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1
The Development of the Brooding Scale

Ji-Hyun Kim,Yanhong Piao,Woo-Sung Kim,Jeong-Jae Park,Nam-In Kang,Keon-Hak Lee,Young-Chul Chung 대한신경정신의학회 2019 PSYCHIATRY INVESTIGATION Vol.16 No.6
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  KCI
  
  스콜라
Objective The purpose of this study was to develop a Brooding Scale (BS) and to confirm its psychometric properties. Methods A preliminary questionnaire was developed based on a literature review and face-to-face interviews with healthy subjects. To evaluate reliability and construct validity, a 15-item BS was administered to 124 healthy subjects. Convergent validity was tested by assessing the relationship between the BS and the Ruminative Response Scale (RRS). Discriminant validity was confirmed in 58 patients with schizophrenia. Results The internal consistency for the BS was excellent. An exploratory factor analysis yielded two factors: the emotional (six items) and cognitive (five items) domains, which explained 33.83% and 23.69% of the variance, respectively. The BS total score and scores for factors 1 and 2 showed significant positive correlations with the RRS. The total score and sub-factor scores of the BS were significantly higher in patients with schizophrenia than in healthy subjects. Conclusion The BS can be used as a reliable and valid tool to assess brooding in healthy adults. In addition, it had good discriminant validity for patients with schizophrenia.
2
조현병 재발방지를 위한 약물치료 전략

김지현(Jihyun Kim),박염홍(Yanhong Piao),신광범(Quangfan Shen),정영철(Young-Chul Chung) 대한신경정신의학회 2018 신경정신의학 Vol.57 No.3
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  KCI
  
  스콜라
Successful treatment is very high in patients with first episode schizophrenia (FES). On the other hand, the problem is a frequent relapse often caused by non-compliance. The non-compliance rate in patients with FES is 40–60% within 1 year. The causes of non-compliance are diverse, such as poor insight, drug side effects, attitude of caregiver, social stigma, etc. Clinicians should be able to provide appropriate psychosocial intervention and long acting injectable antipsychotics (LAI) to overcome non-compliance. Recently, there is solid and accumulating evidence demonstrating superiority of LAI over oral medication in terms of reducing relapse or rehospitalization. In particular, a substantial portion (approximately 30–50%) of patients and caregivers prefer LAI to oral medication. Shared decision-making is the process that clinicians and patients/caregiver should go through in order to obtain the full benefits from LAI.
3
Classification of schizophrenia and normal controls using 3D convolutional neural network and outcome visualization

Oh, Kanghan,Kim, Woosung,Shen, Guangfan,Piao, Yanhong,Kang, Nam-In,Oh, Il-Seok,Chung, Young Chul Elsevier 2019 Schizophrenia Research Vol.212 No.-
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Abstract Background The recent deep learning-based studies on the classification of schizophrenia (SCZ) using MRI data rely on manual extraction of feature vector, which destroys the 3D structure of MRI data. In order to both identify SCZ and find relevant biomarkers, preserving the 3D structure in classification pipeline is critical. Objectives The present study investigated whether the proposed 3D convolutional neural network (CNN) model produces higher accuracy compared to the support vector machine (SVM) and other 3D-CNN models in distinguishing individuals with SCZ spectrum disorders (SSDs) from healthy controls. We sought to construct saliency map using class saliency visualization (CSV) method. Methods Task-based fMRI data were obtained from 103 patients with SSDs and 41 normal controls. To preserve spatial locality, we used 3D activation map as input for the 3D convolutional autoencoder (3D-CAE)-based CNN model. Data on 62 patients with SSDs were used for unsupervised pretraining with 3D-CAE. Data on the remaining 41 patients and 41 normal controls were processed for training and testing with CNN. The performance of our model was analyzed and compared with SVM and other 3D-CNN models. The learned CNN model was visualized using CSV method. Results Using task-based fMRI data, our model achieved 84.15%∼84.43% classification accuracies, outperforming SVM and other 3D-CNN models. The inferior and middle temporal lobes were identified as key regions for classification. Conclusions Our findings suggest that the proposed 3D-CAE-based CNN can classify patients with SSDs and controls with higher accuracy compared to other models. Visualization of salient regions provides important clinical information.