Journal of Yeungnam Medical Science

Review articles

Medical Ethics
Analyzing ethical dimensions of mental disorders: trends and key research areas through bibliometric methods: Bo Wang, Oyyappan Duraipandi; J Yeungnam Med Sci. 2025;42:54. Published online September 5, 2025; DOI: https://doi.org/10.12701/jyms.2025.42.54

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Abstract PDF: This study aimed to explore key ethical issues related to mental disorders through a bibliometric and cluster-based content analysis of existing academic literature. A comprehensive literature search was conducted in the Scopus database (Elsevier) up to December 31, 2024, using ethics-and mental disorder-related keywords. The search was limited to English-language journal articles in medicine, psychology, neuroscience, and other related fields. After title and abstract screening, 1,271 articles were included (κ=0.907). Bibliometric analyses including keyword co-occurrence, citation coupling, and country/author mapping were performed using VOSviewer (Centre for Science and Technology Studies, Leiden University) and Gephi (Gephi Consortium). A cluster-based content analysis was used to interpret the thematic structure of the field. The annual publication volume showed an upward fluctuating trend, with increasing scholarly attention post-1994. Coauthor networks revealed weak centralization, and the core author group remained underdeveloped. Research has been geographically concentrated in North America and Western Europe, particularly in the United States, the United Kingdom, and Canada. Keyword analysis identified six major thematic clusters: (1) conceptual foundations and policy frameworks in mental health ethics, (2) ethical challenges in psychiatric care, (3) research ethics, (4) patient autonomy and rights, (5) end-of-life decision-making and palliative ethics, and (6) neuroethics and emerging biomedical technologies. Recent popular topics include artificial intelligence, epistemic injustice, and medical aid for the dying. This study maps the intellectual structure and evolving focus of the ethical discourse on mental health. These findings highlight the need for ethically responsive frameworks that address patient autonomy, technological advancement, and global equity.

Psychiatry and Mental Health
Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review: Seokho Yun; J Yeungnam Med Sci. 2024;41(4):261-268. Published online September 9, 2024; DOI: https://doi.org/10.12701/jyms.2024.00668

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Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

