• Korean Journal of Psychosomatic Medicine
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• v.25 no.2
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• pp.153-165
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• 2017

Objectives : Recent neuroimaging studies focus on dysfunctions in connectivity between cognitive circuits and emotional circuits: anterior cingulate cortex that connects dorsolateral orbitofrontal cortex and prefrontal cortex to limbic system. Previous studies on pediatric depression using DTI have reported decreased neural connectivity in several brain regions, including the amygdala, anterior cingulate cortex, superior longitudinal fasciculus. We compared the neural connectivity of psychotropic drug naïve adolescent patients with a first onset of major depressive episode with healthy controls using DTI. Methods : Adolescent psychotropic drug naïve patients(n=26, 10 men, 16 women; age range, 13-18 years) who visited the Korea University Guro Hospital and were diagnosed with first onset major depressive disorder were registered. Healthy controls(n=27, 5 males, 22 females; age range, 12-17 years) were recruited. Psychiatric interviews, complete psychometrics including IQ and HAM-D, MRI including diffusion weighted image acquisition were conducted prior to antidepressant administration to the patients. Fractional anisotropy(FA), radial, mean, and axial diffusivity were estimated using DTI. FMRIB Software Library-Tract Based Spatial Statistics was used for statistical analysis. Results : We did not observe any significant difference in whole brain analysis. However, ROI analysis on right superior longitudinal fasciculus resulted in 3 clusters with significant decrease of FA in patients group. Conclusions : The patients with adolescent major depressive disorder showed statistically significant FA decrease in the DTI-based structure compared with healthy control. Therefore we suppose DTI can be used as a bio-marker in psychotropic drug-naïve adolescent patients with first onset major depressive disorder.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.5931-5936
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• 2012

This study was to evaluate the clinical usefulness of the PRESS technique based on the correlation between PRESS technique and biopsy results by applying 3.0T high magnetic field MRS technique for evaluation of non-alcoholic fatty liver disease patients. This experiment were carried out using a 3.0T magnetic resonance imaging equipment. The part data of each spectrum is taken by peak area integration. The part data of resonance peak was used to calculate relative ratio. MR spectral peak in patients with non-alcoholic fatty liver disease is from 0.9 to 1.6 ppm. According to MRS method study result, Patients with NAFLD were obtained with 94% sensitivity and 80% specificity(p=0.000). When compared to normal based on MRS and Biopsy results was valid correlation(r=0.79, p=0.04). Results for NAFLD(r=0.89, p=0.002) also showed a correlation. Therefore, PRESS technique to evaluate patients with non-alcoholic fatty liver disease, the distribution difference between normal liver and fatty liver part is significantly distinguished. Biopsy and MRS fatty liver peak ratio(%) proves high lipid over grade(r = 0.7).

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.507-518
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• 2021

Magnetic resonance imaging (MRI) is a key technology that has been seeing increasing use in studying the structural and functional innerworkings of the brain. Analyzing the variability of brain connectome through tractography analysis has been used to increase our understanding of disease pathology in humans. However, there lacks standardization of analysis methods for small animals such as mice, and lacks scientific consensus in regard to accurate preprocessing strategies and atlas-based neuroinformatics for images. In addition, it is difficult to acquire high resolution images for mice due to how significantly smaller a mouse brain is compared to that of humans. In this study, we present an Allen Mouse Brain Atlas-based image data analysis pipeline for structural connectivity analysis involving structural region segmentation using mouse brain structural images and diffusion tensor images. Each analysis method enabled the analysis of mouse brain image data using reliable software that has already been verified with human and mouse image data. In addition, the pipeline presented in this study is optimized for users to efficiently process data by organizing functions necessary for mouse tractography among complex analysis processes and various functions.