ArticleBrain sciences2023
Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.
Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: trends in explainable artificial intelligence (XAI).BMC psychiatry · 2025Pooled it
- Infraslow Closed-Loop Brain Training for Anxiety and Depression (ISAD): A pilot randomised, sham-controlled trial in adult females with internalizing disorders.Cognitive, affective & behavioral neuroscience · 2025Trial
- Toward Multi-Dimensional Depression Assessment: EEG-Based Machine Learning and Neurophysiological Interpretation for Diagnosis, Severity, and Cognitive Decline.Brain sciences · 2026Article
- An explainable multimodal artificial intelligence model for classifying suicide attempters with borderline personality disorder: a pilot study.Scientific reports · 2025Article
- Role of iron in brain development, aging, and neurodegenerative diseases.Annals of medicine · 2025Review
- The Journey of the Default Mode Network: Development, Function, and Impact on Mental Health.Biology · 2025Review
- Comparative Analysis of High-Frequency and Low-Frequency Transcutaneous Electrical Stimulation of the Right Median Nerve in the Regression of Clinical and Neurophysiological Manifestations of Generalized Anxiety Disorder.Journal of clinical medicine · 2024Article
- Article
- A potential brain functional biomarker distinguishing patients with Crohn's disease with different disease stages: a resting-state fMRI study.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.
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