Perceptual and Cognitive Neuroscience Lab (Shim Lab)
Introduction
The goal of our research is to understand how the human brain gives rise to perception and cognition, and specifically how top-down or feedback processing contributes to this process. Research focuses on how top-down processing serves to gate the entry of information into attention and memory, alter fundamental information about object location and identity, create new representations at early stages of processing where no feedforward information exists, and integrate information from multiple sensory modalities. In order to gain a comprehensive understanding of the cognitive and neural mechanisms that underlie human mental processes, including perception, attention, and memory we combine techniques from neuroimaging (encoding and decoding), vision sciences, and cognitive psychology. This allows us to explore how the brain represents and processes a range of perceptual and cognitive information.
Selected Recent Publication
1. Yu, Q., & Shim, W. M. (2016). Modulating foveal representation can influence visual discrimination in the periphery. Journal of Vision, 16(3):15, 1-12.
2. Chong, E., Familiar, A., & Shim, W. M. (2015). Reconstructing representation of dynamic visual objects in early visual cortex. PNAS, 113, 1453-1458.
3. Uddenberg, S., & Shim, W. M. (2015). Seeing the world through target-tinted glasses: Positive mood broadens perceptual tuning. Emotion, 15, 319-328.
4. Shim, W. M., Jiang, Y. V., & Kanwisher, N. (2013). Redundancy gains in retinotopic cortex. Journal of Neurophysiology, 110, 2227-2235.
5. Shim, W. M., Alvarez, G. A., Vickery, T. J., & Jiang, Y. V. (2010). The number of attentional foci and their precision are dissociated in the posterior parietal cortex. Cerebral Cortex, 20, 1342-1349.
High-resolution fMRI Lab
Introduction
My lab is developing acquisition and analysis methods for high-resolution fMRI in humans. In particular, we are trying to image mesoscopic human brain function using MRI at 7 Tesla. To that end, novel fMRI sequences are developed and tested, analysis pipeline developed, and physiological model of ascending vein effects are applied to the data to remove spatial bias in the fMRI signal. In addition, we are interested in quantitative MRI approaches at 7 Tesla to study subcortical brain organization and cortical brain parcellation in both healthy subjects and patients. Finally, the lab pursues modeling brain connectivity using physiological principles and advanced computational approaches.
Selected Recent Publication
1. J. Polimeni, K. Uludağ. Neuroimaging with Ultra-High Field MRI: Present and Future. NeuroImage 168, 1-532 (http://www.journals.elsevier.com/neuroimage/call-for-papers/neuroimaging-with-ultra-high-field-mri-present-and-future/, 2018, Publisher: Elsevier).
2. K. Uludağ, K. Ugurbil, L. Berliner. Functional MRI: From Nuclear Spins to Brain Function. (http://www.springer.com/gp/book/9781489975904, 2015, Publisher: Springer).
3. I. Marquardt, M. Schneider, O.F. Gulban, D. Ivanov, K. Uludağ. Cortical depth profiles of luminance contrast responses in human V1 and V2 using 7 T fMRI. Human Brain Mapping 39, 2812-27 (2018).
4. S. Kashyap, D. Ivanov, M. Havlicek, B. A. Poser, K. Uludağ. Impact of acquisition and analysis strategies on cortical depth-dependent fMRI. NeuroImage 168, 332-344 (2018).
5. K. Uludağ, P. Blinder. Linking brain vascular physiology to hemodynamic response in ultra-high field MRI. NeuroImage 168, 279-295 (2018).
6. M. Havlicek, A. Roebroeck, K. J. Friston, A. Gardumi, D. Ivanov, K. Uludağ. Physiologically informed dynamic causal models for fMRI. NeuroImage 122, 355-372 (2015).
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