Introduction
We aim to understand how the brain works, what intelligence and attention – both biological and artificial – are, and how human cognition emerges from the brain’s complex systems during development. To address these questions, we combine large-scale brain imaging (primarily fMRI) with computational predictive modeling. We also develop and apply AI techniques to analyze multimodal medical data, with potential applications in clinical and translational research.
Selected Recent Publication
1. Yoo K, Rosenberg MD, Kwon YH, Lin Q, Avery EW, Scheinost D, Constable RT, Chun MM. A brain-based general measure of attention. Nature Human Behaviour. 2022 June; 6: 782-795.
2. Yoo K, Rosenberg MD, Kwon YH, Scheinost D, Constable RT, Chun MM. A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome. NeuroImage. 2022 Aug; 257: 119279.
3. Wang X*, Yoo K*, Chen H, Zou T, Wang H, Gao Q, Meng L, Hu X, Li R. Antagonistic network signature of motor function in Parkinson’s disease revealed by connectome-based predictive modeling. npj Parkinson’s Disease. 2022 Apr; 8: 49.
4. Kwon YH, Yoo K*, Nguyen H, Jeong Y, Chun MM. Predicting multilingual effects on executive function and individual connectomes in children: an ABCD Study. Proceedings of the National Academy of Sciences of the United States of America (PNAS). 2021 Dec; 118(49): e2110811118.
5. Jiang R, Noble S, Sui J, Yoo K, Rosenblatt M, Horien C, Qi S, Liang Q, Sun H, Calhoun VD, Scheinost D. Associations of physical frailty with health outcomes and brain structure in 483,033 middle-aged and older adults: a population-based study from the UK Biobank. The Lancet Digital Health. 2023 June; 5(6): E350-E359.