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주요우울장애 환자에서 소뇌 국소 부위의 부피와 피질 두께의 차이
강지원,한규만,원은수,태우석,함병주,Kang, Ji-Won,Han, Kyu-Man,Won, Eunsoo,Tae, Woo-Suk,Ham, Byung-Joo 대한생물정신의학회 2018 생물정신의학 Vol.25 No.3
Objectives A growing body of evidence has suggested that morphologic changes in cerebellum may be implicated with pathophysiology of major depressive disorder (MDD). The aim of this study is to investigate a difference in the volume and cortical thickness of the specific region of cerebellum between patients with MDD and healthy controls (HC). Methods A total of 127 patients with MDD and 105 HC participated in this study and underwent T1-weighted structural magnetic resonance imaging. We analyzed volume and cortical thickness of each twelve cerebellum regions divided by left and right and the volume and cortical thickness of the whole cerebellum from T1-weigted image of participants. One-way analysis of covariance was used to investigate the volume and cortical thickness difference of total and specific regions between two groups adjusting for age, gender, medication, and total intracranial cavity volume. Results We found that the patients with MDD had significantly greater volume in the left cerebellum lobule III region [false discovery rate (FDR)-corrected p = 0.034] compared to HC. Also, our findings indicate that cortical thickness of left lobule VIIB (FDR-corrected p = 0.032) and lobule VIIIB (FDR-corrected p = 0.032) are significantly thinner in the patients with MDD compared with the HC. No significant volume and cortical thickness differences were observed in other sub-regions of the cerebellum. The volumes and cortical thickness of whole cerebellum between patients with MDD and HC did not differ significantly. Conclusions We observed the region-specific volume and cortical thickness difference in cerebellum between the patients with MDD and HC. The results of our study implicate that the information about structural alterations in cerebellum with further replicative studies might provide a stepping stone toward a specific marker to diagnose MDD.
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제1형 양극성장애 환자에서 대뇌피질 주름 패턴의 변형 : Local Gyrification Index 분석
이준용,한규만,원은수,이민수,함병주,Lee, Junyong,Han, Kyu-Man,Won, Eunsoo,Lee, Min-Soo,Ham, Byung-Joo 대한생물정신의학회 2017 생물정신의학 Vol.24 No.4
Objectives Local gyrification reflects the early neural development of cortical connectivity, and is regarded as a potential neural endophenotype in psychiatric disorders. Several studies have suggested altered local gyrification in patients with bipolar I disorder (BD-I). The purpose of the present study was to investigate the alterations in the cortical gyrification of whole brain cortices in patients with BD-I. Methods Twenty-two patients with BD-I and age and sex-matched 22 healthy controls (HC) were included in this study. All participants underwent T1-weighted structural magnetic resonance imaging (MRI). The local gyrification index (LGI) of 66 cortical regions were analyzed using the FreeSurfer (Athinoula A. Martinos Center for Biomedical Imaging). One-way analysis of covariance (ANCOVA) was used to analyze the difference of LGI values between two groups adjusting for age and sex as covariates. Results The patients with BD-I showed significant hypogyria in the left pars opercularis (uncorrected-p = 0.049), the left rostral anterior cingulate gyrus (uncorrected-p = 0.012), the left caudal anterior cingulate gyrus (uncorrected-p = 0.033). However, these findings were not significant after applying the multiple comparison correction. Severity or duration of illness were not significantly correlated with LGI in the patients with BD-I. Conclusions Our results of lower LGI in the anterior cingulate cortex and the ventrolateral prefrontal cortex in the BD-I group implicate that altered cortical gyrification in neural circuits involved in emotion-processing may contribute to pathophysiology of BD-I.
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황혜진,오종수,방민지,원은수,이강수,최태규,이상혁,Hwang, Hye Jin,Oh, Jongsoo,Bang, Minji,Won, Eunsoo,Lee, Kang Soo,Choi, Tai Kiu,Lee, Sang-Hyuk 대한생물정신의학회 2019 생물정신의학 Vol.26 No.2
Objectives The objective of this study is to investigate differences in clinical characteristics between female panic disorder (PD) patients with abortion history (PD+A) and without abortion history (PD-A). Methods We examined data from 341 female patients diagnosed with PD. We divided the patients with PD into PD+A (82 patients) and PD-A (259 patients) to compare demographic and clinical characteristics. The following instruments were applied : stress coping strategies, NEO-neuroticism, the Anxiety Sensitivity Index-Revised (ASI-R), the Albany Panic and Phobia Questionnaire (APPQ), the Beck Depression Inventory, the Beck Anxiety Inventory (BAI) and the Sheehan Disability Scale. Results Compared to the PD-A, the PD+A group showed no significant difference in coping strategies. However, significantly higher scores in neuroticism, the ASI-R, the APPQ and the BAI were observed. In terms of health-related disability, the PD+A group did not show significant difference. Conclusions Our results suggest that the PD+A group may differ from the PD-A group in trait markers such as neuroticism and anxiety sensitivity, and abortion history may be associated with panic-related symptom severity. Our study suggests that further consideration is needed on such clinical characteristics in PD patients with abortion history.
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멀티 모달 데이터를 이용한 한국형 주요 우울 장애 진단 및 치료 모델
최용화(Yonghwa Choi),김아람(Aram Kim),전민지(Minji Jeon),김선규(Sunkyu Kim),한규만(Kyu-Man Han),원은수(Eunsoo Won),함병주(Byung-Joo Ham),강재우(Jaewoo Kang) 한국정보과학회 2019 정보과학회논문지 Vol.46 No.1
우울증은 현대 사회에서 가장 흔한 정신질환 중 하나로, 반복되는 재발에 따른 만성화로 인해 사회적인 부담을 증가시킨다. 그러나 다양한 요인들이 복합적으로 관여하는 질병이기 때문에 여러 요인을 효율적으로 고려할 수 있는 기계학습 모델이 필요하다. 본 논문에서는 기본 정보, MRI, 유전자, 인지 검사의 4가지 멀티 모달 데이터를 이용해 우울증 여부를 진단하고 항우울제 반응의 정도를 예측할 수 있는 모델을 제안하여 우울증 진단의 경우 AUROC 점수 0.923, 항우울제 반응성 예측의 경우 MSE 0.08의 정확도를 얻었다. 그리고 제안한 모델의 결과를 정량적으로 분석하여 환자의 데이터를 추가할수록 정확한 진단 및 약물 반응성 예측이 가능함을 확인하고, 정성적으로 분석하여 우울증에 관해 기존에 알려진 주요 요인을 찾는 것뿐 아니라 새로운 가설을 제시하였다. Depression is one of the most common mental illnesses in the modern society, and it increases the social burden due to repeated recurrences. However, since there are many pre-disposing factors that cause depression, there is need to develop a machine-learning model that examine these factors effectively. In this paper, we propose a model that can diagnose depression and predict the degree of antidepressant response using four multi modal data including basic information, MRI, genetics, and cognitive test. The model achieved 0.923 AUROC score for diagnosis and 0.08 MSE for prediction of antidepressant response. In addition, the results of the proposed model were quantitatively analyzed, and it confirmed that accurate diagnosis and drug response prediction are possible when the patient’s data is added. Qualitative analysis was also conducted to provide new hypotheses as well as findings on the main factors causing depression.
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