브만사
>
BioLab
BioLab
University of Southern California, NIDLL (NeuroImaging with Deep Learning Lab)
김호성 교수
- 등록2024.12.16
- 조회727
연구실 소개
연구실 홈페이지The Neuroimaging and Deep Learning Lab (NIDLL) is dedicated to advancing research in neuroimaging using deep learning techniques to explore the relationship between poor sleep quality and faster brain aging in adults. Our primary goal is to predict long-term outcomes for neurodegenerative disorders. Using multi-contrast MRI data collected over time, we analyze and quantify brain structural and functional variability.
Hosung Kim, Ph.D. (김호성)
Assistant Professor of Neurology, University of Southern California, USA
Education and Training
2012 Ph.D. in Biomedical Engineering, McGill Universty, Montreal QC, Canada
2014 Postdoctoral fellow in Neruoimaging of Epilepsy, Montreal Neurological Institute, Montreal QC, Canada
2016 Postdoctoral fellow in Neurodevelopment, Clinical Neuroscience, University of California San Francisco, San Francisco, CA, USA
연구분야
Neuroelectrophysiological aging clock (EEG) associated with sleep disorders, comorbidity and brain structural changes
We have developed a novel sleep EEG-based brain age index (BAI) using artificial intelligence to objectively measure brain aging influenced by sleep patterns. Unlike traditional models that rely on structural MRI data, our approach captures dynamic brain functions during sleep, providing unique insights into the aging process. Our brain age prediction model achieves a mean absolute error of 4.8 years, outperforming existing EEG-based biomarkers.
Our research highlights the significant role of sleep architecture—particularly the depth and continuity of sleep—in brain aging. We found that chronic sleep disorders like obstructive sleep apnea (OSA) and insomnia accelerate brain aging, emphasizing the critical importance of diagnosing and treating these conditions to preserve brain health.
Additionally, we discovered that shift workers exhibit specific delta wave patterns linked to accelerated brain aging, offering the first empirical evidence of the long-term brain health risks associated with shift work. We also explored the therapeutic potential of continuous positive airway pressure (CPAP) therapy for individuals with OSA. Our findings show that CPAP treatment can effectively halt the progression of brain aging, underscoring its potential to protect against the long-term consequences of sleep-disordered breathing on brain health.
Imaging of Glymphatic Function in Human Brains
The glymphatic system is a brain fluid-clearance pathway that facilitates the removal of metabolic waste by cerebrospinal fluid (CSF) through periarterial spaces (PAS) and perivenous spaces (PVS). While existing non-invasive imaging techniques like DTI-ALPS index provide whole-brain measures of glymphatic function, they lack region-specific insights critical for understanding neurodegenerative diseases. We propose a novel metric, RaPPiD (Ratio of Perpendicular to Parallel Periarterial Diffusivity), which uses diffusion MRI and T2-weighted imaging to quantify regional glymphatic function by analyzing water diffusivity at voxels of PAS. RaPPiD evaluates the ratio of perpendicular-to-parallel diffusivity, reflecting fluid movement into interstitial spaces.
In a cohort of 579 subjects from the HCP-Aging study, we found significant associations between RaPPiD values and sleep quality indices in the frontoparietal and limbic networks, as well as cardiovascular factors like systolic blood pressure in the dorsal attention network.
RaPPiD provides a refined, region-specific measure of glymphatic function, highlighting its sensitivity to sleep and vascular health. This approach may improve understanding of age-related neurodegeneration and brain clearance mechanisms.
Motion Artifact Correction using Deep Learning
Head motion during MRI acquisition presents significant problems for subsequent neuroimaging analyses. In our most up-to-date work (Sun et al., ISMRM 2025), we aim to develop a Motion-Adaptive Diffusion Model (MADM) to correct motion artifacts in MR, improving image quality. MADM is based on a diffusion model. Gaussian noise is added in the forward process, and a U-Net progressively denoises the images in reverse process. Our model was trained on the MR-ART dataset.
Diffusion Model
What we can do: A generative model used to create images by simulation the diffusion process. Key Idea: Start with random noise, and gradually transform it into a meaningful image. Forward Process: The model adds noise to the images over multiple steps, simulating a diffusion process. Reverse Process: The model learns to reverse the diffusion process, starting from noise and progressively generating a structured output via neural networks
Brain Age Prediction in Various Neurodegenerative Diseases
The focuse of this study is the development of advanced AI methods to predict brain ages in various neurological conditions and understand whether brain aging can be accelerated by those clinical conditions and reversed by treatment or rehabilitation and whether regional pattern of brain age can be modified via brain re-organization and disease compensation mechnisms. Using idividual T1-weighted MRI scans, we extract brain (cortical) surface models, define regions of interest (ROIs), and calculate the regional brain age for each ROI. This method provides a detailed understanding of the pattern of specific brain regions that are affected by various disease progression. Currently, we are applying this technique to study healthy older adults with APOE 4 gene (Im et al., Brain Communications 2024) or Cardiometabolic syndromes (Kang et al., Alzheimer's Research & Therapy) , stroke (Park et al., medArxiv 2024), sleep disorders (including insomnia, obstructive sleep apnea [OSA], and REM sleep behavior disorder [RBD]), and mesial temporal lobe epilepsy (MTLE). For example, in a recent stroke study, we observed that the contralesional hemisphere in patients with severe motor impairment exhibited a younger brain age, suggesting a potential compensatory mechanism. These findings underscore the value of regional brain age analysis in uncovering hidden brain dynamics and guiding targeted therapeutic interventions.
Cutting-Edge Machine Learning Techniques to Analyze Disease Progression
The focus of this study is the application of cutting-edge machine learning techniques to analyze disease progression, particularly in neurodegenerative disorders. Specifically, we utilize the Subtype and Stage Inference (SuStaIn) algorithm, an event-based probabilistic model, to classify patient populations into distinct subtypes and uncover unique progression patterns. In a recent study on Parkinson's disease (PD), we applied SuStaIn to analyze brain MRI data, focusing on cortical thickness and deep gray matter volume. This analysis revealed two distinct subtypes: a "cortex-first" subtype, characterized by cortical thinning preceding subcortical atrophy, and a "deep gray-first" subtype, where subcortical atrophy occurs earlier. These subtypes demonstrated significant differences in clinical features and disease progression rates. This research highlights the potential of data-driven methods to advance personalized medicine and enable more precise, targeted interventions.
연구성과
최근 2년, 2023~ 현재
X Wu, C Xie, F Cheng, Z Li, R Li, D Xu, H Kim, J Zhang, H Liu, M Liu, Comparative evaluation of interpretation methods in surface-based age prediction for neonates, NeuroImage , 2024.
H Jang, MY Chun, J Yun, JP Kim, SH Kang, M Weiner, HJ Kim, DL Na, SW Seo, H Kim, Ethnic differences in the prevalence of amyloid positivity and cognitive trajectories, Alzheimer’s & Dementia, 2024.
O Weaver, D Gano, Y Zhou, H Kim, R Tognatta, Z Yan, JK Ryu, C Brandt, H Kim, Fibrinogen inhibits sonic hedgehog signaling and impairs neonatal cerebellar development after blood–brain barrier disruption, Proceedings of the National Academy of Sciences, 2024.
Y Oh, JS Kim, CH Lyoo, G Park, H Kim, Spatiotemporal progression patterns of dopamine availability and deep gray matter volume in Parkinson disease–related cognitive impairment, Neurology, 2024.
H Kim, WS Ryu, D Schellingerhout, J Park, J Chung, SW Jeong, DS Gwak, Automated segmentation of MRI white matter hyperintensities in 8,421 patients with acute ischemic stroke, American Journal of Neuroradiology, 2024.
Q Shou, S Cen, N Chen, JM Ringman, J Wen, H Kim, DJJ Wang, Diffusion model enables quantitative CBF analysis of Alzheimer’s disease, medRxiv, 2024.
S Yook, SJ Choi, H Lee, EY Joo, H Kim, Long-term night-shift work is associated with accelerated brain aging and worsens N3 sleep in female nurses, Sleep Medicine, 2024.
PE Kim, H Yang, D Kim, L Sunwoo, CK Kim, BJ Kim, JT Kim, WS Ryu, H Kim, Automated prediction of proximal middle cerebral artery occlusions in noncontrast brain computed tomography, Stroke, 2024.
M Liu, M Lu, SY Kim, HJ Lee, BA Duffy, S Yuan, Y Chai, JH Cole, X Wu, H Kim, Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates, European Radiology, 2024.
JS Kim, YS Oh, CH Lyoo, G Park, H Kim, Association and spatiotemporal progression patterns of dopamine availability and deep gray matter volume in Parkinson’s disease–related cognitive impairment, Parkinsonism & Related Disorders, 2024.
G Park, H Jo, Y Chai, HR Park, H Lee, EY Joo, H Kim, Static and dynamic brain morphological changes in isolated REM sleep behavior disorder compared to normal aging, Frontiers in Neuroscience, 2024.
S Yook, HR Park, EY Joo, H Kim, Predicting the impact of CPAP on brain health: a study using the sleep EEG‐derived brain age index, Annals of Clinical and Translational Neurology, 2024.
S Kim, J Ollinger, C Song, S Raiciulescu, S Seenivasan, A Wolfgang, H Kim, JK Werner, P-H Yeh, White matter alterations in military service members with remote mild traumatic brain injury, JAMA Network Open, 2024.
S Yook, SJ Choi, C Zang, EY Joo, H Kim, Are there effects of light exposure on daytime sleep for rotating shift nurses after night shift?: an EEG power analysis, Frontiers in Neuroscience, 2024.
S Yook, D Kim, C Gupte, EY Joo, H Kim, Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity, Sleep Medicine, 2024.
Y Im, SH Kang, G Park, H Yoo, MY Chun, CH Kim, CJ Park, JP Kim, SW Seo, H Kim, Ethnic differences in the effects of apolipoprotein E ɛ4 and vascular risk factors on accelerated brain aging, Brain Communications, 2024.
M Liu, AH Zhu, P Maiti, SI Thomopoulos, S Gadewar, Y Chai, H Kim, N Jahanshad, Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection, Human Brain Mapping, 2023
Q Shou, C Zhao, X Shao, K Jann, H Kim, K G, DJJ Wang et al., Transformer-based deep learning denoising of single and multi-delay 3D arterial spin labeling, Magnetic Resonance in Medicine, 2023
Y Chai, HR Park, H Jo, MY Seo, HY Kim, EY Joo, H Kim, White matter microstructure and connectivity changes after surgery in male adults with obstructive sleep apnea: recovery or reorganization?, Frontiers in Neuroscience, 2023
YS Oh, JS Kim, CH Lyoo, H Kim, Obstructive sleep apnea and striatal dopamine availability in Parkinson's disease, Movement Disorders, 2023
DL Koo, RP Cabeen, SH Yook, SY Cen, EY Joo, H Kim, More extensive white matter disruptions present in untreated obstructive sleep apnea than we thought: A large sample diffusion imaging study, Human Brain Mapping, 2023
Y Chai, H Zhang, G Park, E Lopez, C Zang, J Ha, O Elhawary, H Kim, Semi-supervised learning in perivascular space segmentation using MRI images, Medical Imaging with Deep Learning, 2023
SL Liew, N Schweighofer, JH Cole, A Zavaliangos-Petropulu, BP Lo, H Kim, et al., Association of brain age, lesion volume, and functional outcome in patients with stroke, Neurology, 2023
SH Kang, M Liu, G Park, SY Kim, H Lee, W Matloff, L Zhao, H Yoo, JP Kim, H Kim et al., Different effects of cardiometabolic syndrome on brain age in relation to gender and ethnicity, Alzheimer's research & therapy, 2023
DL Koo, RP Cabeen, SH Yook, SY Cen, EY Joo, H Kim, More extensive white matter disruptions present in untreated obstructive sleep apnea than we thought: A large sample diffusion imaging study, Human Brain Mapping, 2023
SL Liew, N Schweighofer, JH Cole, A Zavaliangos-Petropulu, BP Lo, H Kim et al., Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke, Neurology, 2023
YS Oh, JS Kim, CH Lyoo, H Kim, Obstructive Sleep Apnea and Striatal Dopamine Availability in Parkinson's Disease, Movement Disorders, 2023
S Yook, HR Park, C Park, G Park, DC Lim, J Kim, EY Joo, H Kim, Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders, NeuroImage, 2023
SL Liew, BP Lo, MR Donnelly, A Zavaliangos-Petropulu, JN Jeong, H Kim et al, A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms, Scientific data, 2023
연구실 구성원
지도교수 : 김호성
현재 구성원
Postdoctoral Researchers : Yaqiong Chai, Soonhyun Yook, Gilsoon Park
Project Assistants : Minji Kim,
PhD Students : Cong Zang, Quanhao Sun
Master Students : Harmony Cen
Medical Students : Meghan Nanda, Audrey Yin
Undergraduate Students: Andrew S Kim, Sherry Yue Du, Christina Choi, Jung, Grace Chang, Abby Trang, June Lim, Cheny Yang, Steven Li, Carlos Robles
Visiting Professors: Kyungwook Kang, Seong-Jun Choi, Jung-Won Shin, Tae Won Yang
Contact : hosung.kim@loni.usc.edu
하고싶은 이야기
한국에서 박사 진학을 원하는 학생들과 우리 연구실에 관심이 있는 학생들은 저에게 hosung.kim@loni.usc.edu 연락 바랍니다.
본 정보는 해당 기관에서 제공한 자료를 바탕으로 구성된 내용으로, BRIC에서 자체 생산한 정보가 아님을 밝혀드립니다.
소개된 내용에서 중요하게 판단되는 부분은 해당 기관에 추가적으로 사실 확인 작업을 하시길 부탁드립니다.
내용의 업데이트 또는 변경을 원하시면 관리자(interview@ibric.org)에게 연락 바랍니다.
