CENTER FOR NEUROSCIENCE IMAGING RESEARCH

Lab Name: Medical Image Processing Lab

Introdution

Our lab focuses on developing novel data processing algorithms for neuroimaging. We are particularly interested in image registration, segmentation, and feature extraction for various medical imaging modalities. Neuroimaging data contain millions of voxels and thus robust algorithmic considerations are required to properly explore such high-dimensional data. We are witnessing exponential growth in accumulated data with advances in neuroimaging technology. Thus, the role of data post-processing will be an integral part of advanced neuroimaging research. We also have following research interests; 1) data mining for neuroimaging, 2) medical image analysis for age modeling and neurological disease, 3) medical image analysis for cancer management.

Selected Recent Publications

1. B. Park and H.Park, “Connectivity differences between adult male and female patients with attention deficit hyperactivity disorder according to resting-state fMRI”, Neural Regeneration Research, 2015.

2. B. Park, J. Seo, J. Yi, and H.Park, “Structural and functional brain connectivity of people with obesity and prediction of body mass index using connectivity”, PLoS ONE 10(11): e0141376. doi:10.1371/journal.pone.0141376, 2015.

3. S.-J. Choi*, J.-H. Kim*, J. Seo, H.-S. Kim, J.-M. Lee, and H.Park, “Parametric Response Mapping of Dynamic CT for Predicting Intrahepatic Recurrence of Hepatocellular Carcinoma after Conventional Transcatheter Arterial Chemoembolization”, European Radiology 26(1): 225-234, 2016. (* equal contribution)

4. H.Park, D. Wood, H. Hussain, C. Meyer, R. Shah, T. Johnson, T. Chenevert, and M. Piert, “Introducing Parametric PET/MR Fusion Imaging of Primary Prostate Cancer”, Journal of Nuclear Medicine 53: 546-551, 2012.

5. H.Park, P. H. Bland, and C. R. Meyer, “Construction of an Abdominal Probabilistic Atlas and its application in Segmentation", IEEE Transactions on medical imaging, 22: 483-492, 2003.

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.

Cocoan lab (Computational Cognitive Affective Neuroscience Laboratory)

Introduction

The mission of our lab is to understand pain and emotions in the perspective of Computational, Cognitive, and Affective Neuroscience. We also aim to develop clinically useful neuroimaging models and tools that can be used and shared across different research groups and clinical settings.

Our main research tools include functional Magnetic Resonance Imaging (fMRI), psychophysiology measures (skin conductance, pupilometry, electrocardiogram, respiration), electroencephalogram (EEG), and other behavioral measures such as face recording camera, eye-tracker, etc. Most importantly, we use computational tools to model and understand our affective, cognitive, and behavioral responses.

Selected Recent Publication

Detail

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).