Ho Sung Kim

Title(s)Assistant Professor of Neurology
vCardDownload vCard

    Collapse Biography 
    Collapse Education and Training
    McGill University, Montreal QC, Canada Ph.D.Biomedical Engineering
    Montreal Neurological Institute, Montreal QC, Canada Postdoctoral fellowNeuroimaging of Epilepsy
    University of California, San Francisco, San Francisco CAPostdoctoral fellowNeurodevelopment, Clinical Neuroscience
    Collapse Awards and Honors
    Baxter Foundation2017  - 2018Donald E. and Delia B. Baxter Foundation Faculty Fellowship Award
    Canadian Institutes of Health Research2015  - 2017Banting Postdoctoral Fellowships
    Fonds de la recherche en sante/ Health Research Funds in Quebec (FRSQ)2014  - 2016Post-doctoral fellowship
    Sleep2013Sleep Research Society Abstract Excellence Award
    ISMRM 24th Annual Meeting & Exhibition2016Young Investigator / Trainee Stipends Award
    10th Annual Meeting of Korean Sleep Research Society2013Best Poster presentation award
    American Epilepsy Society (AES 2011)2011Young Investigator Travel Award
    American Epilepsy Society (AES 2010)2010Young Investigator Travel Award
    International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2011)2011Student Travel Award
    Fonds de la recherche en santé / Health Research Funds in Quebec (FRSQ) 2009  - 2010Doctoral Training Awards
    McGill University2007Fellowship for returning graduate students
    Hanyang University1998  - 2000Excellent Student Scholarship

    Collapse Overview 
    Collapse 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.

    Collapse Research 
    Collapse Research Activities and Funding
    Machine learning of multicontrast MRI features to predict neurodevelopmental outcome of preterm neonates
    Baxter Foundation Jul 1, 2017 - Jun 30, 2018
    Role: Principal investigator

