Beom-Sok Jung
Korea Advanced Institute of Science and Technology · 医学
研究室紹介
Professor Beom-Sok Jung's research lab specializes in computational and neuroimaging approaches to understand brain-behavior relationships in neuropsychiatric and neurodegenerative disorders. The lab focuses on applying advanced neuroimaging techniques—such as perfusion MRI, diffusion tensor imaging (DTI), and functional MRI—combined with machine learning and multivariate statistical models to investigate functional and structural brain abnormalities in conditions like depression, schizophrenia, ADHD, Alzheimer’s disease, and Internet Gaming Disorder. A key research direction involves developing interpretable AI models to predict disease phenotypes from brain imaging data, with translational goals in early detection, biomarker discovery, and personalized intervention strategies.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Our results using EmotionDiary demonstrated that the more depressed one is, the more one will read tips and facts about depression. We also confirmed depressed individuals had significantly fewer interactions with others (eg, decreased number of friends and location tagging). Our app, EmotionDiary, can successfully evaluate depressive symptoms as well as provide useful tips and facts to users. These results open the door for examining Facebook activities to identify depressed individuals. We aim
BACKGROUND: The aim of this study was to predict isocitrate dehydrogenase (IDH) genotypes of gliomas using an interpretable deep learning application for dynamic susceptibility contrast (DSC) perfusion MRI. METHODS: Four hundred sixty-three patients with gliomas who underwent preoperative MRI were enrolled in the study. All the patients had immunohistopathologic diagnoses of either IDH-wildtype or IDH-mutant gliomas. Tumor subregions were segmented using a convolutional neural network followed b
Functional studies in schizophrenia demonstrate prominent abnormalities within the left inferior frontal gyrus (IFG) and also suggest the functional connectivity abnormalities in language network including left IFG and superior temporal gyrus during semantic processing. White matter connections between regions involved in the semantic network have also been indicated in schizophrenia. However, an association between functional and anatomical connectivity disruptions within the semantic network i
Our results suggest that medication-naïve ADHD subjects may have delayed maturation of the two functional connections, SN-Sensory/Motor and aDMN-pDMN/prec. Interventions that enhance the functional connectivity of these two connections may merit attention as potential therapeutic or preventive options in both ADHD and TDC.
INTRODUCTION: Cognitive performance in patients with Alzheimer's dementia (AD) and mild cognitive impairment (MCI) has been reported to be related to hippocampal atrophy and microstructural changes in white matter (WM). We aimed to predict the neurocognitive functions of patients with MCI or AD using hippocampal volumes and diffusion tensor imaging (DTI) metrics via partial least squares regression (PLSR). METHODS: = 33). Twenty-four hippocampal subfield volumes and the average values for fracti
Internet use and on-line game play stimulate corticostriatal-limbic circuitry in both healthy subjects and subjects with Internet gaming disorder (IGD). We hypothesized that increased fractional anisotropy (FA) with decreased radial diffusivity (RD) would be observed in IGD subjects, compared with healthy control subjects, and that these white matter indices would be associated with clinical variables including duration of illness and executive function. We screened 181 male patients in order to
Depression diagnosis is one of the most important issues in psychiatry. Depression is a complicated mental illness that varies in symptoms and requires patient cooperation. In the present study, we demonstrated a novel data-driven attempt to diagnose depressive disorder based on clinical questionnaires. It includes deep learning, multi-modal representation, and interpretability to overcome the limitations of the data-driven approach in clinical application. We implemented a shared representation
Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffered from low sensitivity. We developed and validated a deep graph neural network model that increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training