates study in depressed adolescents with non-suicidal self-injury.

Neuroimaging studies have revealed abnormal brain activities in depressed teenagers who engage in non-suicidal self-injury (NSSI). We used resting-state electroencephalography (EEG) microstate analysis, which indicates the brief overlap of brain network activation for exploring the characteristics of large-scale cortical activities in depressed adolescents engaged with NSSI to clarify the underlying temporal mechanism. A modified k-means cluster algorithm was used to segment 64-channel resting-state EEG data into microstates. Data from 27 healthy adolescents, 37 adolescents with major depressive disorder (MDD), and 53 adolescents with both MDD and NSSI were examined in this study. The resting-state microstate parameters were compared among groups using the one-way ANOVA and Spearman correlation. Then the associations between significantly different microstate parameters and the depressive severity and self-harming data in the patient groups were further analyzed. The MDD group had higher contribution (p < 0.01), occurrence (p < 0.01) of microstate A, and higher microstate E→A transition (p < 0.05) than the HC and the NSSI group. The MDD group showed a distinctly longer duration (p < 0.05) of microstate A and microstate A→C transition than the HC. The transition probability from B to C was increased in the NSSI group compared to the HC. In the MDD group, the HAMD correlated with the duration of microstate A (Spearman's rho = 0.34, p = 0.044), as the PHQ-9 correlated with its occurrence (Spearman's rho = 0.37, p = 0.028). This research revealed that whereas depressive adolescents with NSSI and MDD displayed similar patterns with healthy controls in EEG microstate, the MDD group did not. Additionally, the non-random transition from microstate E→A may protect against recent self-harm in adolescents with MDD.