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
Introduction
Neural Basis of Continuous Behavior (NBCB) lab aims to understand humans and animals' internal processes while making continuous and interactive decisions between multiple agents. We use human psychophysics, animal electrophysiology, and computational models to address our scientific question.
Specifically, we want to address normative behavior and neural dynamics of:
prediction and planning
information factorization and generalization across context
social inference and learning
by using the real-time navigation/foraging/hunting task paradigm.
We are open to incorporate methods from various fields, including artificial neural networks and computational ethology (but not limited to).
Selected Recent Publications
1. Yoo, S.B.M., Hayden, B.Y., and Pearson, J.M. (2021). Continuous decisions. Philosophical Transactions Royal Soc B 376, 20190664.
2. Yoo, S. B. M., Tu, J. C., & Hayden, B. Y. Multicentric tracking of multiple agents by anterior cingulate cortex during pursuit and evasion. Nature Communication (2021, accepted)
3. Yoo, S.B.M., Tu, J.C., Piantadosi, S.T., and Hayden, B.Y. (2020). The neural basis of predictive pursuit. Nature Neurosci 23, 252–259.
4. Yoo, S.B.M., and Hayden, B.Y. (2020). The Transition from Evaluation to Selection Involves Neural Subspace Reorganization in Core Reward Regions. Neuron 105, 712-724.e4.
5. Yoo, S.B.M., and Hayden, B.Y. (2018). Economic Choice as an Untangling of Options into Actions. Neuron 99, 434–447.
Computational Learning & Memory Neurosciece Lab
(CLMN Lab)
Research interest
· Computational modeling of human movement control, learning, and memory
· Neuroscientific approach to modulating human learning & memory with non-invasive brain stimulation
· Brain inspired artificial intelligence (Reverse engineering the brain to understand learning and memory)
· Cognitive and neural mechanisms underlying decision making in the framework of reinforcement learning
Selected Recent Publications
1. Choi Y, Shin EY, Kim S*. Spatiotemporal dissociation of fMRI activity in the caudate nucleus underlies human de novo motor skill learning. Proceedings of National Academy of Sciences U. S. A., Vol. 117, Issue 38, 2020
2. Kim S, Nilakantan AS, Hermiller MS, Palumbo R, VanHaerents SA, Voss JL*. Selective and coherent activity increases due to stimulation indicate functional distinctions between episodic memory networks. Science Advances, Vol. 4, Issue 8, 2018
3. Kim S, Ogawa K, Lv J, Schweighofer N*, Imamizu H. Neural substrates related to motor memory with multiple time scales in sensorimotor adaptation. PLoS Biology, Vol. 13, Issue 12, 2015
4. Kim S, Callier T, Tabot GA, Gaunt RA, Tenore FV, Bensmaia SJ*. Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex. Proceedings of National Academy of Sciences U. S. A., Vol. 112, Issue 49, 2015