Kangjoo Lee, PhD
Postdoctoral Associate
Research & Publications
Biography
Research Summary
Brain Connectivity, Brain Metabolism, Machine Learning, Multimodal Functional Neuroimaging, Sleep, Epilepsy
- Understanding the relationship between baseline activity and brain function
- Understanding the relationship between brain metabolism and connectivity
- Understanding state-dependent or pathological brain dynamics
- Development of presurgical biomarker of neurological disorders
- fMRI, EEG, MEG, NIRS, Calcium Imaging, Pupillometry in both clinical and preclinical studies
- Open science, numerical analysis, machine learning for neuroimaging
Coauthors
Selected Publications
- Arousal impacts distributed hubs modulating the integration of brain functional connectivityKangjoo Lee, Corey Horien, David O’Connor, Bronwen Garand-Sheridan, Fuyuze Tokoglu, Dustin Scheinost, Evelyn M.R. Lake, R. Todd Constable bioRxiv 2021.07.12.452041; doi: https://doi.org/10.1101/2021.07.12.452041
- A hitchhiker's guide to working with large, open-source neuroimaging datasets.Horien C, Noble S, Greene AS, Lee K, Barron DS, Gao S, O'Connor D, Salehi M, Dadashkarimi J, Shen X, Lake EMR, Constable RT, Scheinost D. A hitchhiker's guide to working with large, open-source neuroimaging datasets. Nature Human Behaviour 2021, 5:185-193.
- Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol.Horien C, Fontenelle S, Joseph K, Powell N, Nutor C, Fortes D, Butler M, Powell K, Macris D, Lee K, Greene AS, McPartland JC, Volkmar FR, Scheinost D, Chawarska K, Constable RT. Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol. Scientific Reports 2020, 10:21855.
- Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis.Lee K, Khoo HM, Fourcade C, Gotman J, Grova C. Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis. Magnetic Resonance Imaging 2019, 58:97-107.
- Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy.Lee K, Khoo HM, Lina JM, Dubeau F, Gotman J, Grova C. Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy. NeuroImage. Clinical 2018, 20:71-84.
- Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.Lee YB, Lee J, Tak S, Lee K, Na DL, Seo SW, Jeong Y, Ye JC. Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis. NeuroImage 2016, 125:1032-1045.
- SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity.Lee K, Lina JM, Gotman J, Grova C. SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity. NeuroImage 2016, 134:434-449.
- Detection of abnormal resting-state networks in individual patients suffering from focal epilepsy: an initial step toward individual connectivity assessment.Dansereau CL, Bellec P, Lee K, Pittau F, Gotman J, Grova C. Detection of abnormal resting-state networks in individual patients suffering from focal epilepsy: an initial step toward individual connectivity assessment. Frontiers In Neuroscience 2014, 8:419.
- A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion.Lee K, Tak S, Ye JC. A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion. IEEE Transactions On Medical Imaging 2011, 30:1076-89.
- Quantification of CMRO(2) without hypercapnia using simultaneous near-infrared spectroscopy and fMRI measurements.Tak S, Jang J, Lee K, Ye JC. Quantification of CMRO(2) without hypercapnia using simultaneous near-infrared spectroscopy and fMRI measurements. Physics In Medicine And Biology 2010, 55:3249-69.
- Characterization and optimization of a quasi-monolithic detector module with depth-encoding for small animal PETYong Hyun Chung, Seung-Jae Lee, Cheol-Ha Baek, Kang-Joo Lee, and Yong Choi, “Characterization and optimization of a quasi-monolithic detector module with depth-encoding for small animal PET”, J. Korean Phys. Soc., vol. 54, no. 1, pp. 244–249, January 2009