Hosung Kim, MD
My research focuses on developing an analytic platform that assesses aging of brain structures and their structural and functional networks as well as predicting the eventual long-term outcome for neurodevelopment and quantifying the progression of neurodegeneration. To follow-up long-term brain structural modification associated with neurodevelopmental / neurodegenerative disorders, my group develops methods to quantify various aspects of brain anatomical and networking variability using longitudinally collected multi-contrast MRI. My technical expertise on surface-based morphology and texture modeling, network topology analysis, and multivariate statistical modeling consists of essential elements to develop a combination of techniques to accomplish the proposed specific aims. I have also applied various analytic frameworks, including cortical morphometry, voxel-based morphometry, deformation-based morphometry and structural / functional network analyses, to assessment of brain structures in healthy conditions as well as in pathological conditions that often present anatomical variations beyond the range of normal structures. Using a more advanced pattern analysis with machine learning and deep learning on innovative multi-contrast MRI features, my current research seeks to understand the atypical structural and network alterations in various neurological diseases including epilepsy, dementia, and preterm birth and ultimately to predict neurological / brain functional outcome in the patients. In particular, the use of the deep-learning-based neural network (DNN) can reconstruct a smaller size of the high-order feature-set to enhance the performance in prediction as well as brain segmentation and artifact correction.