Eunsoo Moon

Hwagyu Suh, Eunsoo Moon, Hyunju Lim, Kyungwon Kim, Young-Min Lee, Byung-Dae Lee. Clin Psychopharmacol Neurosci 2026;24:641-9. https://doi.org/10.9758/cpn.26.1458

Seulchan Kwak, Byung Dae Lee, Young Min Lee, Eunsoo Moon, Hwagyu Suh, Kyungwon Kim, Siyul Kim. Clin Psychopharmacol Neurosci -0001;0:. https://doi.org/10.9758/cpn.26.1454

Objective Historical theological frameworks, particularly those rooted in Christianity, have conceptualized guilt as a defining component of melancholy. This study compared the network structures of depressive symptoms in guilt-rich and guilt-free presentations of depression using a large-scale cross-national Asian dataset.Methods We analyzed data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants, Phase 3 (REAP-AD3), which included patients with depressive disorders from 11 Asian countries. Network analysis was used to estimate symptom interactions and identify the central symptoms in each group. Participants were dichotomized according to the presence or absence of prominent guilt symptoms, as determined by the “feeling bad about yourself” item of the Patient Health Questionnaire-9 (PHQ-9).Results The network structures of the two groups differed substantially. In the guilt-rich group, depressed mood was the central node that was closely linked to self-blame. By contrast, the guilt-free group exhibited centralized networks with a loss of energy.Conclusion Variations in symptom network structures can be partly explained as broadly consonant with early ideas of Richard Baxter and Robert Burton on melancholia. Clinically, symptoms related to moral judgment and self-criticism may indicate potentially different specific therapeutic attention from individuals with significant guilt. Conversely, for those without pronounced guilt, interventions targeting physical and motivational symptoms such as fatigue might inform hypothesis-driven treatment strategies.

Byung-Dae Lee, Hwan-Wook Shim, Young-Min Lee, Eunsoo Moon, Hwagyu Suh, Kyungwon Kim, Hak-Jin Kim, Kyoungjune Pak, Kyung-Un Choi, Kwang-yeol Baek. Clin Psychopharmacol Neurosci -0001;0:. https://doi.org/10.9758/cpn.26.1447

Kang T, et al. J Korean Med Sci. 2026 Aug;41:e211. https://doi.org/10.3346/jkms.2026.41.e211

Lee E, et al. J Korean Med Sci. 2026 May;41:e139. https://doi.org/10.3346/jkms.2026.41.e139

Objective This study aimed to develop a machine learning-based classification model to differentiate bipolar disorder from major depressive disorder using self-report scales, including the Mood Disorder Questionnaire (MDQ), Bipolar Spectrum Diagnostic Scale (BSDS), and Anger Coping Scale (ACS).Methods A total of 122 bipolar and 67 depressive patients participated. Recursive feature elimination with 1,000 iterations was used to identify the most informative features. Machine learning classifiers assessed combinations of MDQ, BSDS, and ACS items for classification performance.Results The AUC values for MDQ and BSDS were 0.8212 and 0.7934, respectively. Combining MDQ and BSDS increased the AUC to 0.8477, which improved further to 0.8548 when ACS was included. For MDQ, the best performance was achieved when all 13 items were included. In contrast, the combined model of MDQ, BSDS, and ACS showed optimal performance when BSDS items 18 (conflicts with colleagues or police), 19 (alcohol or substance use), and ACS item 15 (beating others) were excluded.Conclusion Integrating anger coping styles with mood symptoms enhanced diagnostic accuracy, particularly when items related to undesirable behaviors were excluded. This machine learning approach shows potential for effectively evaluating bipolarity and underscores the importance of refining self-report scales to optimize diagnostic tools. Future research should incorporate clinical and objective data to enhance classification models.

Jiwon Kim, BSN, Jong-Hwan Park, PhD, Hyunju Lim, MA, Hwagyu Suh, MD, Kyungwon Kim, MD, PhD, Tae Sik Goh, MD, PhD, Myung-Jun Shin, MD, PhD, Eunsoo Moon, MD, PhD. Mood Emot 2025;23:63-71. https://doi.org/10.35986/me.2025.23.3.63

Objective Stress and coping strategies can influence the onset and recurrence of mood episodes. Assessment and classification of stress-coping behaviors might be necessary for intervention to correct maladaptive coping strategies. This study aimed to explore clinical types of stress-coping behaviors in patients with mood disorders using cluster analysis, and compare clinical characteristics among clusters.Methods This study included 252 patients with mood disorders. Types of stress-coping behaviors were classified by cluster analysis using the scores of the Behavioral Checklist for Coping with Stress. Clinical characteristics, measured by Clinical Global Impression Severity, Mood Disorder Questionnaire, and Bipolar Spectrum Diagnostic Scale, Beck Depression Inventory-II, State-Trait Anxiety Inventory, were compared among clusters.Results Stress-coping behaviors were identified into four clusters named Balanced, Withdrawn, Impulsive, and Active Clusters. The Balanced Cluster used personal and social activities predominantly and was coping adaptively and less depressed. The Withdrawn Cluster showed significantly fewer coping behaviors and was more depressed. The Impulsive Cluster used pleasure-seeking behaviors predominantly and personal activities minimally. They showed more bipolarity and were depressed. The Active Cluster used the most numerous coping behaviors. They showed more bipolarity and were not depressed.Conclusion This study showed four types of stress-coping behaviors, such as Balanced, Withdrawn, Impulsive, and Active types, reflecting different clinical characteristics. Future research needs to be conducted to determine whether each type of stress-coping behavior is related to clinical prognosis in prospective studies and whether modification of coping behavior can improve prognosis.