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  • Seong-Gi Kim
  • Professor
  • MR biophysics, Neuroimaging, System neuroscience
  • Department of Biomedical Engineering
  • seonggikimskku.edu
  • http://fbrainmapping.weebly.com/
  • brain mapping, MRI, neuroscience, neurovascular

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  • Functional Brain Mapping Lab

     


    Introduction


    Magnetic resonance imaging (MRI) is a very powerful non-invasive tool to visualize brain morphology, physiology, function and connectivity. However, MRI originates from water protons, thus its biological source is not straightforward. Especially, widely-used blood oxygenation-level dependent (BOLD) fMRI relies on the presumably close relationship between neural activity and hemodynamic responses. Therefore, it is crucial to understand underlying basis of fMRI for proper quantification and determining ultimate limits. Also MRI can image entire brain repeatedly from anesthetized to awake animals, and its readout can be combined with diverse manipulations such as sensory, electrical, chemical and optogenetic stimulation, and pharmacological interventions for answering system-level neural circuits. To obtain multimodal functional neuroimaging data, animal MRI facility (9.4 T and 15.2 T Bruker) is accompanied with a neurophysiology laboratory with electrophysiology, intrinsic optical imager, laser Doppler flowmeter, etc. Our research lab consisting of MR scientists and system neuroscientists focuses on three inter-related research themes; a) the development of physiological and functional MRI techniques, b) the investigation of biophysical and physiological sources of MRI signals (functional MRI, perfusion, diffusion, chemical exchange MRI), and c) the application of neuroimaging techniques to systems neuroscience research. 

     


    Selected Recent Publications


    1. Jin T, Wang P, Zong XP & Kim SG, “MR imaging of the Amide-Proton Transfer effect and the pH-insensitive Nuclear Overhauser Effect at 9.4 T”, Magnetic Resonance in Medicine 69: 760-770, 2013.


    2. Vazquez AV, Fukuda M, Crowley JC & Kim SG, “Neural and hemodynamic responses elicited by forelimb and photo-stimulation in  Channelrhodopsin-2 mice: Insights into the hemodynamic point-spread function”, Cerebral Cortex 24(11): 2908-2919, 2014.


    3. Jin T, Mehrens H, Hendrich KS & Kim SG, “Mapping brain glucose uptake with chemical exchange-sensitive spin-lock magnetic resonance imaging”, Journal of Cerebral Blood and Metabolism 34(8): 1402-1410, 2014.


    4. Iordanova B, Vazquez AL, Poplawsky AJ, Fukuda M, and Kim SG, “Neural and hemodynamic responses to optogenetic and sensory stimulation in the rat somatosensory cortex”,  Journal of Cerebral Blood and Metabolism 35(6): 922-932, 2015.


    5. Poplawsky AJ, Fukuda M, Murphy M & Kim SG, “Layer-specific fMRI responses to excitatory and inhibitory neuronal activities in the olfactory bulb”, J of Neurosci 35(46): 15263-15275, 2015.

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  • Sensorimotor Cognition Lab


     

    Introduction


    My lab is interested in how attention and cognition modulate sensory neural representation and transmission of the neural information to the downstream motor areas in the brain. I use in-vivo neurophysiological recording techniques, computational modeling, and sophisticated behavioral control for understanding neural codes in the cortical/subcortical regions using NHP as an animal model. The lab also pursues to address the same questions using brain imaging techniques, including EEG and fMRI, through collaboration with others in the center.


     

    Selected Recent Publications


    1. Joonyeol Lee and Stephen G. Lisberger, "Gamma synchrony predicts neuron–neuron correlations and correlations with motor behavior in extrastriate visual area MT", Journal of Neuroscience 33: 19677-19688, 2013. 


    2. Joonyeol Lee, Mati Joshua, Javier F. Medina, and Stephen G. Lisberger, "Signal, Noise, and Variation in Neural and Sensory-Motor Latency", Neuron 90: 1-2, 2016.

     

    3. Jin Yang*, Joonyeol Lee*, and Stephen G. Lisberger, "The interaction of Bayesian priors and sensory data and its neural circuit implementation in visually guided movement", Journal of Neuroscience 32: 17632–17645, 2012 *Equal contribution.

    4. Joonyeol Lee and John H. R. Maunsell, "Attentional modulation of MT neurons with single or multiple stimuli in their receptive fields", Journal of Neuroscience, 30: 3058-3066, 2010.

    5. Joonyeol Lee and John H. R. Maunsell, "A normalization model of attentional modulation of single unit responses", PLoS ONE, 4(2): e4651, 2009.

  • Seok-Jun Hong
  • Assistant Professor
  • Computational neuroimaging, Developmental disorders, biophysical brain network modeling
  • Department of Biomedical Engineering
  • hongseokjunskku.edu
  • https://combinelab.net

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  • Information

  • Computational Brain Imaging and Network Modeling Lab
    (COMBINE LAB)


     

    Introduction


    We are the research group of Computational Brain Imaging and Network Modeling (COMBINE) at IBS Center for Neuroscience Imaging Research (CNIR) and Sungkyunkwan University (SKKU) in South Korea. “COMBINE” is not a simply eye-catching acronym for the lab title but represents the main research perspective we are pursuing. Using diverse neuroimaging and computational modeling approaches, our research aims at identifying system-level principles for large-scale organization of the brain and its neurodynamics in both typical and atypcial conditions. In performing the research, we are seeking to combine multi-method (connectomics, computational modeling), multi-modal (structure and function), and multi-scale (circuit-level, large-scale network and behhaviors) analytical approaches to understand brain working principles and capture individual variations in complex behavioral and clinical outcomes. Based on these research tools, ultimately we are targeting to develop effective imaging-based biomarkers for normal cognition and clinical diagnosis.


     

    Selected Recent Publications


    1. Hong SJ, Vogelstein J, Gozzi A, Bernhardt BC, Yeo B.T.T, Milham MP, Di Martino A, Towards Neurosubtypes in Autism. Biological Psychiatry 2020 


    2. Hong SJ, Vos de Wael R, Bethlehem R, Lariviere R, Paquola C, Valk SL, Di Martino A, Milham MP, Smallwood J, Margulies D, Bernhardt BC. Atypical functional connectome hierarchy in autism. Nature Communications. 2019, 10 (1):1022

     

    3. Hong SJ, Lee HM, Gill RS, Bernhardt BC, Bernasconi N, Bernasconi A. A connectome-based mechanistic model of epileptogenic focal cortical developmental malformations. Brain. 2019, 142 (3):688-699

     

     

     

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