    Collapse Bibliographic 
    Collapse Publications
    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Researchers can login to make corrections and additions, or contact us for help.
    List All   |   Timeline
    1. Ben A Duffy, Wenlu Zhang, Haoteng Tang, Lu Zhao, Meng Law, Arthur W Toga, Hosung Kim.Medical Imaging with Deep Learning (MIDL 2018). Retrospective correction of motion artifact affected structural MRI images using deep learning of simulated motion. 2018; (accepted).
    2. Tan YL, Kim H, Lee S, Tihan T, Ver Hoef L, Mueller SG, Barkovich AJ, Xu D, Knowlton R. Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias. Neuroimage. 2018 02 01; 166:10-18. PMID: 29097316.
      View in: PubMed
    3. Kim H, Kim JH, Possin KL, Winer J, Geschwind MD, Xu D, Hess CP. Surface-based morphometry reveals caudate subnuclear structural damage in patients with premotor Huntington disease. Brain Imaging Behav. 2017 Oct; 11(5):1365-1372. PMID: 27730480.
      View in: PubMed
    4. Kim H, Tan Y-L, Lee S, Barkovich AJ, Xu D, Knowlton R.Surface–wise texture patch analysis of combined MRi andPET to detect MRI-negative focal cortical dysplasias. Med. Image. Comp. Comp. Assist. Interv. 2017; 10433:212-220.
    5. Peyvandi S, Kim H, Lau J, Barkovich AJ, Campbell A, Miller S, Xu D, McQuillen P. The association between cardiac physiology, acquired brain injury, and postnatal brain growth in critical congenital heart disease. J Thorac Cardiovasc Surg. 2018 Jan; 155(1):291-300.e3. PMID: 28918207.
      View in: PubMed
    6. Shapiro KA, Kim H, Mandelli ML, Rogers EE, Gano D, Ferriero DM, Barkovich AJ, Gorno-Tempini ML, Glass HC, Xu D. Early changes in brain structure correlate with language outcomes in children with neonatal encephalopathy. Neuroimage Clin. 2017; 15:572-580. PMID: 28924555.
      View in: PubMed
    7. Ito KL, Anglin J, Kim H, Liew S-L.Semi-automated Robust Quantification of Lesions (SRQL) Toolbox. Research Ideas and Outcomes. 2017; 3:e13395 .
    8. Possin KL, Kim H, Geschwind MD, Moskowitz T, Johnson ET, Sha SJ, Apple A, Xu D, Miller BL, Finkbeiner S, Hess CP, Kramer JH. 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. PMID: 28427989.
      View in: PubMed
    9. Kim H, Suh S, Joo EY, Hong SB. Morphological alterations in amygdalo-hippocampal substructures in narcolepsy patients with cataplexy. Brain Imaging Behav. 2016 12; 10(4):984-994. PMID: 26446435.
      View in: PubMed
    10. Kim H, Caldairou B, Bernasconi A, Bernasconi N.Multi-template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling. Neuroinformatics. 2016; in revision.
    11. Cui J, Tymofiyeva O, Desikan R, Flynn T, Kim H, Gano D, Hess CP, Ferriero DM, Barkovich AJ, Xu D. Microstructure of the Default Mode Network in Preterm Infants. AJNR Am J Neuroradiol. 2017 Feb; 38(2):343-348. PMID: 28059709.
      View in: PubMed
    12. Paredes MF, James D, Gil-Perotin S, Kim H, Cotter JA, Ng C, Sandoval K, Rowitch DH, Xu D, McQuillen PS, Garcia-Verdugo JM, Huang EJ, Alvarez-Buylla A. Extensive migration of young neurons into the infant human frontal lobe. Science. 2016 Oct 07; 354(6308). PMID: 27846470.
      View in: PubMed
    13. Caldairou B, Bernhardt BC, Kulaga-Yoskovitz J, Kim H, Bernasconi N, Bernasconi A .A Surface Patch-Based Segmentation Method for Hippocampal Subfields. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. 2016; 9901.
    14. Ho ML, Patton, AC, DeLong DR, Kim H, Gilbertson JR, Felmlee J, Watson RE.Brain Injury in the Preterm and Term Neonate. Current Radiology Reports. 2016; 4(39).
    15. Kim H, Lepage C, Maheshwary R, Jeon S, Evans AC, Hess CP, Barkovich AJ, Xu D. NEOCIVET: Towards accurate morphometry of neonatal gyrification and clinical applications in preterm newborns. Neuroimage. 2016 Sep; 138:28-42. PMID: 27184202; PMCID: PMC4982765 [Available on 09/01/17].
    16. Suh S, Kim H, Dang-Vu TT, Joo E, Shin C. 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. PMID: 26414892.
      View in: PubMed
    17. Kim H, Gano D, Ho ML, Guo XM, Unzueta A, Hess C, Ferriero DM, Xu D, Barkovich AJ. Hindbrain regional growth in preterm newborns and its impairment in relation to brain injury. Hum Brain Mapp. 2016 Feb; 37(2):678-88. PMID: 26589992.
      View in: PubMed
