Sang-Choong Roh, Ji Sun Kim, Sungkean Kim, Yourim Kim, Seung-Hwan Lee. Clin Psychopharmacol Neurosci 2020;18:58-66. https://doi.org/10.9758/cpn.2020.18.1.58

Ji Sun Kim, Young Joon Kwon, Hwa Young Lee, Ho-Sung Lee, Sungkean Kim, Se-hoon Shim. Clin Psychopharmacol Neurosci 2020;18:127-35. https://doi.org/10.9758/cpn.2020.18.1.127

Schizophrenia (SZ) and bipolar disorder (BD) pose diagnostic challenges due to overlapping clinical symptoms and genetic factors, often resulting in misdiagnosis and suboptimal treatment outcomes. This study aimed to identify EEG-based biomarkers that can differentiate SZ from BD using multiscale fuzzy entropy (MFE) and relative power (RP) analyses and to evaluate their diagnostic utility using machine learning. EEG data were collected from 65 patients with SZ and 49 patients with BD under resting-state conditions. The MFE and RP were calculated for the bilateral frontal, central, and parietal regions using 30 s EEG segments. For MFE, the band-scale fuzzy entropy (FuzzyEn) was determined across the theta, alpha, beta, and gamma bands based on simulation results demonstrating an inverse relationship between scale factors and frequency components. RP was derived by segmenting the EEG data into 2 s intervals with a 500 ms moving window. A support vector machine (SVM) was used to differentiate between patients with SZ and BD based on band-scale FuzzyEn and RP. The SVM classifier achieved an accuracy of 78.94%, a sensitivity of 81.53%, and a specificity of 75.51%. Patients with SZ showed higher theta-scale FuzzyEn in the right frontal, left central, and bilateral parietal regions; higher alpha-scale FuzzyEn in the right parietal region; and increased theta power in the bilateral frontal, central, and right parietal regions. These differences remained robust after controlling for medication effects. These findings demonstrate the potential of combining MFE, RP, and machine learning to differentiate between SZ and BD, contributing to improved diagnostic precision in psychiatric disorders.

Objective The prolonged coronavirus disease-2019 (COVID-19) pandemic is likely to cause psychological distress in people. This systematic review aimed to identify the effectiveness of virtual reality (VR)-based psychological intervention among individuals with psychological distress during the COVID-19 crisis. PubMed, Ovid MEDLINE, Cochrane Library, Web of Science, Embase, and PsycINFO databases were searched for articles published until July 2022.Methods The available citations were deduplicated and screened by two authors using the title and abstract information. Eligibility criteria were constructed according to the PICOT guidelines. Empirical studies of all designs and comparator groups were included if they appraised the impact of an immersive VR intervention on any standardized measure indicative of psychological distress (stress, anxiety, depression, and post-traumatic symptoms) or improvements in quality of life in participants, including COVID-19 patients, medical staff working with COVID-19 patients, and people who had experienced strict social distancing during the COVID-19 pandemic.Results The results were discussed using a narrative synthesis because of the heterogeneity between studies. Seven of the studies met the inclusion criteria. There were two randomized controlled trials and five uncontrolled studies on VR interventions.Conclusion All studies reported significant improvement in a wide range of psychological distress during COVID-19, ranging from stress, anxiety, depression, and post-traumatic symptoms to quality of life, supporting the efficacy of VR-based psychological intervention. Our results suggest that VR intervention has potential to ameliorate COVID-19-related psychological distress with efficacy and safety.

Yong-Wook Kim, Sungkean Kim, Min Jin Jin, Chang-Hwan Im, Seung-Hwan Lee. Clin Psychopharmacol Neurosci 2024;22:53-66. https://doi.org/10.9758/cpn.22.1035