A reduced P300 prospectively predicts increased depressive severity in adults with clinical depression

Recent research suggests that depressive disorders in adults are characterized by reductions in flanker P300 amplitude, and that a reduced flanker P300 may also predict worst depressive trajectories over time. The current study extended this work to adolescence-and to evaluate the specificity of the relationship between flanker P300 to depressive symptoms versus anxiety symptoms, and whether the association between flanker P300 and depressive symptoms was moderated by biological sex. To this end, P300 amplitude, depression, anxiety, and sex were assessed in a large sample of 619 adolescents aged 11 to 14. Participants completed a speeded response flanker task while EEG was recorded, as well as self-reported measures of current depression and anxiety symptoms. Reduced P300 amplitude was related to both heightened depression and anxiety symptoms in zero-order correlations. Regression-based analyses suggest that reduced P300 was uniquely related to depressive symptoms. Furthermore, this negative association between P300 and depression was apparent in female adolescents, but not male adolescents. In sum, the current study suggests that flanker P300 amplitude may potentially serve as a neural marker specific to depression in females during adolescence.

Background: Psychiatric diagnosis is formulated by symptomatic classification; disease-specific neurophysiological phenotyping could help with its fundamental treatment. Here, we investigated brain phenotyping in patients with schizophrenia (SZ) and major depressive disorder (MDD) by using electroencephalography (EEG) and conducted machine-learning-based classification of the two diseases by using EEG components.Materials and Methods: We enrolled healthy controls (HCs) (n = 30) and patients with SZ (n = 34) and MDD (n = 33). An auditory P300 (AP300) task was performed, and the N1 and P3 components were extracted. Two-group classification was conducted using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Positive and negative symptoms and depression and/or anxiety symptoms were evaluated.Results: Considering both the results of statistical comparisons and machine learning-based classifications, patients and HCs showed significant differences in AP300, with SZ and MDD showing lower N1 and P3 than HCs. In the sum of amplitudes and cortical sources, the findings for LDA with classification accuracy (SZ vs. HCs: 71.31%, MDD vs. HCs: 74.55%), sensitivity (SZ vs. HCs: 77.67%, MDD vs. HCs: 79.00%), and specificity (SZ vs. HCs: 64.00%, MDD vs. HCs: 69.67%) supported these results. The SVM classifier showed reasonable scores between SZ and HCs and/or MDD and HCs. The comparison between SZ and MDD showed low classification accuracy (59.71%), sensitivity (65.08%), and specificity (54.83%).Conclusions: Patients with SZ and MDD showed deficiencies in N1 and P3 components in the sum of amplitudes and cortical sources, indicating attentional dysfunction in both early and late sensory/cognitive gating input. The LDA and SVM classifiers in the AP300 are useful to distinguish patients with SZ and HCs and/or MDD and HCs.

No abstract