This mini-review summarizes information from eight recent (21st century) articles focused on the improvement of diagnoses for mood disorders. Authors of all articles recognized that a problem in diagnosis exists (Table 1) and made suggestions for improvements in existing systems (Table 2). Most of the research employed predictive analytical schemes and many of the proposed diagnostic symptoms were biological (Table 3). A model for success in diagnosis is applied to mood disorders. It is noted that an increase in diagnostic accuracy depends on both better identification of symptoms and better delineation of disorders.
References
[1]
American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). American Psychiatric Publishing.
[2]
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K. et al. (2010). Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. American Journal of Psychiatry, 167, 748-751. https://doi.org/10.1176/appi.ajp.2010.09091379
[3]
Kim, S., Lee, H., & Lee, K. (2021). Screening of Mood Symptoms Using MMPI-2-RF Scales: An Application of Machine Learning Techniques. Journal of Personalized Medicine, 11, Article 812. https://doi.org/10.3390/jpm11080812
[4]
Lee, H., Cho, C., Lee, T., Jeong, J., Yeom, J. W., Kim, S. et al. (2023). Prediction of Impending Mood Episode Recurrence Using Real-Time Digital Phenotypes in Major Depression and Bipolar Disorders in South Korea: A Prospective Nationwide Cohort Study. Psychological Medicine, 53, 5636-5644. https://doi.org/10.1017/s0033291722002847
[5]
Le-Niculescu, H., Roseberry, K., Gill, S. S., Levey, D. F., Phalen, P. L., Mullen, J. et al. (2021). Precision Medicine for Mood Disorders: Objective Assessment, Risk Prediction, Pharmacogenomics, and Repurposed Drugs. Molecular Psychiatry, 26, 2776-2804. https://doi.org/10.1038/s41380-021-01061-w
[6]
Maes, M. H., & Stoyanov, D. (2022). False Dogmas in Mood Disorders Research: Towards a Nomothetic Network Approach. World Journal of Psychiatry, 12, 651-667. https://doi.org/10.5498/wjp.v12.i5.651
[7]
Maes, M., Moraes, J. B., Bonifacio, K. L., Barbosa, D. S., Vargas, H. O., Michelin, A. P. et al. (2021). Towards a New Model and Classification of Mood Disorders Based on Risk Resilience, Neuro-Affective Toxicity, Staging, and Phenome Features Using the Nomothetic Network Psychiatry Approach. Metabolic Brain Disease, 36, 509-521. https://doi.org/10.1007/s11011-020-00656-6
[8]
Sakrajda, K., & Szczepankiewicz, A. (2021). Inflammation-Related Changes in Mood Disorders and the Immunomodulatory Role of Lithium. International Journal of Molecular Sciences, 22, Article 1532. https://doi.org/10.3390/ijms22041532
[9]
Song, Y. W., Lee, H. S., Kim, S., Kim, K., Kim, B. N., & Kim, J. S. (2024). How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers. Clinical Psychopharmacology and Neuroscience: The Official Scientific Journal of the Korean College of Neuropsychopharmacology, 22, 416-430. https://doi.org/10.9758/cpn.24.1165
[10]
Song, Y. W., Lee, H. S., Kim, S., Kim, K., Kim, B., & Kim, J. S. (2024). How to Solve Clinical Challenges in Mood Disorders; Machine Learning Approaches Using Electrophysiological Markers. Clinical Psychopharmacology and Neuroscience, 22, 416-430. https://doi.org/10.9758/cpn.24.1165
[11]
Todeva-Radneva, A., Aryutova, K., Kandilarova, S., Paunova, R., & Stoyanov, D. (2021). The Translational Potential of Non-Coding RNAs and Multimodal MRI Data Sets as Diagnostic and Differential Diagnostic Biomarkers for Mood Disorders. Current Topics in Medicinal Chemistry, 21, 949-963. https://doi.org/10.2174/1568026621666210521144534
[12]
Tomasik, J., Han, S. Y. S., Barton-Owen, G., Mirea, D., Martin-Key, N. A., Rustogi, N. et al. (2021). A Machine Learning Algorithm to Differentiate Bipolar Disorder from Major Depressive Disorder Using an Online Mental Health Questionnaire and Blood Biomarker Data. Translational Psychiatry, 11, Article No. 41. https://doi.org/10.1038/s41398-020-01181-x
[13]
World Health Organization (2004). ICD-10: International Statistical Classification of Diseases and Related Health Problems (2nd ed.). World Health Organization.
[14]
Zheng, J., Zong, X., Tang, L., Guo, H., Zhao, P., Womer, F. Y. et al. (2024). Characterizing the Distinct Imaging Phenotypes, Clinical Behavior, and Genetic Vulnerability of Brain Maturational Subtypes in Mood Disorders. Psychological Medicine, 54, 2774-2784. https://doi.org/10.1017/s0033291724000886