    18. Chen Y, Kim H, Bok R, Sukumar S, Mu X, Sheldon RA, Barkovich AJ, Ferriero DM, Xu D. Pyruvate to Lactate Metabolic Changes during Neurodevelopment Measured Dynamically Using Hyperpolarized 13C Imaging in Juvenile Murine Brain. Dev Neurosci. 2016; 38(1):34-40. PMID: 26550989; PMCID: PMC4732911 [Available on 01/01/17].
    19. Kim H, Joo E, Suh S, Kim JH, Kim ST, Hong SB. Effects of long-term treatment on brain volume in patients with obstructive sleep apnea syndrome. Hum Brain Mapp. 2016 Jan; 37(1):395-409. PMID: 26503297; PMCID: PMC4715739 [Available on 01/01/17].
    20. Kim H, Lepage C, Evans AC, Barkovich AJ, Xu D.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.
    21. Kim H, Caldairou B, Hwang JW, Mansi T, Hong SJ, Bernasconi N, Bernasconi A. Accurate cortical tissue classification on MRI by modeling cortical folding patterns. Hum Brain Mapp. 2015 Sep; 36(9):3563-74. PMID: 26037453.
      View in: PubMed
    22. Joo EY, Kim H, Suh S, Hong SB. 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. PMID: 25061247.
      View in: PubMed
    23. Hong SJ, Kim H, Schrader D, Bernasconi N, Bernhardt BC, Bernasconi A. Automated detection of cortical dysplasia type II in MRI-negative epilepsy. Neurology. 2014 Jul 01; 83(1):48-55. PMID: 24898923; PMCID: PMC4114179.
    24. Kim H, Bernhardt BC, Kulaga-Yoskovitz J, Caldairou B, Bernasconi A, Bernasconi N. 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. PMID: 25485376.
      View in: PubMed
    25. Bernhardt BC, Kim H, Bernasconi N. Patterns of subregional mesiotemporal disease progression in temporal lobe epilepsy. Neurology. 2013 Nov 19; 81(21):1840-7. PMID: 24142475.
      View in: PubMed
    26. Kim H, Mansi T, Bernasconi N. Disentangling hippocampal shape anomalies in epilepsy. Front Neurol. 2013; 4:131. PMID: 24062718; PMCID: PMC3769634.
    27. Concha L, Kim H, Bernasconi A, Bernhardt BC, Bernasconi N. Spatial patterns of water diffusion along white matter tracts in temporal lobe epilepsy. Neurology. 2012 Jul 31; 79(5):455-62. PMID: 22815555; PMCID: PMC3405250.
    28. Kim H, Mansi T, Bernasconi N, Bernasconi A. Surface-based multi-template automated hippocampal segmentation: application to temporal lobe epilepsy. Med Image Anal. 2012 Oct; 16(7):1445-55. PMID: 22613821.
      View in: PubMed
    29. Bernhardt BC, Bernasconi N, Kim H, Bernasconi A. Mapping thalamocortical network pathology in temporal lobe epilepsy. Neurology. 2012 Jan 10; 78(2):129-36. PMID: 22205759.
      View in: PubMed
    30. Kim H, Chupin M, Colliot O, Bernhardt BC, Bernasconi N, Bernasconi A. Automatic hippocampal segmentation in temporal lobe epilepsy: impact of developmental abnormalities. Neuroimage. 2012 Feb 15; 59(4):3178-86. PMID: 22155377.
      View in: PubMed
    31. Kim H, Mansi T, Bernasconi N, Bernasconi A. 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. PMID: 22003730.
      View in: PubMed
    32. Kim H, Mansi T, Bernasconi A, Bernasconi N. 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. PMID: 21995048.
      View in: PubMed
    33. Voets NL, Bernhardt BC, Kim H, Yoon U, Bernasconi N. Increased temporolimbic cortical folding complexity in temporal lobe epilepsy. Neurology. 2011 Jan 11; 76(2):138-44. PMID: 21148116; PMCID: PMC3030232.
    34. Gerardin E, Chételat G, Chupin M, Cuingnet R, Desgranges B, Kim HS, Niethammer M, Dubois B, Lehéricy S, Garnero L, Eustache F, Colliot O. 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. PMID: 19463957; PMCID: PMC3001345.
    35. Bernhardt BC, Worsley KJ, Kim H, Evans AC, Bernasconi A, Bernasconi N. Longitudinal and cross-sectional analysis of atrophy in pharmacoresistant temporal lobe epilepsy. Neurology. 2009 May 19; 72(20):1747-54. PMID: 19246420; PMCID: PMC2827310.
    36. Sankar T, Bernasconi N, Kim H, Bernasconi A. Temporal lobe epilepsy: differential pattern of damage in temporopolar cortex and white matter. Hum Brain Mapp. 2008 Aug; 29(8):931-44. PMID: 17636561.
      View in: PubMed
    37. Kim H, Bernasconi N, Bernhardt B, Colliot O, Bernasconi A. Basal temporal sulcal morphology in healthy controls and patients with temporal lobe epilepsy. Neurology. 2008 May 27; 70(22 Pt 2):2159-65. PMID: 18505994.
      View in: PubMed
    38. Kim H, Besson P, Colliot O, Bernasconi A, Bernasconi N. 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. PMID: 18979844.
      View in: PubMed