Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

The impact of COVID-19 stressors on psychological distress and suicidality in a nationwide community survey in Taiwan.

COVID-19 stressors and psychological stress response are important correlates of suicide risks under the COVID-19 pandemic. This study aimed to investigate the prevalence of COVID-19 stress, its impact on mental health and associated risk factors among the general population during the outbreak of COVID-19 in July 2020 throughout Taiwan. A nationwide population-based survey was conducted using a computer-assisted telephone interview system with a stratified, proportional randomization method for the survey. The questionnaire comprised demographic variables, psychological distress assessed by the five-item Brief Symptom Rating Scale and independent psychosocial variables including COVID-19 stressors, loneliness, suicidality, and health-related self-efficacy. In total, 2094 respondents completed the survey (female 51%). The COVID-19 stress was experienced among 45.4% of the participants, with the most prevalent stressors related to daily life and job/financial concerns. Higher levels of suicidality, loneliness, and a lower level of self-efficacy had significantly higher odds of having COVID-19 stress. The structural equation model revealed that COVID-19 stress was moderately associated with psychological distress and mediated by other psychosocial risk factors. The findings call for more attention on strategies of stress management and mental health promotion for the public to prevent larger scales of psychological consequences in future waves of the COVID-19 pandemic.