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 multicontrast 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.