Cheol E. Han, Ph.D.

2016- Electronics and Information Engineering Korea University at Sejong 
motor control, computational neuroscience, rehabilitation, network science, MRI, connectome
"Cheol E Han"
Mean distance: 14.88 (cluster 17)


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Michael A. Arbib grad student 2004-2009 USC
 (PhD, Thesis title: Modeling human reaching and grasping: Cortex, rehabilitation and lateralization.)
Nicolas Schweighofer grad student 2004-2009 USC
Marcus Kaiser post-doc 2010-2012 Seoul National University
Joon-Kyung Seong research scientist 2013-2015 Korea University, Seoul
 (Research Professor)
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Kim S, Han CE, Kim B, et al. (2021) Effort, success, and side of lesion determine arm choice in chronic stroke survivors with mild-to-moderate impairment. Journal of Neurophysiology
Lee S, Kim D, Youn H, et al. (2021) Brain network analysis reveals that amyloidopathy affects comorbid cognitive dysfunction in older adults with depression. Scientific Reports. 11: 4299
Giannakakis E, Han CE, Weber B, et al. (2020) Towards simulations of long-term behavior of neural networks: Modeling synaptic plasticity of connections within and between human brain regions. Neurocomputing. 416: 38-44
Giannakakis E, Hutchings F, Papasavvas CA, et al. (2020) Computational modelling of the long-term effects of brain stimulation on the local and global structural connectivity of epileptic patients. Plos One. 15: e0221380
Choi M, Youn H, Kim D, et al. (2019) Comparison of neurodegenerative types using different brain MRI analysis metrics in older adults with normal cognition, mild cognitive impairment, and Alzheimer's dementia. Plos One. 14: e0220739
Kim D, Lee S, Choi M, et al. (2019) Diffusion tensor imaging reveals abnormal brain networks in elderly subjects with subjective cognitive deficits. Neurological Sciences : Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Lee P, Choi M, Kim D, et al. (2019) Comparison Between Brain Sub-Networks Decomposed by Auto Encoder and Graph Auto Encoder with Non-Negative Weight Constraint and Sparse Encoding Journal of the Institute of Electronics and Information Engineers. 56: 99-108
Youn H, Lee J, Choi MW, et al. (2019) P4-275: Comparison Of Neurodegenerative Types Using Four Brain Mri Analysis Methods In The Different Stages Of Alzheimer'S Disease-Related Cognitive Decline Alzheimers & Dementia. 15
Lee P, Choi M, Kim D, et al. (2019) Comparison between brain sub-networks decomposed by auto encoder (AE) and graph auto encoder (GAE) with non-negative weight constraints and sparse encoding Ibro Reports. 6: S179
Kim D, Lee J, Choi M, et al. (2019) Discriminating coupling between structural connectivity and functional connectivity in the brain networks of juvenile myoclonic epilepsy Ibro Reports. 6: S108
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