Clinical characteristics and sociodemographic features of psychotic major depression

Prevalence and risk factors for psychotic symptoms in young, first-episode and drug-naïve patients with major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a common psychiatric disorder worldwide. Psychotic depression has been reported to be frequently under-diagnosed due to poor recognition of psychotic features. Therefore, the purpose of this study was to reveal the rate and risk factors of psychotic symptoms in young, drug-naïve patients with major depressive disorder at the time of their first episode.

METHODS

A total of 917 patients were recruited and divided into psychotic and non-psychotic subgroups based on the Positive and Negative Syndrome Scale (PANSS) positive subscale score. Anxiety symptoms and depressive symptoms were measured by the Hamilton Anxiety Rating Scale (HAMA) and the 17-item Hamilton Depression Rating Scale (HAMD-17), respectively. Several biochemical indicators such as total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FBG), thyroid stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) were also measured.

RESULTS

The rate of psychotic symptoms among young adult MDD patients was 9.1%. There were significant differences in TSH (p<0.001), FBG (p<0.001), TC (p<0.0001), TG (p = 0.001), HDL-C (p = 0.049), LDL-C (p = 0.010), diastolic blood pressure (DP) (p<0.001), systolic blood pressure (SP) (p<0.001), and HAMD total score (p<0.001) between young MDD patients with and without psychotic depression. HAMD, TSH, TC, and severe anxiety were independently associated with psychotic symptoms in young adult MDD patients. In addition, among young MDD patients, the rate of suicide attempts in the psychotic subgroup was much higher than in the non-psychotic subgroup (45.8% vs. 16.9%).

CONCLUSIONS

Our findings suggest that psychotic symptoms are common in young MDD patients. Several clinical variables and biochemical indicators are associated with the occurrence of psychotic symptoms in young MDD patients.

Resting-state EEG-based convolutional neural network for the diagnosis of depression and its severity

Purpose: The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations.

Methods: In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python and MATLAB software applications. The questionnaire included the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ).

Results: A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70–0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in the major depression group than those in the non-major depression group (p < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65–0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, p = 0.003) and SDS scores (r = 0.765, p < 0.001).

Conclusion: Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity.