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Ho Sung Kim

Assistant Professor of Neurology

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Email hosungki@usc.edu
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Overview

My research spans an interdisciplinary cross-section of Medical Image Processing, Machine learning, and Neuroscience covering clinical neurology and neuropsychiatry. In the fields of medical image processing and analysis, I have studied on multicontrast image registration and segmentation, surface modeling of cortical/subcortical structures which are the prerequisite techniques to proceed the analysis of structural and functional brain imaging studies.
My projects that have been recently launched at USC-INI and USC-LONI include mainly three domains of the research field: 1) Prediction of neurodevelopmental outcome in neonates with various clinical conditions such as preterm birth, hypoxia-ischemia, and congenital heart disease: This project expands in line with my team’s expertise in neurodevelopment, neuroimaging, computational imaging feature modeling and machine learning (particularly DEEP learning); 2) Neuroimaging data quality controls (image QC): My team dedicates its efforts to implementation of online-based LONI-QC system that allows the public to evaluate their own data as well as to automated QC feature that will ultimately predict the accuracy of brain image post-processing and the sensitivity in the subsequently biological/clinical analysis to given target pathophysiology, and 3) Prediction of brain age and accelerated aging due to neurodegeneration: combination of brain imaging data and covolutional neural network-based deep-learning can estimate the brain age for individual images. extending this model with a statistical hazard model, we aim to determine risk scores for aging subjects who potentially develop a neurodegenerative disease.
In other clinical/neuroscientific applications, my team has applied various advanced analytic frameworks, including cortical morphometry, voxel-based morphometry, deformation-based morphometry and structural network analysis, to the assessment of brain structure in healthy conditions as well as pathological conditions, which often present anatomical variations beyond the range of normal structures.
My team continues to expand aforementioned techniques to the analysis of BIG DATA of brain imaging data to better understand mechanisms involved in various diseases and disorders such as stroke, epilepsy, dementia, sleep disorders, as well as long-term deafness and sudden hearing loss.

Awards

  • Baxter Foundation: Donald E. and Delia B. Baxter Foundation Faculty Fellowship Award, 2017-2018
  • ISMRM 24th Annual Meeting & Exhibition: Young Investigator / Trainee Stipends Award, 2016
  • Canadian Institutes of Health Research: Banting Postdoctoral Fellowships, 2015-2017
  • Fonds de la recherche en sante/ Health Research Funds in Quebec (FRSQ): Post-doctoral fellowship, 2014-2016
  • Sleep: Sleep Research Society Abstract Excellence Award, 2013
  • 10th Annual Meeting of Korean Sleep Research Society: Best Poster presentation award, 2013
  • American Epilepsy Society (AES 2011): Young Investigator Travel Award, 2011
  • International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2011): Student Travel Award, 2011
  • American Epilepsy Society (AES 2010): Young Investigator Travel Award, 2010
  • Fonds de la recherche en santé / Health Research Funds in Quebec (FRSQ) : Doctoral Training Awards, 2009-2010
  • McGill University: Fellowship for returning graduate students, 2007
  • Hanyang University: Excellent Student Scholarship, 1998-2000

Publications

  • The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke Hum Brain Mapp. 2022 01; 43(1):129-148. . View in PubMed
  • Cortical reorganization following auditory deprivation predicts cochlear implant performance in postlingually deaf adults Hum Brain Mapp. 2021 01; 42(1):233-244. . View in PubMed
  • Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions Neuroimage. 2021 04 15; 230:117756. . View in PubMed
  • Robust Cortical Thickness Morphometry of Neonatal Brain and Systematic Evaluation Using Multi-Site MRI Datasets Front Neurosci. 2021; 15:650082. . View in PubMed
  • A five-year longitudinal study reveals progressive cortical thinning in narcolepsy and faster cortical thinning in relation to early-onset Brain Imaging Behav. 2020 Feb; 14(1):200-212. . View in PubMed
  • Imputation Strategy for Reliable Regional MRI Morphological Measurements Neuroinformatics. 2020 01; 18(1):59-70. . View in PubMed
  • Disruption and Compensation of Sulcation-based Covariance Networks in Neonatal Brain Growth after Perinatal Injury Cereb Cortex. 2020 11 03; 30(12):6238-6253. . View in PubMed
  • White matter tract-specific alterations in male patients with untreated obstructive sleep apnea are associated with worse cognitive function Sleep. 2020 03 12; 43(3). . View in PubMed
  • A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data Hum Brain Mapp. 2019 11 01; 40(16):4669-4685. . View in PubMed
  • Beyond sleepy: structural and functional changes of the default-mode network in idiopathic hypersomnia Sleep. 2019 10 21; 42(11). . View in PubMed
  • Age-Related Differences in Brain Morphology and the Modifiers in Middle-Aged and Older Adults Cereb Cortex. 2019 09 13; 29(10):4169-4193. . View in PubMed
  • The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data Front Neuroinform. 2019; 13:60. . View in PubMed
  • The association between cardiac physiology, acquired brain injury, and postnatal brain growth in critical congenital heart diseaseJ Thorac Cardiovasc Surg. 2018 01; 155(1):291-300. e3. . View in PubMed
  • Cochlear Implantation in Postlingually Deaf Adults is Time-sensitive Towards Positive Outcome: Prediction using Advanced Machine Learning Techniques Sci Rep. 2018 12 20; 8(1):18004. . View in PubMed
  • A large, open source dataset of stroke anatomical brain images and manual lesion segmentations Sci Data. 2018 02 20; 5:180011. . View in PubMed
  • Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias Neuroimage. 2018 02 01; 166:10-18. . View in PubMed
  • Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling Front Neuroinform. 2018; 12:39. . View in PubMed
  • Medical Imaging with Deep Learning (MIDL 2018) Retrospective correction of motion artifact affected structural MRI images using deep learning of simulated motion. 2018; (accepted). . View in PubMed
  • Surface-based morphometry reveals caudate subnuclear structural damage in patients with premotor Huntington disease Brain Imaging Behav. 2017 Oct; 11(5):1365-1372. . View in PubMed
  • Egocentric and allocentric visuospatial working memory in premotor Huntington’s disease: A double dissociation with caudate and hippocampal volumes Neuropsychologia. 2017 Jul 01; 101:57-64. . View in PubMed
  • Microstructure of the Default Mode Network in Preterm Infants AJNR Am J Neuroradiol. 2017 Feb; 38(2):343-348. . View in PubMed
  • Early changes in brain structure correlate with language outcomes in children with neonatal encephalopathy Neuroimage Clin. 2017; 15:572-580. . View in PubMed
  • Semi-automated Robust Quantification of Lesions (SRQL) Toolbox Research Ideas and Outcomes. 2017; 3:e13395. . View in PubMed
  • Surface–wise texture patch analysis of combined MRi andPET to detect MRI-negative focal cortical dysplasiasMed. Image. Comp. Comp. Assist. Interv. 2017; 10433:212-220. . View in PubMed
  • Extensive migration of young neurons into the infant human frontal lobe Science. 2016 Oct 07; 354(6308). . View in PubMed
  • NEOCIVET: Towards accurate morphometry of neonatal gyrification and clinical applications in preterm newborns Neuroimage. 2016 Sep; 138:28-42. . View in PubMed
  • Hindbrain regional growth in preterm newborns and its impairment in relation to brain injury Hum Brain Mapp. 2016 Feb; 37(2):678-88. . View in PubMed
  • Morphological alterations in amygdalo-hippocampal substructures in narcolepsy patients with cataplexy Brain Imaging Behav. 2016 12; 10(4):984-994. . View in PubMed
  • Effects of long-term treatment on brain volume in patients with obstructive sleep apnea syndrome Hum Brain Mapp. 2016 Jan; 37(1):395-409. . View in PubMed
  • Pyruvate to Lactate Metabolic Changes during Neurodevelopment Measured Dynamically Using Hyperpolarized 13C Imaging in Juvenile Murine Brain Dev Neurosci. 2016; 38(1):34-40. . View in PubMed
  • A Surface Patch-Based Segmentation Method for Hippocampal Subfields Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. 2016; 9901. . View in PubMed
  • Multi-template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling Neuroinformatics. 2016; in revision. . View in PubMed
  • Cortical Thinning and Altered Cortico-Cortical Structural Covariance of the Default Mode Network in Patients with Persistent Insomnia Symptoms Sleep. 2016 Jan 01; 39(1):161-71. . View in PubMed
  • Brain Injury in the Preterm and Term Neonate Current Radiology Reports. 2016; 4(39). . View in PubMed
  • Accurate cortical tissue classification on MRI by modeling cortical folding patterns Hum Brain Mapp. 2015 Sep; 36(9):3563-74. . View in PubMed
  • NEOCIVET: Extraction of cortical surface and analysis of neonatal gyrification using a modified CIVET pipeline Medical Image Computing and Computer-Assisted Intervention. 2015; 9351:571-9. . View in PubMed
  • Automated detection of cortical dysplasia type II in MRI-negative epilepsy Neurology. 2014 Jul 01; 83(1):48-55. . View in PubMed
  • Hippocampal substructural vulnerability to sleep disturbance and cognitive impairment in patients with chronic primary insomnia: magnetic resonance imaging morphometry Sleep. 2014 Jul 01; 37(7):1189-98. . View in PubMed
  • Multivariate hippocampal subfield analysis of local MRI intensity and volume: application to temporal lobe epilepsy Med Image Comput Comput Assist Interv. 2014; 17(Pt 2):170-8. . View in PubMed
  • Patterns of subregional mesiotemporal disease progression in temporal lobe epilepsy Neurology. 2013 Nov 19; 81(21):1840-7. . View in PubMed
  • Disentangling hippocampal shape anomalies in epilepsy Front Neurol. 2013; 4:131. . View in PubMed
  • Surface-based multi-template automated hippocampal segmentation: application to temporal lobe epilepsy Med Image Anal. 2012 Oct; 16(7):1445-55. . View in PubMed
  • Spatial patterns of water diffusion along white matter tracts in temporal lobe epilepsy Neurology. 2012 Jul 31; 79(5):455-62. . View in PubMed
  • Automatic hippocampal segmentation in temporal lobe epilepsy: impact of developmental abnormalities Neuroimage. 2012 Feb 15; 59(4):3178-86. . View in PubMed
  • Mapping thalamocortical network pathology in temporal lobe epilepsy Neurology. 2012 Jan 10; 78(2):129-36. . View in PubMed
  • Increased temporolimbic cortical folding complexity in temporal lobe epilepsy Neurology. 2011 Jan 11; 76(2):138-44. . View in PubMed
  • Vertex-wise shape analysis of the hippocampus: disentangling positional differences from volume changes Med Image Comput Comput Assist Interv. 2011; 14(Pt 2):352-9. . View in PubMed
  • Robust surface-based multi-template automated algorithm to segment healthy and pathological hippocampi Med Image Comput Comput Assist Interv. 2011; 14(Pt 3):445-53. . View in PubMed
  • Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging Neuroimage. 2009 Oct 01; 47(4):1476-86. . View in PubMed
  • Longitudinal and cross-sectional analysis of atrophy in pharmacoresistant temporal lobe epilepsy Neurology. 2009 May 19; 72(20):1747-54. . View in PubMed
  • Temporal lobe epilepsy: differential pattern of damage in temporopolar cortex and white matter Hum Brain Mapp. 2008 Aug; 29(8):931-44. . View in PubMed
  • Basal temporal sulcal morphology in healthy controls and patients with temporal lobe epilepsy Neurology. 2008 May 27; 70(22 Pt 2):2159-65. . View in PubMed
  • Surface-based vector analysis using heat equation interpolation: a new approach to quantify local hippocampal volume changes Med Image Comput Comput Assist Interv. 2008; 11(Pt 1):1008-15. . View in PubMed

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