Depressive disorder (DD) and generalized anxiety disorder (GAD), two prominent mental health conditions, are commonly diagnosed using subjective methods such as scales and interviews. Previous research indicated that machine learning (ML) can enhance our understanding of their underlying mechanisms. This study seeks to investigate the mechanisms of DD, GAD, and healthy controls (HC) while constructing a diagnostic framework for triple classifications. Specifically, the experiment involved collecting electroencephalogram (EEG) signals from 42 DD patients, 45 GAD patients, and 38 HC adults. The Phase Lag Index (PLI) was employed to quantify brain functional connectivity and analyze differences in functional connectivity among three groups. This study also explored the impact of time window feature computations on classification performance, including the XGBoost, CatBoost, LightGBM, and ensemble models. In order to enhance classification performance, a feature optimization algorithm based on Autogluon-Tabular was proposed. The results indicate that a 12 s time window provides optimal classification performance for the three groups, achieving the highest accuracy of 97.33% with the ensemble model. The analysis further reveals a significant reorganization of the brain, with the most pronounced changes observed in the frontal lobe and beta rhythm. These findings support the hypothesis of abnormal brain functional connectivity in DD and GAD, contributing valuable insights into the neural mechanisms underlying DD and GAD.

Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment strategies. To address this need, this study aims to establish a GAD grading and quantification diagnostic model by integrating an electroencephalogram (EEG) and ensemble learning. In this context, a total of 39 normal subjects and 80 GAD patients were recruited and divided into four groups: normal control, mild GAD, moderate GAD, and severe GAD. Ten minutes resting state EEG data were collected for every subject. Functional connectivity features were extracted from each EEG segment with different time windows. Then, ensemble learning was employed for GAD classification studies and brain mechanism analysis. Hence, the results showed that the Catboost model with a 10 s time window achieved an impressive 98.1% accuracy for four-level classification. Particularly, it was found that those functional connections situated between the frontal and temporal lobes were significantly more abundant than in other regions, with the beta rhythm being the most prominent. The analysis framework and findings of this study provide substantial evidence for the applications of artificial intelligence in the clinical diagnosis of GAD.

The assessment of depression severity still relies primarily on subjective rating scales, with a lack of objective quantitative biomarkers. This study aimed to develop a deep learning model based on resting-state electroencephalography (EEG) for automated and quantitative prediction of depression severity. Resting-state EEG data were collected from 70 patients with depressive disorder (DD) and 30 healthy controls (HC). A novel deep learning framework-PLI_GE_gMLP-was proposed. By integrating phase lag index (PLI), graph embedding (GE), and gated multilayer perceptron (gMLP), the model effectively captures both the spatiotemporal dependencies and functional connectivity features of EEG signals for depression severity prediction. The PLI_GE_gMLP model achieved a mean absolute error (MAE) of 4.30 in predicting depression severity, significantly outperforming various traditional machine learning (e.g., RF, XGBoost, LightGBM) and deep learning models (e.g., ResNet, GENet). SHAP (Shapley Additive Explanations) interpretability analysis further revealed that functional connectivity in the frontal and temporal lobes, particularly PLI features in the Beta and Theta bands, contributed most significantly to the predictions, aligning closely with the neuropathological mechanisms of DD. Through the synergistic integration of GE and gMLP, the proposed PLI_GE_gMLP model enables accurate and interpretable prediction of depression severity.