Citations

Citations to this article as recorded by

Neural Efficiency and Attentional Instability in Gaming Disorder: A Task-Based Occipital EEG and Machine Learning Study
Riaz Muhammad, Ezekiel Edward Nettey-Oppong, Muhammad Usman, Saeed Ahmed Khan Abro, Toufique Ahmed Soomro, Ahmed Ali
Bioengineering.2026; 13(2): 152. CrossRef
Development and validation of a multimodal data collection system for adolescent mental health management
Siyeon Ko, Kyoungsu Oh, Uhyeong Won, Jung-A Oh, Nak-Jung Kwon, Hyun-sook Park, Young-A Ji, Sungjin Kim, Yonghwan Moon, Nayoung Park, Dohyoung Kim, Euijun Yang, Kyungmin Na, Yeonju Kim, Youngho Lee, Hyekyung Woo
DIGITAL HEALTH.2026;[Epub] CrossRef
Multi-omics biomarkers in psychiatric disorders diagnosis and stratification
Seyyed Hossein Khatami, Sanam Anoosheh, Marzieh Khodaparast, Amir Maghsoudloonejad, Ehsan Dadgostar, Amir Asadi, Mahya Kaveh, Malihe Mehdinejad Haghighi
Clinica Chimica Acta.2026; 585: 120887. CrossRef
Lymphocyte subpopulations and EEG asymmetry
Matisse Ducharme, Reza Zomorrodi, George Nader, Corinne Fischer, Philip Gerretsen, Ariel Graff, Daniel Blumberger, Vincenzo De Luca
Journal of Neural Transmission.2026;[Epub] CrossRef
Beta power as a neural correlate of sensory features in autistic individuals
Julie Chaudet, Julien Pichot, Amandine Pedoux, Mathis Fleury, Anna Maruani, Valérie Vantalon, Elise Humeau, Thomas Bourgeron, Josselin Houenou, Guillaume Dumas, Edouard Duchesnay, Richard Delorme, Anton Iftimovici, Aline Lefebvre
Journal of Neurodevelopmental Disorders.2026;[Epub] CrossRef
Role of Electroencephalographic Biomarkers as Predictors of Post-Stroke Cognitive Outcomes in Patients with Cerebral Infarction: Literature Review
Di Pu, Yan Xiong
Neuropsychiatric Disease and Treatment.2026; Volume 22: 1. CrossRef
Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals
Gulay Tasci, Prabal Datta Barua, Dahiru Tanko, Tugce Keles, Suat Tas, Ilknur Sercek, Suheda Kaya, Kubra Yildirim, Yunus Talu, Burak Tasci, Filiz Ozsoy, Nida Gonen, Irem Tasci, Sengul Dogan, Turker Tuncer
Diagnostics.2025; 15(2): 154. CrossRef
Innovative Therapeutic Approaches in Severe Adolescent Depression: Neuroimaging and Pharmacological Insights
Andrei-Gabriel Zanfir, Simona-Corina Trifu
Balneo and PRM Research Journal.2025; 16(Vol 16 No.): 765. CrossRef
Epileptic Seizure Detection Using Machine Learning: A Systematic Review and Meta-Analysis
Lin Bai, Gerhard Litscher, Xiaoning Li
Brain Sciences.2025; 15(6): 634. CrossRef
A Systematic Review of Mental Health Monitoring and Intervention Using Unsupervised Deep Learning on EEG Data
Akhila Reddy Yadulla, Guna Sekhar Sajja, Santosh Reddy Addula, Mohan Harish Maturi, Geeta Sandeep Nadella, Elyson De La Cruz, Karthik Meduri, Hari Gonaygunta
Psychology International.2025; 7(3): 61. CrossRef
A recent advances on autism spectrum disorders in diagnosing based on machine learning and deep learning
Hajir Ammar Hatim, Zaid Abdi Alkareem Alyasseri, Norziana Jamil
Artificial Intelligence Review.2025;[Epub] CrossRef
High alpha oscillations in portable prefrontal EEG indicate gender-sensitive biomarkers for emotional disorders
Shu Tang, Chuanliang Han, Xuebing Li
Scientific Reports.2025;[Epub] CrossRef
Interhemispheric EEG coherence as a candidate biomarker in gambling disorder: evidence of frontal hyperconnectivity and posterior disconnectivity
Eda Yılmazer, Metin Çinaroğlu, Selami Varol Ülker, Sultan Tarlacı
Frontiers in Neuroscience.2025;[Epub] CrossRef
HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage
Jian Shi, Danyang Chen, Xingwei Zhao, Zhixian Zhao, Shengjie Li, Yeguang Xu, Tao Ding, Zheng Zhu, Peng Zhang, Qing Ye, Yingxin Tang, Ping Zhang, Bo Tao, Zhouping Tang
Scientific Data.2025;[Epub] CrossRef
Predicting Major Depressive Disorder Using Neural Networks from Spectral Measures of EEG Data
Igor Kozulin, Ekaterina Merkulova, Vasiliy Savostyanov, Haonan Shi, Xinyi Wang, Andrey Bocharov, Alexander Savostyanov
Bioengineering.2025; 12(11): 1251. CrossRef

Original article

Psychiatry and Mental Health
Impact of COVID-19 on the development of major mental disorders in patients visiting a university hospital: a retrospective observational study: Hee-Cheol Kim; J Yeungnam Med Sci. 2024;41(2):86-95. Published online February 6, 2024; DOI: https://doi.org/10.12701/jyms.2023.01256

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Abstract PDF

Background
This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on the development of major mental disorders in patients visiting a university hospital.
Methods
The study participants were patients with COVID-19 (n=5,006) and those without COVID-19 (n=367,162) registered in the database of Keimyung University Dongsan Hospital and standardized with the Observational Medical Outcomes Partnership Common Data Model. Data on major mental disorders that developed in both groups over the 5-year follow-up period were extracted using the FeederNet computer program. A multivariate Cox proportional hazards model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for the incidence of major mental disorders.
Results
The incidences of dementia and sleep, anxiety, and depressive disorders were significantly higher in the COVID-19 group than in the control group. The incidence rates per 1,000 patient-years in the COVID-19 group vs. the control group were 12.71 vs. 3.76 for dementia, 17.42 vs. 7.91 for sleep disorders, 6.15 vs. 3.41 for anxiety disorders, and 8.30 vs. 5.78 for depressive disorders. There was no significant difference in the incidence of schizophrenia or bipolar disorder between the two groups. COVID-19 infection increased the risk of mental disorders in the following order: dementia (HR, 3.49; 95% CI, 2.45–4.98), sleep disorders (HR, 2.27; 95% CI, 1.76–2.91), anxiety disorders (HR, 1.90; 95% CI, 1.25–2.84), and depressive disorders (HR, 1.54; 95% CI, 1.09–2.15).
Conclusion
This study showed that the major mental disorders associated with COVID-19 were dementia and sleep, anxiety, and depressive disorders.

Citations

Citations to this article as recorded by

Association between COVID-19 infection and risk of mental disorders: a systematic review and meta-analysis
Jiayi Liu, Honghao Lai, Weilong Zhao, Jiajie Huang, Bei Pan, Janne Estill, Long Ge
General Hospital Psychiatry.2025; 97: 130. CrossRef

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